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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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2
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Goverde CA, Pacesa M, Goldbach N, Dornfeld LJ, Balbi PEM, Georgeon S, Rosset S, Kapoor S, Choudhury J, Dauparas J, Schellhaas C, Kozlov S, Baker D, Ovchinnikov S, Vecchio AJ, Correia BE. Computational design of soluble and functional membrane protein analogues. Nature 2024; 631:449-458. [PMID: 38898281 PMCID: PMC11236705 DOI: 10.1038/s41586-024-07601-y] [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: 05/09/2023] [Accepted: 05/23/2024] [Indexed: 06/21/2024]
Abstract
De novo design of complex protein folds using solely computational means remains a substantial challenge1. Here we use a robust deep learning pipeline to design complex folds and soluble analogues of integral membrane proteins. Unique membrane topologies, such as those from G-protein-coupled receptors2, are not found in the soluble proteome, and we demonstrate that their structural features can be recapitulated in solution. Biophysical analyses demonstrate the high thermal stability of the designs, and experimental structures show remarkable design accuracy. The soluble analogues were functionalized with native structural motifs, as a proof of concept for bringing membrane protein functions to the soluble proteome, potentially enabling new approaches in drug discovery. In summary, we have designed complex protein topologies and enriched them with functionalities from membrane proteins, with high experimental success rates, leading to a de facto expansion of the functional soluble fold space.
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Affiliation(s)
- Casper A Goverde
- Laboratory of Protein Design and Immunoengineering, École Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Martin Pacesa
- Laboratory of Protein Design and Immunoengineering, École Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Nicolas Goldbach
- Laboratory of Protein Design and Immunoengineering, École Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Lars J Dornfeld
- Laboratory of Protein Design and Immunoengineering, École Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Petra E M Balbi
- Laboratory of Protein Design and Immunoengineering, École Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Sandrine Georgeon
- Laboratory of Protein Design and Immunoengineering, École Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Stéphane Rosset
- Laboratory of Protein Design and Immunoengineering, École Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Srajan Kapoor
- Department of Structural Biology, University at Buffalo, Buffalo, NY, USA
| | - Jagrity Choudhury
- Department of Structural Biology, University at Buffalo, Buffalo, NY, USA
| | - Justas Dauparas
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Christian Schellhaas
- Laboratory of Protein Design and Immunoengineering, École Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Simon Kozlov
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
| | - Sergey Ovchinnikov
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Alex J Vecchio
- Department of Structural Biology, University at Buffalo, Buffalo, NY, USA
| | - Bruno E Correia
- Laboratory of Protein Design and Immunoengineering, École Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics, Lausanne, Switzerland.
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3
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Wankowicz SA, Ravikumar A, Sharma S, Riley BT, Raju A, Flowers J, Hogan D, van den Bedem H, Keedy DA, Fraser JS. Uncovering Protein Ensembles: Automated Multiconformer Model Building for X-ray Crystallography and Cryo-EM. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.06.28.546963. [PMID: 37425870 PMCID: PMC10327213 DOI: 10.1101/2023.06.28.546963] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
In their folded state, biomolecules exchange between multiple conformational states that are crucial for their function. Traditional structural biology methods, such as X-ray crystallography and cryogenic electron microscopy (cryo-EM), produce density maps that are ensemble averages, reflecting molecules in various conformations. Yet, most models derived from these maps explicitly represent only a single conformation, overlooking the complexity of biomolecular structures. To accurately reflect the diversity of biomolecular forms, there is a pressing need to shift towards modeling structural ensembles that mirror the experimental data. However, the challenge of distinguishing signal from noise complicates manual efforts to create these models. In response, we introduce the latest enhancements to qFit, an automated computational strategy designed to incorporate protein conformational heterogeneity into models built into density maps. These algorithmic improvements in qFit are substantiated by superior R f r e e and geometry metrics across a wide range of proteins. Importantly, unlike more complex multicopy ensemble models, the multiconformer models produced by qFit can be manually modified in most major model building software (e.g. Coot) and fit can be further improved by refinement using standard pipelines (e.g. Phenix, Refmac, Buster). By reducing the barrier of creating multiconformer models, qFit can foster the development of new hypotheses about the relationship between macromolecular conformational dynamics and function.
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Affiliation(s)
- Stephanie A. Wankowicz
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Ashraya Ravikumar
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Shivani Sharma
- Structural Biology Initiative, CUNY Advanced Science Research Center, New York, NY 10031
- Ph.D. Program in Biology, The Graduate Center – City University of New York, New York, NY 10016
| | - Blake T. Riley
- Structural Biology Initiative, CUNY Advanced Science Research Center, New York, NY 10031
| | - Akshay Raju
- Structural Biology Initiative, CUNY Advanced Science Research Center, New York, NY 10031
| | - Jessica Flowers
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Daniel Hogan
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Henry van den Bedem
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
- Atomwise, Inc., San Francisco, CA, United States
| | - Daniel A. Keedy
- Structural Biology Initiative, CUNY Advanced Science Research Center, New York, NY 10031
- Department of Chemistry and Biochemistry, City College of New York, New York, NY 10031
- Ph.D. Programs in Biochemistry, Biology, and Chemistry, The Graduate Center – City University of New York, New York, NY 10016
| | - James S. Fraser
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
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4
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Wilson E, Cava JK, Chowell D, Raja R, Mangalaparthi KK, Pandey A, Curtis M, Anderson KS, Singharoy A. The electrostatic landscape of MHC-peptide binding revealed using inception networks. Cell Syst 2024; 15:362-373.e7. [PMID: 38554709 DOI: 10.1016/j.cels.2024.03.001] [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: 04/04/2023] [Revised: 11/24/2023] [Accepted: 03/05/2024] [Indexed: 04/02/2024]
Abstract
Predictive modeling of macromolecular recognition and protein-protein complementarity represents one of the cornerstones of biophysical sciences. However, such models are often hindered by the combinatorial complexity of interactions at the molecular interfaces. Exemplary of this problem is peptide presentation by the highly polymorphic major histocompatibility complex class I (MHC-I) molecule, a principal component of immune recognition. We developed human leukocyte antigen (HLA)-Inception, a deep biophysical convolutional neural network, which integrates molecular electrostatics to capture non-bonded interactions for predicting peptide binding motifs across 5,821 MHC-I alleles. These predictions of generated motifs correlate strongly with experimental peptide binding and presentation data. Beyond molecular interactions, the study demonstrates the application of predicted motifs in analyzing MHC-I allele associations with HIV disease progression and patient response to immune checkpoint inhibitors. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- Eric Wilson
- School of Molecular Sciences, Arizona State University, Tempe, AZ 85207, USA; The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - John Kevin Cava
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ 85207, USA
| | - Diego Chowell
- The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Remya Raja
- Department of Immunology, Mayo Clinic, Scottsdale, AZ 85259, USA
| | - Kiran K Mangalaparthi
- Department of Laboratory Medicine and Pathology, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA
| | - Akhilesh Pandey
- Department of Laboratory Medicine and Pathology, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA; Center for Individualized Medicine, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA; Manipal Academy of Higher Education, Manipal 576104, Karnataka, India
| | - Marion Curtis
- Department of Immunology, Mayo Clinic, Scottsdale, AZ 85259, USA; College of Medicine and Science, Mayo Clinic, Scottsdale, AZ 85259, USA; Department of Cancer Biology, Mayo Clinic, Scottsdale, AZ 85259, USA
| | - Karen S Anderson
- School of Life Sciences, Arizona State University, Tempe, AZ 85207, USA.
| | - Abhishek Singharoy
- School of Molecular Sciences, Arizona State University, Tempe, AZ 85207, USA.
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5
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Karnchanapandh K, Sanachai K, Poo-Arporn RP, Rungrotmongkol T. Enhancing bezlotoxumab binding to C. difficile toxin B2: insights from computational simulations and mutational analyses for antibody design. J Biomol Struct Dyn 2024:1-11. [PMID: 38511411 DOI: 10.1080/07391102.2024.2329785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 03/06/2024] [Indexed: 03/22/2024]
Abstract
Clostridioides difficile infection (CDI) is a significant concern caused by widespread antibiotic use, resulting in diarrhea and inflammation from the gram-positive anaerobic bacterium C. difficile. Although bezlotoxumab (Bez), a monoclonal antibody (mAb), was developed to address CDI recurrences, the recurrence rate remains high, partly due to reduced neutralization efficiency against toxin B2. In this study, we aimed to enhance the binding of Bez to C. difficile toxin B2 by combining computational simulations and mutational analyses. We identified specific mutations in Bez, including S28R, S31W/K, Y32R, S56W and G103D/S in the heavy chain (Hc), and S32F/H/R/W/Y in the light chain (Lc), which significantly improved binding to toxin B2 and formed critical protein-protein interactions. Through molecular dynamics simulations, several single mutations, such as HcS28R, LcS32H, LcS32R, LcS32W and LcS32Y, exhibited superior binding affinities to toxin B2 compared to Bez wild-type (WT), primarily attributed to Coulombic interactions. Combining the HcS28R mutation with four different mutations at residue LcS32 led to even greater binding affinities in double mutants (MTs), particularly HcS28R/LcS32H, HcS28R/LcS32R and HcS28R/LcS32Y, reinforcing protein-protein binding. Analysis of per-residue decomposition free energy highlighted key residues contributing significantly to enhanced binding interactions, emphasizing the role of electrostatic interactions. These findings offer insights into rational Bez MT design for improved toxin B2 binding, providing a foundation for developing more effective antibodies to neutralize toxin B2 and combat-related infections.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Kun Karnchanapandh
- Program in Bioinformatics and Computational Biology, Graduate School, Chulalongkorn University, Bangkok, Thailand
| | - Kamonpan Sanachai
- Department of Biochemistry, Faculty of Science, Khon Kaen University, Khon Kaen, Thailand
| | - Rungtiva P Poo-Arporn
- Biological Engineering Program, Faculty of Engineering, King Mongkut's University of Technology Thonburi, Bangkok, Thailand
| | - Thanyada Rungrotmongkol
- Program in Bioinformatics and Computational Biology, Graduate School, Chulalongkorn University, Bangkok, Thailand
- Center of Excellence in Biocatalyst and Sustainable Biotechnology, Department of Biochemistry, Faculty of Science, Chulalongkorn University, Bangkok, Thailand
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6
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Goverde CA, Pacesa M, Goldbach N, Dornfeld LJ, Balbi PEM, Georgeon S, Rosset S, Kapoor S, Choudhury J, Dauparas J, Schellhaas C, Kozlov S, Baker D, Ovchinnikov S, Vecchio AJ, Correia BE. Computational design of soluble functional analogues of integral membrane proteins. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.05.09.540044. [PMID: 38496615 PMCID: PMC10942269 DOI: 10.1101/2023.05.09.540044] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
De novo design of complex protein folds using solely computational means remains a significant challenge. Here, we use a robust deep learning pipeline to design complex folds and soluble analogues of integral membrane proteins. Unique membrane topologies, such as those from GPCRs, are not found in the soluble proteome and we demonstrate that their structural features can be recapitulated in solution. Biophysical analyses reveal high thermal stability of the designs and experimental structures show remarkable design accuracy. The soluble analogues were functionalized with native structural motifs, standing as a proof-of-concept for bringing membrane protein functions to the soluble proteome, potentially enabling new approaches in drug discovery. In summary, we designed complex protein topologies and enriched them with functionalities from membrane proteins, with high experimental success rates, leading to a de facto expansion of the functional soluble fold space.
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7
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Harmalkar A, Lyskov S, Gray JJ. Reliable protein-protein docking with AlphaFold, Rosetta, and replica-exchange. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.28.551063. [PMID: 37546760 PMCID: PMC10402144 DOI: 10.1101/2023.07.28.551063] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Despite the recent breakthrough of AlphaFold (AF) in the field of protein sequence-to-structure prediction, modeling protein interfaces and predicting protein complex structures remains challenging, especially when there is a significant conformational change in one or both binding partners. Prior studies have demonstrated that AF-multimer (AFm) can predict accurate protein complexes in only up to 43% of cases.1 In this work, we combine AlphaFold as a structural template generator with a physics-based replica exchange docking algorithm. Using a curated collection of 254 available protein targets with both unbound and bound structures, we first demonstrate that AlphaFold confidence measures (pLDDT) can be repurposed for estimating protein flexibility and docking accuracy for multimers. We incorporate these metrics within our ReplicaDock 2.0 protocol2 to complete a robust in-silico pipeline for accurate protein complex structure prediction. AlphaRED (AlphaFold-initiated Replica Exchange Docking) successfully docks failed AF predictions including 97 failure cases in Docking Benchmark Set 5.5. AlphaRED generates CAPRI acceptable-quality or better predictions for 66% of benchmark targets. Further, on a subset of antigen-antibody targets, which is challenging for AFm (19% success rate), AlphaRED demonstrates a success rate of 51%. This new strategy demonstrates the success possible by integrating deep-learning based architectures trained on evolutionary information with physics-based enhanced sampling. The pipeline is available at github.com/Graylab/AlphaRED.
