351
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Dietler J, Schubert R, Krafft TGA, Meiler S, Kainrath S, Richter F, Schweimer K, Weyand M, Janovjak H, Möglich A. A Light-Oxygen-Voltage Receptor Integrates Light and Temperature. J Mol Biol 2021; 433:167107. [PMID: 34146595 DOI: 10.1016/j.jmb.2021.167107] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 05/31/2021] [Accepted: 06/09/2021] [Indexed: 10/21/2022]
Abstract
Sensory photoreceptors enable organisms to adjust their physiology, behavior, and development in response to light, generally with spatiotemporal acuity and reversibility. These traits underlie the use of photoreceptors as genetically encoded actuators to alter by light the state and properties of heterologous organisms. Subsumed as optogenetics, pertinent approaches enable regulating diverse cellular processes, not least gene expression. Here, we controlled the widely used Tet repressor by coupling to light-oxygen-voltage (LOV) modules that either homodimerize or dissociate under blue light. Repression could thus be elevated or relieved, and consequently protein expression was modulated by light. Strikingly, the homodimeric RsLOV module from Rhodobacter sphaeroides not only dissociated under light but intrinsically reacted to temperature. The limited light responses of wild-type RsLOV at 37 °C were enhanced in two variants that exhibited closely similar photochemistry and structure. One variant improved the weak homodimerization affinity of 40 µM by two-fold and thus also bestowed light sensitivity on a receptor tyrosine kinase. Certain photoreceptors, exemplified by RsLOV, can evidently moonlight as temperature sensors which immediately bears on their application in optogenetics and biotechnology. Properly accounted for, the temperature sensitivity can be leveraged for the construction of signal-responsive cellular circuits.
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Affiliation(s)
- Julia Dietler
- Department of Biochemistry, University of Bayreuth, 95447 Bayreuth, Germany
| | - Roman Schubert
- Biophysical Chemistry, Humboldt-University Berlin, 10115 Berlin, Germany
| | - Tobias G A Krafft
- Department of Biochemistry, University of Bayreuth, 95447 Bayreuth, Germany
| | - Simone Meiler
- Department of Biochemistry, University of Bayreuth, 95447 Bayreuth, Germany
| | - Stephanie Kainrath
- Australian Regenerative Medicine Institute (ARMI), Monash University, Clayton, Victoria 3800, Australia
| | - Florian Richter
- Biophysical Chemistry, Humboldt-University Berlin, 10115 Berlin, Germany
| | - Kristian Schweimer
- Biopolymers, University of Bayreuth, 95447 Bayreuth, Germany; North-Bavarian NMR Center, University of Bayreuth, 95447 Bayreuth, Germany
| | - Michael Weyand
- Department of Biochemistry, University of Bayreuth, 95447 Bayreuth, Germany
| | - Harald Janovjak
- Australian Regenerative Medicine Institute (ARMI), Monash University, Clayton, Victoria 3800, Australia
| | - Andreas Möglich
- Department of Biochemistry, University of Bayreuth, 95447 Bayreuth, Germany; Biophysical Chemistry, Humboldt-University Berlin, 10115 Berlin, Germany; Bayreuth Center for Biochemistry & Molecular Biology, University of Bayreuth, 95447 Bayreuth, Germany; North-Bavarian NMR Center, University of Bayreuth, 95447 Bayreuth, Germany.
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352
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Nance ML, Labonte JW, Adolf-Bryfogle J, Gray JJ. Development and Evaluation of GlycanDock: A Protein-Glycoligand Docking Refinement Algorithm in Rosetta. J Phys Chem B 2021; 125:10.1021/acs.jpcb.1c00910. [PMID: 34133179 PMCID: PMC8742512 DOI: 10.1021/acs.jpcb.1c00910] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Carbohydrate chains are ubiquitous in the complex molecular processes of life. These highly diverse chains are recognized by a variety of protein receptors, enabling glycans to regulate many biological functions. High-resolution structures of protein-glycoligand complexes reveal the atomic details necessary to understand this level of molecular recognition and inform application-focused scientific and engineering pursuits. When experimental challenges hinder high-throughput determination of quality structures, computational tools can, in principle, fill the gap. In this work, we introduce GlycanDock, a residue-centric protein-glycoligand docking refinement algorithm developed within the Rosetta macromolecular modeling and design software suite. We performed a benchmark docking assessment using a set of 109 experimentally determined protein-glycoligand complexes as well as 62 unbound protein structures. The GlycanDock algorithm can sample and discriminate among protein-glycoligand models of native-like structural accuracy with statistical reliability from starting structures of up to 7 Å root-mean-square deviation in the glycoligand ring atoms. We show that GlycanDock-refined models qualitatively replicated the known binding specificity of a bacterial carbohydrate-binding module. Finally, we present a protein-glycoligand docking pipeline for generating putative protein-glycoligand complexes when only the glycoligand sequence and unbound protein structure are known. In combination with other carbohydrate modeling tools, the GlycanDock docking refinement algorithm will accelerate research in the glycosciences.
