1
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Hwang W, Austin SL, Blondel A, Boittier ED, Boresch S, Buck M, Buckner J, Caflisch A, Chang HT, Cheng X, Choi YK, Chu JW, Crowley MF, Cui Q, Damjanovic A, Deng Y, Devereux M, Ding X, Feig MF, Gao J, Glowacki DR, Gonzales JE, Hamaneh MB, Harder ED, Hayes RL, Huang J, Huang Y, Hudson PS, Im W, Islam SM, Jiang W, Jones MR, Käser S, Kearns FL, Kern NR, Klauda JB, Lazaridis T, Lee J, Lemkul JA, Liu X, Luo Y, MacKerell AD, Major DT, Meuwly M, Nam K, Nilsson L, Ovchinnikov V, Paci E, Park S, Pastor RW, Pittman AR, Post CB, Prasad S, Pu J, Qi Y, Rathinavelan T, Roe DR, Roux B, Rowley CN, Shen J, Simmonett AC, Sodt AJ, Töpfer K, Upadhyay M, van der Vaart A, Vazquez-Salazar LI, Venable RM, Warrensford LC, Woodcock HL, Wu Y, Brooks CL, Brooks BR, Karplus M. CHARMM at 45: Enhancements in Accessibility, Functionality, and Speed. J Phys Chem B 2024. [PMID: 39303207 DOI: 10.1021/acs.jpcb.4c04100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/22/2024]
Abstract
Since its inception nearly a half century ago, CHARMM has been playing a central role in computational biochemistry and biophysics. Commensurate with the developments in experimental research and advances in computer hardware, the range of methods and applicability of CHARMM have also grown. This review summarizes major developments that occurred after 2009 when the last review of CHARMM was published. They include the following: new faster simulation engines, accessible user interfaces for convenient workflows, and a vast array of simulation and analysis methods that encompass quantum mechanical, atomistic, and coarse-grained levels, as well as extensive coverage of force fields. In addition to providing the current snapshot of the CHARMM development, this review may serve as a starting point for exploring relevant theories and computational methods for tackling contemporary and emerging problems in biomolecular systems. CHARMM is freely available for academic and nonprofit research at https://academiccharmm.org/program.
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Affiliation(s)
- Wonmuk Hwang
- Department of Biomedical Engineering, Texas A&M University, College Station, Texas 77843, United States
- Department of Materials Science and Engineering, Texas A&M University, College Station, Texas 77843, United States
- Department of Physics and Astronomy, Texas A&M University, College Station, Texas 77843, United States
- Center for AI and Natural Sciences, Korea Institute for Advanced Study, Seoul 02455, Republic of Korea
| | - Steven L Austin
- Department of Chemistry, University of South Florida, Tampa, Florida 33620, United States
| | - Arnaud Blondel
- Institut Pasteur, Université Paris Cité, CNRS UMR3825, Structural Bioinformatics Unit, 28 rue du Dr. Roux F-75015 Paris, France
| | - Eric D Boittier
- Department of Chemistry, University of Basel, Klingelbergstrasse 80, CH-4056 Basel, Switzerland
| | - Stefan Boresch
- Faculty of Chemistry, Department of Computational Biological Chemistry, University of Vienna, Wahringerstrasse 17, 1090 Vienna, Austria
| | - Matthias Buck
- Department of Physiology and Biophysics, Case Western Reserve University, School of Medicine, Cleveland, Ohio 44106, United States
| | - Joshua Buckner
- Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Amedeo Caflisch
- Department of Biochemistry, University of Zürich, CH-8057 Zürich, Switzerland
| | - Hao-Ting Chang
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan, ROC
| | - Xi Cheng
- Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Yeol Kyo Choi
- Department of Biological Sciences, Lehigh University, Bethlehem, Pennsylvania 18015, United States
| | - Jhih-Wei Chu
- Institute of Bioinformatics and Systems Biology, Department of Biological Science and Technology, Institute of Molecular Medicine and Bioengineering, and Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B), National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan, ROC
| | - Michael F Crowley
- Renewable Resources and Enabling Sciences Center, National Renewable Energy Laboratory, Golden, Colorado 80401, United States
| | - Qiang Cui
- Department of Chemistry, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, United States
- Department of Physics, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, United States
- Department of Biomedical Engineering, Boston University, 44 Cummington Mall, Boston, Massachusetts 02215, United States
| | - Ana Damjanovic
- Department of Biophysics, Johns Hopkins University, Baltimore, Maryland 21218, United States
- Department of Physics and Astronomy, Johns Hopkins University, Baltimore, Maryland 21218, United States
- Laboratory of Computational Biology, National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Yuqing Deng
- Shanghai R&D Center, DP Technology, Ltd., Shanghai 201210, China
| | - Mike Devereux
- Department of Chemistry, University of Basel, Klingelbergstrasse 80, CH-4056 Basel, Switzerland
| | - Xinqiang Ding
- Department of Chemistry, Tufts University, Medford, Massachusetts 02155, United States
| | - Michael F Feig
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824, United States
| | - Jiali Gao
- School of Chemical Biology & Biotechnology, Peking University Shenzhen Graduate School, Shenzhen, Guangdong 518055, China
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, Guangdong 518055, China
- Department of Chemistry and Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455, United States
| | - David R Glowacki
- CiTIUS Centro Singular de Investigación en Tecnoloxías Intelixentes da USC, 15705 Santiago de Compostela, Spain
| | - James E Gonzales
- Department of Biomedical Engineering, Texas A&M University, College Station, Texas 77843, United States
- Laboratory of Computational Biology, National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Mehdi Bagerhi Hamaneh
- Department of Physiology and Biophysics, Case Western Reserve University, School of Medicine, Cleveland, Ohio 44106, United States
| | | | - Ryan L Hayes
- Department of Chemical and Biomolecular Engineering, University of California, Irvine, Irvine, California 92697, United States
- Department of Pharmaceutical Sciences, University of California, Irvine, Irvine, California 92697, United States
| | - Jing Huang
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China
| | - Yandong Huang
- College of Computer Engineering, Jimei University, Xiamen 361021, China
| | - Phillip S Hudson
- Department of Chemistry, University of South Florida, Tampa, Florida 33620, United States
- Medicine Design, Pfizer Inc., Cambridge, Massachusetts 02139, United States
| | - Wonpil Im
- Department of Biological Sciences, Lehigh University, Bethlehem, Pennsylvania 18015, United States
| | - Shahidul M Islam
- Department of Chemistry, Delaware State University, Dover, Delaware 19901, United States
| | - Wei Jiang
- Computational Science Division, Argonne National Laboratory, Argonne, Illinois 60439, United States
| | - Michael R Jones
- Laboratory of Computational Biology, National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Silvan Käser
- Department of Chemistry, University of Basel, Klingelbergstrasse 80, CH-4056 Basel, Switzerland
| | - Fiona L Kearns
- Department of Chemistry, University of South Florida, Tampa, Florida 33620, United States
| | - Nathan R Kern
- Department of Biological Sciences, Lehigh University, Bethlehem, Pennsylvania 18015, United States
| | - Jeffery B Klauda
- Department of Chemical and Biomolecular Engineering, Institute for Physical Science and Technology, Biophysics Program, University of Maryland, College Park, Maryland 20742, United States
| | - Themis Lazaridis
- Department of Chemistry, City College of New York, New York, New York 10031, United States
| | - Jinhyuk Lee
- Disease Target Structure Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea
- Department of Bioinformatics, KRIBB School of Bioscience, University of Science and Technology, Daejeon 34141, Republic of Korea
| | - Justin A Lemkul
- Department of Biochemistry, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, United States
| | - Xiaorong Liu
- Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Yun Luo
- Department of Biotechnology and Pharmaceutical Sciences, College of Pharmacy, Western University of Health Sciences, Pomona, California 91766, United States
| | - Alexander D MacKerell
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, Maryland 21201, United States
| | - Dan T Major
- Department of Chemistry and Institute for Nanotechnology & Advanced Materials, Bar-Ilan University, Ramat-Gan 52900, Israel
| | - Markus Meuwly
- Department of Chemistry, University of Basel, Klingelbergstrasse 80, CH-4056 Basel, Switzerland
- Department of Chemistry, Brown University, Providence, Rhode Island 02912, United States
| | - Kwangho Nam
- Department of Chemistry and Biochemistry, University of Texas at Arlington, Arlington, Texas 76019, United States
| | - Lennart Nilsson
- Karolinska Institutet, Department of Biosciences and Nutrition, SE-14183 Huddinge, Sweden
| | - Victor Ovchinnikov
- Harvard University, Department of Chemistry and Chemical Biology, Cambridge, Massachusetts 02138, United States
| | - Emanuele Paci
- Dipartimento di Fisica e Astronomia, Universitá di Bologna, Bologna 40127, Italy
| | - Soohyung Park
- Department of Biological Sciences, Lehigh University, Bethlehem, Pennsylvania 18015, United States
| | - Richard W Pastor
- Laboratory of Computational Biology, National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Amanda R Pittman
- Department of Chemistry, University of South Florida, Tampa, Florida 33620, United States
| | - Carol Beth Post
- Borch Department of Medicinal Chemistry and Molecular Pharmacology, Purdue University, West Lafayette, Indiana 47907, United States
| | - Samarjeet Prasad
- Laboratory of Computational Biology, National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Jingzhi Pu
- Department of Chemistry and Chemical Biology, Indiana University Indianapolis, Indianapolis, Indiana 46202, United States
| | - Yifei Qi
- School of Pharmacy, Fudan University, Shanghai 201203, China
| | | | - Daniel R Roe
- Laboratory of Computational Biology, National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Benoit Roux
- Department of Chemistry, University of Chicago, Chicago, Illinois 60637, United States
| | | | - Jana Shen
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, Maryland 21201, United States
| | - Andrew C Simmonett
- Laboratory of Computational Biology, National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Alexander J Sodt
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Kai Töpfer
- Department of Chemistry, University of Basel, Klingelbergstrasse 80, CH-4056 Basel, Switzerland
| | - Meenu Upadhyay
- Department of Chemistry, University of Basel, Klingelbergstrasse 80, CH-4056 Basel, Switzerland
| | - Arjan van der Vaart
- Department of Chemistry, University of South Florida, Tampa, Florida 33620, United States
| | | | - Richard M Venable
- Laboratory of Computational Biology, National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Luke C Warrensford
- Department of Chemistry, University of South Florida, Tampa, Florida 33620, United States
| | - H Lee Woodcock
- Department of Chemistry, University of South Florida, Tampa, Florida 33620, United States
| | - Yujin Wu
- Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Charles L Brooks
- Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Bernard R Brooks
- Laboratory of Computational Biology, National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Martin Karplus
- Harvard University, Department of Chemistry and Chemical Biology, Cambridge, Massachusetts 02138, United States
- Laboratoire de Chimie Biophysique, ISIS, Université de Strasbourg, 67000 Strasbourg, France
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2
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Hardy BJ, Curnow P. Computational design of de novo bioenergetic membrane proteins. Biochem Soc Trans 2024; 52:1737-1745. [PMID: 38958574 DOI: 10.1042/bst20231347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Revised: 06/11/2024] [Accepted: 06/17/2024] [Indexed: 07/04/2024]
Abstract
The major energy-producing reactions of biochemistry occur at biological membranes. Computational protein design now provides the opportunity to elucidate the underlying principles of these processes and to construct bioenergetic pathways on our own terms. Here, we review recent achievements in this endeavour of 'synthetic bioenergetics', with a particular focus on new enabling tools that facilitate the computational design of biocompatible de novo integral membrane proteins. We use recent examples to showcase some of the key computational approaches in current use and highlight that the overall philosophy of 'surface-swapping' - the replacement of solvent-facing residues with amino acids bearing lipid-soluble hydrophobic sidechains - is a promising avenue in membrane protein design. We conclude by highlighting outstanding design challenges and the emerging role of AI in sequence design and structure ideation.
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Affiliation(s)
| | - Paul Curnow
- School of Biochemistry, University of Bristol, Bristol, U.K
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3
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Hardy BJ, Dubiel P, Bungay EL, Rudin M, Williams C, Arthur CJ, Guberman‐Pfeffer MJ, Sofia Oliveira A, Curnow P, Anderson JLR. Delineating redox cooperativity in water-soluble and membrane multiheme cytochromes through protein design. Protein Sci 2024; 33:e5113. [PMID: 38980168 PMCID: PMC11232281 DOI: 10.1002/pro.5113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2024] [Revised: 06/26/2024] [Accepted: 06/27/2024] [Indexed: 07/10/2024]
Abstract
Nature has evolved diverse electron transport proteins and multiprotein assemblies essential to the generation and transduction of biological energy. However, substantially modifying or adapting these proteins for user-defined applications or to gain fundamental mechanistic insight can be hindered by their inherent complexity. De novo protein design offers an attractive route to stripping away this confounding complexity, enabling us to probe the fundamental workings of these bioenergetic proteins and systems, while providing robust, modular platforms for constructing completely artificial electron-conducting circuitry. Here, we use a set of de novo designed mono-heme and di-heme soluble and membrane proteins to delineate the contributions of electrostatic micro-environments and dielectric properties of the surrounding protein medium on the inter-heme redox cooperativity that we have previously reported. Experimentally, we find that the two heme sites in both the water-soluble and membrane constructs have broadly equivalent redox potentials in isolation, in agreement with Poisson-Boltzmann Continuum Electrostatics calculations. BioDC, a Python program for the estimation of electron transfer energetics and kinetics within multiheme cytochromes, also predicts equivalent heme sites, and reports that burial within the low dielectric environment of the membrane strengthens heme-heme electrostatic coupling. We conclude that redox cooperativity in our diheme cytochromes is largely driven by heme electrostatic coupling and confirm that this effect is greatly strengthened by burial in the membrane. These results demonstrate that while our de novo proteins present minimalist, new-to-nature constructs, they enable the dissection and microscopic examination of processes fundamental to the function of vital, yet complex, bioenergetic assemblies.
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Affiliation(s)
| | | | | | - May Rudin
- School of BiochemistryUniversity of BristolBristolUK
| | | | | | | | | | - Paul Curnow
- School of BiochemistryUniversity of BristolBristolUK
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4
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Samanta R, Harmalkar A, Prathima P, Gray JJ. Advancing membrane-associated protein docking with improved sampling and scoring in Rosetta. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.09.602802. [PMID: 39026849 PMCID: PMC11257521 DOI: 10.1101/2024.07.09.602802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
The oligomerization of protein macromolecules on cell membranes plays a fundamental role in regulating cellular function. From modulating signal transduction to directing immune response, membrane proteins (MPs) play a crucial role in biological processes and are often the target of many pharmaceutical drugs. Despite their biological relevance, the challenges in experimental determination have hampered the structural availability of membrane proteins and their complexes. Computational docking provides a promising alternative to model membrane protein complex structures. Here, we present Rosetta-MPDock, a flexible transmembrane (TM) protein docking protocol that captures binding-induced conformational changes. Rosetta-MPDock samples large conformational ensembles of flexible monomers and docks them within an implicit membrane environment. We benchmarked this method on 29 TM-protein complexes of variable backbone flexibility. These complexes are classified based on the root-mean-square deviation between the unbound and bound states (RMSDUB) as: rigid (RMSDUB <1.2 Å), moderately-flexible (RMSDUB ∈ [1.2, 2.2) Å), and flexible targets (RMSDUB > 2.2 Å). In a local docking scenario, i.e. with membrane protein partners starting ≈10 Å apart embedded in the membrane in their unbound conformations, Rosetta-MPDock successfully predicts the correct interface (success defined as achieving 3 near-native structures in the 5 top-ranked models) for 67% moderately flexible targets and 60% of the highly flexible targets, a substantial improvement from the existing membrane protein docking methods. Further, by integrating AlphaFold2-multimer for structure determination and using Rosetta-MPDock for docking and refinement, we demonstrate improved success rates over the benchmark targets from 64% to 73%. Rosetta-MPDock advances the capabilities for membrane protein complex structure prediction and modeling to tackle key biological questions and elucidate functional mechanisms in the membrane environment. The benchmark set and the code is available for public use at github.com/Graylab/MPDock.