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Affiliation(s)
- Ameya Harmalkar
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA
| | - Sergey Lyskov
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA
- Program in Molecular Biophysics, The Johns Hopkins University, Baltimore, MD 21218, USA
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8
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Vaissier Welborn V. Understanding Cysteine Reactivity in Protein Environments with Electric Fields. J Phys Chem B 2023; 127:9936-9942. [PMID: 37962274 DOI: 10.1021/acs.jpcb.3c05749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
The role cysteine residues play in proteins is mediated by their protonation state, whereby the thiolate form of the side chain is highly reactive while the thiol form is more inert. However, the pKa of cysteine residues is hard to predict as it can differ widely from its reference value in solution, an effect that is accentuated by local effects in the heterogeneous protein environment. Here, we present a new approach to the prediction of cysteine reactivity based on electric field calculations at the thiol/thiolate group. We validated our approach by predicting the protonation state of cysteine residues in different protein environments (in the active site, at the protein surface, and buried within the protein interior), including Cys-25 in papaya protease omega, which was proven problematic for the more traditional constant pH molecular dynamics (MD) technique. We predict pKa shifts consistent with experimental observations, and the decomposition of the electric fields into contributions from molecular fragments provides a direct handle to rationalize local pH and pKa effects in proteins without introducing parameters other than those of the force field used for MD simulations.
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Affiliation(s)
- Valerie Vaissier Welborn
- Department of Chemistry, Virginia Tech, Blacksburg, Virginia 24060, United States
- Macromolecules Innovation Institute (MII),Virginia Tech, Blacksburg, Virginia 24060, United States
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9
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Du S, Wankowicz SA, Yabukarski F, Doukov T, Herschlag D, Fraser JS. Refinement of multiconformer ensemble models from multi-temperature X-ray diffraction data. Methods Enzymol 2023; 688:223-254. [PMID: 37748828 PMCID: PMC10637719 DOI: 10.1016/bs.mie.2023.06.009] [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] [Indexed: 09/27/2023]
Abstract
Conformational ensembles underlie all protein functions. Thus, acquiring atomic-level ensemble models that accurately represent conformational heterogeneity is vital to deepen our understanding of how proteins work. Modeling ensemble information from X-ray diffraction data has been challenging, as traditional cryo-crystallography restricts conformational variability while minimizing radiation damage. Recent advances have enabled the collection of high quality diffraction data at ambient temperatures, revealing innate conformational heterogeneity and temperature-driven changes. Here, we used diffraction datasets for Proteinase K collected at temperatures ranging from 313 to 363 K to provide a tutorial for the refinement of multiconformer ensemble models. Integrating automated sampling and refinement tools with manual adjustments, we obtained multiconformer models that describe alternative backbone and sidechain conformations, their relative occupancies, and interconnections between conformers. Our models revealed extensive and diverse conformational changes across temperature, including increased bound peptide ligand occupancies, different Ca2+ binding site configurations and altered rotameric distributions. These insights emphasize the value and need for multiconformer model refinement to extract ensemble information from diffraction data and to understand ensemble-function relationships.
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Affiliation(s)
- Siyuan Du
- Department of Biochemistry, Stanford University, Stanford, CA, United States; Department of Chemistry, Stanford University, Stanford, CA, United States
| | - Stephanie A Wankowicz
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, United States
| | - Filip Yabukarski
- Department of Biochemistry, Stanford University, Stanford, CA, United States; Bristol-Myers Squibb, San Diego, CA, United States
| | - Tzanko Doukov
- Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Menlo Park, CA, United States
| | - Daniel Herschlag
- Department of Biochemistry, Stanford University, Stanford, CA, United States; Department of Chemical Engineering, Stanford University, Stanford, CA, United States; Stanford ChEM-H, Stanford University, Stanford, CA, United States
| | - James S Fraser
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, United States; Quantitative Biosciences Institute, University of California, San Francisco, CA, United States.
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Narkhede YB, Bhardwaj A, Motsa BB, Saxena R, Sharma T, Chapagain PP, Stahelin RV, Wiest O. Elucidating Residue-Level Determinants Affecting Dimerization of Ebola Virus Matrix Protein Using High-Throughput Site Saturation Mutagenesis and Biophysical Approaches. J Phys Chem B 2023; 127:6449-6461. [PMID: 37458567 DOI: 10.1021/acs.jpcb.3c01759] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/28/2023]
Abstract
The Ebola virus (EBOV) is a filamentous virus that acquires its lipid envelope from the plasma membrane of the host cell it infects. EBOV assembly and budding from the host cell plasma membrane are mediated by a peripheral protein, known as the matrix protein VP40. VP40 is a 326 amino acid protein with two domains that are loosely linked. The VP40 N-terminal domain (NTD) contains a hydrophobic α-helix, which mediates VP40 dimerization. The VP40 C-terminal domain has a cationic patch, which mediates interactions with anionic lipids and a hydrophobic region that mediates VP40 dimer-dimer interactions. The VP40 dimer is necessary for trafficking to the plasma membrane inner leaflet and interactions with anionic lipids to mediate the VP40 assembly and oligomerization. Despite significant structural information available on the VP40 dimer structure, little is known on how the VP40 dimer is stabilized and how residues outside the NTD hydrophobic portion of the α-helical dimer interface contribute to dimer stability. To better understand how VP40 dimer stability is maintained, we performed computational studies using per-residue energy decomposition and site saturation mutagenesis. These studies revealed a number of novel keystone residues for VP40 dimer stability just adjacent to the α-helical dimer interface as well as distant residues in the VP40 CTD that can stabilize the VP40 dimer form. Experimental studies with representative VP40 mutants in vitro and in cells were performed to test computational predictions that reveal residues that alter VP40 dimer stability. Taken together, these studies provide important biophysical insights into VP40 dimerization and may be useful in strategies to weaken or alter the VP40 dimer structure as a means of inhibiting the EBOV assembly.
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Affiliation(s)
- Yogesh B Narkhede
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, United States
| | - Atul Bhardwaj
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, United States
| | - Balindile B Motsa
- Department of Medicinal Chemistry & Molecular Pharmacology, Purdue Institute of Inflammation, Immunology, and Infectious Disease, Purdue University, West Lafayette, Indiana 47907, United States
| | - Roopashi Saxena
- Department of Medicinal Chemistry & Molecular Pharmacology, Purdue Institute of Inflammation, Immunology, and Infectious Disease, Purdue University, West Lafayette, Indiana 47907, United States
| | | | | | - Robert V Stahelin
- Department of Medicinal Chemistry & Molecular Pharmacology, Purdue Institute of Inflammation, Immunology, and Infectious Disease, Purdue University, West Lafayette, Indiana 47907, United States
| | - Olaf Wiest
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, United States
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11
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Mohseni Behbahani Y, Laine E, Carbone A. Deep Local Analysis deconstructs protein-protein interfaces and accurately estimates binding affinity changes upon mutation. Bioinformatics 2023; 39:i544-i552. [PMID: 37387162 DOI: 10.1093/bioinformatics/btad231] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/01/2023] Open
Abstract
MOTIVATION The spectacular recent advances in protein and protein complex structure prediction hold promise for reconstructing interactomes at large-scale and residue resolution. Beyond determining the 3D arrangement of interacting partners, modeling approaches should be able to unravel the impact of sequence variations on the strength of the association. RESULTS In this work, we report on Deep Local Analysis, a novel and efficient deep learning framework that relies on a strikingly simple deconstruction of protein interfaces into small locally oriented residue-centered cubes and on 3D convolutions recognizing patterns within cubes. Merely based on the two cubes associated with the wild-type and the mutant residues, DLA accurately estimates the binding affinity change for the associated complexes. It achieves a Pearson correlation coefficient of 0.735 on about 400 mutations on unseen complexes. Its generalization capability on blind datasets of complexes is higher than the state-of-the-art methods. We show that taking into account the evolutionary constraints on residues contributes to predictions. We also discuss the influence of conformational variability on performance. Beyond the predictive power on the effects of mutations, DLA is a general framework for transferring the knowledge gained from the available non-redundant set of complex protein structures to various tasks. For instance, given a single partially masked cube, it recovers the identity and physicochemical class of the central residue. Given an ensemble of cubes representing an interface, it predicts the function of the complex. AVAILABILITY AND IMPLEMENTATION Source code and models are available at http://gitlab.lcqb.upmc.fr/DLA/DLA.git.
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Affiliation(s)
- Yasser Mohseni Behbahani
- Laboratory of Computational and Quantitative Biology (LCQB), UMR 7238, Sorbonne Université, CNRS, IBPS, Paris 75005, France
| | - Elodie Laine
- Laboratory of Computational and Quantitative Biology (LCQB), UMR 7238, Sorbonne Université, CNRS, IBPS, Paris 75005, France
| | - Alessandra Carbone
- Laboratory of Computational and Quantitative Biology (LCQB), UMR 7238, Sorbonne Université, CNRS, IBPS, Paris 75005, France
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12
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Clayton J, de Oliveira VM, Ibrahim MF, Sun X, Mahinthichaichan P, Shen M, Hilgenfeld R, Shen J. Integrative Approach to Dissect the Drug Resistance Mechanism of the H172Y Mutation of SARS-CoV-2 Main Protease. J Chem Inf Model 2023; 63:3521-3533. [PMID: 37199464 PMCID: PMC10237302 DOI: 10.1021/acs.jcim.3c00344] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Indexed: 05/19/2023]
Abstract
Nirmatrelvir is an orally available inhibitor of SARS-CoV-2 main protease (Mpro) and the main ingredient of Paxlovid, a drug approved by the U.S. Food and Drug Administration for high-risk COVID-19 patients. Recently, a rare natural mutation, H172Y, was found to significantly reduce nirmatrelvir's inhibitory activity. As the COVID-19 cases skyrocket in China and the selective pressure of antiviral therapy builds in the US, there is an urgent need to characterize and understand how the H172Y mutation confers drug resistance. Here, we investigated the H172Y Mpro's conformational dynamics, folding stability, catalytic efficiency, and inhibitory activity using all-atom constant pH and fixed-charge molecular dynamics simulations, alchemical and empirical free energy calculations, artificial neural networks, and biochemical experiments. Our data suggest that the mutation significantly weakens the S1 pocket interactions with the N-terminus and perturbs the conformation of the oxyanion loop, leading to a decrease in the thermal stability and catalytic efficiency. Importantly, the perturbed S1 pocket dynamics weaken the nirmatrelvir binding in the P1 position, which explains the decreased inhibitory activity of nirmatrelvir. Our work demonstrates the predictive power of the combined simulation and artificial intelligence approaches, and together with biochemical experiments, they can be used to actively surveil continually emerging mutations of SARS-CoV-2 Mpro and assist the optimization of antiviral drugs. The presented approach, in general, can be applied to characterize mutation effects on any protein drug targets.
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Affiliation(s)
- Joseph Clayton
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, Maryland 21201, USA
| | - Vinicius Martins de Oliveira
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, Maryland 21201, USA
| | | | - Xinyuanyuan Sun
- Institute of Molecular Medicine, University of Lübeck, Lübeck 23562, Germany
| | - Paween Mahinthichaichan
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, Maryland 21201, USA
| | - Mingzhe Shen
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, Maryland 21201, USA
| | - Rolf Hilgenfeld
- Institute for Molecular Medicine, University of Lübeck, Lübeck 23562, Germany
- German Center for Infection Research (DZIF), Hamburg – Lübeck – Borstel – Riems Site, University of Lübeck, Lübeck 23562, Germany
| | - Jana Shen
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, Maryland 21201, USA
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13
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Stalke A, Behrendt A, Hennig F, Gohlke H, Buhl N, Reinkens T, Baumann U, Schlegelberger B, Illig T, Pfister ED, Skawran B. Functional characterization of novel or yet uncharacterized ATP7B missense variants detected in patients with clinical Wilson's disease. Clin Genet 2023. [PMID: 37157876 DOI: 10.1111/cge.14352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 04/14/2023] [Accepted: 04/20/2023] [Indexed: 05/10/2023]
Abstract
Wilson's disease (WD, MIM#277900) is an autosomal recessive disorder resulting in copper excess caused by biallelic variants in the ATP7B gene (MIM#606882) encoding a copper transporting P-type ATPase. ATP7B variants of unknown significance (VUS) are detected frequently, sometimes impeding a clear diagnosis. Functional analyses can help to classify these variants as benign or pathogenic. Additionally, variants already classified as (likely) pathogenic benefit from functional analyses to understand their pathomechanism, thus contribute to the development of personalized treatment approaches in the future. We described clinical features of six WD patients and functionally characterized five ATP7B missense variants (two VUS, three yet uncharacterized likely pathogenic variants), detected in these patients. We determined the protein level, copper export capacity, and cellular localization in an in vitro model and potential structural consequences using an ATP7B protein model based on AlphaFold. Our analyses give insight into the pathomechanism and allowed reclassification for the two VUS to likely pathogenic and for two of the three likely pathogenic variants to pathogenic.