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Affiliation(s)
- Morgan L. Nance
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Jason W. Labonte
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
- Department of Chemistry, Franklin & Marshall College, Lancaster, Pennsylvania 17603, United States
- Department of Chemistry, Gettysburg College, Gettysburg, Pennsylvania 17325, United States
| | - Jared Adolf-Bryfogle
- Protein Design Lab, Institute for Protein Innovation, Boston, Massachusetts 02115, United States
- Division of Hematology/Oncology, Boston Children’s Hospital, Boston, Massachusetts 02115, United States
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts 02115, United States
| | - Jeffrey J. Gray
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, Maryland 21218, United States
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
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353
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Scherer M, Fleishman SJ, Jones PR, Dandekar T, Bencurova E. Computational Enzyme Engineering Pipelines for Optimized Production of Renewable Chemicals. Front Bioeng Biotechnol 2021; 9:673005. [PMID: 34211966 PMCID: PMC8239229 DOI: 10.3389/fbioe.2021.673005] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Accepted: 05/06/2021] [Indexed: 11/13/2022] Open
Abstract
To enable a sustainable supply of chemicals, novel biotechnological solutions are required that replace the reliance on fossil resources. One potential solution is to utilize tailored biosynthetic modules for the metabolic conversion of CO2 or organic waste to chemicals and fuel by microorganisms. Currently, it is challenging to commercialize biotechnological processes for renewable chemical biomanufacturing because of a lack of highly active and specific biocatalysts. As experimental methods to engineer biocatalysts are time- and cost-intensive, it is important to establish efficient and reliable computational tools that can speed up the identification or optimization of selective, highly active, and stable enzyme variants for utilization in the biotechnological industry. Here, we review and suggest combinations of effective state-of-the-art software and online tools available for computational enzyme engineering pipelines to optimize metabolic pathways for the biosynthesis of renewable chemicals. Using examples relevant for biotechnology, we explain the underlying principles of enzyme engineering and design and illuminate future directions for automated optimization of biocatalysts for the assembly of synthetic metabolic pathways.
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Affiliation(s)
- Marc Scherer
- Department of Bioinformatics, Julius-Maximilians University of Würzburg, Würzburg, Germany
| | - Sarel J Fleishman
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Patrik R Jones
- Department of Life Sciences, Imperial College London, London, United Kingdom
| | - Thomas Dandekar
- Department of Bioinformatics, Julius-Maximilians University of Würzburg, Würzburg, Germany
| | - Elena Bencurova
- Department of Bioinformatics, Julius-Maximilians University of Würzburg, Würzburg, Germany
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354
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Khakzad H, Happonen L, Malmström J, Malmström L. Cheetah-MS: a web server to model protein complexes using tandem cross-linking mass spectrometry data. Bioinformatics 2021; 37:4871-4872. [PMID: 34128979 PMCID: PMC8665757 DOI: 10.1093/bioinformatics/btab449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 05/07/2021] [Accepted: 06/14/2021] [Indexed: 11/13/2022] Open
Abstract
Summary Protein–protein interactions (PPIs) are central in many biological processes but difficult to characterize, especially in complex, unfractionated samples. Chemical cross-linking combined with mass spectrometry (MS) and computational modeling is gaining recognition as a viable tool in protein interaction studies. Here, we introduce Cheetah-MS, a web server for predicting the PPIs in a complex mixture of samples. It combines the capability and sensitivity of MS to analyze complex samples with the power and resolution of protein–protein docking. It produces the quaternary structure of the PPI of interest by analyzing tandem MS/MS data (also called MS2). Combining MS analysis and modeling increases the sensitivity and, importantly, facilitates the interpretation of the results. Availability and implementation Cheetah-MS is freely available as a web server at https://www.txms.org.