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Affiliation(s)
- Rituparna Samanta
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA
- Current affiliation: University of South Florida, Tampa, FL, USA
| | - Ameya Harmalkar
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA
- Current affiliation: Generate Biomedicines Inc., Cambridge, MA, USA
| | - Priyamvada Prathima
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA
- Current affiliation: Department of Immunology, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
| | - Jeffrey J. Gray
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA
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5
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Hermosilla AM, Berner C, Ovchinnikov S, Vorobieva AA. Validation of de novo designed water-soluble and transmembrane β-barrels by in silico folding and melting. Protein Sci 2024; 33:e5033. [PMID: 38864690 PMCID: PMC11168064 DOI: 10.1002/pro.5033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 04/14/2024] [Accepted: 05/08/2024] [Indexed: 06/13/2024]
Abstract
In silico validation of de novo designed proteins with deep learning (DL)-based structure prediction algorithms has become mainstream. However, formal evidence of the relationship between a high-quality predicted model and the chance of experimental success is lacking. We used experimentally characterized de novo water-soluble and transmembrane β-barrel designs to show that AlphaFold2 and ESMFold excel at different tasks. ESMFold can efficiently identify designs generated based on high-quality (designable) backbones. However, only AlphaFold2 can predict which sequences have the best chance of experimentally folding among similar designs. We show that ESMFold can generate high-quality structures from just a few predicted contacts and introduce a new approach based on incremental perturbation of the prediction ("in silico melting"), which can reveal differences in the presence of favorable contacts between designs. This study provides a new insight on DL-based structure prediction models explainability and on how they could be leveraged for the design of increasingly complex proteins; in particular membrane proteins which have historically lacked basic in silico validation tools.
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Affiliation(s)
- Alvaro Martin Hermosilla
- Structural Biology BrusselsVrije Universiteit BrusselBrusselsBelgium
- VIB‐VUB Center for Structural BiologyBrusselsBelgium
| | - Carolin Berner
- Structural Biology BrusselsVrije Universiteit BrusselBrusselsBelgium
- VIB‐VUB Center for Structural BiologyBrusselsBelgium
| | - Sergey Ovchinnikov
- John Harvard Distinguished Science Fellowship ProgramHarvard UniversityCambridgeMassachusettsUSA
- Present address:
Department of BiologyMassachusetts Institute of TechnologyCambridgeMassachusettsUSA
| | - Anastassia A. Vorobieva
- Structural Biology BrusselsVrije Universiteit BrusselBrusselsBelgium
- VIB‐VUB Center for Structural BiologyBrusselsBelgium
- VIB Center for AI and Computational BiologyBelgium
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6
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Kellogg GE. Three-Dimensional Interaction Homology: Deconstructing Residue-Residue and Residue-Lipid Interactions in Membrane Proteins. Molecules 2024; 29:2838. [PMID: 38930903 PMCID: PMC11207109 DOI: 10.3390/molecules29122838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2024] [Revised: 06/09/2024] [Accepted: 06/10/2024] [Indexed: 06/28/2024] Open
Abstract
A method is described to deconstruct the network of hydropathic interactions within and between a protein's sidechain and its environment into residue-based three-dimensional maps. These maps encode favorable and unfavorable hydrophobic and polar interactions, in terms of spatial positions for optimal interactions, relative interaction strength, as well as character. In addition, these maps are backbone angle-dependent. After map calculation and clustering, a finite number of unique residue sidechain interaction maps exist for each backbone conformation, with the number related to the residue's size and interaction complexity. Structures for soluble proteins (~749,000 residues) and membrane proteins (~387,000 residues) were analyzed, with the latter group being subdivided into three subsets related to the residue's position in the membrane protein: soluble domain, core-facing transmembrane domain, and lipid-facing transmembrane domain. This work suggests that maps representing residue types and their backbone conformation can be reassembled to optimize the medium-to-high resolution details of a protein structure. In particular, the information encoded in maps constructed from the lipid-facing transmembrane residues appears to paint a clear picture of the protein-lipid interactions that are difficult to obtain experimentally.
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Affiliation(s)
- Glen E Kellogg
- Department of Medicinal Chemistry, Virginia Commonwealth University, Richmond, VA 23298-0540, USA
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7
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Mravic M, He L, Kratochvil HT, Hu H, Nick SE, Bai W, Edwards A, Jo H, Wu Y, DiMaio D, DeGrado WF. De novo-designed transmembrane proteins bind and regulate a cytokine receptor. Nat Chem Biol 2024; 20:751-760. [PMID: 38480980 PMCID: PMC11142920 DOI: 10.1038/s41589-024-01562-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 01/25/2024] [Indexed: 05/30/2024]
Abstract
Transmembrane (TM) domains as simple as a single span can perform complex biological functions using entirely lipid-embedded chemical features. Computational design has the potential to generate custom tool molecules directly targeting membrane proteins at their functional TM regions. Thus far, designed TM domain-targeting agents have been limited to mimicking the binding modes and motifs of natural TM interaction partners. Here, we demonstrate the design of de novo TM proteins targeting the erythropoietin receptor (EpoR) TM domain in a custom binding topology competitive with receptor homodimerization. The TM proteins expressed in mammalian cells complex with EpoR and inhibit erythropoietin-induced cell proliferation. In vitro, the synthetic TM domain complex outcompetes EpoR homodimerization. Structural characterization reveals that the complex involves the intended amino acids and agrees with our designed molecular model of antiparallel TM helices at 1:1 stoichiometry. Thus, membrane protein TM regions can now be targeted in custom-designed topologies.
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Affiliation(s)
- Marco Mravic
- Department of Pharmaceutical Chemistry, School of Pharmacy, University of California, San Francisco, CA, USA.
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA.
| | - Li He
- Department of Genetics, Yale School of Medicine, New Haven, CT, USA
| | - Huong T Kratochvil
- Department of Pharmaceutical Chemistry, School of Pharmacy, University of California, San Francisco, CA, USA
- Department of Chemistry, University of North Carolina Chapel Hill, Chapel Hill, NC, USA
| | - Hailin Hu
- Department of Pharmaceutical Chemistry, School of Pharmacy, University of California, San Francisco, CA, USA
- School of Medicine, Tsinghua University, Beijing, China
| | - Sarah E Nick
- Department of Pharmaceutical Chemistry, School of Pharmacy, University of California, San Francisco, CA, USA
| | - Weiya Bai
- Department of Genetics, Yale School of Medicine, New Haven, CT, USA
| | - Anne Edwards
- Department of Genetics, Yale School of Medicine, New Haven, CT, USA
| | - Hyunil Jo
- Department of Pharmaceutical Chemistry, School of Pharmacy, University of California, San Francisco, CA, USA
| | - Yibing Wu
- Department of Pharmaceutical Chemistry, School of Pharmacy, University of California, San Francisco, CA, USA
| | - Daniel DiMaio
- Department of Genetics, Yale School of Medicine, New Haven, CT, USA.
- Department of Therapeutic Radiology, Yale School of Medicine, New Haven, CT, USA.
- Department of Molecular Biophysics & Biochemistry, Yale University, New Haven, CT, USA.
- Yale Cancer Center, New Haven, CT, USA.
| | - William F DeGrado
- Department of Pharmaceutical Chemistry, School of Pharmacy, University of California, San Francisco, CA, USA.
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8
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Woods H, Leman JK, Meiler J. Modeling membrane geometries implicitly in Rosetta. Protein Sci 2024; 33:e4908. [PMID: 38358133 PMCID: PMC10868433 DOI: 10.1002/pro.4908] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 01/05/2024] [Accepted: 01/08/2024] [Indexed: 02/16/2024]
Abstract
Interactions between membrane proteins (MPs) and lipid bilayers are critical for many cellular functions. In the Rosetta molecular modeling suite, the implicit membrane energy function is based on a "slab" model, which represent the membrane as a flat bilayer. However, in nature membranes often have a curvature that is important for function and/or stability. Even more prevalent, in structural biology research MPs are reconstituted in model membrane systems such as micelles, bicelles, nanodiscs, or liposomes. Thus, we have modified the existing membrane energy potentials within the RosettaMP framework to allow users to model MPs in different membrane geometries. We show that these modifications can be utilized in core applications within Rosetta such as structure refinement, protein-protein docking, and protein design. For MP structures found in curved membranes, refining these structures in curved, implicit membranes produces higher quality models with structures closer to experimentally determined structures. For MP systems embedded in multiple membranes, representing both membranes results in more favorable scores compared to only representing one of the membranes. Modeling MPs in geometries mimicking the membrane model system used in structure determination can improve model quality and model discrimination.
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Affiliation(s)
- Hope Woods
- Center of Structural Biology, Vanderbilt UniversityNashvilleTennesseeUSA
- Chemical and Physical Biology ProgramVanderbilt UniversityNashvilleTennesseeUSA
| | | | - Jens Meiler
- Center of Structural Biology, Vanderbilt UniversityNashvilleTennesseeUSA
- Department of ChemistryVanderbilt UniversityNashvilleTennesseeUSA
- Institute for Drug Discovery, Leipzig University Medical SchoolLeipzigGermany
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9
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Ngo K, Lopez Mateos D, Han Y, Rouen KC, Ahn SH, Wulff H, Clancy CE, Yarov-Yarovoy V, Vorobyov I. Elucidating molecular mechanisms of protoxin-II state-specific binding to the human NaV1.7 channel. J Gen Physiol 2024; 156:e202313368. [PMID: 38127314 PMCID: PMC10737443 DOI: 10.1085/jgp.202313368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 09/08/2023] [Accepted: 12/04/2023] [Indexed: 12/23/2023] Open
Abstract
Human voltage-gated sodium (hNaV) channels are responsible for initiating and propagating action potentials in excitable cells, and mutations have been associated with numerous cardiac and neurological disorders. hNaV1.7 channels are expressed in peripheral neurons and are promising targets for pain therapy. The tarantula venom peptide protoxin-II (PTx2) has high selectivity for hNaV1.7 and is a valuable scaffold for designing novel therapeutics to treat pain. Here, we used computational modeling to study the molecular mechanisms of the state-dependent binding of PTx2 to hNaV1.7 voltage-sensing domains (VSDs). Using Rosetta structural modeling methods, we constructed atomistic models of the hNaV1.7 VSD II and IV in the activated and deactivated states with docked PTx2. We then performed microsecond-long all-atom molecular dynamics (MD) simulations of the systems in hydrated lipid bilayers. Our simulations revealed that PTx2 binds most favorably to the deactivated VSD II and activated VSD IV. These state-specific interactions are mediated primarily by PTx2's residues R22, K26, K27, K28, and W30 with VSD and the surrounding membrane lipids. Our work revealed important protein-protein and protein-lipid contacts that contribute to high-affinity state-dependent toxin interaction with the channel. The workflow presented will prove useful for designing novel peptides with improved selectivity and potency for more effective and safe treatment of pain.
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Affiliation(s)
- Khoa Ngo
- Biophysics Graduate Group, University of California, Davis, Davis, CA, USA
- Department of Physiology and Membrane Biology, University of California, Davis, Davis, CA, USA
| | - Diego Lopez Mateos
- Biophysics Graduate Group, University of California, Davis, Davis, CA, USA
- Department of Physiology and Membrane Biology, University of California, Davis, Davis, CA, USA
| | - Yanxiao Han
- Department of Physiology and Membrane Biology, University of California, Davis, Davis, CA, USA
| | - Kyle C. Rouen
- Biophysics Graduate Group, University of California, Davis, Davis, CA, USA
- Department of Physiology and Membrane Biology, University of California, Davis, Davis, CA, USA
| | - Surl-Hee Ahn
- Department of Chemical Engineering, University of California, Davis, Davis, CA, USA
| | - Heike Wulff
- Department of Pharmacology, University of California, Davis, Davis, CA, USA
| | - Colleen E. Clancy
- Department of Physiology and Membrane Biology, University of California, Davis, Davis, CA, USA
- Department of Pharmacology, University of California, Davis, Davis, CA, USA
- Center for Precision Medicine and Data Science, University of California, Davis, Davis, CA, USA
| | - Vladimir Yarov-Yarovoy
- Department of Physiology and Membrane Biology, University of California, Davis, Davis, CA, USA
- Department of Anesthesiology and Pain Medicine, University of California, Davis, Davis, CA, USA
| | - Igor Vorobyov
- Department of Physiology and Membrane Biology, University of California, Davis, Davis, CA, USA
- Department of Pharmacology, University of California, Davis, Davis, CA, USA
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10
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Sinclair M, Stein RA, Sheehan JH, Hawes EM, O’Brien RM, Tajkhorshid E, Claxton DP. Integrative analysis of pathogenic variants in glucose-6-phosphatase based on an AlphaFold2 model. PNAS NEXUS 2024; 3:pgae036. [PMID: 38328777 PMCID: PMC10849595 DOI: 10.1093/pnasnexus/pgae036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 01/09/2024] [Indexed: 02/09/2024]
Abstract
Mediating the terminal reaction of gluconeogenesis and glycogenolysis, the integral membrane protein glucose-6-phosphate catalytic subunit 1 (G6PC1) regulates hepatic glucose production by catalyzing hydrolysis of glucose-6-phosphate (G6P) within the lumen of the endoplasmic reticulum. Consistent with its vital contribution to glucose homeostasis, inactivating mutations in G6PC1 causes glycogen storage disease (GSD) type 1a characterized by hepatomegaly and severe hypoglycemia. Despite its physiological importance, the structural basis of G6P binding to G6PC1 and the molecular disruptions induced by missense mutations within the active site that give rise to GSD type 1a are unknown. In this study, we determine the atomic interactions governing G6P binding as well as explore the perturbations imposed by disease-linked missense variants by subjecting an AlphaFold2 G6PC1 structural model to molecular dynamics simulations and in silico predictions of thermodynamic stability validated with robust in vitro and in situ biochemical assays. We identify a collection of side chains, including conserved residues from the signature phosphatidic acid phosphatase motif, that contribute to a hydrogen bonding and van der Waals network stabilizing G6P in the active site. The introduction of GSD type 1a mutations modified the thermodynamic landscape, altered side chain packing and substrate-binding interactions, and induced trapping of catalytic intermediates. Our results, which corroborate the high quality of the AF2 model as a guide for experimental design and to interpret outcomes, not only confirm the active-site structural organization but also identify previously unobserved mechanistic contributions of catalytic and noncatalytic side chains.
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Affiliation(s)
- Matt Sinclair
- Theoretical and Computational Biophysics Group, NIH Center for Macromolecular Modeling and Visualization, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Department of Biochemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Richard A Stein
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN 37232, USA
- Center for Applied Artificial Intelligence in Protein Dynamics, Vanderbilt University, Nashville, TN 37240, USA
| | - Jonathan H Sheehan
- Center for Structural Biology, Vanderbilt University, Nashville, TN 37240, USA
- Division of Infectious Diseases, Department of Internal Medicine, Washington University School of Medicine, St Louis, MO 63110, USA
| | - Emily M Hawes
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN 37232, USA
| | - Richard M O’Brien
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN 37232, USA
| | - Emad Tajkhorshid
- Theoretical and Computational Biophysics Group, NIH Center for Macromolecular Modeling and Visualization, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Department of Biochemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Derek P Claxton
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN 37232, USA
- Center for Applied Artificial Intelligence in Protein Dynamics, Vanderbilt University, Nashville, TN 37240, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN 37240, USA
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11
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Samanta R, Gray JJ. Implicit model to capture electrostatic features of membrane environment. PLoS Comput Biol 2024; 20:e1011296. [PMID: 38252688 PMCID: PMC10833867 DOI: 10.1371/journal.pcbi.1011296] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Revised: 02/01/2024] [Accepted: 12/13/2023] [Indexed: 01/24/2024] Open
Abstract
Membrane protein structure prediction and design are challenging due to the complexity of capturing the interactions in the lipid layer, such as those arising from electrostatics. Accurately capturing electrostatic energies in the low-dielectric membrane often requires expensive Poisson-Boltzmann calculations that are not scalable for membrane protein structure prediction and design. In this work, we have developed a fast-to-compute implicit energy function that considers the realistic characteristics of different lipid bilayers, making design calculations tractable. This method captures the impact of the lipid head group using a mean-field-based approach and uses a depth-dependent dielectric constant to characterize the membrane environment. This energy function Franklin2023 (F23) is built upon Franklin2019 (F19), which is based on experimentally derived hydrophobicity scales in the membrane bilayer. We evaluated the performance of F23 on five different tests probing (1) protein orientation in the bilayer, (2) stability, and (3) sequence recovery. Relative to F19, F23 has improved the calculation of the tilt angle of membrane proteins for 90% of WALP peptides, 15% of TM-peptides, and 25% of the adsorbed peptides. The performances for stability and design tests were equivalent for F19 and F23. The speed and calibration of the implicit model will help F23 access biophysical phenomena at long time and length scales and accelerate the membrane protein design pipeline.