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Affiliation(s)
- Amelie Stalke
- Department of Human Genetics, Hannover Medical School, Hannover, Germany
- Department of Pediatric Gastroenterology and Hepatology, Division of Kidney, Liver, and Metabolic Diseases, Hannover Medical School, Hannover, Germany
| | - Annika Behrendt
- Institute for Pharmaceutical and Medicinal Chemistry, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Finja Hennig
- Department of Human Genetics, Hannover Medical School, Hannover, Germany
| | - Holger Gohlke
- Institute for Pharmaceutical and Medicinal Chemistry, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Bio- and Geosciences (IBG-4: Bioinformatics), Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Nicole Buhl
- Department of Human Genetics, Hannover Medical School, Hannover, Germany
- Department of Pediatric Gastroenterology and Hepatology, Division of Kidney, Liver, and Metabolic Diseases, Hannover Medical School, Hannover, Germany
| | - Thea Reinkens
- Department of Human Genetics, Hannover Medical School, Hannover, Germany
| | - Ulrich Baumann
- Department of Pediatric Gastroenterology and Hepatology, Division of Kidney, Liver, and Metabolic Diseases, Hannover Medical School, Hannover, Germany
| | | | - Thomas Illig
- Hannover Unified Bank, Hannover Medical School, Hannover, Germany
| | - Eva-Doreen Pfister
- Department of Pediatric Gastroenterology and Hepatology, Division of Kidney, Liver, and Metabolic Diseases, Hannover Medical School, Hannover, Germany
| | - Britta Skawran
- Department of Human Genetics, Hannover Medical School, Hannover, Germany
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14
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Du S, Wankowicz SA, Yabukarski F, Doukov T, Herschlag D, Fraser JS. Refinement of Multiconformer Ensemble Models from Multi-temperature X-ray Diffraction Data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.05.539620. [PMID: 37205593 PMCID: PMC10187334 DOI: 10.1101/2023.05.05.539620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Conformational ensembles underlie all protein functions. Thus, acquiring atomic-level ensemble models that accurately represent conformational heterogeneity is vital to deepen our understanding of how proteins work. Modeling ensemble information from X-ray diffraction data has been challenging, as traditional cryo-crystallography restricts conformational variability while minimizing radiation damage. Recent advances have enabled the collection of high quality diffraction data at ambient temperatures, revealing innate conformational heterogeneity and temperature-driven changes. Here, we used diffraction datasets for Proteinase K collected at temperatures ranging from 313 to 363K to provide a tutorial for the refinement of multiconformer ensemble models. Integrating automated sampling and refinement tools with manual adjustments, we obtained multiconformer models that describe alternative backbone and sidechain conformations, their relative occupancies, and interconnections between conformers. Our models revealed extensive and diverse conformational changes across temperature, including increased bound peptide ligand occupancies, different Ca2+ binding site configurations and altered rotameric distributions. These insights emphasize the value and need for multiconformer model refinement to extract ensemble information from diffraction data and to understand ensemble-function relationships.
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Affiliation(s)
- Siyuan Du
- Department of Biochemistry, Stanford University, Stanford, California 94305, United States
- Department of Chemistry, Stanford University, Stanford, California 94305, United States
| | - Stephanie A. Wankowicz
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, California 94143, United States
| | - Filip Yabukarski
- Department of Biochemistry, Stanford University, Stanford, California 94305, United States
- Bristol-Myers Squibb, San Diego, California 92121, United States
| | - Tzanko Doukov
- Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Menlo Park, California 94025, United States
| | - Daniel Herschlag
- Department of Biochemistry, Stanford University, Stanford, California 94305, United States
- Department of Chemical Engineering, Stanford University, Stanford, California 94305, United States
- Stanford ChEM-H, Stanford University, Stanford, California 94305, United States
| | - James S. Fraser
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, California 94143, United States
- Quantitative Biosciences Institute, University of California, San Francisco, California 94143, United States
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15
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O'Donnell T, Cazals F. Enhanced conformational exploration of protein loops using a global parameterization of the backbone geometry. J Comput Chem 2023; 44:1094-1104. [PMID: 36733189 DOI: 10.1002/jcc.27067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Accepted: 12/22/2022] [Indexed: 02/04/2023]
Abstract
Flexible loops are paramount to protein functions, with action modes ranging from localized dynamics contributing to the free energy of the system, to large amplitude conformational changes accounting for the repositioning whole secondary structure elements or protein domains. However, generating diverse and low energy loops remains a difficult problem. This work introduces a novel paradigm to sample loop conformations, in the spirit of the hit-and-run (HAR) Markov chain Monte Carlo technique. The algorithm uses a decomposition of the loop into tripeptides, and a novel characterization of necessary conditions for Tripeptide Loop Closure to admit solutions. Denoting m the number of tripeptides, the algorithm works in an angular space of dimension 12 m. In this space, the hyper-surfaces associated with the aforementioned necessary conditions are used to run a HAR-like sampling technique. On classical loop cases up to 15 amino acids, our parameter free method compares favorably to previous work, generating more diverse conformational ensembles. We also report experiments on a 30 amino acids long loop, a size not processed in any previous work.
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Affiliation(s)
- Timothée O'Donnell
- Algorithms-Biology-Structure, Centre Inria at Université Côte d'Azur, Sophia Antipolis, France
| | - Frédéric Cazals
- Algorithms-Biology-Structure, Centre Inria at Université Côte d'Azur, Sophia Antipolis, France
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16
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Mullapudi V, Vaquer-Alicea J, Bommareddy V, Vega AR, Ryder BD, White CL, Diamond MI, Joachimiak LA. Network of hotspot interactions cluster tau amyloid folds. Nat Commun 2023; 14:895. [PMID: 36797278 PMCID: PMC9935906 DOI: 10.1038/s41467-023-36572-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 02/06/2023] [Indexed: 02/18/2023] Open
Abstract
Cryogenic electron microscopy has revealed unprecedented molecular insight into the conformations of β-sheet-rich protein amyloids linked to neurodegenerative diseases. It remains unknown how a protein can adopt a diversity of folds and form multiple distinct fibrillar structures. Here we develop an in silico alanine scan method to estimate the relative energetic contribution of each amino acid in an amyloid assembly. We apply our method to twenty-seven ex vivo and in vitro fibril structural polymorphs of the microtubule-associated protein tau. We uncover networks of energetically important interactions involving amyloid-forming motifs that stabilize the different fibril folds. We evaluate our predictions in cellular and in vitro aggregation assays. Using a machine learning approach, we classify the structures based on residue energetics to identify distinguishing and unifying features. Our energetic profiling suggests that minimal sequence elements control the stability of tau fibrils, allowing future design of protein sequences that fold into unique structures.
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Affiliation(s)
- Vishruth Mullapudi
- Center for Alzheimer's and Neurodegenerative Diseases, Peter O'Donnell Jr. Brain Institute, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Jaime Vaquer-Alicea
- Center for Alzheimer's and Neurodegenerative Diseases, Peter O'Donnell Jr. Brain Institute, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Vaibhav Bommareddy
- Center for Alzheimer's and Neurodegenerative Diseases, Peter O'Donnell Jr. Brain Institute, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Anthony R Vega
- Center for Alzheimer's and Neurodegenerative Diseases, Peter O'Donnell Jr. Brain Institute, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Bryan D Ryder
- Center for Alzheimer's and Neurodegenerative Diseases, Peter O'Donnell Jr. Brain Institute, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
- Molecular Biophysics Graduate Program, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Charles L White
- Center for Alzheimer's and Neurodegenerative Diseases, Peter O'Donnell Jr. Brain Institute, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Marc I Diamond
- Center for Alzheimer's and Neurodegenerative Diseases, Peter O'Donnell Jr. Brain Institute, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Lukasz A Joachimiak
- Center for Alzheimer's and Neurodegenerative Diseases, Peter O'Donnell Jr. Brain Institute, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.
- Department of Biochemistry, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.
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17
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Boorla VS, Chowdhury R, Ramasubramanian R, Ameglio B, Frick R, Gray JJ, Maranas CD. De novo design and Rosetta-based assessment of high-affinity antibody variable regions (Fv) against the SARS-CoV-2 spike receptor binding domain (RBD). Proteins 2023; 91:196-208. [PMID: 36111441 PMCID: PMC9538105 DOI: 10.1002/prot.26422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 08/17/2022] [Accepted: 09/06/2022] [Indexed: 01/11/2023]
Abstract
The continued emergence of new SARS-CoV-2 variants has accentuated the growing need for fast and reliable methods for the design of potentially neutralizing antibodies (Abs) to counter immune evasion by the virus. Here, we report on the de novo computational design of high-affinity Ab variable regions (Fv) through the recombination of VDJ genes targeting the most solvent-exposed hACE2-binding residues of the SARS-CoV-2 spike receptor binding domain (RBD) protein using the software tool OptMAVEn-2.0. Subsequently, we carried out computational affinity maturation of the designed variable regions through amino acid substitutions for improved binding with the target epitope. Immunogenicity of designs was restricted by preferring designs that match sequences from a 9-mer library of "human Abs" based on a human string content score. We generated 106 different antibody designs and reported in detail on the top five that trade-off the greatest computational binding affinity for the RBD with human string content scores. We further describe computational evaluation of the top five designs produced by OptMAVEn-2.0 using a Rosetta-based approach. We used Rosetta SnugDock for local docking of the designs to evaluate their potential to bind the spike RBD and performed "forward folding" with DeepAb to assess their potential to fold into the designed structures. Ultimately, our results identified one designed Ab variable region, P1.D1, as a particularly promising candidate for experimental testing. This effort puts forth a computational workflow for the de novo design and evaluation of Abs that can quickly be adapted to target spike epitopes of emerging SARS-CoV-2 variants or other antigenic targets.
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Affiliation(s)
- Veda Sheersh Boorla
- Department of Chemical Engineering, The Pennsylvania State University, University Park. PA 16802
| | - Ratul Chowdhury
- Department of Chemical Engineering, The Pennsylvania State University, University Park. PA 16802
| | | | - Brandon Ameglio
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD, USA
| | - Rahel Frick
- 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
| | - Costas D. Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park. PA 16802
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18
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Sora V, Laspiur AO, Degn K, Arnaudi M, Utichi M, Beltrame L, De Menezes D, Orlandi M, Stoltze UK, Rigina O, Sackett PW, Wadt K, Schmiegelow K, Tiberti M, Papaleo E. RosettaDDGPrediction for high-throughput mutational scans: From stability to binding. Protein Sci 2023; 32:e4527. [PMID: 36461907 PMCID: PMC9795540 DOI: 10.1002/pro.4527] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 11/25/2022] [Accepted: 11/25/2022] [Indexed: 12/05/2022]
Abstract
Reliable prediction of free energy changes upon amino acid substitutions (ΔΔGs) is crucial to investigate their impact on protein stability and protein-protein interaction. Advances in experimental mutational scans allow high-throughput studies thanks to multiplex techniques. On the other hand, genomics initiatives provide a large amount of data on disease-related variants that can benefit from analyses with structure-based methods. Therefore, the computational field should keep the same pace and provide new tools for fast and accurate high-throughput ΔΔG calculations. In this context, the Rosetta modeling suite implements effective approaches to predict folding/unfolding ΔΔGs in a protein monomer upon amino acid substitutions and calculate the changes in binding free energy in protein complexes. However, their application can be challenging to users without extensive experience with Rosetta. Furthermore, Rosetta protocols for ΔΔG prediction are designed considering one variant at a time, making the setup of high-throughput screenings cumbersome. For these reasons, we devised RosettaDDGPrediction, a customizable Python wrapper designed to run free energy calculations on a set of amino acid substitutions using Rosetta protocols with little intervention from the user. Moreover, RosettaDDGPrediction assists with checking completed runs and aggregates raw data for multiple variants, as well as generates publication-ready graphics. We showed the potential of the tool in four case studies, including variants of uncertain significance in childhood cancer, proteins with known experimental unfolding ΔΔGs values, interactions between target proteins and disordered motifs, and phosphomimetics. RosettaDDGPrediction is available, free of charge and under GNU General Public License v3.0, at https://github.com/ELELAB/RosettaDDGPrediction.