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Affiliation(s)
- Hamed Khakzad
- Equipe Signalisation Calcique et Infections Microbiennes, École Normale Supérieure Paris-Saclay, Gif-sur-Yvette, 91190, France.,Institut National de la Santé et de la Recherche Médicale U1282, Gif-sur-Yvette, 91190, France
| | - Lotta Happonen
- Division of Infection Medicine, Department of Clinical Sciences, Lund University, Sweden
| | - Johan Malmström
- Division of Infection Medicine, Department of Clinical Sciences, Lund University, Sweden
| | - Lars Malmström
- Division of Infection Medicine, Department of Clinical Sciences, Lund University, Sweden
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355
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Suh D, Lee JW, Choi S, Lee Y. Recent Applications of Deep Learning Methods on Evolution- and Contact-Based Protein Structure Prediction. Int J Mol Sci 2021; 22:6032. [PMID: 34199677 PMCID: PMC8199773 DOI: 10.3390/ijms22116032] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Revised: 05/29/2021] [Accepted: 05/29/2021] [Indexed: 01/23/2023] Open
Abstract
The new advances in deep learning methods have influenced many aspects of scientific research, including the study of the protein system. The prediction of proteins' 3D structural components is now heavily dependent on machine learning techniques that interpret how protein sequences and their homology govern the inter-residue contacts and structural organization. Especially, methods employing deep neural networks have had a significant impact on recent CASP13 and CASP14 competition. Here, we explore the recent applications of deep learning methods in the protein structure prediction area. We also look at the potential opportunities for deep learning methods to identify unknown protein structures and functions to be discovered and help guide drug-target interactions. Although significant problems still need to be addressed, we expect these techniques in the near future to play crucial roles in protein structural bioinformatics as well as in drug discovery.
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Affiliation(s)
- Donghyuk Suh
- Global AI Drug Discovery Center, School of Pharmaceutical Sciences, College of Pharmacy and Graduate, Ewha Womans University, Seoul 03760, Korea; (D.S.); (J.W.L.); (S.C.)
| | - Jai Woo Lee
- Global AI Drug Discovery Center, School of Pharmaceutical Sciences, College of Pharmacy and Graduate, Ewha Womans University, Seoul 03760, Korea; (D.S.); (J.W.L.); (S.C.)
| | - Sun Choi
- Global AI Drug Discovery Center, School of Pharmaceutical Sciences, College of Pharmacy and Graduate, Ewha Womans University, Seoul 03760, Korea; (D.S.); (J.W.L.); (S.C.)
| | - Yoonji Lee
- College of Pharmacy, Chung-Ang University, Seoul 06974, Korea
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356
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Biehn SE, Limpikirati P, Vachet RW, Lindert S. Utilization of Hydrophobic Microenvironment Sensitivity in Diethylpyrocarbonate Labeling for Protein Structure Prediction. Anal Chem 2021; 93:8188-8195. [PMID: 34061512 DOI: 10.1021/acs.analchem.1c00395] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Diethylpyrocarbonate (DEPC) labeling analyzed with mass spectrometry can provide important insights into higher order protein structures. It has been previously shown that neighboring hydrophobic residues promote a local increase in DEPC concentration such that serine, threonine, and tyrosine residues are more likely to be labeled despite low solvent exposure. In this work, we developed a Rosetta algorithm that used the knowledge of labeled and unlabeled serine, threonine, and tyrosine residues and assessed their local hydrophobic environment to improve protein structure prediction. Additionally, DEPC-labeled histidine and lysine residues with higher relative solvent accessible surface area values (i.e., more exposed) were scored favorably. Application of our score term led to reductions of the root-mean-square deviations (RMSDs) of the lowest scoring models. Additionally, models that scored well tended to have lower RMSDs. A detailed tutorial describing our protocol and required command lines is included. Our work demonstrated the considerable potential of DEPC covalent labeling data to be used for accurate higher order structure determination.
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Affiliation(s)
- Sarah E Biehn
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, Ohio 43210, United States
| | - Patanachai Limpikirati
- Department of Food and Pharmaceutical Chemistry, Faculty of Pharmaceutical Sciences, Chulalongkorn University, Bangkok 10330, Thailand
| | - Richard W Vachet
- Department of Chemistry, University of Massachusetts, Amherst, Massachusetts 01003, United States
| | - Steffen Lindert
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, Ohio 43210, United States
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357
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Yachnin BJ, Mulligan VK, Khare SD, Bailey-Kellogg C. MHCEpitopeEnergy, a Flexible Rosetta-Based Biotherapeutic Deimmunization Platform. J Chem Inf Model 2021; 61:2368-2382. [PMID: 33900750 PMCID: PMC8225355 DOI: 10.1021/acs.jcim.1c00056] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
As non-"self" macromolecules, biotherapeutics can trigger an immune response that can reduce drug efficacy, require patients to be taken off therapy, or even cause life-threatening reactions. To enable the flexible and facile design of protein biotherapeutics while reducing the prevalence of T-cell epitopes that drive immune recognition, we have integrated into the Rosetta protein design suite a new scoring term that allows design protocols to account for predicted or experimentally identified epitopes in the optimized objective function. This flexible scoring term can be used in any Rosetta design trajectory, can be targeted to specific regions of a protein, and can be readily extended to work with a variety of epitope predictors. By performing extensive design runs with varied design parameter choices for three case study proteins as well as a larger diverse benchmark, we show that the incorporation of this scoring term enables the effective exploration of an alternative, deimmunized sequence space to discover diverse proteins that are potentially highly deimmunized while retaining physical and chemical qualities similar to those yielded by equivalent nondeimmunizing sequence design protocols.