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Affiliation(s)
- Rituparna Samanta
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Jeffrey J. Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, Maryland, United States of America
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, Maryland, United States of America
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12
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Nordquist E, Zhang G, Barethiya S, Ji N, White KM, Han L, Jia Z, Shi J, Cui J, Chen J. Incorporating physics to overcome data scarcity in predictive modeling of protein function: A case study of BK channels. PLoS Comput Biol 2023; 19:e1011460. [PMID: 37713443 PMCID: PMC10529646 DOI: 10.1371/journal.pcbi.1011460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Revised: 09/27/2023] [Accepted: 08/24/2023] [Indexed: 09/17/2023] Open
Abstract
Machine learning has played transformative roles in numerous chemical and biophysical problems such as protein folding where large amount of data exists. Nonetheless, many important problems remain challenging for data-driven machine learning approaches due to the limitation of data scarcity. One approach to overcome data scarcity is to incorporate physical principles such as through molecular modeling and simulation. Here, we focus on the big potassium (BK) channels that play important roles in cardiovascular and neural systems. Many mutants of BK channel are associated with various neurological and cardiovascular diseases, but the molecular effects are unknown. The voltage gating properties of BK channels have been characterized for 473 site-specific mutations experimentally over the last three decades; yet, these functional data by themselves remain far too sparse to derive a predictive model of BK channel voltage gating. Using physics-based modeling, we quantify the energetic effects of all single mutations on both open and closed states of the channel. Together with dynamic properties derived from atomistic simulations, these physical descriptors allow the training of random forest models that could reproduce unseen experimentally measured shifts in gating voltage, ∆V1/2, with a RMSE ~ 32 mV and correlation coefficient of R ~ 0.7. Importantly, the model appears capable of uncovering nontrivial physical principles underlying the gating of the channel, including a central role of hydrophobic gating. The model was further evaluated using four novel mutations of L235 and V236 on the S5 helix, mutations of which are predicted to have opposing effects on V1/2 and suggest a key role of S5 in mediating voltage sensor-pore coupling. The measured ∆V1/2 agree quantitatively with prediction for all four mutations, with a high correlation of R = 0.92 and RMSE = 18 mV. Therefore, the model can capture nontrivial voltage gating properties in regions where few mutations are known. The success of predictive modeling of BK voltage gating demonstrates the potential of combining physics and statistical learning for overcoming data scarcity in nontrivial protein function prediction.
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Affiliation(s)
- Erik Nordquist
- Department of Chemistry, University of Massachusetts Amherst, Amherst, Massachusetts, United States of America
| | - Guohui Zhang
- Department of Biomedical Engineering, Center for the Investigation of Membrane Excitability Disorders, Cardiac Bioelectricity and Arrhythmia Center, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Shrishti Barethiya
- Department of Chemistry, University of Massachusetts Amherst, Amherst, Massachusetts, United States of America
| | - Nathan Ji
- Department of Biology, Boston College, Chestnut Hill, Massachusetts, United States of America
| | - Kelli M. White
- Department of Biomedical Engineering, Center for the Investigation of Membrane Excitability Disorders, Cardiac Bioelectricity and Arrhythmia Center, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Lu Han
- Department of Biomedical Engineering, Center for the Investigation of Membrane Excitability Disorders, Cardiac Bioelectricity and Arrhythmia Center, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Zhiguang Jia
- Department of Chemistry, University of Massachusetts Amherst, Amherst, Massachusetts, United States of America
| | - Jingyi Shi
- Department of Biomedical Engineering, Center for the Investigation of Membrane Excitability Disorders, Cardiac Bioelectricity and Arrhythmia Center, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Jianmin Cui
- Department of Biomedical Engineering, Center for the Investigation of Membrane Excitability Disorders, Cardiac Bioelectricity and Arrhythmia Center, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Jianhan Chen
- Department of Chemistry, University of Massachusetts Amherst, Amherst, Massachusetts, United States of America
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13
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Sinclair M, Stein RA, Sheehan JH, Hawes EM, O'Brien RM, Tajkhorshid E, Claxton DP. Molecular mechanisms of catalytic inhibition for active site mutations in glucose-6-phosphatase catalytic subunit 1 linked to glycogen storage disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.13.532485. [PMID: 36993754 PMCID: PMC10054992 DOI: 10.1101/2023.03.13.532485] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Mediating the terminal reaction of gluconeogenesis and glycogenolysis, the integral membrane protein G6PC1 regulates hepatic glucose production by catalyzing hydrolysis of glucose-6-phosphate (G6P) within the lumen of the endoplasmic reticulum. Consistent with its vital contribution to glucose homeostasis, inactivating mutations in G6PC1 cause glycogen storage disease (GSD) type 1a characterized by hepatomegaly and severe hypoglycemia. Despite its physiological importance, the structural basis of G6P binding to G6PC1 and the molecular disruptions induced by missense mutations within the active site that give rise to GSD type 1a are unknown. Exploiting a computational model of G6PC1 derived from the groundbreaking structure prediction algorithm AlphaFold2 (AF2), we combine molecular dynamics (MD) simulations and computational predictions of thermodynamic stability with a robust in vitro screening platform to define the atomic interactions governing G6P binding as well as explore the energetic perturbations imposed by disease-linked variants. We identify a collection of side chains, including conserved residues from the signature phosphatidic acid phosphatase motif, that contribute to a hydrogen bonding and van der Waals network stabilizing G6P in the active site. Introduction of GSD type 1a mutations into the G6PC1 sequence elicits changes in G6P binding energy, thermostability and structural properties, suggesting multiple pathways of catalytic impairment. Our results, which corroborate the high quality of the AF2 model as a guide for experimental design and to interpret outcomes, not only confirm active site structural organization but also suggest novel mechanistic contributions of catalytic and non-catalytic side chains.
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14
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Samanta R, Gray JJ. Implicit model to capture electrostatic features of membrane environment. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.26.546486. [PMID: 37425950 PMCID: PMC10327106 DOI: 10.1101/2023.06.26.546486] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Membrane protein structure prediction and design are challenging due to the complexity of capturing the interactions in the lipid layer, such as those arising from electrostatics. Accurately capturing electrostatic energies in the low-dielectric membrane often requires expensive Poisson-Boltzmann calculations that are not scalable for membrane protein structure prediction and design. In this work, we have developed a fast-to-compute implicit energy function that considers the realistic characteristics of different lipid bilayers, making design calculations tractable. This method captures the impact of the lipid head group using a mean-field-based approach and uses a depth-dependent dielectric constant to characterize the membrane environment. This energy function Franklin2023 (F23) is built upon Franklin2019 (F19), which is based on experimentally derived hydrophobicity scales in the membrane bilayer. We evaluated the performance of F23 on five different tests probing (1) protein orientation in the bilayer, (2) stability, and (3) sequence recovery. Relative to F19, F23 has improved the calculation of the tilt angle of membrane proteins for 90% of WALP peptides, 15% of TM-peptides, and 25% of the adsorbed peptides. The performances for stability and design tests were equivalent for F19 and F23. The speed and calibration of the implicit model will help F23 access biophysical phenomena at long time and length scales and accelerate the membrane protein design pipeline.
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Affiliation(s)
- Rituparna Samanta
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, Maryland 21218, United States
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, Maryland, United States
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15
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Nordquist E, Zhang G, Barethiya S, Ji N, White KM, Han L, Jia Z, Shi J, Cui J, Chen J. Incorporating physics to overcome data scarcity in predictive modeling of protein function: a case study of BK channels. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.24.546384. [PMID: 37425916 PMCID: PMC10327070 DOI: 10.1101/2023.06.24.546384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Machine learning has played transformative roles in numerous chemical and biophysical problems such as protein folding where large amount of data exists. Nonetheless, many important problems remain challenging for data-driven machine learning approaches due to the limitation of data scarcity. One approach to overcome data scarcity is to incorporate physical principles such as through molecular modeling and simulation. Here, we focus on the big potassium (BK) channels that play important roles in cardiovascular and neural systems. Many mutants of BK channel are associated with various neurological and cardiovascular diseases, but the molecular effects are unknown. The voltage gating properties of BK channels have been characterized for 473 site-specific mutations experimentally over the last three decades; yet, these functional data by themselves remain far too sparse to derive a predictive model of BK channel voltage gating. Using physics-based modeling, we quantify the energetic effects of all single mutations on both open and closed states of the channel. Together with dynamic properties derived from atomistic simulations, these physical descriptors allow the training of random forest models that could reproduce unseen experimentally measured shifts in gating voltage, ΔV 1/2 , with a RMSE ∼ 32 mV and correlation coefficient of R ∼ 0.7. Importantly, the model appears capable of uncovering nontrivial physical principles underlying the gating of the channel, including a central role of hydrophobic gating. The model was further evaluated using four novel mutations of L235 and V236 on the S5 helix, mutations of which are predicted to have opposing effects on V 1/2 and suggest a key role of S5 in mediating voltage sensor-pore coupling. The measured ΔV 1/2 agree quantitatively with prediction for all four mutations, with a high correlation of R = 0.92 and RMSE = 18 mV. Therefore, the model can capture nontrivial voltage gating properties in regions where few mutations are known. The success of predictive modeling of BK voltage gating demonstrates the potential of combining physics and statistical learning for overcoming data scarcity in nontrivial protein function prediction. Author Summary Deep machine learning has brought many exciting breakthroughs in chemistry, physics and biology. These models require large amount of training data and struggle when the data is scarce. The latter is true for predictive modeling of the function of complex proteins such as ion channels, where only hundreds of mutational data may be available. Using the big potassium (BK) channel as a biologically important model system, we demonstrate that a reliable predictive model of its voltage gating property could be derived from only 473 mutational data by incorporating physics-derived features, which include dynamic properties from molecular dynamics simulations and energetic quantities from Rosetta mutation calculations. We show that the final random forest model captures key trends and hotspots in mutational effects of BK voltage gating, such as the important role of pore hydrophobicity. A particularly curious prediction is that mutations of two adjacent residues on the S5 helix would always have opposite effects on the gating voltage, which was confirmed by experimental characterization of four novel mutations. The current work demonstrates the importance and effectiveness of incorporating physics in predictive modeling of protein function with scarce data.
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Affiliation(s)
- Erik Nordquist
- Department of Chemistry, University of Massachusetts Amherst, Amherst, Massachusetts, USA
| | - Guohui Zhang
- Department of Biomedical Engineering, Center for the Investigation of Membrane Excitability Disorders, Cardiac Bioelectricity and Arrhythmia Center, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Shrishti Barethiya
- Department of Chemistry, University of Massachusetts Amherst, Amherst, Massachusetts, USA
| | - Nathan Ji
- Department of Biology, Boston College, Chestnut Hill, Massachusetts, USA
| | - Kelli M White
- Department of Biomedical Engineering, Center for the Investigation of Membrane Excitability Disorders, Cardiac Bioelectricity and Arrhythmia Center, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Lu Han
- Department of Biomedical Engineering, Center for the Investigation of Membrane Excitability Disorders, Cardiac Bioelectricity and Arrhythmia Center, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Zhiguang Jia
- Department of Chemistry, University of Massachusetts Amherst, Amherst, Massachusetts, USA
| | - Jingyi Shi
- Department of Biomedical Engineering, Center for the Investigation of Membrane Excitability Disorders, Cardiac Bioelectricity and Arrhythmia Center, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Jianmin Cui
- Department of Biomedical Engineering, Center for the Investigation of Membrane Excitability Disorders, Cardiac Bioelectricity and Arrhythmia Center, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Jianhan Chen
- Department of Chemistry, University of Massachusetts Amherst, Amherst, Massachusetts, USA
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16
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Ngo K, Mateos DL, Han Y, Rouen KC, Ahn SH, Wulff H, Clancy CE, Yarov-Yarovoy V, Vorobyov I. Elucidating Molecular Mechanisms of Protoxin-2 State-specific Binding to the Human Na V1.7 Channel. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.27.530360. [PMID: 36909474 PMCID: PMC10002706 DOI: 10.1101/2023.02.27.530360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
Abstract
Human voltage-gated sodium (hNaV) channels are responsible for initiating and propagating action potentials in excitable cells and mutations have been associated with numerous cardiac and neurological disorders. hNaV1.7 channels are expressed in peripheral neurons and are promising targets for pain therapy. The tarantula venom peptide protoxin-2 (PTx2) has high selectivity for hNaV1.7 and serves as a valuable scaffold to design novel therapeutics to treat pain. Here, we used computational modeling to study the molecular mechanisms of the state-dependent binding of PTx2 to hNaV1.7 voltage-sensing domains (VSDs). Using Rosetta structural modeling methods, we constructed atomistic models of the hNaV1.7 VSD II and IV in the activated and deactivated states with docked PTx2. We then performed microsecond-long all-atom molecular dynamics (MD) simulations of the systems in hydrated lipid bilayers. Our simulations revealed that PTx2 binds most favorably to the deactivated VSD II and activated VSD IV. These state-specific interactions are mediated primarily by PTx2's residues R22, K26, K27, K28, and W30 with VSD as well as the surrounding membrane lipids. Our work revealed important protein-protein and protein-lipid contacts that contribute to high-affinity state-dependent toxin interaction with the channel. The workflow presented will prove useful for designing novel peptides with improved selectivity and potency for more effective and safe treatment of pain.
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Affiliation(s)
- Khoa Ngo
- Biophysics Graduate Group, University of California, Davis
- Department of Physiology and Membrane Biology, University of California, Davis
| | - Diego Lopez Mateos
- Biophysics Graduate Group, University of California, Davis
- Department of Physiology and Membrane Biology, University of California, Davis
| | - Yanxiao Han
- Department of Physiology and Membrane Biology, University of California, Davis
| | - Kyle C. Rouen
- Biophysics Graduate Group, University of California, Davis
- Department of Physiology and Membrane Biology, University of California, Davis
| | - Surl-Hee Ahn
- Department of Chemical Engineering, University of California, Davis
| | - Heike Wulff
- Department of Pharmacology, University of California, Davis
| | - Colleen E. Clancy
- Department of Physiology and Membrane Biology, University of California, Davis
- Department of Pharmacology, University of California, Davis
- Center for Precision Medicine and Data Science, University of California, Davis
| | - Vladimir Yarov-Yarovoy
- Department of Physiology and Membrane Biology, University of California, Davis
- Department of Anesthesiology and Pain Medicine, University of California, Davis
| | - Igor Vorobyov
- Department of Physiology and Membrane Biology, University of California, Davis
- Department of Pharmacology, University of California, Davis
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17
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Koehler Leman J, Künze G. Recent Advances in NMR Protein Structure Prediction with ROSETTA. Int J Mol Sci 2023; 24:ijms24097835. [PMID: 37175539 PMCID: PMC10178863 DOI: 10.3390/ijms24097835] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 04/15/2023] [Accepted: 04/21/2023] [Indexed: 05/15/2023] Open
Abstract
Nuclear magnetic resonance (NMR) spectroscopy is a powerful method for studying the structure and dynamics of proteins in their native state. For high-resolution NMR structure determination, the collection of a rich restraint dataset is necessary. This can be difficult to achieve for proteins with high molecular weight or a complex architecture. Computational modeling techniques can complement sparse NMR datasets (<1 restraint per residue) with additional structural information to elucidate protein structures in these difficult cases. The Rosetta software for protein structure modeling and design is used by structural biologists for structure determination tasks in which limited experimental data is available. This review gives an overview of the computational protocols available in the Rosetta framework for modeling protein structures from NMR data. We explain the computational algorithms used for the integration of different NMR data types in Rosetta. We also highlight new developments, including modeling tools for data from paramagnetic NMR and hydrogen-deuterium exchange, as well as chemical shifts in CS-Rosetta. Furthermore, strategies are discussed to complement and improve structure predictions made by the current state-of-the-art AlphaFold2 program using NMR-guided Rosetta modeling.