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Affiliation(s)
- Valentina Sora
- Cancer Structural Biology, Danish Cancer Society Research CenterCopenhagenDenmark
- Cancer Systems Biology, Section for Bioinformatics, Department of Health and TechnologyTechnical University of DenmarkLyngbyDenmark
| | - Adrian Otamendi Laspiur
- Cancer Systems Biology, Section for Bioinformatics, Department of Health and TechnologyTechnical University of DenmarkLyngbyDenmark
| | - Kristine Degn
- Cancer Systems Biology, Section for Bioinformatics, Department of Health and TechnologyTechnical University of DenmarkLyngbyDenmark
| | - Matteo Arnaudi
- Cancer Structural Biology, Danish Cancer Society Research CenterCopenhagenDenmark
- Cancer Systems Biology, Section for Bioinformatics, Department of Health and TechnologyTechnical University of DenmarkLyngbyDenmark
| | - Mattia Utichi
- Cancer Structural Biology, Danish Cancer Society Research CenterCopenhagenDenmark
- Cancer Systems Biology, Section for Bioinformatics, Department of Health and TechnologyTechnical University of DenmarkLyngbyDenmark
| | - Ludovica Beltrame
- Cancer Structural Biology, Danish Cancer Society Research CenterCopenhagenDenmark
- Cancer Systems Biology, Section for Bioinformatics, Department of Health and TechnologyTechnical University of DenmarkLyngbyDenmark
| | - Dayana De Menezes
- Cancer Systems Biology, Section for Bioinformatics, Department of Health and TechnologyTechnical University of DenmarkLyngbyDenmark
| | - Matteo Orlandi
- Cancer Systems Biology, Section for Bioinformatics, Department of Health and TechnologyTechnical University of DenmarkLyngbyDenmark
| | - Ulrik Kristoffer Stoltze
- Department of Clinical GeneticsCopenhagen University Hospital RigshospitaletCopenhagenDenmark
- Department of Pediatrics and Adolescent MedicineUniversity Hospital RigshospitaletCopenhagenDenmark
- Institute of Clinical Medicine, Faculty of MedicineUniversity of CopenhagenCopenhagenDenmark
| | - Olga Rigina
- Cancer Systems Biology, Section for Bioinformatics, Department of Health and TechnologyTechnical University of DenmarkLyngbyDenmark
| | - Peter Wad Sackett
- Cancer Systems Biology, Section for Bioinformatics, Department of Health and TechnologyTechnical University of DenmarkLyngbyDenmark
| | - Karin Wadt
- Department of Clinical GeneticsCopenhagen University Hospital RigshospitaletCopenhagenDenmark
- Institute of Clinical Medicine, Faculty of MedicineUniversity of CopenhagenCopenhagenDenmark
| | - Kjeld Schmiegelow
- Department of Pediatrics and Adolescent MedicineUniversity Hospital RigshospitaletCopenhagenDenmark
- Institute of Clinical Medicine, Faculty of MedicineUniversity of CopenhagenCopenhagenDenmark
| | - Matteo Tiberti
- Cancer Structural Biology, Danish Cancer Society Research CenterCopenhagenDenmark
| | - Elena Papaleo
- Cancer Structural Biology, Danish Cancer Society Research CenterCopenhagenDenmark
- Cancer Systems Biology, Section for Bioinformatics, Department of Health and TechnologyTechnical University of DenmarkLyngbyDenmark
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19
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King E, Maxel S, Zhang Y, Kenney KC, Cui Y, Luu E, Siegel JB, Weiss GA, Luo R, Li H. Orthogonal glycolytic pathway enables directed evolution of noncanonical cofactor oxidase. Nat Commun 2022; 13:7282. [PMID: 36435948 PMCID: PMC9701214 DOI: 10.1038/s41467-022-35021-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 11/15/2022] [Indexed: 11/28/2022] Open
Abstract
Noncanonical cofactor biomimetics (NCBs) such as nicotinamide mononucleotide (NMN+) provide enhanced scalability for biomanufacturing. However, engineering enzymes to accept NCBs is difficult. Here, we establish a growth selection platform to evolve enzymes to utilize NMN+-based reducing power. This is based on an orthogonal, NMN+-dependent glycolytic pathway in Escherichia coli which can be coupled to any reciprocal enzyme to recycle the ensuing reduced NMN+. With a throughput of >106 variants per iteration, the growth selection discovers a Lactobacillus pentosus NADH oxidase variant with ~10-fold increase in NMNH catalytic efficiency and enhanced activity for other NCBs. Molecular modeling and experimental validation suggest that instead of directly contacting NCBs, the mutations optimize the enzyme's global conformational dynamics to resemble the WT with the native cofactor bound. Restoring the enzyme's access to catalytically competent conformation states via deep navigation of protein sequence space with high-throughput evolution provides a universal route to engineer NCB-dependent enzymes.
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Affiliation(s)
- Edward King
- Department of Molecular Biology and Biochemistry, University of California Irvine, Irvine, CA, 92697, USA
| | - Sarah Maxel
- Department Chemical and Biomolecular Engineering University of California Irvine, Irvine, CA, 92697, USA
| | - Yulai Zhang
- Department Chemical and Biomolecular Engineering University of California Irvine, Irvine, CA, 92697, USA
| | - Karissa C Kenney
- Department of Chemistry, University of California Irvine, Irvine, CA, 92697, USA
| | - Youtian Cui
- Genome Center, University of California Davis, Davis, CA, 95616, USA
| | - Emma Luu
- Genome Center, University of California Davis, Davis, CA, 95616, USA
| | - Justin B Siegel
- Genome Center, University of California Davis, Davis, CA, 95616, USA
- Department of Chemistry, Molecular Medicine University of California, Davis, Davis, CA, USA
- Department of Biochemistry and Molecular Medicine University of California, Davis, Davis, CA, USA
| | - Gregory A Weiss
- Department of Molecular Biology and Biochemistry, University of California Irvine, Irvine, CA, 92697, USA
- Department of Chemistry, University of California Irvine, Irvine, CA, 92697, USA
- Department of Pharmaceutical Sciences, University of California Irvine, Irvine, CA, 92697, USA
| | - Ray Luo
- Department of Molecular Biology and Biochemistry, University of California Irvine, Irvine, CA, 92697, USA
- Department Chemical and Biomolecular Engineering University of California Irvine, Irvine, CA, 92697, USA
- Department Materials Science and Engineering, University of California Irvine, Irvine, CA, 92697, USA
- Department of Biomedical Engineering, University of California Irvine, Irvine, CA, 92697, USA
| | - Han Li
- Department Chemical and Biomolecular Engineering University of California Irvine, Irvine, CA, 92697, USA.
- Department of Biomedical Engineering, University of California Irvine, Irvine, CA, 92697, USA.
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20
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Pacini L, Lesieur C. GCAT: A network model of mutational influences between amino acid positions in PSD95pdz3. Front Mol Biosci 2022; 9:1035248. [PMID: 36387271 PMCID: PMC9659846 DOI: 10.3389/fmolb.2022.1035248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 10/13/2022] [Indexed: 12/05/2022] Open
Abstract
Proteins exist for more than 3 billion years: proof of a sustainable design. They have mechanisms coping with internal perturbations (e.g., amino acid mutations), which tie genetic backgrounds to diseases or drug therapy failure. One difficulty to grasp these mechanisms is the asymmetry of amino acid mutational impact: a mutation at position i in the sequence, which impact a position j does not imply that the mutation at position j impacts the position i. Thus, to distinguish the influence of the mutation of i on j from the influence of the mutation of j on i, position mutational influences must be represented with directions. Using the X ray structure of the third PDZ domain of PDS-95 (Protein Data Bank 1BE9) and in silico mutations, we build a directed network called GCAT that models position mutational influences. In the GCAT, a position is a node with edges that leave the node (out-edges) for the influences of the mutation of the position on other positions and edges that enter the position (in-edges) for the influences of the mutation of other positions on the position. 1BE9 positions split into four influence categories called G, C, A and T going from positions influencing on average less other positions and influenced on average by less other positions (category C) to positions influencing on average more others positions and influenced on average by more other positions (category T). The four categories depict position neighborhoods in the protein structure with different tolerance to mutations.
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Affiliation(s)
- Lorenza Pacini
- University Lyon, CNRS, INSA Lyon, Ecole Centrale de Lyon, UMR5005, Université Claude Bernard Lyon 1, Villeurbanne, France
- Institut Rhônalpin des Systèmes Complexes, IXXI-ENS-Lyon, Lyon, France
| | - Claire Lesieur
- University Lyon, CNRS, INSA Lyon, Ecole Centrale de Lyon, UMR5005, Université Claude Bernard Lyon 1, Villeurbanne, France
- Institut Rhônalpin des Systèmes Complexes, IXXI-ENS-Lyon, Lyon, France
- *Correspondence: Claire Lesieur,
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21
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Ochoa R, Cossio P, Fox T. Protocol for iterative optimization of modified peptides bound to protein targets. J Comput Aided Mol Des 2022; 36:825-835. [PMID: 36258137 PMCID: PMC9640467 DOI: 10.1007/s10822-022-00482-1] [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: 07/20/2022] [Accepted: 10/03/2022] [Indexed: 12/02/2022]
Abstract
Peptides are commonly used as therapeutic agents. However, they suffer from easy degradation and instability. Replacing natural by non-natural amino acids can avoid these problems, and potentially improve the affinity towards the target protein. Here, we present a computational pipeline to optimize peptides based on adding non-natural amino acids while improving their binding affinity. The workflow is an iterative computational evolution algorithm, inspired by the PARCE protocol, that performs single-point mutations on the peptide sequence using modules from the Rosetta framework. The modifications can be guided based on the structural properties or previous knowledge of the biological system. At each mutation step, the affinity to the protein is estimated by sampling the complex conformations and applying a consensus metric using various open protein-ligand scoring functions. The mutations are accepted based on the score differences, allowing for an iterative optimization of the initial peptide. The sampling/scoring scheme was benchmarked with a set of protein-peptide complexes where experimental affinity values have been reported. In addition, a basic application using a known protein-peptide complex is also provided. The structure- and dynamic-based approach allows users to optimize bound peptides, with the option to personalize the code for further applications. The protocol, called mPARCE, is available at: https://github.com/rochoa85/mPARCE/.
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Affiliation(s)
- Rodrigo Ochoa
- Biophysics of Tropical Diseases, Max Planck Tandem Group, University of Antioquia, Medellín, 050010, Colombia. .,Medicinal Chemistry, Boehringer Ingelheim Pharma GmbH & Co KG, 88397, Biberach/Riss, Germany.
| | - Pilar Cossio
- Biophysics of Tropical Diseases, Max Planck Tandem Group, University of Antioquia, Medellín, 050010, Colombia.,Center for Computational Mathematics, Flatiron Institute, New York, 10010, USA.,Center for Computational Biology, Flatiron Institute, New York, 10010, USA
| | - Thomas Fox
- Medicinal Chemistry, Boehringer Ingelheim Pharma GmbH & Co KG, 88397, Biberach/Riss, Germany
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22
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Ferraz MVF, Viana IFT, Coêlho DF, da Cruz CHB, de Arruda Lima M, de Luna Aragão MA, Lins RD. Association strength of E6 to E6AP/p53 complex correlates with HPV‐mediated oncogenesis risk. Biopolymers 2022; 113:e23524. [DOI: 10.1002/bip.23524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 07/26/2022] [Accepted: 08/01/2022] [Indexed: 11/06/2022]
Affiliation(s)
- Matheus Vitor Ferreira Ferraz
- Aggeu Magalhães Institute Oswaldo Cruz Foundation Recife Brazil
- Department of Fundamental Chemistry Federal University of Pernambuco Recife Brazil
| | | | - Danilo Fernandes Coêlho
- Aggeu Magalhães Institute Oswaldo Cruz Foundation Recife Brazil
- Department of Fundamental Chemistry Federal University of Pernambuco Recife Brazil
| | | | | | | | - Roberto Dias Lins
- Aggeu Magalhães Institute Oswaldo Cruz Foundation Recife Brazil
- Department of Fundamental Chemistry Federal University of Pernambuco Recife Brazil
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23
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Graham TGW, Ferrie JJ, Dailey GM, Tjian R, Darzacq X. Detecting molecular interactions in live-cell single-molecule imaging with proximity-assisted photoactivation (PAPA). eLife 2022; 11:e76870. [PMID: 35976226 PMCID: PMC9531946 DOI: 10.7554/elife.76870] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 08/16/2022] [Indexed: 11/13/2022] Open
Abstract
Single-molecule imaging provides a powerful way to study biochemical processes in live cells, yet it remains challenging to track single molecules while simultaneously detecting their interactions. Here, we describe a novel property of rhodamine dyes, proximity-assisted photoactivation (PAPA), in which one fluorophore (the 'sender') can reactivate a second fluorophore (the 'receiver') from a dark state. PAPA requires proximity between the two fluorophores, yet it operates at a longer average intermolecular distance than Förster resonance energy transfer (FRET). We show that PAPA can be used in live cells both to detect protein-protein interactions and to highlight a subpopulation of labeled protein complexes in which two different labels are in proximity. In proof-of-concept experiments, PAPA detected the expected correlation between androgen receptor self-association and chromatin binding at the single-cell level. These results establish a new way in which a photophysical property of fluorophores can be harnessed to study molecular interactions in single-molecule imaging of live cells.
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Affiliation(s)
- Thomas GW Graham
- Department of Molecular and Cell Biology, University of California, BerkeleyBerkeleyUnited States
| | - John Joseph Ferrie
- Department of Molecular and Cell Biology, University of California, BerkeleyBerkeleyUnited States
| | - Gina M Dailey
- Department of Molecular and Cell Biology, University of California, BerkeleyBerkeleyUnited States
| | - Robert Tjian
- Department of Molecular and Cell Biology, University of California, BerkeleyBerkeleyUnited States
- Howard Hughes Medical Institute, University of California, BerkeleyBerkeleyUnited States
| | - Xavier Darzacq
- Department of Molecular and Cell Biology, University of California, BerkeleyBerkeleyUnited States
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24
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Gumpper RH, Fay JF, Roth BL. Molecular insights into the regulation of constitutive activity by RNA editing of 5HT 2C serotonin receptors. Cell Rep 2022; 40:111211. [PMID: 35977511 PMCID: PMC9422347 DOI: 10.1016/j.celrep.2022.111211] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 06/01/2022] [Accepted: 07/21/2022] [Indexed: 11/25/2022] Open
Abstract
RNA editing is a process by which post-transcriptional changes of mRNA nucleotides alter protein function through modification of the amino acid content. The 5HT2C serotonin receptor, which undergoes 32 distinct RNA-editing events leading to 24 protein isoforms, is a notable example of this process. These 5HT2C isoforms display differences in constitutive activity, agonist/inverse agonist potencies, and efficacies. To elucidate the molecular mechanisms responsible for these effects of RNA editing, we present four active-state 5HT2C-transducer-coupled structures of three representative isoforms (INI, VGV, and VSV) with the selective drug lorcaserin (Belviq) and the classic psychedelic psilocin. We also provide a comprehensive analysis of agonist activation and constitutive activity across all 24 protein isoforms. Collectively, these findings reveal a unique hydrogen-bonding network located on intracellular loop 2 that is subject to RNA editing, which differentially affects GPCR constitutive and agonist signaling activities. It is established that the 5HT2C receptor undergoes RNA editing leading to 24 isoforms. Several isoforms exhibit changes in basal activity and are linked to pathologies. Gumpper et al. have done a systematic structure-function characterization of all the isoforms revealing the underlying mechanisms that govern basal activity of the 5HT2C receptor.
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Affiliation(s)
- Ryan H Gumpper
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA.
| | - Jonathan F Fay
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA
| | - Bryan L Roth
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA.
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25
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Magi Meconi G, Sasselli IR, Bianco V, Onuchic JN, Coluzza I. Key aspects of the past 30 years of protein design. REPORTS ON PROGRESS IN PHYSICS. PHYSICAL SOCIETY (GREAT BRITAIN) 2022; 85:086601. [PMID: 35704983 DOI: 10.1088/1361-6633/ac78ef] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 06/15/2022] [Indexed: 06/15/2023]
Abstract
Proteins are the workhorse of life. They are the building infrastructure of living systems; they are the most efficient molecular machines known, and their enzymatic activity is still unmatched in versatility by any artificial system. Perhaps proteins' most remarkable feature is their modularity. The large amount of information required to specify each protein's function is analogically encoded with an alphabet of just ∼20 letters. The protein folding problem is how to encode all such information in a sequence of 20 letters. In this review, we go through the last 30 years of research to summarize the state of the art and highlight some applications related to fundamental problems of protein evolution.
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Affiliation(s)
- Giulia Magi Meconi
- Computational Biophysics Lab, Center for Cooperative Research in Biomaterials (CIC biomaGUNE), Basque Research and Technology Alliance (BRTA), Paseo de Miramon 182, 20014, Donostia-San Sebastián, Spain
| | - Ivan R Sasselli
- Computational Biophysics Lab, Center for Cooperative Research in Biomaterials (CIC biomaGUNE), Basque Research and Technology Alliance (BRTA), Paseo de Miramon 182, 20014, Donostia-San Sebastián, Spain
| | | | - Jose N Onuchic
- Center for Theoretical Biological Physics, Department of Physics & Astronomy, Department of Chemistry, Department of Biosciences, Rice University, Houston, TX 77251, United States of America
| | - Ivan Coluzza
- BCMaterials, Basque Center for Materials, Applications and Nanostructures, Bld. Martina Casiano, UPV/EHU Science Park, Barrio Sarriena s/n, 48940 Leioa, Spain
- Basque Foundation for Science, Ikerbasque, 48009, Bilbao, Spain
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26
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Harmalkar A, Mahajan SP, Gray JJ. Induced fit with replica exchange improves protein complex structure prediction. PLoS Comput Biol 2022; 18:e1010124. [PMID: 35658008 PMCID: PMC9200320 DOI: 10.1371/journal.pcbi.1010124] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 06/15/2022] [Accepted: 04/20/2022] [Indexed: 11/19/2022] Open
Abstract
Despite the progress in prediction of protein complexes over the last decade, recent blind protein complex structure prediction challenges revealed limited success rates (less than 20% models with DockQ score > 0.4) on targets that exhibit significant conformational change upon binding. To overcome limitations in capturing backbone motions, we developed a new, aggressive sampling method that incorporates temperature replica exchange Monte Carlo (T-REMC) and conformational sampling techniques within docking protocols in Rosetta. Our method, ReplicaDock 2.0, mimics induced-fit mechanism of protein binding to sample backbone motions across putative interface residues on-the-fly, thereby recapitulating binding-partner induced conformational changes. Furthermore, ReplicaDock 2.0 clocks in at 150-500 CPU hours per target (protein-size dependent); a runtime that is significantly faster than Molecular Dynamics based approaches. For a benchmark set of 88 proteins with moderate to high flexibility (unbound-to-bound iRMSD over 1.2 Å), ReplicaDock 2.0 successfully docks 61% of moderately flexible complexes and 35% of highly flexible complexes. Additionally, we demonstrate that by biasing backbone sampling particularly towards residues comprising flexible loops or hinge domains, highly flexible targets can be predicted to under 2 Å accuracy. This indicates that additional gains are possible when mobile protein segments are known.
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Affiliation(s)
- Ameya Harmalkar
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Sai Pooja Mahajan
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Jeffrey J. Gray
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, Maryland, United States of America
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27
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Zheng Y, Vaissier Welborn V. Tuning the Catalytic Activity of Synthetic Enzyme KE15 with DNA. J Phys Chem B 2022; 126:3407-3413. [PMID: 35483007 DOI: 10.1021/acs.jpcb.2c00765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Efficiency improvement of synthetic enzymes through scaffold modifications suffers from limitations in terms of effectiveness, cost, and potential devastating consequences for protein structural stability. Here, we propose an alternative to scaffold modification, within electrostatic preorganization theory, where the enzyme's greater environment is designed to support the evolution of the reaction in the active site. We demonstrate the feasibility of such an approach by placing a (polar) DNA fragment in the surroundings of the Kemp eliminase enzyme KE15 (structure from Houk's group) and computing the resulting change in catalytic activity. We find that the introduction of a DNA fragment magnifies the contribution of protein residues to the stabilization of the transition state, estimated from electric field calculations with polarizable molecular dynamics. Our randomly generated test systems reveal a 2.0 kcal/mol reduction in activation energy, suggesting that even more significant catalytic improvements could be made by optimizing DNA size, sequence, and orientation with respect to the enzyme, validating our approach.
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Affiliation(s)
- Yi Zheng
- Department of Chemistry, Virginia Tech, Blacksburg, Virginia 24060, United States
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28
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Fiore KE, Patist MJ, Giannakoulias S, Huang CH, Verma H, Khatri B, Cheng RP, Chatterjee J, Petersson EJ. Structural impact of thioamide incorporation into a β-hairpin. RSC Chem Biol 2022; 3:582-591. [PMID: 35656485 PMCID: PMC9092430 DOI: 10.1039/d1cb00229e] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 03/24/2022] [Indexed: 11/21/2022] Open
Abstract
The thioamide is a naturally-occurring single atom substitution of the canonical amide bond. The exchange of oxygen to sulfur alters the amide's physical and chemical characteristics, thereby expanding its functionality. Incorporation of thioamides in prevalent secondary structures has demonstrated that they can either have stabilizing, destabilizing, or neutral effects. We performed a systematic investigation of the structural impact of thioamide incorporation in a β-hairpin scaffold with nuclear magnetic resonance (NMR). Thioamides as hydrogen bond donors did not increase the foldedness of the more stable "YKL" variant of this scaffold. In the less stable "HPT" variant of the scaffold, the thioamide could be stabilizing as a hydrogen bond donor and destabilizing as a hydrogen bond acceptor, but the extent of the perturbation depended upon the position of incorporation. To better understand these effects we performed structural modelling of the macrocyclic folded HPT variants. Finally, we compare the thioamide effects that we observe to previous studies of both side-chain and backbone perturbations to this β-hairpin scaffold to provide context for our observations.
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Affiliation(s)
- Kristen E Fiore
- Department of Chemistry, University of Pennsylvania 231 S. 34th Street Philadelphia 19104 USA
| | - Martijn J Patist
- Department of Chemistry, University of Pennsylvania 231 S. 34th Street Philadelphia 19104 USA
| | - Sam Giannakoulias
- Department of Chemistry, University of Pennsylvania 231 S. 34th Street Philadelphia 19104 USA
| | - Cheng-Hsin Huang
- Department of Chemistry, National Taiwan University No. 1, Sec. 4, Roosevelt Road Taipei 10617 Taiwan
| | - Hitesh Verma
- Molecular Biophysics Unit, Indian Institute of Science Bangalore 560012 India
| | - Bhavesh Khatri
- Molecular Biophysics Unit, Indian Institute of Science Bangalore 560012 India
| | - Richard P Cheng
- Department of Chemistry, National Taiwan University No. 1, Sec. 4, Roosevelt Road Taipei 10617 Taiwan
| | - Jayanta Chatterjee
- Molecular Biophysics Unit, Indian Institute of Science Bangalore 560012 India
| | - E James Petersson
- Department of Chemistry, University of Pennsylvania 231 S. 34th Street Philadelphia 19104 USA
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29
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Lawal MM, Vaissier Welborn V. Structural dynamics support electrostatic interactions in the active site of Adenylate Kinase. Chembiochem 2022; 23:e202200097. [PMID: 35303385 DOI: 10.1002/cbic.202200097] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 03/17/2022] [Indexed: 11/12/2022]
Abstract
Electrostatic preorganization as well as structural and dynamic heterogeneity are often used to rationalize the remarkable catalytic efficiency of enzymes. However, they are often presented as incompatible because the generation of permanent electrostatic effects implies that the protein structure remains rigid. Here, we use a metric, electric fields, that can treat electrostatic contributions and dynamics effects on equal footing, for a unique perspective on enzymatic catalysis. We find that the residues that contribute the most to electrostatic interactions with the substrate in the active site of Adenylate Kinase (our working example) are also the most flexible residues. Further, entropy-tuning mutations raise flexibility at the picosecond timescale where more conformations can be visited on short time periods, thereby softening the sharp heterogeneity normally visible at the microsecond timescale.
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Affiliation(s)
| | - Valerie Vaissier Welborn
- Virginia Polytechnic Institute and State University, Chemistry, Davidson 421A, 1040 Drillfield Drive, 24073, Blacksburg, UNITED STATES
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30
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ElGamacy M. Accelerating therapeutic protein design. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2022; 130:85-118. [PMID: 35534117 DOI: 10.1016/bs.apcsb.2022.01.004] [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
Protein structures provide for defined microenvironments that can support complex pharmacological functions, otherwise unachievable by small molecules. The advent of therapeutic proteins has thus greatly broadened the range of manageable disorders. Leveraging the knowledge and recent advances in de novo protein design methods has the prospect of revolutionizing how protein drugs are discovered and developed. This review lays out the main challenges facing therapeutic proteins discovery and development, and how present and future advancements of protein design can accelerate the protein drug pipelines.
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Affiliation(s)
- Mohammad ElGamacy
- University Hospital Tübingen, Division of Translational Oncology, Tübingen, Germany; Max Planck Institute for Biology, Tübingen, Germany.
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31
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Bouchiba Y, Ruffini M, Schiex T, Barbe S. Computational Design of Miniprotein Binders. Methods Mol Biol 2022; 2405:361-382. [PMID: 35298822 DOI: 10.1007/978-1-0716-1855-4_17] [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] [Indexed: 06/14/2023]
Abstract
Miniprotein binders hold a great interest as a class of drugs that bridges the gap between monoclonal antibodies and small molecule drugs. Like monoclonal antibodies, they can be designed to bind to therapeutic targets with high affinity, but they are more stable and easier to produce and to administer. In this chapter, we present a structure-based computational generic approach for miniprotein inhibitor design. Specifically, we describe step-by-step the implementation of the approach for the design of miniprotein binders against the SARS-CoV-2 coronavirus, using available structural data on the SARS-CoV-2 spike receptor binding domain (RBD) in interaction with its native target, the human receptor ACE2. Structural data being increasingly accessible around many protein-protein interaction systems, this method might be applied to the design of miniprotein binders against numerous therapeutic targets. The computational pipeline exploits provable and deterministic artificial intelligence-based protein design methods, with some recent additions in terms of binding energy estimation, multistate design and diverse library generation.
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Affiliation(s)
- Younes Bouchiba
- TBI, Université de Toulouse, CNRS, INRAE, INSA, ANITI, Toulouse, France
| | - Manon Ruffini
- TBI, Université de Toulouse, CNRS, INRAE, INSA, ANITI, Toulouse, France
- Université Fédérale de Toulouse, ANITI, INRAE, UR 875, Toulouse, France
| | - Thomas Schiex
- Université Fédérale de Toulouse, ANITI, INRAE, UR 875, Toulouse, France
| | - Sophie Barbe
- TBI, Université de Toulouse, CNRS, INRAE, INSA, ANITI, Toulouse, France.