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Affiliation(s)
- Julia Koehler Leman
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY 10010, USA
| | - Georg Künze
- Institute for Drug Discovery, Medical Faculty, University of Leipzig, Brüderstr. 34, D-04103 Leipzig, Germany
- Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstr. 16-18, D-04107 Leipzig, Germany
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18
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Liessmann F, Künze G, Meiler J. Improving the Modeling of Extracellular Ligand Binding Pockets in RosettaGPCR for Conformational Selection. Int J Mol Sci 2023; 24:7788. [PMID: 37175495 PMCID: PMC10178219 DOI: 10.3390/ijms24097788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 04/19/2023] [Accepted: 04/22/2023] [Indexed: 05/15/2023] Open
Abstract
G protein-coupled receptors (GPCRs) are the largest class of drug targets and undergo substantial conformational changes in response to ligand binding. Despite recent progress in GPCR structure determination, static snapshots fail to reflect the conformational space of putative binding pocket geometries to which small molecule ligands can bind. In comparative modeling of GPCRs in the absence of a ligand, often a shrinking of the orthosteric binding pocket is observed. However, the exact prediction of the flexible orthosteric binding site is crucial for adequate structure-based drug discovery. In order to improve ligand docking and guide virtual screening experiments in computer-aided drug discovery, we developed RosettaGPCRPocketSize. The algorithm creates a conformational ensemble of biophysically realistic conformations of the GPCR binding pocket between the TM bundle, which is consistent with a knowledge base of expected pocket geometries. Specifically, tetrahedral volume restraints are defined based on information about critical residues in the orthosteric binding site and their experimentally observed range of Cα-Cα-distances. The output of RosettaGPCRPocketSize is an ensemble of binding pocket geometries that are filtered by energy to ensure biophysically probable arrangements, which can be used for docking simulations. In a benchmark set, pocket shrinkage observed in the default RosettaGPCR was reduced by up to 80% and the binding pocket volume range and geometric diversity were increased. Compared to models from four different GPCR homology model databases (RosettaGPCR, GPCR-Tasser, GPCR-SSFE, and GPCRdb), the here-created models showed more accurate volumes of the orthosteric pocket when evaluated with respect to the crystallographic reference structure. Furthermore, RosettaGPCRPocketSize was able to generate an improved realistic pocket distribution. However, while being superior to other homology models, the accuracy of generated model pockets was comparable to AlphaFold2 models. Furthermore, in a docking benchmark using small-molecule ligands with a higher molecular weight between 400 and 700 Da, a higher success rate in creating native-like binding poses was observed. In summary, RosettaGPCRPocketSize can generate GPCR models with realistic orthosteric pocket volumes, which are useful for structure-based drug discovery applications.
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Affiliation(s)
- Fabian Liessmann
- Institute for Drug Discovery, Medical Faculty, Leipzig University, 04103 Leipzig, Germany; (F.L.)
| | - Georg Künze
- Institute for Drug Discovery, Medical Faculty, Leipzig University, 04103 Leipzig, Germany; (F.L.)
| | - Jens Meiler
- Institute for Drug Discovery, Medical Faculty, Leipzig University, 04103 Leipzig, Germany; (F.L.)
- Department of Chemistry, Vanderbilt University, Nashville, TN 37235, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN 37235, USA
- Center for Scalable Data Analytics and Artificial Intelligence, Leipzig University, 04105 Leipzig, Germany
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19
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Overduin M, Kervin TA, Klarenbach Z, Adra TRC, Bhat RK. Comprehensive classification of proteins based on structures that engage lipids by COMPOSEL. Biophys Chem 2023; 295:106971. [PMID: 36801589 DOI: 10.1016/j.bpc.2023.106971] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Accepted: 02/05/2023] [Indexed: 02/11/2023]
Abstract
Structures can now be predicted for any protein using programs like AlphaFold and Rosetta, which rely on a foundation of experimentally determined structures of architecturally diverse proteins. The accuracy of such artificial intelligence and machine learning (AI/ML) approaches benefits from the specification of restraints which assist in navigating the universe of folds to converge on models most representative of a given protein's physiological structure. This is especially pertinent for membrane proteins, with structures and functions that depend on their presence in lipid bilayers. Structures of proteins in their membrane environments could conceivably be predicted from AI/ML approaches with user-specificized parameters that describe each element of the architecture of a membrane protein accompanied by its lipid environment. We propose the Classification Of Membrane Proteins based On Structures Engaging Lipids (COMPOSEL), which builds on existing nomenclature types for monotopic, bitopic, polytopic and peripheral membrane proteins as well as lipids. Functional and regulatory elements are also defined in the scripts, as shown with membrane fusing synaptotagmins, multidomain PDZD8 and Protrudin proteins that recognize phosphoinositide (PI) lipids, the intrinsically disordered MARCKS protein, caveolins, the β barrel assembly machine (BAM), an adhesion G-protein coupled receptor (aGPCR) and two lipid modifying enzymes - diacylglycerol kinase DGKε and fatty aldehyde dehydrogenase FALDH. This demonstrates how COMPOSEL communicates lipid interactivity as well as signaling mechanisms and binding of metabolites, drug molecules, polypeptides or nucleic acids to describe the operations of any protein. Moreover COMPOSEL can be scaled to express how genomes encode membrane structures and how our organs are infiltrated by pathogens such as SARS-CoV-2.
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Affiliation(s)
- Michael Overduin
- Department of Biochemistry, University of Alberta, Edmonton, AB, Canada.
| | - Troy A Kervin
- Department of Biochemistry, University of Alberta, Edmonton, AB, Canada
| | | | - Trixie Rae C Adra
- Department of Biochemistry, University of Alberta, Edmonton, AB, Canada
| | - Rakesh K Bhat
- Department of Biochemistry, University of Alberta, Edmonton, AB, Canada
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20
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Marlow B, Kuenze G, Meiler J, Koehler Leman J. Docking cholesterol to integral membrane proteins with Rosetta. PLoS Comput Biol 2023; 19:e1010947. [PMID: 36972273 PMCID: PMC10042369 DOI: 10.1371/journal.pcbi.1010947] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 02/14/2023] [Indexed: 03/29/2023] Open
Abstract
Lipid molecules such as cholesterol interact with the surface of integral membrane proteins (IMP) in a mode different from drug-like molecules in a protein binding pocket. These differences are due to the lipid molecule's shape, the membrane's hydrophobic environment, and the lipid's orientation in the membrane. We can use the recent increase in experimental structures in complex with cholesterol to understand protein-cholesterol interactions. We developed the RosettaCholesterol protocol consisting of (1) a prediction phase using an energy grid to sample and score native-like binding poses and (2) a specificity filter to calculate the likelihood that a cholesterol interaction site may be specific. We used a multi-pronged benchmark (self-dock, flip-dock, cross-dock, and global-dock) of protein-cholesterol complexes to validate our method. RosettaCholesterol improved sampling and scoring of native poses over the standard RosettaLigand baseline method in 91% of cases and performs better regardless of benchmark complexity. On the β2AR, our method found one likely-specific site, which is described in the literature. The RosettaCholesterol protocol quantifies cholesterol binding site specificity. Our approach provides a starting point for high-throughput modeling and prediction of cholesterol binding sites for further experimental validation.
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Affiliation(s)
- Brennica Marlow
- Center for Structural Biology, Vanderbilt University, Nashville, Tennessee, United States of America
- Chemical and Physical Biology Program, Vanderbilt University, Nashville, Tennessee, United States of America
| | - Georg Kuenze
- Center for Structural Biology, Vanderbilt University, Nashville, Tennessee, United States of America
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee, United States of America
- Institute for Drug Discovery, Leipzig University Medical School, Leipzig, Germany
| | - Jens Meiler
- Center for Structural Biology, Vanderbilt University, Nashville, Tennessee, United States of America
- Chemical and Physical Biology Program, Vanderbilt University, Nashville, Tennessee, United States of America
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee, United States of America
- Institute for Drug Discovery, Leipzig University Medical School, Leipzig, Germany
| | - Julia Koehler Leman
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, New York, United States of America
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21
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Mravic M, He L, Kratochvil H, Hu H, Nick SE, Bai W, Edwards A, Jo H, Wu Y, DiMaio D, DeGrado WF. Designed Transmembrane Proteins Inhibit the Erythropoietin Receptor in a Custom Binding Topology. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.13.526773. [PMID: 36824741 PMCID: PMC9949092 DOI: 10.1101/2023.02.13.526773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/17/2023]
Abstract
Transmembrane (TM) domains as simple as a single span can perform complex biological functions using entirely lipid-embedded chemical features. Computational design has potential to generate custom tool molecules directly targeting membrane proteins at their functional TM regions. Thus far, designed TM domain-targeting agents have been limited to mimicking binding modes and motifs of natural TM interaction partners. Here, we demonstrate the design of de novo TM proteins targeting the erythropoietin receptor (EpoR) TM domain in a custom binding topology competitive with receptor homodimerization. The TM proteins expressed in mammalian cells complex with EpoR and inhibit erythropoietin-induced cell proliferation. In vitro, the synthetic TM domain complex outcompetes EpoR homodimerization. Structural characterization reveals that the complex involves the intended amino acids and agrees with our designed molecular model of antiparallel TM helices at 1:1 stoichiometry. Thus, membrane protein TM regions can now be targeted in custom designed topologies.
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22
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Mateos DL, Yarov-Yarovoy V. Structural modeling of peptide toxin-ion channel interactions using RosettaDock. Proteins 2023. [PMID: 36729043 DOI: 10.1002/prot.26474] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 12/09/2022] [Accepted: 01/30/2023] [Indexed: 02/03/2023]
Abstract
Voltage-gated ion channels play essential physiological roles in action potential generation and propagation. Peptidic toxins from animal venoms target ion channels and provide useful scaffolds for the rational design of novel channel modulators with enhanced potency and subtype selectivity. Despite recent progress in obtaining experimental structures of peptide toxin-ion channel complexes, structural determination of peptide toxins bound to ion channels in physiologically important states remains challenging. Here we describe an application of RosettaDock approach to the structural modeling of peptide toxins interactions with ion channels. We tested this approach on 10 structures of peptide toxin-ion channel complexes and demonstrated that it can sample near-native structures in all tested cases. Our approach will be useful for improving the understanding of the molecular mechanism of natural peptide toxin modulation of ion channel gating and for the structural modeling of novel peptide-based ion channel modulators.
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Affiliation(s)
- Diego Lopez Mateos
- Department of Physiology and Membrane Biology, University of California Davis, Davis, California, USA.,Biophysics Graduate Group, University of California Davis, Davis, California, USA
| | - Vladimir Yarov-Yarovoy
- Department of Physiology and Membrane Biology, University of California Davis, Davis, California, USA.,Biophysics Graduate Group, University of California Davis, Davis, California, USA.,Department of Anesthesiology and Pain Medicine, University of California Davis, Davis, California, USA
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23
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Niitsu A, Sugita Y. Towards de novo design of transmembrane α-helical assemblies using structural modelling and molecular dynamics simulation. Phys Chem Chem Phys 2023; 25:3595-3606. [PMID: 36647771 DOI: 10.1039/d2cp03972a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Computational de novo protein design involves iterative processes consisting of amino acid sequence design, structural modelling and scoring, and design validation by synthesis and experimental characterisation. Recent advances in protein structure prediction and modelling methods have enabled the highly efficient and accurate design of water-soluble proteins. However, the design of membrane proteins remains a major challenge. To advance membrane protein design, considering the higher complexity of membrane protein folding, stability, and dynamic interactions between water, ions, lipids, and proteins is an important task. For introducing explicit solvents and membranes to these design methods, all-atom molecular dynamics (MD) simulations of designed proteins provide useful information that cannot be obtained experimentally. In this review, we first describe two major approaches to designing transmembrane α-helical assemblies, consensus and de novo design. We further illustrate recent MD studies of membrane protein folding related to protein design, as well as advanced treatments in molecular models and conformational sampling techniques in the simulations. Finally, we discuss the possibility to introduce MD simulations after the existing static modelling and screening of design decoys as an additional step for refinement of the design, which considers membrane protein folding dynamics and interactions with explicit membranes.
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Affiliation(s)
- Ai Niitsu
- Theoretical Molecular Science Laboratory, RIKEN Cluster for Pioneering Research, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan.
| | - Yuji Sugita
- Theoretical Molecular Science Laboratory, RIKEN Cluster for Pioneering Research, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan. .,Computational Biophysics Research Team, RIKEN Center for Computational Science, 7-1-26 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan.,Laboratory for Biomolecular Function Simulation, RIKEN Center for Biosystems Dynamics Research, 6-7-1 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
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24
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Yüksel S, Bonus M, Schwabe T, Pfleger C, Zimmer T, Enke U, Saß I, Gohlke H, Benndorf K, Kusch J. Uncoupling of Voltage- and Ligand-Induced Activation in HCN2 Channels by Glycine Inserts. Front Physiol 2022; 13:895324. [PMID: 36091400 PMCID: PMC9452628 DOI: 10.3389/fphys.2022.895324] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Accepted: 06/20/2022] [Indexed: 11/24/2022] Open
Abstract
Hyperpolarization-activated cyclic nucleotide-modulated (HCN) channels are tetramers that generate electrical rhythmicity in special brain neurons and cardiomyocytes. The channels are activated by membrane hyperpolarization. The binding of cAMP to the four available cyclic nucleotide-binding domains (CNBD) enhances channel activation. We analyzed in the present study the mechanism of how the effect of cAMP binding is transmitted to the pore domain. Our strategy was to uncouple the C-linker (CL) from the channel core by inserting one to five glycine residues between the S6 gate and the A′-helix (constructs 1G to 5G). We quantified in full-length HCN2 channels the resulting functional effects of the inserted glycines by current activation as well as the structural dynamics and statics using molecular dynamics simulations and Constraint Network Analysis. We show functionally that already in 1G the cAMP effect on activation is lost and that with the exception of 3G and 5G the concentration-activation relationships are shifted to depolarized voltages with respect to HCN2. The strongest effect was found for 4G. Accordingly, the activation kinetics were accelerated by all constructs, again with the strongest effect in 4G. The simulations reveal that the average residue mobility of the CL and CNBD domains is increased in all constructs and that the junction between the S6 and A′-helix is turned into a flexible hinge, resulting in a destabilized gate in all constructs. Moreover, for 3G and 4G, there is a stronger downward displacement of the CL-CNBD than in HCN2 and the other constructs, resulting in an increased kink angle between S6 and A′-helix, which in turn loosens contacts between the S4-helix and the CL. This is suggested to promote a downward movement of the S4-helix, similar to the effect of hyperpolarization. In addition, exclusively in 4G, the selectivity filter in the upper pore region and parts of the S4-helix are destabilized. The results provide new insights into the intricate activation of HCN2 channels.
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Affiliation(s)
- Sezin Yüksel
- Universitätsklinikum Jena, Institut für Physiologie II, Jena, Germany
| | - Michele Bonus
- Institut für Pharmazeutische und Medizinische Chemie, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany
| | - Tina Schwabe
- Universitätsklinikum Jena, Institut für Physiologie II, Jena, Germany
| | - Christopher Pfleger
- Institut für Pharmazeutische und Medizinische Chemie, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany
| | - Thomas Zimmer
- Universitätsklinikum Jena, Institut für Physiologie II, Jena, Germany
| | - Uta Enke
- Universitätsklinikum Jena, Institut für Physiologie II, Jena, Germany
| | - Inga Saß
- Universitätsklinikum Jena, Institut für Physiologie II, Jena, Germany
| | - Holger Gohlke
- Institut für Pharmazeutische und Medizinische Chemie, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany
- John von Neumann Institute for Computing (NIC), Jülich Supercomputing Centre (JSC), Institute of Biological Information Processing (IBI-7: Structural Biochemistry) and Institute of Bio- and Geosciences (IBG-4: Bioinformatics), Forschungszentrum Jülich GmbH, Jülich, Germany
- *Correspondence: Holger Gohlke, ; Klaus Benndorf, ; Jana Kusch,
| | - Klaus Benndorf
- Universitätsklinikum Jena, Institut für Physiologie II, Jena, Germany
- *Correspondence: Holger Gohlke, ; Klaus Benndorf, ; Jana Kusch,
| | - Jana Kusch
- Universitätsklinikum Jena, Institut für Physiologie II, Jena, Germany
- *Correspondence: Holger Gohlke, ; Klaus Benndorf, ; Jana Kusch,
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25
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Computational design of transmembrane proteins. Curr Opin Struct Biol 2022; 74:102381. [DOI: 10.1016/j.sbi.2022.102381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 02/28/2022] [Accepted: 03/17/2022] [Indexed: 11/03/2022]
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26
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Lubin JH, Zardecki C, Dolan EM, Lu C, Shen Z, Dutta S, Westbrook JD, Hudson BP, Goodsell DS, Williams JK, Voigt M, Sarma V, Xie L, Venkatachalam T, Arnold S, Alfaro Alvarado LH, Catalfano K, Khan A, McCarthy E, Staggers S, Tinsley B, Trudeau A, Singh J, Whitmore L, Zheng H, Benedek M, Currier J, Dresel M, Duvvuru A, Dyszel B, Fingar E, Hennen EM, Kirsch M, Khan AA, Labrie‐Cleary C, Laporte S, Lenkeit E, Martin K, Orellana M, Ortiz‐Alvarez de la Campa M, Paredes I, Wheeler B, Rupert A, Sam A, See K, Soto Zapata S, Craig PA, Hall BL, Jiang J, Koeppe JR, Mills SA, Pikaart MJ, Roberts R, Bromberg Y, Hoyer JS, Duffy S, Tischfield J, Ruiz FX, Arnold E, Baum J, Sandberg J, Brannigan G, Khare SD, Burley SK. Evolution of the SARS-CoV-2 proteome in three dimensions (3D) during the first 6 months of the COVID-19 pandemic. Proteins 2022; 90:1054-1080. [PMID: 34580920 PMCID: PMC8661935 DOI: 10.1002/prot.26250] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 08/26/2021] [Accepted: 09/16/2021] [Indexed: 01/18/2023]
Abstract
Understanding the molecular evolution of the SARS-CoV-2 virus as it continues to spread in communities around the globe is important for mitigation and future pandemic preparedness. Three-dimensional structures of SARS-CoV-2 proteins and those of other coronavirusess archived in the Protein Data Bank were used to analyze viral proteome evolution during the first 6 months of the COVID-19 pandemic. Analyses of spatial locations, chemical properties, and structural and energetic impacts of the observed amino acid changes in >48 000 viral isolates revealed how each one of 29 viral proteins have undergone amino acid changes. Catalytic residues in active sites and binding residues in protein-protein interfaces showed modest, but significant, numbers of substitutions, highlighting the mutational robustness of the viral proteome. Energetics calculations showed that the impact of substitutions on the thermodynamic stability of the proteome follows a universal bi-Gaussian distribution. Detailed results are presented for potential drug discovery targets and the four structural proteins that comprise the virion, highlighting substitutions with the potential to impact protein structure, enzyme activity, and protein-protein and protein-nucleic acid interfaces. Characterizing the evolution of the virus in three dimensions provides testable insights into viral protein function and should aid in structure-based drug discovery efforts as well as the prospective identification of amino acid substitutions with potential for drug resistance.