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32
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Vu O, Bender BJ, Pankewitz L, Huster D, Beck-Sickinger AG, Meiler J. The Structural Basis of Peptide Binding at Class A G Protein-Coupled Receptors. Molecules 2021; 27:molecules27010210. [PMID: 35011444 PMCID: PMC8746363 DOI: 10.3390/molecules27010210] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 12/15/2021] [Accepted: 12/18/2021] [Indexed: 11/16/2022] Open
Abstract
G protein-coupled receptors (GPCRs) represent the largest membrane protein family and a significant target class for therapeutics. Receptors from GPCRs’ largest class, class A, influence virtually every aspect of human physiology. About 45% of the members of this family endogenously bind flexible peptides or peptides segments within larger protein ligands. While many of these peptides have been structurally characterized in their solution state, the few studies of peptides in their receptor-bound state suggest that these peptides interact with a shared set of residues and undergo significant conformational changes. For the purpose of understanding binding dynamics and the development of peptidomimetic drug compounds, further studies should investigate the peptide ligands that are complexed to their cognate receptor.
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Affiliation(s)
- Oanh Vu
- Deparment of Chemistry, Vanderbilt University, Nashville, TN 37235, USA;
- Center for Structural Biology, Vanderbilt University, Nashville, TN 37232, USA; (B.J.B.); (L.P.)
| | - Brian Joseph Bender
- Center for Structural Biology, Vanderbilt University, Nashville, TN 37232, USA; (B.J.B.); (L.P.)
- Department of Pharmacology, Vanderbilt University, Nashville, TN 37232, USA
| | - Lisa Pankewitz
- Center for Structural Biology, Vanderbilt University, Nashville, TN 37232, USA; (B.J.B.); (L.P.)
| | - Daniel Huster
- Institute for Medical Physics and Biophysics, Medical Department, Leipzig University, Härtelstr. 16–18, D-04107 Leipzig, Germany;
| | - Annette G. Beck-Sickinger
- Faculty of Life Sciences, Institute of Biochemistry, Leipzig University, Brüderstr. 34, D-04103 Leipzig, Germany;
| | - Jens Meiler
- Deparment of Chemistry, Vanderbilt University, Nashville, TN 37235, USA;
- Center for Structural Biology, Vanderbilt University, Nashville, TN 37232, USA; (B.J.B.); (L.P.)
- Department of Pharmacology, Vanderbilt University, Nashville, TN 37232, USA
- Leipzig University Medical Center, Institute for Drug Discovery, Departments of Chemistry and Computer Science, Leipzig University, Brüderstr. 34, D-04103 Leipzig, Germany
- Correspondence:
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33
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Abstract
Colicins are protein antibiotics deployed by Escherichia coli to eliminate competing strains. Colicins frequently exploit outer membrane (OM) nutrient transporters to penetrate the selectively permeable bacterial cell envelope. Here, by applying live-cell fluorescence imaging, we were able to monitor the entry of the pore-forming toxin colicin B (ColB) into E. coli and localize it within the periplasm. We further demonstrate that single-stranded DNA coupled to ColB can also be transported to the periplasm, emphasizing that the import routes of colicins can be exploited to carry large cargo molecules into bacteria. Moreover, we characterize the molecular mechanism of ColB association with its OM receptor FepA by applying a combination of photoactivated cross-linking, mass spectrometry, and structural modeling. We demonstrate that complex formation is coincident with large-scale conformational changes in the colicin. Thereafter, active transport of ColB through FepA involves the colicin taking the place of the N-terminal half of the plug domain that normally occludes this iron transporter. IMPORTANCE Decades of excessive use of readily available antibiotics has generated a global problem of antibiotic resistance and, hence, an urgent need for novel antibiotic solutions. Bacteriocins are protein-based antibiotics produced by bacteria to eliminate closely related competing bacterial strains. Bacteriocin toxins have evolved to bypass the complex cell envelope in order to kill bacterial cells. Here, we uncover the cellular penetration mechanism of a well-known but poorly understood bacteriocin called colicin B that is active against Escherichia coli. Moreover, we demonstrate that the colicin B-import pathway can be exploited to deliver conjugated DNA cargo into bacterial cells. Our work leads to a better understanding of the way bacteriocins, as potential alternative antibiotics, execute their mode of action as well as highlighting how they might even be exploited in the genomic manipulation of Gram-negative bacteria.
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34
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Havranek B, Chan KK, Wu A, Procko E, Islam SM. Computationally Designed ACE2 Decoy Receptor Binds SARS-CoV-2 Spike (S) Protein with Tight Nanomolar Affinity. J Chem Inf Model 2021; 61:4656-4669. [PMID: 34427448 PMCID: PMC8409145 DOI: 10.1021/acs.jcim.1c00783] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Indexed: 12/25/2022]
Abstract
Even with the availability of vaccines, therapeutic options for COVID-19 still remain highly desirable, especially in hospitalized patients with moderate or severe disease. Soluble ACE2 (sACE2) is a promising therapeutic candidate that neutralizes SARS CoV-2 infection by acting as a decoy. Using computational mutagenesis, we designed a number of sACE2 derivatives carrying three to four mutations. The top-predicted sACE2 decoy based on the in silico mutagenesis scan was subjected to molecular dynamics and free-energy calculations for further validation. After illuminating the mechanism of increased binding for our designed sACE2 derivative, the design was verified experimentally by flow cytometry and BLI-binding experiments. The computationally designed sACE2 decoy (ACE2-FFWF) bound the receptor-binding domain of SARS-CoV-2 tightly with low nanomolar affinity and ninefold affinity enhancement over the wild type. Furthermore, cell surface expression was slightly greater than wild-type ACE2, suggesting that the design is well-folded and stable. Having an arsenal of high-affinity sACE2 derivatives will help to buffer against the emergence of SARS CoV-2 variants. Here, we show that computational methods have become sufficiently accurate for the design of therapeutics for current and future viral pandemics.
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Affiliation(s)
- Brandon Havranek
- Department of Chemistry, University of
Illinois at Chicago, Chicago, Illinois 60607, United
States
| | - Kui K. Chan
- Orthogonal Biologics Inc.,
Urbana, Illinois 61801, United States
| | - Austin Wu
- Department of Computer Science,
Northwestern University, Evanston, Illinois 60208,
United States
| | - Erik Procko
- Department of Biochemistry and Cancer Center at
Illinois, University of Illinois, Urbana, Illinois 61801,
United States
| | - Shahidul M. Islam
- Department of Chemistry, University of
Illinois at Chicago, Chicago, Illinois 60607, United
States
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35
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Cai T, Sun H, Qiao J, Zhu L, Zhang F, Zhang J, Tang Z, Wei X, Yang J, Yuan Q, Wang W, Yang X, Chu H, Wang Q, You C, Ma H, Sun Y, Li Y, Li C, Jiang H, Wang Q, Ma Y. Cell-free chemoenzymatic starch synthesis from carbon dioxide. Science 2021; 373:1523-1527. [PMID: 34554807 DOI: 10.1126/science.abh4049] [Citation(s) in RCA: 180] [Impact Index Per Article: 60.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
[Figure: see text].
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Affiliation(s)
- Tao Cai
- Department of Strategic and Integrative Research, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China.,National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China
| | - Hongbing Sun
- Department of Strategic and Integrative Research, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China.,National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China
| | - Jing Qiao
- Department of Strategic and Integrative Research, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China.,National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China
| | - Leilei Zhu
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China.,National Engineering Laboratory for Industrial Enzymes, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
| | - Fan Zhang
- Department of Strategic and Integrative Research, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China.,National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China
| | - Jie Zhang
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China.,National Engineering Laboratory for Industrial Enzymes, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
| | - Zijing Tang
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China.,National Engineering Laboratory for Industrial Enzymes, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
| | - Xinlei Wei
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China.,National Engineering Laboratory for Industrial Enzymes, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
| | - Jiangang Yang
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China.,National Engineering Laboratory for Industrial Enzymes, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
| | - Qianqian Yuan
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China.,CAS Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
| | - Wangyin Wang
- State Key Laboratory of Catalysis, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
| | - Xue Yang
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China.,CAS Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
| | - Huanyu Chu
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China.,CAS Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
| | - Qian Wang
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China.,CAS Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
| | - Chun You
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China.,National Engineering Laboratory for Industrial Enzymes, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
| | - Hongwu Ma
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China.,CAS Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
| | - Yuanxia Sun
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China.,National Engineering Laboratory for Industrial Enzymes, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
| | - Yin Li
- Department of Strategic and Integrative Research, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China.,National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China
| | - Can Li
- State Key Laboratory of Catalysis, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
| | - Huifeng Jiang
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China.,CAS Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
| | - Qinhong Wang
- Department of Strategic and Integrative Research, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China.,National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China.,CAS Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
| | - Yanhe Ma
- Department of Strategic and Integrative Research, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China.,National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China.,National Engineering Laboratory for Industrial Enzymes, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
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36
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Yin R, Guest JD, Taherzadeh G, Gowthaman R, Mittra I, Quackenbush J, Pierce BG. Structural and energetic profiling of SARS-CoV-2 receptor binding domain antibody recognition and the impact of circulating variants. PLoS Comput Biol 2021; 17:e1009380. [PMID: 34491988 PMCID: PMC8448325 DOI: 10.1371/journal.pcbi.1009380] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 09/17/2021] [Accepted: 08/25/2021] [Indexed: 11/19/2022] Open
Abstract
The SARS-CoV-2 pandemic highlights the need for a detailed molecular understanding of protective antibody responses. This is underscored by the emergence and spread of SARS-CoV-2 variants, including Alpha (B.1.1.7) and Delta (B.1.617.2), some of which appear to be less effectively targeted by current monoclonal antibodies and vaccines. Here we report a high resolution and comprehensive map of antibody recognition of the SARS-CoV-2 spike receptor binding domain (RBD), which is the target of most neutralizing antibodies, using computational structural analysis. With a dataset of nonredundant experimentally determined antibody-RBD structures, we classified antibodies by RBD residue binding determinants using unsupervised clustering. We also identified the energetic and conservation features of epitope residues and assessed the capacity of viral variant mutations to disrupt antibody recognition, revealing sets of antibodies predicted to effectively target recently described viral variants. This detailed structure-based reference of antibody RBD recognition signatures can inform therapeutic and vaccine design strategies.
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Affiliation(s)
- Rui Yin
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland, United States of America
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland, United States of America
| | - Johnathan D. Guest
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland, United States of America
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland, United States of America
| | - Ghazaleh Taherzadeh
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland, United States of America
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland, United States of America
| | - Ragul Gowthaman
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland, United States of America
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland, United States of America
| | - Ipsa Mittra
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland, United States of America
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland, United States of America
| | - Jane Quackenbush
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland, United States of America
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland, United States of America
| | - Brian G. Pierce
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland, United States of America
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland, United States of America
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37
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Bouchiba Y, Cortés J, Schiex T, Barbe S. Molecular flexibility in computational protein design: an algorithmic perspective. Protein Eng Des Sel 2021; 34:6271252. [PMID: 33959778 DOI: 10.1093/protein/gzab011] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 03/12/2021] [Accepted: 03/29/2021] [Indexed: 12/19/2022] Open
Abstract
Computational protein design (CPD) is a powerful technique for engineering new proteins, with both great fundamental implications and diverse practical interests. However, the approximations usually made for computational efficiency, using a single fixed backbone and a discrete set of side chain rotamers, tend to produce rigid and hyper-stable folds that may lack functionality. These approximations contrast with the demonstrated importance of molecular flexibility and motions in a wide range of protein functions. The integration of backbone flexibility and multiple conformational states in CPD, in order to relieve the inaccuracies resulting from these simplifications and to improve design reliability, are attracting increased attention. However, the greatly increased search space that needs to be explored in these extensions defines extremely challenging computational problems. In this review, we outline the principles of CPD and discuss recent effort in algorithmic developments for incorporating molecular flexibility in the design process.
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Affiliation(s)
- Younes Bouchiba
- Toulouse Biotechnology Institute, TBI, CNRS, INRAE, INSA, ANITI, Toulouse 31400, France.,Laboratoire d'Analyse et d'Architecture des Systèmes, LAAS CNRS, Université de Toulouse, CNRS, Toulouse 31400, France
| | - Juan Cortés
- Laboratoire d'Analyse et d'Architecture des Systèmes, LAAS CNRS, Université de Toulouse, CNRS, Toulouse 31400, France
| | - Thomas Schiex
- Université de Toulouse, ANITI, INRAE, UR MIAT, F-31320, Castanet-Tolosan, France
| | - Sophie Barbe
- Toulouse Biotechnology Institute, TBI, CNRS, INRAE, INSA, ANITI, Toulouse 31400, France
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38
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Moysa A, Steczkiewicz K, Niedzialek D, Hammerschmid D, Zhukova L, Sobott F, Dadlez M. A model of full-length RAGE in complex with S100B. Structure 2021; 29:989-1002.e6. [PMID: 33887170 DOI: 10.1016/j.str.2021.04.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2020] [Revised: 02/03/2021] [Accepted: 04/02/2021] [Indexed: 01/10/2023]
Abstract
The receptor for advanced glycation end products (RAGE) is an immunoglobulin-type multiligand transmembrane protein expressed in numerous cell types, including the central nervous system cells. RAGE interaction with S100B, released during brain tissue damage, leads to RAGE upregulation and initialization of a spiral proinflammatory associated with different neural disorders. Here, we present the structural characterization of the hetero-oligomeric complex of the full-length RAGE with S100B, obtained by a combination of mass spectrometry-based methods and molecular modeling. We predict that RAGE functions as a tightly packed tetramer exposing a positively charged surface formed by V domains for S100B binding. Based on HDX results we demonstrate an allosteric coupling of the distal extracellular V domains and the transmembrane region, indicating a possible mechanism of signal transmission by RAGE across the membrane. Our model provides an insight into RAGE-ligand interactions, providing a basis for the rational design of the therapeutic modifiers of its activity.