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Affiliation(s)
- Joseph H. Lubin
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Department of Chemistry and Chemical BiologyRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Christine Zardecki
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Elliott M. Dolan
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Department of Chemistry and Chemical BiologyRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Changpeng Lu
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Zhuofan Shen
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Department of Chemistry and Chemical BiologyRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Shuchismita Dutta
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Rutgers Cancer Institute of New Jersey, Robert Wood Johnson Medical School, Rutgers, The State University of New JerseyNew BrunswickNew JerseyUSA
| | - John D. Westbrook
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Rutgers Cancer Institute of New Jersey, Robert Wood Johnson Medical School, Rutgers, The State University of New JerseyNew BrunswickNew JerseyUSA
| | - Brian P. Hudson
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - David S. Goodsell
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Rutgers Cancer Institute of New Jersey, Robert Wood Johnson Medical School, Rutgers, The State University of New JerseyNew BrunswickNew JerseyUSA
- The Scripps Research InstituteLa JollaCaliforniaUSA
| | - Jonathan K. Williams
- Department of Chemistry and Chemical BiologyRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Maria Voigt
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Vidur Sarma
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Lingjun Xie
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Department of Chemistry and Chemical BiologyRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Thejasvi Venkatachalam
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Steven Arnold
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | | | | | - Aaliyah Khan
- University of Maryland Baltimore CountyBaltimoreMarylandUSA
| | | | | | | | | | | | | | - Helen Zheng
- Watchung Hills Regional High SchoolWarrenNew JerseyUSA
| | | | | | - Mark Dresel
- Department of Chemistry and Chemical BiologyRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | | | | | | | | | | | | | | | | | - Evan Lenkeit
- Department of Chemistry and Chemical BiologyRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | | | | | | | | | | | | | - Andrew Sam
- Department of Chemistry and Chemical BiologyRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Katherine See
- Rochester Institute of TechnologyRochesterNew YorkUSA
| | | | - Paul A. Craig
- Rochester Institute of TechnologyRochesterNew YorkUSA
| | | | - Jennifer Jiang
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | | | | | | | | | - Yana Bromberg
- Department of Biochemistry and MicrobiologyRutgers, The State University of New JerseyNew BrunswickNew JerseyUSA
| | - J. Steen Hoyer
- Department of Ecology, Evolution and Natural Resources, School of Environmental and Biological SciencesRutgers, The State University of New JerseyNew BrunswickNew JerseyUSA
| | - Siobain Duffy
- Department of Ecology, Evolution and Natural Resources, School of Environmental and Biological SciencesRutgers, The State University of New JerseyNew BrunswickNew JerseyUSA
| | - Jay Tischfield
- Department of GeneticsRutgers, The State University of New Jersey, and Human Genetics Institute of New JerseyPiscatawayNew JerseyUSA
| | - Francesc X. Ruiz
- Center for Advanced Biotechnology and MedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Eddy Arnold
- Department of Chemistry and Chemical BiologyRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Center for Advanced Biotechnology and MedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Jean Baum
- Department of Chemistry and Chemical BiologyRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Jesse Sandberg
- Center for Computational and Integrative BiologyRutgers, The State University of New JerseyCamdenNew JerseyUSA
| | - Grace Brannigan
- Center for Computational and Integrative BiologyRutgers, The State University of New JerseyCamdenNew JerseyUSA
- Department of PhysicsRutgers, The State University of New JerseyCamdenNew JerseyUSA
| | - Sagar D. Khare
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Department of Chemistry and Chemical BiologyRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Rutgers Cancer Institute of New Jersey, Robert Wood Johnson Medical School, Rutgers, The State University of New JerseyNew BrunswickNew JerseyUSA
| | - Stephen K. Burley
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Department of Chemistry and Chemical BiologyRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Rutgers Cancer Institute of New Jersey, Robert Wood Johnson Medical School, Rutgers, The State University of New JerseyNew BrunswickNew JerseyUSA
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer CenterUniversity of CaliforniaSan Diego, La JollaCaliforniaUSA
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27
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Marafi D, Fatih JM, Kaiyrzhanov R, Ferla MP, Gijavanekar C, Al-Maraghi A, Liu N, Sites E, Alsaif HS, Al-Owain M, Zakkariah M, El-Anany E, Guliyeva U, Guliyeva S, Gaba C, Haseeb A, Alhashem AM, Danish E, Karageorgou V, Beetz C, Subhi AA, Mullegama SV, Torti E, Sebastin M, Breilyn MS, Duberstein S, Abdel-Hamid MS, Mitani T, Du H, Rosenfeld JA, Jhangiani SN, Coban Akdemir Z, Gibbs RA, Taylor JC, Fakhro KA, Hunter JV, Pehlivan D, Zaki MS, Gleeson JG, Maroofian R, Houlden H, Posey JE, Sutton VR, Alkuraya FS, Elsea SH, Lupski JR. Biallelic variants in SLC38A3 encoding a glutamine transporter cause epileptic encephalopathy. Brain 2022; 145:909-924. [PMID: 34605855 PMCID: PMC9050560 DOI: 10.1093/brain/awab369] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 07/13/2021] [Accepted: 08/26/2021] [Indexed: 11/14/2022] Open
Abstract
The solute carrier (SLC) superfamily encompasses >400 transmembrane transporters involved in the exchange of amino acids, nutrients, ions, metals, neurotransmitters and metabolites across biological membranes. SLCs are highly expressed in the mammalian brain; defects in nearly 100 unique SLC-encoding genes (OMIM: https://www.omim.org) are associated with rare Mendelian disorders including developmental and epileptic encephalopathy and severe neurodevelopmental disorders. Exome sequencing and family-based rare variant analyses on a cohort with neurodevelopmental disorders identified two siblings with developmental and epileptic encephalopathy and a shared deleterious homozygous splicing variant in SLC38A3. The gene encodes SNAT3, a sodium-coupled neutral amino acid transporter and a principal transporter of the amino acids asparagine, histidine, and glutamine, the latter being the precursor for the neurotransmitters GABA and glutamate. Additional subjects with a similar developmental and epileptic encephalopathy phenotype and biallelic predicted-damaging SLC38A3 variants were ascertained through GeneMatcher and collaborations with research and clinical molecular diagnostic laboratories. Untargeted metabolomic analysis was performed to identify novel metabolic biomarkers. Ten individuals from seven unrelated families from six different countries with deleterious biallelic variants in SLC38A3 were identified. Global developmental delay, intellectual disability, hypotonia, and absent speech were common features while microcephaly, epilepsy, and visual impairment were present in the majority. Epilepsy was drug-resistant in half. Metabolomic analysis revealed perturbations of glutamate, histidine, and nitrogen metabolism in plasma, urine, and CSF of selected subjects, potentially representing biomarkers of disease. Our data support the contention that SLC38A3 is a novel disease gene for developmental and epileptic encephalopathy and illuminate the likely pathophysiology of the disease as perturbations in glutamine homeostasis.
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Affiliation(s)
- Dana Marafi
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
- Department of Pediatrics, Faculty of Medicine, Kuwait University, P.O. Box 24923, 13110 Safat, Kuwait
- Correspondence to: Dana Marafi, MD, MSc Department of Pediatrics, Faculty of Medicine, Kuwait University P.O. Box 24923, 13110 Safat, Kuwait E-mail:
| | - Jawid M Fatih
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Rauan Kaiyrzhanov
- Department of Neuromuscular Disorders Institute of Neurology, University College London, Queen Square, London, UK
| | - Matteo P Ferla
- NIHR Oxford Biomedical Research Centre, Oxford OX4 2PG, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Charul Gijavanekar
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
- Baylor Genetics Laboratory, Houston, TX 77030, USA
| | | | - Ning Liu
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
- Baylor Genetics Laboratory, Houston, TX 77030, USA
| | - Emily Sites
- Division of Molecular and Human Genetics, Nationwide Children's Hospital, Columbus, OH 43205, USA
| | - Hessa S Alsaif
- Department of Translational Genomics, Center for Genomic Medicine, King Faisal Specialist Hospital and Research Center, Riyadh 11211, Saudi Arabia
| | - Mohammad Al-Owain
- Department of Medical Genomics, Center for Genomic Medicine, King Faisal Specialist Hospital and Research Center, Riyadh 11211, Saudi Arabia
- Department of Anatomy and Cell Biology, College of Medicine, Alfaisal University 11533, Riyadh, Saudi Arabia
| | - Mohamed Zakkariah
- Section of Child Neurology, Department of Pediatrics, Al-adan Hospital, Riqqa, Kuwait
| | - Ehab El-Anany
- Section of Child Neurology, Department of Pediatrics, Al-adan Hospital, Riqqa, Kuwait
| | | | | | - Colette Gaba
- Department of Pediatrics, Bon Secours Mercy Health, Toledo, OH 43608, USA
| | - Ateeq Haseeb
- Mercy Children’s Hospital, Toledo, OH 43608, USA
| | - Amal M Alhashem
- Division of Medical Genetic and Metabolic Medicine, Department of Pediatrics, Prince Sultan Medical Military City, Riyadh, Saudi Arabia
| | - Enam Danish
- Department of Ophthalmology, King Fahad Armed Forces Hospital, Jeddah, Saudi Arabia
| | | | | | - Alaa A Subhi
- Neurosciences Department, King Faisal Specialist Hospital and Research Center, Jeddah, Saudi Arabia
| | | | | | - Monisha Sebastin
- Albert Einstein College of Medicine and the Children's Hospital at Montefiore, Bronx, New York 10467, USA
- Division of Genetics, Department of Pediatrics, Montefiore Medical Center and Albert Einstein College of Medicine, Bronx, New York, 10467, USA
| | - Margo Sheck Breilyn
- Albert Einstein College of Medicine and the Children's Hospital at Montefiore, Bronx, New York 10467, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Susan Duberstein
- Isabelle Rapin Division of Child Neurology in the Saul R Korey Department of Neurology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Mohamed S Abdel-Hamid
- Department of Medical Molecular Genetics, Human Genetics and Genome Research Division, National Research Centre, Cairo, Egypt
| | - Tadahiro Mitani
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Haowei Du
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Jill A Rosenfeld
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
- Baylor Genetics Laboratory, Houston, TX 77030, USA
| | - Shalini N Jhangiani
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Zeynep Coban Akdemir
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
- Human Genetics Center, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Richard A Gibbs
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Jenny C Taylor
- NIHR Oxford Biomedical Research Centre, Oxford OX4 2PG, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Khalid A Fakhro
- Department of Human Genetics, Sidra Medicine, Doha 26999, Qatar
- Department of Genetic Medicine, Weill Cornell Medical College, Doha, Qatar
- College of Health and Life Sciences, Hamad Bin Khalifa University, Doha 34110, Qatar
| | - Jill V Hunter
- E.B. Singleton Department of Pediatric Radiology, Texas Children’s Hospital, Houston, TX 77030, USA
- Department of Radiology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Davut Pehlivan
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
- Texas Children's Hospital, Houston, TX 77030, USA
- Section of Pediatric Neurology and Developmental Neuroscience, Department of Pediatrics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Maha S Zaki
- Department of Clinical Genetics, Human Genetics and Genome Research Division, National Research Centre, Cairo, Egypt
| | - Joseph G Gleeson
- Rady Children's Institute for Genomic Medicine, Howard Hughes Medical Institute, University of California, San Diego, CA 92123, USA
| | - Reza Maroofian
- Department of Neuromuscular Disorders Institute of Neurology, University College London, Queen Square, London, UK
| | - Henry Houlden
- Department of Neuromuscular Disorders Institute of Neurology, University College London, Queen Square, London, UK
| | - Jennifer E Posey
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - V Reid Sutton
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
- Baylor Genetics Laboratory, Houston, TX 77030, USA
- Texas Children's Hospital, Houston, TX 77030, USA
| | - Fowzan S Alkuraya
- Department of Translational Genomics, Center for Genomic Medicine, King Faisal Specialist Hospital and Research Center, Riyadh 11211, Saudi Arabia
| | - Sarah H Elsea
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
- Baylor Genetics Laboratory, Houston, TX 77030, USA
| | - James R Lupski
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
- Texas Children's Hospital, Houston, TX 77030, USA
- Department of Pediatrics, Baylor College of Medicine, Houston, TX 77030, USA
- Correspondence may also be addressed to: James R. Lupski, MD, PhD, DSc (hon) Department of Molecular and Human Genetics, Baylor College of Medicine One Baylor Plaza, Room 604B, Houston, TX 77030, USA E-mail:
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Verteporfin is a Substrate-Selective γ-Secretase Inhibitor that Binds the Amyloid Precursor Protein Transmembrane Domain. J Biol Chem 2022; 298:101792. [PMID: 35247387 PMCID: PMC8968665 DOI: 10.1016/j.jbc.2022.101792] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 02/22/2022] [Accepted: 02/26/2022] [Indexed: 11/23/2022] Open
Abstract
This work reports substrate-selective inhibition of a protease with broad substrate specificity based on direct binding of a small molecule inhibitor to the substrate. The target for these studies was γ-secretase protease, which cleaves dozens of different single span membrane protein substrates, including both the C99 domain of the human amyloid precursor protein and the Notch receptor. Substrate-specific inhibition of C99 cleavage is desirable to reduce production of the amyloid-β polypeptide without inhibiting Notch cleavage, a major source of toxicity associated with broad specificity γ-secretase inhibitors. In order to identify a C99-selective inhibitors of the human γ-secretase, we conducted an NMR-based screen of FDA-approved drugs against C99 in model membranes. From this screen, we identified the small molecule verteporfin with these properties. We observed that verteporfin formed a direct 1:1 complex with C99, with a KD of 15-47 μM (depending on the membrane mimetic used), and that it did not bind the transmembrane domain of the Notch-1 receptor. Biochemical assays showed that direct binding of verteporfin to C99 inhibits γ-secretase cleavage of C99 with IC50 values in the range of 15- 164 μM, while Notch-1 cleavage was inhibited only at higher concentrations, and likely via a mechanism that does not involve binding to Notch-1. This work documents a robust NMR-based approach to discovery of small molecule binders to single-span membrane proteins and confirmed that it is possible to inhibit γ-secretase in a substrate-specific manner.
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29
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Boral A, Khamaru M, Mitra D. Designing synthetic transcription factors: A structural perspective. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2022; 130:245-287. [PMID: 35534109 DOI: 10.1016/bs.apcsb.2021.12.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
In this chapter, we discuss different design strategies of synthetic proteins, especially synthetic transcription factors. Design and engineering of synthetic transcription factors is particularly relevant for the need-based manipulation of gene expression. With recent advances in structural biology techniques and with the emergence of other precision biochemical/physical tools, accurate knowledge on structure-function relations is increasingly becoming available. Besides discussing the underlying principles of design, we go through individual cases, especially those involving four major groups of transcription factors-basic leucine zippers, zinc fingers, helix-turn-helix and homeodomains. We further discuss how synthetic biology can come together with structural biology to alter the genetic blueprint of life.
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Affiliation(s)
- Aparna Boral
- Department of Life Sciences, Presidency University, Kolkata, West Bengal, India
| | - Madhurima Khamaru
- Department of Life Sciences, Presidency University, Kolkata, West Bengal, India
| | - Devrani Mitra
- Department of Life Sciences, Presidency University, Kolkata, West Bengal, India.