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Affiliation(s)
- Alexander Moysa
- Institute of Biochemistry and Biophysics, PAS, Pawinskiego 5a, 02-109 Warsaw, Poland.
| | - Kamil Steczkiewicz
- Institute of Biochemistry and Biophysics, PAS, Pawinskiego 5a, 02-109 Warsaw, Poland.
| | - Dorota Niedzialek
- Institute of Biochemistry and Biophysics, PAS, Pawinskiego 5a, 02-109 Warsaw, Poland
| | - Dietmar Hammerschmid
- Department of Chemistry, King's College London, 7 Trinity Street, SE1 1DB London, UK; Department of Chemistry, Biomolecular & Analytical Mass Spectrometry Group, University of Antwerp, Groenenborgerlaan 171, 2020 Antwerp, Belgium
| | - Lilia Zhukova
- Institute of Biochemistry and Biophysics, PAS, Pawinskiego 5a, 02-109 Warsaw, Poland
| | - Frank Sobott
- Astbury Centre for Structural Molecular Biology and School of Molecular and Cellular Biology, University of Leeds, Woodhouse Lane, LS2 9JT Leeds, UK; Department of Chemistry, Biomolecular & Analytical Mass Spectrometry Group, University of Antwerp, Groenenborgerlaan 171, 2020 Antwerp, Belgium
| | - Michal Dadlez
- Institute of Biochemistry and Biophysics, PAS, Pawinskiego 5a, 02-109 Warsaw, Poland
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39
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Ochoa R, Laskowski RA, Thornton JM, Cossio P. Impact of Structural Observables From Simulations to Predict the Effect of Single-Point Mutations in MHC Class II Peptide Binders. Front Mol Biosci 2021; 8:636562. [PMID: 34222328 PMCID: PMC8253603 DOI: 10.3389/fmolb.2021.636562] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 02/15/2021] [Indexed: 11/23/2022] Open
Abstract
The prediction of peptide binders to Major Histocompatibility Complex (MHC) class II receptors is of great interest to study autoimmune diseases and for vaccine development. Most approaches predict the affinities using sequence-based models trained on experimental data and multiple alignments from known peptide substrates. However, detecting activity differences caused by single-point mutations is a challenging task. In this work, we used interactions calculated from simulations to build scoring matrices for quickly estimating binding differences by single-point mutations. We modelled a set of 837 peptides bound to an MHC class II allele, and optimized the sampling of the conformations using the Rosetta backrub method by comparing the results to molecular dynamics simulations. From the dynamic trajectories of each complex, we averaged and compared structural observables for each amino acid at each position of the 9°mer peptide core region. With this information, we generated the scoring-matrices to predict the sign of the binding differences. We then compared the performance of the best scoring-matrix to different computational methodologies that range in computational costs. Overall, the prediction of the activity differences caused by single mutated peptides was lower than 60% for all the methods. However, the developed scoring-matrix in combination with existing methods reports an increase in the performance, up to 86% with a scoring method that uses molecular dynamics.
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Affiliation(s)
- Rodrigo Ochoa
- Biophysics of Tropical Diseases, Max Planck Tandem Group, University of Antioquia UdeA, Medellin, Colombia.,European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, United Kingdom
| | - Roman A Laskowski
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, United Kingdom
| | - Janet M Thornton
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, United Kingdom
| | - Pilar Cossio
- Biophysics of Tropical Diseases, Max Planck Tandem Group, University of Antioquia UdeA, Medellin, Colombia.,Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Frankfurt am Main, Germany
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40
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Schoeder C, Schmitz S, Adolf-Bryfogle J, Sevy AM, Finn JA, Sauer MF, Bozhanova NG, Mueller BK, Sangha AK, Bonet J, Sheehan JH, Kuenze G, Marlow B, Smith ST, Woods H, Bender BJ, Martina CE, del Alamo D, Kodali P, Gulsevin A, Schief WR, Correia BE, Crowe JE, Meiler J, Moretti R. Modeling Immunity with Rosetta: Methods for Antibody and Antigen Design. Biochemistry 2021; 60:825-846. [PMID: 33705117 PMCID: PMC7992133 DOI: 10.1021/acs.biochem.0c00912] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 03/02/2021] [Indexed: 01/16/2023]
Abstract
Structure-based antibody and antigen design has advanced greatly in recent years, due not only to the increasing availability of experimentally determined structures but also to improved computational methods for both prediction and design. Constant improvements in performance within the Rosetta software suite for biomolecular modeling have given rise to a greater breadth of structure prediction, including docking and design application cases for antibody and antigen modeling. Here, we present an overview of current protocols for antibody and antigen modeling using Rosetta and exemplify those by detailed tutorials originally developed for a Rosetta workshop at Vanderbilt University. These tutorials cover antibody structure prediction, docking, and design and antigen design strategies, including the addition of glycans in Rosetta. We expect that these materials will allow novice users to apply Rosetta in their own projects for modeling antibodies and antigens.
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Affiliation(s)
- Clara
T. Schoeder
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
| | - Samuel Schmitz
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
| | - Jared Adolf-Bryfogle
- Department
of Immunology and Microbiology, The Scripps
Research Institute, La Jolla, California 92037, United States
- IAVI
Neutralizing Antibody Center, The Scripps
Research Institute, La Jolla, California 92037, United States
| | - Alexander M. Sevy
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
- Chemical
and Physical Biology Program, Vanderbilt
University, Nashville, Tennessee 37232-0301, United States
- Vanderbilt
Vaccine Center, Vanderbilt University Medical
Center, Nashville, Tennessee 37232-0417, United States
| | - Jessica A. Finn
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
- Vanderbilt
Vaccine Center, Vanderbilt University Medical
Center, Nashville, Tennessee 37232-0417, United States
- Department
of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee 37232, United States
| | - Marion F. Sauer
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
- Chemical
and Physical Biology Program, Vanderbilt
University, Nashville, Tennessee 37232-0301, United States
- Vanderbilt
Vaccine Center, Vanderbilt University Medical
Center, Nashville, Tennessee 37232-0417, United States
| | - Nina G. Bozhanova
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
| | - Benjamin K. Mueller
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
| | - Amandeep K. Sangha
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
| | - Jaume Bonet
- Institute
of Bioengineering, École Polytechnique
Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
| | - Jonathan H. Sheehan
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
| | - Georg Kuenze
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
- Institute
for Drug Discovery, University Leipzig Medical
School, 04103 Leipzig, Germany
| | - Brennica Marlow
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
- Chemical
and Physical Biology Program, Vanderbilt
University, Nashville, Tennessee 37232-0301, United States
| | - Shannon T. Smith
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
- Chemical
and Physical Biology Program, Vanderbilt
University, Nashville, Tennessee 37232-0301, United States
| | - Hope Woods
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
- Chemical
and Physical Biology Program, Vanderbilt
University, Nashville, Tennessee 37232-0301, United States
| | - Brian J. Bender
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
- Department
of Pharmacology, Vanderbilt University, Nashville, Tennessee 37212, United States
| | - Cristina E. Martina
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
| | - Diego del Alamo
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
- Chemical
and Physical Biology Program, Vanderbilt
University, Nashville, Tennessee 37232-0301, United States
| | - Pranav Kodali
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
| | - Alican Gulsevin
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
| | - William R. Schief
- Department
of Immunology and Microbiology, The Scripps
Research Institute, La Jolla, California 92037, United States
- IAVI
Neutralizing Antibody Center, The Scripps
Research Institute, La Jolla, California 92037, United States
| | - Bruno E. Correia
- Institute
of Bioengineering, École Polytechnique
Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
| | - James E. Crowe
- Vanderbilt
Vaccine Center, Vanderbilt University Medical
Center, Nashville, Tennessee 37232-0417, United States
- Department
of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee 37232, United States
- Department
of Pediatrics, Vanderbilt University Medical
Center, Nashville, Tennessee 37232, United States
| | - Jens Meiler
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
- Institute
for Drug Discovery, University Leipzig Medical
School, 04103 Leipzig, Germany
| | - Rocco Moretti
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
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41
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Sormani G, Harteveld Z, Rosset S, Correia B, Laio A. A Rosetta-based protein design protocol converging to natural sequences. J Chem Phys 2021; 154:074114. [DOI: 10.1063/5.0039240] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023] Open
Affiliation(s)
| | - Zander Harteveld
- Institute of Bioengineering, Ecole Polytechnique Federale de Lausanne, Lausanne CH-1015, Switzerland and Swiss Institute of Bioinformatics (SIB), Lausanne CH-1015, Switzerland
| | - Stéphane Rosset
- Institute of Bioengineering, Ecole Polytechnique Federale de Lausanne, Lausanne CH-1015, Switzerland and Swiss Institute of Bioinformatics (SIB), Lausanne CH-1015, Switzerland
| | - Bruno Correia
- Institute of Bioengineering, Ecole Polytechnique Federale de Lausanne, Lausanne CH-1015, Switzerland and Swiss Institute of Bioinformatics (SIB), Lausanne CH-1015, Switzerland
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42
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Toplak A, Teixeira de Oliveira EF, Schmidt M, Rozeboom HJ, Wijma HJ, Meekels LKM, de Visser R, Janssen DB, Nuijens T. From thiol-subtilisin to omniligase: Design and structure of a broadly applicable peptide ligase. Comput Struct Biotechnol J 2021; 19:1277-1287. [PMID: 33717424 PMCID: PMC7921005 DOI: 10.1016/j.csbj.2021.02.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2020] [Revised: 02/01/2021] [Accepted: 02/01/2021] [Indexed: 11/05/2022] Open
Abstract
Omniligase-1 is a broadly applicable enzyme for peptide bond formation between an activated acyl donor peptide and a non-protected acyl acceptor peptide. The enzyme is derived from an earlier subtilisin variant called peptiligase by several rounds of protein engineering aimed at increasing synthetic yields and substrate range. To examine the contribution of individual mutations on S/H ratio and substrate scope in peptide synthesis, we selected peptiligase variant M222P/L217H as a starting enzyme and introduced successive mutations. Mutation A225N in the S1′ pocket and F189W of the S2′ pocket increased the synthesis to hydrolysis (S/H) ratio and overall coupling efficiency, whereas the I107V mutation was added to S4 pocket to increase the reaction rate. The final omniligase variants appeared to have a very broad substrate range, coupling more than 250 peptides in a 400-member library of acyl acceptors, as indicated by a high-throughput FRET assay. Crystal structures and computational modelling could rationalize the exceptional properties of omniligase-1 in peptide synthesis
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Affiliation(s)
- Ana Toplak
- EnzyPep B.V., Brightlands Campus Urmonderbaan 22, 6167 RD Geleen, The Netherlands
| | - Eduardo F Teixeira de Oliveira
- Biotransformation and Biocatalysis, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Nijenborgh 4, 9747 AG Groningen, The Netherlands
| | - Marcel Schmidt
- EnzyPep B.V., Brightlands Campus Urmonderbaan 22, 6167 RD Geleen, The Netherlands
| | - Henriëtte J Rozeboom
- Biotransformation and Biocatalysis, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Nijenborgh 4, 9747 AG Groningen, The Netherlands
| | - Hein J Wijma
- Biotransformation and Biocatalysis, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Nijenborgh 4, 9747 AG Groningen, The Netherlands
| | - Linda K M Meekels
- EnzyPep B.V., Brightlands Campus Urmonderbaan 22, 6167 RD Geleen, The Netherlands
| | - Rowin de Visser
- EnzyPep B.V., Brightlands Campus Urmonderbaan 22, 6167 RD Geleen, The Netherlands
| | - Dick B Janssen
- Biotransformation and Biocatalysis, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Nijenborgh 4, 9747 AG Groningen, The Netherlands
| | - Timo Nuijens
- EnzyPep B.V., Brightlands Campus Urmonderbaan 22, 6167 RD Geleen, The Netherlands
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43
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Michael E, Polydorides S, Simonson T, Archontis G. Hybrid MC/MD for protein design. J Chem Phys 2021; 153:054113. [PMID: 32770896 DOI: 10.1063/5.0013320] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
Computational protein design relies on simulations of a protein structure, where selected amino acids can mutate randomly, and mutations are selected to enhance a target property, such as stability. Often, the protein backbone is held fixed and its degrees of freedom are modeled implicitly to reduce the complexity of the conformational space. We present a hybrid method where short molecular dynamics (MD) segments are used to explore conformations and alternate with Monte Carlo (MC) moves that apply mutations to side chains. The backbone is fully flexible during MD. As a test, we computed side chain acid/base constants or pKa's in five proteins. This problem can be considered a special case of protein design, with protonation/deprotonation playing the role of mutations. The solvent was modeled as a dielectric continuum. Due to cost, in each protein we allowed just one side chain position to change its protonation state and the other position to change its type or mutate. The pKa's were computed with a standard method that scans a range of pH values and with a new method that uses adaptive landscape flattening (ALF) to sample all protonation states in a single simulation. The hybrid method gave notably better accuracy than standard, fixed-backbone MC. ALF decreased the computational cost a factor of 13.