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30
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Nguyen PT, Nguyen HM, Wagner KM, Stewart RG, Singh V, Thapa P, Chen YJ, Lillya MW, Ton AT, Kondo R, Ghetti A, Pennington MW, Hammock B, Griffith TN, Sack JT, Wulff H, Yarov-Yarovoy V. Computational design of peptides to target Na V1.7 channel with high potency and selectivity for the treatment of pain. eLife 2022; 11:81727. [PMID: 36576241 PMCID: PMC9831606 DOI: 10.7554/elife.81727] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Accepted: 12/23/2022] [Indexed: 12/29/2022] Open
Abstract
The voltage-gated sodium NaV1.7 channel plays a key role as a mediator of action potential propagation in C-fiber nociceptors and is an established molecular target for pain therapy. ProTx-II is a potent and moderately selective peptide toxin from tarantula venom that inhibits human NaV1.7 activation. Here we used available structural and experimental data to guide Rosetta design of potent and selective ProTx-II-based peptide inhibitors of human NaV1.7 channels. Functional testing of designed peptides using electrophysiology identified the PTx2-3127 and PTx2-3258 peptides with IC50s of 7 nM and 4 nM for hNaV1.7 and more than 1000-fold selectivity over human NaV1.1, NaV1.3, NaV1.4, NaV1.5, NaV1.8, and NaV1.9 channels. PTx2-3127 inhibits NaV1.7 currents in mouse and human sensory neurons and shows efficacy in rat models of chronic and thermal pain when administered intrathecally. Rationally designed peptide inhibitors of human NaV1.7 channels have transformative potential to define a new class of biologics to treat pain.
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Affiliation(s)
- Phuong T Nguyen
- Department of Physiology and Membrane Biology, University of California DavisDavisUnited States
| | - Hai M Nguyen
- Department of Pharmacology, University of California DavisDavisUnited States
| | - Karen M Wagner
- Department of Entomology and Nematology & Comprehensive Cancer Center, University of California DavisDavisUnited States
| | - Robert G Stewart
- Department of Physiology and Membrane Biology, University of California DavisDavisUnited States
| | - Vikrant Singh
- Department of Pharmacology, University of California DavisDavisUnited States
| | - Parashar Thapa
- Department of Physiology and Membrane Biology, University of California DavisDavisUnited States
| | - Yi-Je Chen
- Department of Pharmacology, University of California DavisDavisUnited States
| | - Mark W Lillya
- Department of Physiology and Membrane Biology, University of California DavisDavisUnited States
| | | | | | | | | | - Bruce Hammock
- Department of Entomology and Nematology & Comprehensive Cancer Center, University of California DavisDavisUnited States
| | - Theanne N Griffith
- Department of Physiology and Membrane Biology, University of California DavisDavisUnited States
| | - Jon T Sack
- Department of Physiology and Membrane Biology, University of California DavisDavisUnited States,Department of Anesthesiology and Pain Medicine, University of California DavisDavisUnited States
| | - Heike Wulff
- Department of Pharmacology, University of California DavisDavisUnited States
| | - Vladimir Yarov-Yarovoy
- Department of Physiology and Membrane Biology, University of California DavisDavisUnited States,Department of Anesthesiology and Pain Medicine, University of California DavisDavisUnited States,Biophysics Graduate Group, University of California DavisDavisUnited States
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31
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Wang C, Yang J, Lu Y. Modularize and Unite: Toward Creating a Functional Artificial Cell. Front Mol Biosci 2021; 8:781986. [PMID: 34912849 PMCID: PMC8667554 DOI: 10.3389/fmolb.2021.781986] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 11/17/2021] [Indexed: 11/17/2022] Open
Abstract
An artificial cell is a simplified model of a living system, bringing breakthroughs into both basic life science and applied research. The bottom-up strategy instructs the construction of an artificial cell from nonliving materials, which could be complicated and interdisciplinary considering the inherent complexity of living cells. Although significant progress has been achieved in the past 2 decades, the area is still facing some problems, such as poor compatibility with complex bio-systems, instability, and low standardization of the construction method. In this review, we propose creating artificial cells through the integration of different functional modules. Furthermore, we divide the function requirements of an artificial cell into four essential parts (metabolism, energy supplement, proliferation, and communication) and discuss the present researches. Then we propose that the compartment and the reestablishment of the communication system would be essential for the reasonable integration of functional modules. Although enormous challenges remain, the modular construction would facilitate the simplification and standardization of an artificial cell toward a natural living system. This function-based strategy would also broaden the application of artificial cells and represent the steps of imitating and surpassing nature.
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Affiliation(s)
- Chen Wang
- Key Laboratory of Industrial Biocatalysis, Department of Chemical Engineering, Ministry of Education, Tsinghua University, Beijing, China
| | - Junzhu Yang
- Key Laboratory of Industrial Biocatalysis, Department of Chemical Engineering, Ministry of Education, Tsinghua University, Beijing, China
| | - Yuan Lu
- Key Laboratory of Industrial Biocatalysis, Department of Chemical Engineering, Ministry of Education, Tsinghua University, Beijing, China
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32
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Koehler Leman J, Lyskov S, Lewis SM, Adolf-Bryfogle J, Alford RF, Barlow K, Ben-Aharon Z, Farrell D, Fell J, Hansen WA, Harmalkar A, Jeliazkov J, Kuenze G, Krys JD, Ljubetič A, Loshbaugh AL, Maguire J, Moretti R, Mulligan VK, Nance ML, Nguyen PT, Ó Conchúir S, Roy Burman SS, Samanta R, Smith ST, Teets F, Tiemann JKS, Watkins A, Woods H, Yachnin BJ, Bahl CD, Bailey-Kellogg C, Baker D, Das R, DiMaio F, Khare SD, Kortemme T, Labonte JW, Lindorff-Larsen K, Meiler J, Schief W, Schueler-Furman O, Siegel JB, Stein A, Yarov-Yarovoy V, Kuhlman B, Leaver-Fay A, Gront D, Gray JJ, Bonneau R. Ensuring scientific reproducibility in bio-macromolecular modeling via extensive, automated benchmarks. Nat Commun 2021; 12:6947. [PMID: 34845212 PMCID: PMC8630030 DOI: 10.1038/s41467-021-27222-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Accepted: 11/02/2021] [Indexed: 01/14/2023] Open
Abstract
Each year vast international resources are wasted on irreproducible research. The scientific community has been slow to adopt standard software engineering practices, despite the increases in high-dimensional data, complexities of workflows, and computational environments. Here we show how scientific software applications can be created in a reproducible manner when simple design goals for reproducibility are met. We describe the implementation of a test server framework and 40 scientific benchmarks, covering numerous applications in Rosetta bio-macromolecular modeling. High performance computing cluster integration allows these benchmarks to run continuously and automatically. Detailed protocol captures are useful for developers and users of Rosetta and other macromolecular modeling tools. The framework and design concepts presented here are valuable for developers and users of any type of scientific software and for the scientific community to create reproducible methods. Specific examples highlight the utility of this framework, and the comprehensive documentation illustrates the ease of adding new tests in a matter of hours.
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Affiliation(s)
- Julia Koehler Leman
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, 10010, USA.
- Department of Biology, New York University, New York, NY, 10003, USA.
| | - Sergey Lyskov
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Steven M Lewis
- Cyrus Biotechnology, 1201 Second Ave, Suite 900, Seattle, WA, 98101, USA
| | - Jared Adolf-Bryfogle
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, 92037, USA
- IAVI Neutralizing Antibody Center, Scripps Research, La Jolla, CA, 92037, USA
| | - Rebecca F Alford
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Kyle Barlow
- Graduate Program in Bioinformatics, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Ziv Ben-Aharon
- Department of Microbiology and Molecular Genetics, Hebrew University, Hadassah Medical School, POB 12272, Jerusalem, 91120, Israel
| | - Daniel Farrell
- Department of Biochemistry, University of Washington, Seattle, WA, 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA, 98195, USA
| | - Jason Fell
- Genome Center, University of California, Davis, CA, 95616, USA
- Department of Biochemistry & Molecular Medicine, University of California, Davis, CA, 95616, USA
- Department of Chemistry, University of California, Davis, CA, 95616, USA
| | - William A Hansen
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, 08904, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, 08904, USA
| | - Ameya Harmalkar
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Jeliazko Jeliazkov
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Georg Kuenze
- Department of Chemistry, Vanderbilt University, Nashville, TN, 37235, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37235, USA
- Institute for Drug Discovery, Medical School, Leipzig University, 04103, Leipzig, Germany
| | - Justyna D Krys
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw, Pasteura 1, 02-093, Warsaw, Poland
| | - Ajasja Ljubetič
- Department of Biochemistry, University of Washington, Seattle, WA, 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA, 98195, USA
| | - Amanda L Loshbaugh
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, 94158, USA
- Biophysics Graduate Program, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Jack Maguire
- Program in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Rocco Moretti
- Department of Chemistry, Vanderbilt University, Nashville, TN, 37235, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37235, USA
| | - Vikram Khipple Mulligan
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, 10010, USA
| | - Morgan L Nance
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Phuong T Nguyen
- Department of Physiology and Membrane Biology, School of Medicine, University of California, Davis, CA, 95616, USA
| | - Shane Ó Conchúir
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Shourya S Roy Burman
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Rituparna Samanta
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Shannon T Smith
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37235, USA
- Chemical and Physical Biology Program, Vanderbilt University, Nashville, TN, 37235, USA
| | - Frank Teets
- Department of Bioochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27516, USA
| | - Johanna K S Tiemann
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, DK-2200, Copenhagen N., Denmark
| | - Andrew Watkins
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Hope Woods
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37235, USA
- Chemical and Physical Biology Program, Vanderbilt University, Nashville, TN, 37235, USA
| | - Brahm J Yachnin
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, 08904, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, 08904, USA
| | - Christopher D Bahl
- Institute for Protein Innovation, Boston, MA, 02115, USA
- Division of Hematology/Oncology, Boston Children's Hospital, Boston, MA, 02115, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, 02115, USA
| | | | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA, 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA, 98195, USA
| | - Rhiju Das
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Frank DiMaio
- Department of Biochemistry, University of Washington, Seattle, WA, 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA, 98195, USA
| | - Sagar D Khare
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, 08904, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, 08904, USA
| | - Tanja Kortemme
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, 94158, USA
- Biophysics Graduate Program, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Jason W Labonte
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Kresten Lindorff-Larsen
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, DK-2200, Copenhagen N., Denmark
| | - Jens Meiler
- Department of Chemistry, Vanderbilt University, Nashville, TN, 37235, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37235, USA
- Institute for Drug Discovery, Medical School, Leipzig University, 04103, Leipzig, Germany
| | - William Schief
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, 92037, USA
- IAVI Neutralizing Antibody Center, Scripps Research, La Jolla, CA, 92037, USA
| | - Ora Schueler-Furman
- Department of Microbiology and Molecular Genetics, Hebrew University, Hadassah Medical School, POB 12272, Jerusalem, 91120, Israel
| | - Justin B Siegel
- Genome Center, University of California, Davis, CA, 95616, USA
- Department of Biochemistry & Molecular Medicine, University of California, Davis, CA, 95616, USA
- Department of Chemistry, University of California, Davis, CA, 95616, USA
| | - Amelie Stein
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, DK-2200, Copenhagen N., Denmark
| | - Vladimir Yarov-Yarovoy
- Department of Physiology and Membrane Biology, School of Medicine, University of California, Davis, CA, 95616, USA
| | - Brian Kuhlman
- Department of Bioochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27516, USA
| | - Andrew Leaver-Fay
- Department of Bioochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27516, USA
| | - Dominik Gront
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw, Pasteura 1, 02-093, Warsaw, Poland
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA.
| | - Richard Bonneau
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, 10010, USA.
- Department of Biology, New York University, New York, NY, 10003, USA.
- Department of Computer Science, New York University, New York, NY, 10003, USA.
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33
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Mulnaes D, Schott-Verdugo S, Koenig F, Gohlke H. TopProperty: Robust Metaprediction of Transmembrane and Globular Protein Features Using Deep Neural Networks. J Chem Theory Comput 2021; 17:7281-7289. [PMID: 34663069 DOI: 10.1021/acs.jctc.1c00685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Transmembrane proteins (TMPs) are critical components of cellular life. However, due to experimental challenges, the number of experimentally resolved TMP structures is severely underrepresented in databases compared to their cellular abundance. Prediction of (per-residue) features such as transmembrane topology, membrane exposure, secondary structure, and solvent accessibility can be a useful starting point for experimental design or protein structure prediction but often requires different computational tools for different features or types of proteins. We present TopProperty, a metapredictor that predicts all of these features for TMPs or globular proteins. TopProperty is trained on datasets without bias toward a high number of sequence homologs, and the predictions are significantly better than the evaluated state-of-the-art primary predictors on all quality metrics. TopProperty eliminates the need for protein type- or feature-tailored tools, specifically for TMPs. TopProperty is freely available as a web server and standalone at https://cpclab.uni-duesseldorf.de/topsuite/.
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Affiliation(s)
- Daniel Mulnaes
- Institut für Pharmazeutische und Medizinische Chemie, Heinrich-Heine-Universität Düsseldorf, Düsseldorf 40225, Germany
| | - Stephan Schott-Verdugo
- John von Neumann Institute for Computing (NIC), Jülich Supercomputing Centre (JSC), Institute of Biological Information Processing (IBI-7: Structural Bioinformatics), and Institute of Bio- and Geosciences (IBG-4: Bioinformatics), Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Str., Jülich 52425, Germany
| | - Filip Koenig
- Institut für Pharmazeutische und Medizinische Chemie, Heinrich-Heine-Universität Düsseldorf, Düsseldorf 40225, Germany
| | - Holger Gohlke
- Institut für Pharmazeutische und Medizinische Chemie, Heinrich-Heine-Universität Düsseldorf, Düsseldorf 40225, Germany.,John von Neumann Institute for Computing (NIC), Jülich Supercomputing Centre (JSC), Institute of Biological Information Processing (IBI-7: Structural Bioinformatics), and Institute of Bio- and Geosciences (IBG-4: Bioinformatics), Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Str., Jülich 52425, Germany
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34
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Moe-Lange J, Gappel NM, Machado M, Wudick MM, Sies CSA, Schott-Verdugo SN, Bonus M, Mishra S, Hartwig T, Bezrutczyk M, Basu D, Farmer EE, Gohlke H, Malkovskiy A, Haswell ES, Lercher MJ, Ehrhardt DW, Frommer WB, Kleist TJ. Interdependence of a mechanosensitive anion channel and glutamate receptors in distal wound signaling. SCIENCE ADVANCES 2021; 7:eabg4298. [PMID: 34516872 PMCID: PMC8442888 DOI: 10.1126/sciadv.abg4298] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Glutamate has dual roles in metabolism and signaling; thus, signaling functions must be isolatable and distinct from metabolic fluctuations, as seen in low-glutamate domains at synapses. In plants, wounding triggers electrical and calcium (Ca2+) signaling, which involve homologs of mammalian glutamate receptors. The hydraulic dispersal and squeeze-cell hypotheses implicate pressure as a key component of systemic signaling. Here, we identify the stretch-activated anion channel MSL10 as necessary for proper wound-induced electrical and Ca2+ signaling. Wound gene induction, genetics, and Ca2+ imaging indicate that MSL10 acts in the same pathway as the glutamate receptor–like proteins (GLRs). Analogous to mammalian NMDA glutamate receptors, GLRs may serve as coincidence detectors gated by the combined requirement for ligand binding and membrane depolarization, here mediated by stretch activation of MSL10. This study provides a molecular genetic basis for a role of mechanical signal perception and the transmission of long-distance electrical and Ca2+ signals in plants.
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Affiliation(s)
- Jacob Moe-Lange
- Department of Biology, Stanford University, Stanford, CA 94305, USA
- Institute for Molecular Physiology, Heinrich-Heine-Universität Düsseldorf, 40225 Düsseldorf, Germany
- Department of Plant Biology, Carnegie Science, Stanford, CA 94305, USA
| | - Nicoline M. Gappel
- Institute for Molecular Physiology, Heinrich-Heine-Universität Düsseldorf, 40225 Düsseldorf, Germany
| | - Mackenzie Machado
- Department of Plant Biology, Carnegie Science, Stanford, CA 94305, USA
| | - Michael M. Wudick
- Institute for Molecular Physiology, Heinrich-Heine-Universität Düsseldorf, 40225 Düsseldorf, Germany
| | - Cosima S. A. Sies
- Institute for Molecular Physiology, Heinrich-Heine-Universität Düsseldorf, 40225 Düsseldorf, Germany
| | - Stephan N. Schott-Verdugo
- Institute for Pharmaceutical and Medicinal Chemistry, Heinrich-Heine-Universität Düsseldorf, 40225 Düsseldorf, Germany
- Centro de Bioinformática y Simulación Molecular (CBSM), Facultad de Ingeniería, Universidad de Talca, 2 Norte 685, CL-3460000 Talca, Chile
- John von Neumann Institute for Computing (NIC), Jülich Supercomputing Centre (JSC), Institute of Biological Information Processing (IBI-7: Structural Bioinformatics), and Institute of Bio- and Geosciences (IBG-4: Bioinformatics), Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Str., 52425 Jülich, Germany
| | - Michele Bonus
- Institute for Pharmaceutical and Medicinal Chemistry, Heinrich-Heine-Universität Düsseldorf, 40225 Düsseldorf, Germany
| | - Swastik Mishra
- Computational Cell Biology, Heinrich-Heine-Universität Düsseldorf, 40225 Düsseldorf, Germany
| | - Thomas Hartwig
- Institute for Molecular Physiology, Heinrich-Heine-Universität Düsseldorf, 40225 Düsseldorf, Germany
| | - Margaret Bezrutczyk
- Institute for Molecular Physiology, Heinrich-Heine-Universität Düsseldorf, 40225 Düsseldorf, Germany
| | - Debarati Basu
- NSF Center for Engineering Mechanobiology, Department of Biology, Washington University in St. Louis, Box 1137, One Brookings Drive, St. Louis, MO 63130, USA
| | - Edward E. Farmer
- Department of Plant Molecular Biology, University of Lausanne, CH-1015 Lausanne, Switzerland
| | - Holger Gohlke
- Institute for Pharmaceutical and Medicinal Chemistry, Heinrich-Heine-Universität Düsseldorf, 40225 Düsseldorf, Germany
- John von Neumann Institute for Computing (NIC), Jülich Supercomputing Centre (JSC), Institute of Biological Information Processing (IBI-7: Structural Bioinformatics), and Institute of Bio- and Geosciences (IBG-4: Bioinformatics), Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Str., 52425 Jülich, Germany
| | - Andrey Malkovskiy
- Department of Plant Biology, Carnegie Science, Stanford, CA 94305, USA
| | - Elizabeth S. Haswell
- NSF Center for Engineering Mechanobiology, Department of Biology, Washington University in St. Louis, Box 1137, One Brookings Drive, St. Louis, MO 63130, USA
| | - Martin J. Lercher
- Computational Cell Biology, Heinrich-Heine-Universität Düsseldorf, 40225 Düsseldorf, Germany
| | - David W. Ehrhardt
- Department of Plant Biology, Carnegie Science, Stanford, CA 94305, USA
| | - Wolf B. Frommer
- Department of Biology, Stanford University, Stanford, CA 94305, USA
- Institute for Molecular Physiology, Heinrich-Heine-Universität Düsseldorf, 40225 Düsseldorf, Germany
- Institute of Transformative Bio-Molecules (WPI-ITbM), Nagoya University, Chikusa, Nagoya 464-8601, Japan
- Corresponding author.
| | - Thomas J. Kleist
- Institute for Molecular Physiology, Heinrich-Heine-Universität Düsseldorf, 40225 Düsseldorf, Germany
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35
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Michael E, Simonson T. How much can physics do for protein design? Curr Opin Struct Biol 2021; 72:46-54. [PMID: 34461593 DOI: 10.1016/j.sbi.2021.07.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Revised: 07/22/2021] [Accepted: 07/25/2021] [Indexed: 01/03/2023]
Abstract
Physics and physical chemistry are an important thread in computational protein design, complementary to knowledge-based tools. They provide molecular mechanics scoring functions that need little or no ad hoc parameter readjustment, methods to thoroughly sample equilibrium ensembles, and different levels of approximation for conformational flexibility. They led recently to the successful redesign of a small protein using a physics-based folded state energy. Adaptive Monte Carlo or molecular dynamics schemes were discovered where protein variants are populated as per their ligand-binding free energy or catalytic efficiency. Molecular dynamics have been used for backbone flexibility. Implicit solvent models have been refined, polarizable force fields applied, and many physical insights obtained.
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Affiliation(s)
- Eleni Michael
- Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole Polytechnique, 91128, Palaiseau, France
| | - Thomas Simonson
- Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole Polytechnique, 91128, Palaiseau, France.
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36
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Alford RF, Samanta R, Gray JJ. Diverse Scientific Benchmarks for Implicit Membrane Energy Functions. J Chem Theory Comput 2021; 17:5248-5261. [PMID: 34310137 DOI: 10.1021/acs.jctc.0c00646] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Energy functions are fundamental to biomolecular modeling. Their success depends on robust physical formalisms, efficient optimization, and high-resolution data for training and validation. Over the past 20 years, progress in each area has advanced soluble protein energy functions. Yet, energy functions for membrane proteins lag behind due to sparse and low-quality data, leading to overfit tools. To overcome this challenge, we assembled a suite of 12 tests on independent data sets varying in size, diversity, and resolution. The tests probe an energy function's ability to capture membrane protein orientation, stability, sequence, and structure. Here, we present the tests and use the franklin2019 energy function to demonstrate them. We then identify areas for energy function improvement and discuss potential future integration with machine-learning-based optimization methods. The tests are available through the Rosetta Benchmark Server (https://benchmark.graylab.jhu.edu/) and GitHub (https://github.com/rfalford12/Implicit-Membrane-Energy-Function-Benchmark).
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Affiliation(s)
- Rebecca F Alford
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States
| | - Rituparna Samanta
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States.,Program in Molecular Biophysics, Johns Hopkins University, Baltimore, Maryland, United States
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37
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Principles and Methods in Computational Membrane Protein Design. J Mol Biol 2021; 433:167154. [PMID: 34271008 DOI: 10.1016/j.jmb.2021.167154] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Revised: 07/03/2021] [Accepted: 07/06/2021] [Indexed: 01/13/2023]
Abstract
After decades of progress in computational protein design, the design of proteins folding and functioning in lipid membranes appears today as the next frontier. Some notable successes in the de novo design of simplified model membrane protein systems have helped articulate fundamental principles of protein folding, architecture and interaction in the hydrophobic lipid environment. These principles are reviewed here, together with the computational methods and approaches that were used to identify them. We provide an overview of the methodological innovations in the generation of new protein structures and functions and in the development of membrane-specific energy functions. We highlight the opportunities offered by new machine learning approaches applied to protein design, and by new experimental characterization techniques applied to membrane proteins. Although membrane protein design is in its infancy, it appears more reachable than previously thought.
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38
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del Alamo D, Jagessar KL, Meiler J, Mchaourab HS. Methodology for rigorous modeling of protein conformational changes by Rosetta using DEER distance restraints. PLoS Comput Biol 2021; 17:e1009107. [PMID: 34133419 PMCID: PMC8238229 DOI: 10.1371/journal.pcbi.1009107] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Revised: 06/28/2021] [Accepted: 05/24/2021] [Indexed: 12/20/2022] Open
Abstract
We describe an approach for integrating distance restraints from Double Electron-Electron Resonance (DEER) spectroscopy into Rosetta with the purpose of modeling alternative protein conformations from an initial experimental structure. Fundamental to this approach is a multilateration algorithm that harnesses sets of interconnected spin label pairs to identify optimal rotamer ensembles at each residue that fit the DEER decay in the time domain. Benchmarked relative to data analysis packages, the algorithm yields comparable distance distributions with the advantage that fitting the DEER decay and rotamer ensemble optimization are coupled. We demonstrate this approach by modeling the protonation-dependent transition of the multidrug transporter PfMATE to an inward facing conformation with a deviation to the experimental structure of less than 2Å Cα RMSD. By decreasing spin label rotamer entropy, this approach engenders more accurate Rosetta models that are also more closely clustered, thus setting the stage for more robust modeling of protein conformational changes.
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Affiliation(s)
- Diego del Alamo
- Department of Chemistry and Center for Structural Biology, Vanderbilt University, Nashville, Tennessee, United States of America
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee, United States of America
| | - Kevin L. Jagessar
- Department of Chemistry and Center for Structural Biology, Vanderbilt University, Nashville, Tennessee, United States of America
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee, United States of America
| | - Jens Meiler
- Department of Chemistry and Center for Structural Biology, Vanderbilt University, Nashville, Tennessee, United States of America
- Institute for Drug Discovery, Leipzig University Medical School, Leipzig, Germany
| | - Hassane S. Mchaourab
- Department of Chemistry and Center for Structural Biology, Vanderbilt University, Nashville, Tennessee, United States of America
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee, United States of America
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39
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Jiang V, Khare SD, Banta S. Computational structure prediction provides a plausible mechanism for electron transfer by the outer membrane protein Cyc2 from Acidithiobacillus ferrooxidans. Protein Sci 2021; 30:1640-1652. [PMID: 33969560 DOI: 10.1002/pro.4106] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 04/30/2021] [Accepted: 05/03/2021] [Indexed: 12/14/2022]
Abstract
Cyc2 is the key protein in the outer membrane of Acidithiobacillus ferrooxidans that mediates electron transfer between extracellular inorganic iron and the intracellular central metabolism. This cytochrome c is specific for iron and interacts with periplasmic proteins to complete a reversible electron transport chain. A structure of Cyc2 has not yet been characterized experimentally. Here we describe a structural model of Cyc2, and associated proteins, to highlight a plausible mechanism for the ferrous iron electron transfer chain. A comparative modeling protocol specific for trans membrane beta barrel (TMBB) proteins in acidophilic conditions (pH ~ 2) was applied to the primary sequence of Cyc2. The proposed structure has three main regimes: Extracellular loops exposed to low-pH conditions, a TMBB, and an N-terminal cytochrome-like region within the periplasmic space. The Cyc2 model was further refined by identifying likely iron and heme docking sites. This represents the first computational model of Cyc2 that accounts for the membrane microenvironment and the acidity in the extracellular matrix. This approach can be used to model other TMBBs which can be critical for chemolithotrophic microbial growth.
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Affiliation(s)
- Virginia Jiang
- Department of Chemical Engineering, Columbia University in the City of New York, New York, New York, USA
| | - Sagar D Khare
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA
| | - Scott Banta
- Department of Chemical Engineering, Columbia University in the City of New York, New York, New York, USA
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40
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Marx DC, Fleming KG. Membrane proteins enter the fold. Curr Opin Struct Biol 2021; 69:124-130. [PMID: 33975156 DOI: 10.1016/j.sbi.2021.03.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 03/15/2021] [Accepted: 03/21/2021] [Indexed: 11/19/2022]
Abstract
Membrane proteins have historically been recalcitrant to biophysical folding studies. However, recent adaptations of methods from the soluble protein folding field have found success in their applications to transmembrane proteins composed of both α-helical and β-barrel conformations. Avoiding aggregation is critical for the success of these experiments. Altogether these studies are leading to discoveries of folding trajectories, foundational stabilizing forces and better-defined endpoints that enable more accurate interpretation of thermodynamic data. Increased information on membrane protein folding in the cell shows that the emerging biophysical principles are largely recapitulated even in the complex biological environment.
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Affiliation(s)
- Dagan C Marx
- TC Jenkins Department of Biophysics, Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD, 21218, United States
| | - Karen G Fleming
- TC Jenkins Department of Biophysics, Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD, 21218, United States.
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41
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Meinen BA, Bahl CD. Breakthroughs in computational design methods open up new frontiers for de novo protein engineering. Protein Eng Des Sel 2021; 34:6243354. [DOI: 10.1093/protein/gzab007] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 02/16/2021] [Accepted: 03/08/2021] [Indexed: 02/03/2023] Open
Abstract
Abstract
Proteins catalyze the majority of chemical reactions in organisms, and harnessing this power has long been the focus of the protein engineering field. Computational protein design aims to create new proteins and functions in silico, and in doing so, accelerate the process, reduce costs and enable more sophisticated engineering goals to be accomplished. Challenges that very recently seemed impossible are now within reach thanks to several landmark advances in computational protein design methods. Here, we summarize these new methods, with a particular emphasis on de novo protein design advancements occurring within the past 5 years.
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Affiliation(s)
- Ben A Meinen
- Institute for Protein Innovation, Harvard Institutes of Medicine 4 Blackfan Circle, Room 941 Boston, MA 02115-5701 Boston, USA
- Division of Hematology/Oncology, Boston Children's Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Christopher D Bahl
- Institute for Protein Innovation, Harvard Institutes of Medicine 4 Blackfan Circle, Room 941 Boston, MA 02115-5701 Boston, USA
- Division of Hematology/Oncology, Boston Children's Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
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42
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Gulsevin A, Meiler J. Prediction of amphipathic helix-membrane interactions with Rosetta. PLoS Comput Biol 2021; 17:e1008818. [PMID: 33730029 PMCID: PMC8007005 DOI: 10.1371/journal.pcbi.1008818] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2020] [Revised: 03/29/2021] [Accepted: 02/18/2021] [Indexed: 01/17/2023] Open
Abstract
Amphipathic helices have hydrophobic and hydrophilic/charged residues situated on opposite faces of the helix. They can anchor peripheral membrane proteins to the membrane, be attached to integral membrane proteins, or exist as independent peptides. Despite the widespread presence of membrane-interacting amphipathic helices, there is no computational tool within Rosetta to model their interactions with membranes. In order to address this need, we developed the AmphiScan protocol with PyRosetta, which runs a grid search to find the most favorable position of an amphipathic helix with respect to the membrane. The performance of the algorithm was tested in benchmarks with the RosettaMembrane, ref2015_memb, and franklin2019 score functions on six engineered and 44 naturally-occurring amphipathic helices using membrane coordinates from the OPM and PDBTM databases, OREMPRO server, and MD simulations for comparison. The AmphiScan protocol predicted the coordinates of amphipathic helices within less than 3Å of the reference structures and identified membrane-embedded residues with a Matthews Correlation Constant (MCC) of up to 0.57. Overall, AmphiScan stands as fast, accurate, and highly-customizable protocol that can be pipelined with other Rosetta and Python applications. Amphipathic helices are important targets as antibacterial peptides and as domains of membrane proteins that play a role in sensing the membrane environment. Understanding how amphipathic helices interact with membrane enables us to design better peptides and understand how membrane proteins use them to interact with their environment. However, there is a limited number of tools available for the modeling of amphipathic helices in membranes. Implicit membrane models can be used for this purpose as simplistic representations of the membrane environment. In this work, we developed the AmphiScan protocol that can be used to predict membrane coordinates of amphipathic helices starting with a helix structure in an implicit membrane environment. We benchmarked the performance of AmphiScan on engineered LK peptides, naturally-occurring amphipathic helices, and hydrophobic and hydrophilic peptides. Our approach provides a reliable and customizable tool to model amphipathic helix–membrane interactions, and pose a platform for the screening of amphipathic helix properties in silico.
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Affiliation(s)
- Alican Gulsevin
- Department of Chemistry, Center for Structural Biology, Vanderbilt University, Nashville, Tennessee, United States of America
| | - Jens Meiler
- Department of Chemistry, Center for Structural Biology, Vanderbilt University, Nashville, Tennessee, United States of America
- Institute for Drug Discovery, Leipzig University Medical School, 04103 Leipzig, Germany
- * E-mail:
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43
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Marx DC, Fleming KG. Local Bilayer Hydrophobicity Modulates Membrane Protein Stability. J Am Chem Soc 2021; 143:764-772. [DOI: 10.1021/jacs.0c09412] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Affiliation(s)
- Dagan C. Marx
- Thomas C. Jenkins Department of Biophysics, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Karen G. Fleming
- Thomas C. Jenkins Department of Biophysics, Johns Hopkins University, Baltimore, Maryland 21218, United States
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44
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Abstract
Protein engineering can yield new molecular tools for nanotechnology and therapeutic applications through modulating physiochemical and biological properties. Engineering membrane proteins is especially attractive because they perform key cellular processes including transport, nutrient uptake, removal of toxins, respiration, motility, and signaling. In this chapter, we describe two protocols for membrane protein engineering with the Rosetta software: (1) ΔΔG calculations for single point mutations and (2) sequence optimization in different membrane lipid compositions. These modular protocols are easily adaptable for more complex problems and serve as a foundation for efficient membrane protein engineering calculations.
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Affiliation(s)
- Rebecca F Alford
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA. .,Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD, USA.
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45
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Pan X, Kortemme T. Recent advances in de novo protein design: Principles, methods, and applications. J Biol Chem 2021; 296:100558. [PMID: 33744284 PMCID: PMC8065224 DOI: 10.1016/j.jbc.2021.100558] [Citation(s) in RCA: 93] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 03/12/2021] [Accepted: 03/16/2021] [Indexed: 02/06/2023] Open
Abstract
The computational de novo protein design is increasingly applied to address a number of key challenges in biomedicine and biological engineering. Successes in expanding applications are driven by advances in design principles and methods over several decades. Here, we review recent innovations in major aspects of the de novo protein design and include how these advances were informed by principles of protein architecture and interactions derived from the wealth of structures in the Protein Data Bank. We describe developments in de novo generation of designable backbone structures, optimization of sequences, design scoring functions, and the design of the function. The advances not only highlight design goals reachable now but also point to the challenges and opportunities for the future of the field.