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Affiliation(s)
- Eleni Michael
- Department of Physics, University of Cyprus, P.O 20537, CY678 Nicosia, Cyprus
| | - Savvas Polydorides
- Department of Physics, University of Cyprus, P.O 20537, CY678 Nicosia, Cyprus
| | - Thomas Simonson
- Laboratoire de Biochimie (CNRS UMR7654), Ecole Polytechnique, Palaiseau, France
| | - Georgios Archontis
- Department of Physics, University of Cyprus, P.O 20537, CY678 Nicosia, Cyprus
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44
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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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45
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Riley BT, Wankowicz SA, de Oliveira SHP, van Zundert GCP, Hogan DW, Fraser JS, Keedy DA, van den Bedem H. qFit 3: Protein and ligand multiconformer modeling for X-ray crystallographic and single-particle cryo-EM density maps. Protein Sci 2021; 30:270-285. [PMID: 33210433 PMCID: PMC7737783 DOI: 10.1002/pro.4001] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2020] [Revised: 11/10/2020] [Accepted: 11/17/2020] [Indexed: 01/04/2023]
Abstract
New X-ray crystallography and cryo-electron microscopy (cryo-EM) approaches yield vast amounts of structural data from dynamic proteins and their complexes. Modeling the full conformational ensemble can provide important biological insights, but identifying and modeling an internally consistent set of alternate conformations remains a formidable challenge. qFit efficiently automates this process by generating a parsimonious multiconformer model. We refactored qFit from a distributed application into software that runs efficiently on a small server, desktop, or laptop. We describe the new qFit 3 software and provide some examples. qFit 3 is open-source under the MIT license, and is available at https://github.com/ExcitedStates/qfit-3.0.
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Affiliation(s)
- Blake T. Riley
- Structural Biology InitiativeCUNY Advanced Science Research CenterNew YorkNew YorkUSA
| | - Stephanie A. Wankowicz
- Department of Bioengineering and Therapeutic SciencesUniversity of California San FranciscoSan FranciscoCaliforniaUSA
- Biophysics Graduate ProgramUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | | | | | - Daniel W. Hogan
- Department of Bioengineering and Therapeutic SciencesUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - James S. Fraser
- Department of Bioengineering and Therapeutic SciencesUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Daniel A. Keedy
- Structural Biology InitiativeCUNY Advanced Science Research CenterNew YorkNew YorkUSA
- Department of Chemistry and BiochemistryCity College of New YorkNew YorkNew YorkUSA
- Ph.D. Programs in Biochemistry, Biology, and ChemistryThe Graduate Center, City University of New YorkNew YorkUSA
| | - Henry van den Bedem
- Department of Bioengineering and Therapeutic SciencesUniversity of California San FranciscoSan FranciscoCaliforniaUSA
- Atomwise, Inc.San FranciscoCaliforniaUSA
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Ochoa R, Magnitov M, Laskowski RA, Cossio P, Thornton JM. An automated protocol for modelling peptide substrates to proteases. BMC Bioinformatics 2020; 21:586. [PMID: 33375946 PMCID: PMC7771086 DOI: 10.1186/s12859-020-03931-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Accepted: 12/09/2020] [Indexed: 11/21/2022] Open
Abstract
BACKGROUND Proteases are key drivers in many biological processes, in part due to their specificity towards their substrates. However, depending on the family and molecular function, they can also display substrate promiscuity which can also be essential. Databases compiling specificity matrices derived from experimental assays have provided valuable insights into protease substrate recognition. Despite this, there are still gaps in our knowledge of the structural determinants. Here, we compile a set of protease crystal structures with bound peptide-like ligands to create a protocol for modelling substrates bound to protease structures, and for studying observables associated to the binding recognition. RESULTS As an application, we modelled a subset of protease-peptide complexes for which experimental cleavage data are available to compare with informational entropies obtained from protease-specificity matrices. The modelled complexes were subjected to conformational sampling using the Backrub method in Rosetta, and multiple observables from the simulations were calculated and compared per peptide position. We found that some of the calculated structural observables, such as the relative accessible surface area and the interaction energy, can help characterize a protease's substrate recognition, giving insights for the potential prediction of novel substrates by combining additional approaches. CONCLUSION Overall, our approach provides a repository of protease structures with annotated data, and an open source computational protocol to reproduce the modelling and dynamic analysis of the protease-peptide complexes.
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Affiliation(s)
- Rodrigo Ochoa
- Biophysics of Tropical Diseases, Max Planck Tandem Group, University of Antioquia, 050010, Medellín, Colombia.
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.
| | - Mikhail Magnitov
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
- Department of Biological and Medical Physics, Moscow Institute of Physics and Technology (National Research University), Dolgoprudny, Russia, 141701
| | - Roman A Laskowski
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Pilar Cossio
- Biophysics of Tropical Diseases, Max Planck Tandem Group, University of Antioquia, 050010, Medellín, Colombia
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, 60438, Frankfurt am Main, Germany
| | - Janet M Thornton
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
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Harmalkar A, Gray JJ. Advances to tackle backbone flexibility in protein docking. Curr Opin Struct Biol 2020; 67:178-186. [PMID: 33360497 DOI: 10.1016/j.sbi.2020.11.011] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 11/18/2020] [Accepted: 11/25/2020] [Indexed: 12/11/2022]
Abstract
Computational docking methods can provide structural models of protein-protein complexes, but protein backbone flexibility upon association often thwarts accurate predictions. In recent blind challenges, medium or high accuracy models were submitted in less than 20% of the 'difficult' targets (with significant backbone change or uncertainty). Here, we describe recent developments in protein-protein docking and highlight advances that tackle backbone flexibility. In molecular dynamics and Monte Carlo approaches, enhanced sampling techniques have reduced time-scale limitations. Internal coordinate formulations can now capture realistic motions of monomers and complexes using harmonic dynamics. And machine learning approaches adaptively guide docking trajectories or generate novel binding site predictions from deep neural networks trained on protein interfaces. These tools poise the field to break through the longstanding challenge of correctly predicting complex structures with significant conformational change.
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Affiliation(s)
- Ameya Harmalkar
- 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, Institute for Nanobiotechnology, and Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA.
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Karami Y, Saighi P, Vanderhaegen R, Gerlier D, Longhi S, Laine E, Carbone A. Predicting substitutions to modulate disorder and stability in coiled-coils. BMC Bioinformatics 2020; 21:573. [PMID: 33349244 PMCID: PMC7751101 DOI: 10.1186/s12859-020-03867-x] [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: 10/29/2020] [Accepted: 11/09/2020] [Indexed: 11/20/2022] Open
Abstract
Background Coiled-coils are described as stable structural motifs, where two or more helices wind around each other. However, coiled-coils are associated with local mobility and intrinsic disorder. Intrinsically disordered regions in proteins are characterized by lack of stable secondary and tertiary structure under physiological conditions in vitro. They are increasingly recognized as important for protein function. However, characterizing their behaviour in solution and determining precisely the extent of disorder of a protein region remains challenging, both experimentally and computationally. Results In this work, we propose a computational framework to quantify the extent of disorder within a coiled-coil in solution and to help design substitutions modulating such disorder. Our method relies on the analysis of conformational ensembles generated by relatively short all-atom Molecular Dynamics (MD) simulations. We apply it to the phosphoprotein multimerisation domains (PMD) of Measles virus (MeV) and Nipah virus (NiV), both forming tetrameric left-handed coiled-coils. We show that our method can help quantify the extent of disorder of the C-terminus region of MeV and NiV PMDs from MD simulations of a few tens of nanoseconds, and without requiring an extensive exploration of the conformational space. Moreover, this study provided a conceptual framework for the rational design of substitutions aimed at modulating the stability of the coiled-coils. By assessing the impact of four substitutions known to destabilize coiled-coils, we derive a set of rules to control MeV PMD structural stability and cohesiveness. We therefore design two contrasting substitutions, one increasing the stability of the tetramer and the other increasing its flexibility. Conclusions Our method can be considered as a platform to reason about how to design substitutions aimed at regulating flexibility and stability.
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Affiliation(s)
- Yasaman Karami
- CNRS, IBPS, UMR 7238, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), Sorbonne Université, 75005, Paris, France. .,Institute of Computing and Data Sciences (ISCD), Sorbonne Université, 75005, Paris, France.
| | - Paul Saighi
- CNRS, IBPS, UMR 7238, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), Sorbonne Université, 75005, Paris, France
| | - Rémy Vanderhaegen
- CNRS, IBPS, UMR 7238, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), Sorbonne Université, 75005, Paris, France
| | - Denis Gerlier
- CIRI, International Center for Infectiology Research, INSERM, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, Ecole Normale Supérieure de Lyon, Univ Lyon, Lyon, France
| | - Sonia Longhi
- CNRS, Architecture et Fonction des Macromolécules Biologiques (AFMB), UMR 7257, Aix-Marseille University, Marseille, France
| | - Elodie Laine
- CNRS, IBPS, UMR 7238, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), Sorbonne Université, 75005, Paris, France.
| | - Alessandra Carbone
- CNRS, IBPS, UMR 7238, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), Sorbonne Université, 75005, Paris, France. .,Institut Universitaire de France, 75005, Paris, France.
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Yoon J, Ferrie JJ, Petersson EJ. Improved Modeling of Thioamide FRET Quenching by Including Conformational Restriction and Coulomb Coupling. J Phys Chem B 2020; 124:10653-10662. [PMID: 33196192 DOI: 10.1021/acs.jpcb.0c06865] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Thioamide-containing amino acids have been shown to quench a wide range of fluorophores through distinct mechanisms. Here, we quantitatively analyze the mechanism through which the thioamide functional group quenches the fluorescence of p-cyanophenylalanine (Cnf), tyrosine (Tyr), and tryptophan (Trp). By comparing PyRosetta simulations to published experiments performed on polyproline ruler peptides, we corroborate previous findings that both Cnf and Tyr quenching occurs via Förster resonance energy transfer (FRET), while Trp quenching occurs through an alternate mechanism such as Dexter transfer. Additionally, optimization of the peptide sampling scheme and comparison of thioamides attached to the peptide backbone and side chain revealed that the significant conformational restriction associated with the thioamide moiety results in a high sensitivity of the apparent FRET efficiency to underlying conformational differences. Moreover, by computing FRET efficiencies from structural models using a variety of approaches, we find that quantitative accuracy in the role of Coulomb coupling is required to explain contributions to the observed quenching efficiency from individual structures on a detailed level. Last, we demonstrate that these additional considerations improve our ability to predict thioamide quenching efficiencies observed during binding of thioamide-labeled peptides to fluorophore-labeled variants of calmodulin.
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Affiliation(s)
- Jimin Yoon
- Department of Chemistry, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - John J Ferrie
- Department of Chemistry, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - E James Petersson
- Department of Chemistry, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
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Pan X, Thompson MC, Zhang Y, Liu L, Fraser JS, Kelly MJS, Kortemme T. Expanding the space of protein geometries by computational design of de novo fold families. Science 2020; 369:1132-1136. [PMID: 32855341 DOI: 10.1126/science.abc0881] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Accepted: 07/14/2020] [Indexed: 01/03/2023]
Abstract
Naturally occurring proteins vary the precise geometries of structural elements to create distinct shapes optimal for function. We present a computational design method, loop-helix-loop unit combinatorial sampling (LUCS), that mimics nature's ability to create families of proteins with the same overall fold but precisely tunable geometries. Through near-exhaustive sampling of loop-helix-loop elements, LUCS generates highly diverse geometries encompassing those found in nature but also surpassing known structure space. Biophysical characterization showed that 17 (38%) of 45 tested LUCS designs encompassing two different structural topologies were well folded, including 16 with designed non-native geometries. Four experimentally solved structures closely matched the designs. LUCS greatly expands the designable structure space and offers a new paradigm for designing proteins with tunable geometries that may be customizable for novel functions.
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Affiliation(s)
- Xingjie Pan
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA. .,UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, San Francisco, CA, USA
| | - Michael C Thompson
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA
| | - Yang Zhang
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA
| | - Lin Liu
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA
| | - James S Fraser
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA.,Quantitative Biosciences Institute, University of California, San Francisco, CA, USA
| | - Mark J S Kelly
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA, USA
| | - Tanja Kortemme
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA. .,UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, San Francisco, CA, USA.,Quantitative Biosciences Institute, University of California, San Francisco, CA, USA.,Chan Zuckerberg Biohub, San Francisco, CA, USA
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