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Affiliation(s)
- Xingjie Pan
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, USA; UC Berkeley - UCSF Graduate Program in Bioengineering, University of California San Francisco, San Francisco, California, USA.
| | - Tanja Kortemme
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, USA; UC Berkeley - UCSF Graduate Program in Bioengineering, University of California San Francisco, San Francisco, California, USA; Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, California, USA.
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46
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Lubin JH, Zardecki C, Dolan EM, Lu C, Shen Z, Dutta S, Westbrook JD, Hudson BP, Goodsell DS, Williams JK, Voigt M, Sarma V, Xie L, Venkatachalam T, Arnold S, Alvarado LHA, Catalfano K, Khan A, McCarthy E, Staggers S, Tinsley B, Trudeau A, Singh J, Whitmore L, Zheng H, Benedek M, Currier J, Dresel M, Duvvuru A, Dyszel B, Fingar E, Hennen EM, Kirsch M, Khan AA, Labrie-Cleary C, Laporte S, Lenkeit E, Martin K, Orellana M, de la Campa MOA, Paredes I, Wheeler B, Rupert A, Sam A, See K, Zapata SS, Craig PA, Hall BL, Jiang J, Koeppe JR, Mills SA, Pikaart MJ, Roberts R, Bromberg Y, Hoyer JS, Duffy S, Tischfield J, Ruiz FX, Arnold E, Baum J, Sandberg J, Brannigan G, Khare SD, Burley SK. Evolution of the SARS-CoV-2 proteome in three dimensions (3D) during the first six months of the COVID-19 pandemic. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2020. [PMID: 33299989 DOI: 10.1101/2020.12.01.406637] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Three-dimensional structures of SARS-CoV-2 and other coronaviral proteins archived in the Protein Data Bank were used to analyze viral proteome evolution during the first six months of the COVID-19 pandemic. Analyses of spatial locations, chemical properties, and structural and energetic impacts of the observed amino acid changes in >48,000 viral proteome sequences showed how each one of the 29 viral study proteins have undergone amino acid changes. Structural models computed for every unique sequence variant revealed that most substitutions map to protein surfaces and boundary layers with a minority affecting hydrophobic cores. Conservative changes were observed more frequently in cores versus boundary layers/surfaces. Active sites and protein-protein interfaces showed modest numbers of substitutions. Energetics calculations showed that the impact of substitutions on the thermodynamic stability of the proteome follows a universal bi-Gaussian distribution. Detailed results are presented for six drug discovery targets and four structural proteins comprising the virion, highlighting substitutions with the potential to impact protein structure, enzyme activity, and functional interfaces. Characterizing the evolution of the virus in three dimensions provides testable insights into viral protein function and should aid in structure-based drug discovery efforts as well as the prospective identification of amino acid substitutions with potential for drug resistance.
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47
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Integrative modeling of membrane-associated protein assemblies. Nat Commun 2020; 11:6210. [PMID: 33277503 PMCID: PMC7718903 DOI: 10.1038/s41467-020-20076-5] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Accepted: 11/13/2020] [Indexed: 01/03/2023] Open
Abstract
Membrane proteins are among the most challenging systems to study with experimental structural biology techniques. The increased number of deposited structures of membrane proteins has opened the route to modeling their complexes by methods such as docking. Here, we present an integrative computational protocol for the modeling of membrane-associated protein assemblies. The information encoded by the membrane is represented by artificial beads, which allow targeting of the docking toward the binding-competent regions. It combines efficient, artificial intelligence-based rigid-body docking by LightDock with a flexible final refinement with HADDOCK to remove potential clashes at the interface. We demonstrate the performance of this protocol on eighteen membrane-associated complexes, whose interface lies between the membrane and either the cytosolic or periplasmic regions. In addition, we provide a comparison to another state-of-the-art docking software, ZDOCK. This protocol should shed light on the still dark fraction of the interactome consisting of membrane proteins.
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48
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Adolf-Bryfogle J, Teets FD, Bahl CD. Toward complete rational control over protein structure and function through computational design. Curr Opin Struct Biol 2020; 66:170-177. [PMID: 33276237 DOI: 10.1016/j.sbi.2020.10.015] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 10/08/2020] [Accepted: 10/19/2020] [Indexed: 11/28/2022]
Abstract
The grand challenge of protein design is a general method for producing a polypeptide with arbitrary functionality, conformation, and biochemical properties. To that end, a wide variety of methods have been developed for the improvement of native proteins, the design of ideal proteins de novo, and the redesign of suboptimal proteins with better-performing substructures. These methods employ informatic comparisons of function-structure-sequence relationships as well as knowledge-based evaluation of protein properties to narrow the immense protein sequence search space down to an enumerable and often manually evaluable set of structures that meet specified criteria. While arbitrary manipulation of protein-protein interfaces and molecular catalysis remains an unsolved problem, and no protein shape or behavior manipulation algorithm is universally applicable, the promising results thus far are a strong indicator that a general approach to the arbitrary manipulation of polypeptides is within reach.
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Affiliation(s)
- Jared Adolf-Bryfogle
- Institute for Protein Innovation, Boston, MA 02115, USA; Division of Hematology/Oncology, Boston Children's Hospital, Boston, MA, USA; Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Frank D Teets
- Institute for Protein Innovation, Boston, MA 02115, USA; Division of Hematology/Oncology, Boston Children's Hospital, Boston, MA, USA; Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Christopher D Bahl
- Institute for Protein Innovation, Boston, MA 02115, USA; Division of Hematology/Oncology, Boston Children's Hospital, Boston, MA, USA; Department of Pediatrics, Harvard Medical School, Boston, MA, USA.
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49
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Cho E, Lu Y. Compartmentalizing Cell-Free Systems: Toward Creating Life-Like Artificial Cells and Beyond. ACS Synth Biol 2020; 9:2881-2901. [PMID: 33095011 DOI: 10.1021/acssynbio.0c00433] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Building an artificial cell is a research area that is rigorously studied in the field of synthetic biology. It has brought about much attention with the aim of ultimately constructing a natural cell-like structure. In particular, with the more mature cell-free platforms and various compartmentalization methods becoming available, achieving this aim seems not far away. In this review, we discuss the various types of artificial cells capable of hosting several cellular functions. Different compartmental boundaries and the mature and evolving technologies that are used for compartmentalization are examined, and exciting recent advances that overcome or have the potential to address current challenges are discussed. Ultimately, we show how compartmentalization and cell-free systems have, and will, come together to fulfill the goal to assemble a fully synthetic cell that displays functionality and complexity as advanced as that in nature. The development of such artificial cell systems will offer insight into the fundamental study of evolutionary biology and the sea of applications as a result. Although several challenges remain, emerging technologies such as artificial intelligence also appear to help pave the way to address them and achieve the ultimate goal.
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Affiliation(s)
- Eunhee Cho
- Key Lab of Industrial Biocatalysis, Ministry of Education, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
| | - Yuan Lu
- Key Lab of Industrial Biocatalysis, Ministry of Education, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
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Leman JK, Weitzner BD, Lewis SM, Adolf-Bryfogle J, Alam N, Alford RF, Aprahamian M, Baker D, Barlow KA, Barth P, Basanta B, Bender BJ, Blacklock K, Bonet J, Boyken SE, Bradley P, Bystroff C, Conway P, Cooper S, Correia BE, Coventry B, Das R, De Jong RM, DiMaio F, Dsilva L, Dunbrack R, Ford AS, Frenz B, Fu DY, Geniesse C, Goldschmidt L, Gowthaman R, Gray JJ, Gront D, Guffy S, Horowitz S, Huang PS, Huber T, Jacobs TM, Jeliazkov JR, Johnson DK, Kappel K, Karanicolas J, Khakzad H, Khar KR, Khare SD, Khatib F, Khramushin A, King IC, Kleffner R, Koepnick B, Kortemme T, Kuenze G, Kuhlman B, Kuroda D, Labonte JW, Lai JK, Lapidoth G, Leaver-Fay A, Lindert S, Linsky T, London N, Lubin JH, Lyskov S, Maguire J, Malmström L, Marcos E, Marcu O, Marze NA, Meiler J, Moretti R, Mulligan VK, Nerli S, Norn C, Ó'Conchúir S, Ollikainen N, Ovchinnikov S, Pacella MS, Pan X, Park H, Pavlovicz RE, Pethe M, Pierce BG, Pilla KB, Raveh B, Renfrew PD, Burman SSR, Rubenstein A, Sauer MF, Scheck A, Schief W, Schueler-Furman O, Sedan Y, Sevy AM, Sgourakis NG, Shi L, Siegel JB, Silva DA, Smith S, Song Y, Stein A, Szegedy M, Teets FD, Thyme SB, Wang RYR, Watkins A, Zimmerman L, Bonneau R. Macromolecular modeling and design in Rosetta: recent methods and frameworks. Nat Methods 2020; 17:665-680. [PMID: 32483333 PMCID: PMC7603796 DOI: 10.1038/s41592-020-0848-2] [Citation(s) in RCA: 427] [Impact Index Per Article: 106.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Accepted: 04/22/2020] [Indexed: 12/12/2022]
Abstract
The Rosetta software for macromolecular modeling, docking and design is extensively used in laboratories worldwide. During two decades of development by a community of laboratories at more than 60 institutions, Rosetta has been continuously refactored and extended. Its advantages are its performance and interoperability between broad modeling capabilities. Here we review tools developed in the last 5 years, including over 80 methods. We discuss improvements to the score function, user interfaces and usability. Rosetta is available at http://www.rosettacommons.org.
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Affiliation(s)
- Julia Koehler Leman
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA.
- Department of Biology, New York University, New York, New York, USA.
| | - Brian D Weitzner
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Lyell Immunopharma Inc., Seattle, WA, USA
| | - Steven M Lewis
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Biochemistry, Duke University, Durham, NC, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Jared Adolf-Bryfogle
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, USA
| | - Nawsad Alam
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Rebecca F Alford
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Melanie Aprahamian
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, OH, USA
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Kyle A Barlow
- Graduate Program in Bioinformatics, University of California San Francisco, San Francisco, CA, USA
| | - Patrick Barth
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Baylor College of Medicine, Department of Pharmacology, Houston, TX, USA
| | - Benjamin Basanta
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Biological Physics Structure and Design PhD Program, University of Washington, Seattle, WA, USA
| | - Brian J Bender
- Department of Pharmacology, Vanderbilt University, Nashville, TN, USA
| | - Kristin Blacklock
- Institute of Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Jaume Bonet
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Scott E Boyken
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Lyell Immunopharma Inc., Seattle, WA, USA
| | - Phil Bradley
- Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Chris Bystroff
- Department of Biological Sciences, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Patrick Conway
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Seth Cooper
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Bruno E Correia
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Brian Coventry
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Rhiju Das
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Frank DiMaio
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Lorna Dsilva
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Roland Dunbrack
- Institute for Cancer Research, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Alexander S Ford
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Brandon Frenz
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Darwin Y Fu
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
| | - Caleb Geniesse
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Ragul Gowthaman
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, USA
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD, USA
| | - Dominik Gront
- Faculty of Chemistry, Biological and Chemical Research Centre, University of Warsaw, Warsaw, Poland
| | - Sharon Guffy
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Scott Horowitz
- Department of Chemistry & Biochemistry, University of Denver, Denver, CO, USA
- The Knoebel Institute for Healthy Aging, University of Denver, Denver, CO, USA
| | - Po-Ssu Huang
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Thomas Huber
- Research School of Chemistry, Australian National University, Canberra, Australian Capital Territory, Australia
| | - Tim M Jacobs
- Program in Bioinformatics and Computational Biology, Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - David K Johnson
- Center for Computational Biology, University of Kansas, Lawrence, KS, USA
| | - Kalli Kappel
- Biophysics Program, Stanford University, Stanford, CA, USA
| | - John Karanicolas
- Institute for Cancer Research, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Hamed Khakzad
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Institute for Computational Science, University of Zurich, Zurich, Switzerland
- S3IT, University of Zurich, Zurich, Switzerland
| | - Karen R Khar
- Cyrus Biotechnology, Seattle, WA, USA
- Center for Computational Biology, University of Kansas, Lawrence, KS, USA
| | - Sagar D Khare
- Institute of Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Department of Chemistry and Chemical Biology, The State University of New Jersey, Piscataway, NJ, USA
- Center for Integrative Proteomics Research, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Computational Biology and Molecular Biophysics Program, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Firas Khatib
- Department of Computer and Information Science, University of Massachusetts Dartmouth, Dartmouth, MA, USA
| | - Alisa Khramushin
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Indigo C King
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Robert Kleffner
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Brian Koepnick
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Tanja Kortemme
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Georg Kuenze
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA
| | - Brian Kuhlman
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Daisuke Kuroda
- Medical Device Development and Regulation Research Center, School of Engineering, University of Tokyo, Tokyo, Japan
- Department of Bioengineering, School of Engineering, University of Tokyo, Tokyo, Japan
| | - Jason W Labonte
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Chemistry, Franklin & Marshall College, Lancaster, PA, USA
| | - Jason K Lai
- Baylor College of Medicine, Department of Pharmacology, Houston, TX, USA
| | - Gideon Lapidoth
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Andrew Leaver-Fay
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Steffen Lindert
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, OH, USA
| | - Thomas Linsky
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Nir London
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Joseph H Lubin
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Sergey Lyskov
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jack Maguire
- Program in Bioinformatics and Computational Biology, Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Lars Malmström
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Institute for Computational Science, University of Zurich, Zurich, Switzerland
- S3IT, University of Zurich, Zurich, Switzerland
- Division of Infection Medicine, Department of Clinical Sciences Lund, Faculty of Medicine, Lund University, Lund, Sweden
| | - Enrique Marcos
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Research in Biomedicine Barcelona, The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Orly Marcu
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Nicholas A Marze
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jens Meiler
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA
- Departments of Chemistry, Pharmacology and Biomedical Informatics, Vanderbilt University, Nashville, TN, USA
- Institute for Chemical Biology, Vanderbilt University, Nashville, TN, USA
| | - Rocco Moretti
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
| | - Vikram Khipple Mulligan
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Santrupti Nerli
- Department of Computer Science, University of California Santa Cruz, Santa Cruz, CA, USA
| | - Christoffer Norn
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Shane Ó'Conchúir
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Noah Ollikainen
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Sergey Ovchinnikov
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Molecular and Cellular Biology Program, University of Washington, Seattle, WA, USA
| | - Michael S Pacella
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Xingjie Pan
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Hahnbeom Park
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Ryan E Pavlovicz
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Manasi Pethe
- Department of Chemistry and Chemical Biology, The State University of New Jersey, Piscataway, NJ, USA
- Center for Integrative Proteomics Research, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Brian G Pierce
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, USA
| | - Kala Bharath Pilla
- Research School of Chemistry, Australian National University, Canberra, Australian Capital Territory, Australia
| | - Barak Raveh
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - P Douglas Renfrew
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA
| | - Shourya S Roy Burman
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Aliza Rubenstein
- Institute of Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Computational Biology and Molecular Biophysics Program, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Marion F Sauer
- Chemical and Physical Biology Program, Vanderbilt Vaccine Center, Vanderbilt University, Nashville, TN, USA
| | - Andreas Scheck
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - William Schief
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, USA
| | - Ora Schueler-Furman
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Yuval Sedan
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Alexander M Sevy
- Chemical and Physical Biology Program, Vanderbilt Vaccine Center, Vanderbilt University, Nashville, TN, USA
| | - Nikolaos G Sgourakis
- Department of Chemistry and Biochemistry, University of California Santa Cruz, Santa Cruz, CA, USA
| | - Lei Shi
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Justin B Siegel
- Department of Chemistry, University of California, Davis, Davis, CA, USA
- Department of Biochemistry and Molecular Medicine, University of California, Davis, Davis, California, USA
- Genome Center, University of California, Davis, Davis, CA, USA
| | | | - Shannon Smith
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
| | - Yifan Song
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Amelie Stein
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Maria Szegedy
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Frank D Teets
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Summer B Thyme
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Ray Yu-Ruei Wang
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Andrew Watkins
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
| | - Lior Zimmerman
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Richard Bonneau
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA.
- Department of Biology, New York University, New York, New York, USA.
- Department of Computer Science, New York University, New York, NY, USA.
- Center for Data Science, New York University, New York, NY, USA.
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