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Affiliation(s)
- Brahm J. Yachnin
- Department of Chemistry and Chemical Biology and Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA
| | - Vikram Khipple Mulligan
- Center for Computational Biology, Flatiron Institute, 162 Fifth Avenue, New York, NY, 10010, USA
| | - Sagar D. Khare
- Department of Chemistry and Chemical Biology and Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA
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358
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Machine learning in protein structure prediction. Curr Opin Chem Biol 2021; 65:1-8. [PMID: 34015749 DOI: 10.1016/j.cbpa.2021.04.005] [Citation(s) in RCA: 101] [Impact Index Per Article: 33.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 04/10/2021] [Indexed: 12/31/2022]
Abstract
Prediction of protein structure from sequence has been intensely studied for many decades, owing to the problem's importance and its uniquely well-defined physical and computational bases. While progress has historically ebbed and flowed, the past two years saw dramatic advances driven by the increasing "neuralization" of structure prediction pipelines, whereby computations previously based on energy models and sampling procedures are replaced by neural networks. The extraction of physical contacts from the evolutionary record; the distillation of sequence-structure patterns from known structures; the incorporation of templates from homologs in the Protein Databank; and the refinement of coarsely predicted structures into finely resolved ones have all been reformulated using neural networks. Cumulatively, this transformation has resulted in algorithms that can now predict single protein domains with a median accuracy of 2.1 Å, setting the stage for a foundational reconfiguration of the role of biomolecular modeling within the life sciences.
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359
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Schlick T, Portillo-Ledesma S, Myers CG, Beljak L, Chen J, Dakhel S, Darling D, Ghosh S, Hall J, Jan M, Liang E, Saju S, Vohr M, Wu C, Xu Y, Xue E. Biomolecular Modeling and Simulation: A Prospering Multidisciplinary Field. Annu Rev Biophys 2021; 50:267-301. [PMID: 33606945 PMCID: PMC8105287 DOI: 10.1146/annurev-biophys-091720-102019] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
We reassess progress in the field of biomolecular modeling and simulation, following up on our perspective published in 2011. By reviewing metrics for the field's productivity and providing examples of success, we underscore the productive phase of the field, whose short-term expectations were overestimated and long-term effects underestimated. Such successes include prediction of structures and mechanisms; generation of new insights into biomolecular activity; and thriving collaborations between modeling and experimentation, including experiments driven by modeling. We also discuss the impact of field exercises and web games on the field's progress. Overall, we note tremendous success by the biomolecular modeling community in utilization of computer power; improvement in force fields; and development and application of new algorithms, notably machine learning and artificial intelligence. The combined advances are enhancing the accuracy andscope of modeling and simulation, establishing an exemplary discipline where experiment and theory or simulations are full partners.
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Affiliation(s)
- Tamar Schlick
- Department of Chemistry, New York University, New York, New York 10003, USA;
- Courant Institute of Mathematical Sciences, New York University, New York, New York 10012, USA
- New York University-East China Normal University Center for Computational Chemistry, New York University Shanghai, Shanghai 200122, China
| | | | - Christopher G Myers
- Department of Chemistry, New York University, New York, New York 10003, USA;
| | - Lauren Beljak
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Justin Chen
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Sami Dakhel
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Daniel Darling
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Sayak Ghosh
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Joseph Hall
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Mikaeel Jan
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Emily Liang
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Sera Saju
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Mackenzie Vohr
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Chris Wu
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Yifan Xu
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Eva Xue
- College of Arts and Science, New York University, New York, New York 10003, USA
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360
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Frappier V, Keating AE. Data-driven computational protein design. Curr Opin Struct Biol 2021; 69:63-69. [PMID: 33910104 DOI: 10.1016/j.sbi.2021.03.009] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2020] [Revised: 03/18/2021] [Accepted: 03/19/2021] [Indexed: 01/28/2023]
Abstract
Computational protein design can generate proteins not found in nature that adopt desired structures and perform novel functions. Although proteins could, in theory, be designed with ab initio methods, practical success has come from using large amounts of data that describe the sequences, structures, and functions of existing proteins and their variants. We present recent creative uses of multiple-sequence alignments, protein structures, and high-throughput functional assays in computational protein design. Approaches range from enhancing structure-based design with experimental data to building regression models to training deep neural nets that generate novel sequences. Looking ahead, deep learning will be increasingly important for maximizing the value of data for protein design.
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Affiliation(s)
- Vincent Frappier
- Generate Biomedicines, 26 Landsdowne Street, Cambridge, MA, 02139, USA
| | - Amy E Keating
- MIT Departments of Biology and Biological Engineering, 77 Massachusetts Ave., Cambridge, MA, 02139, USA.
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361
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Kyrilis FL, Belapure J, Kastritis PL. Detecting Protein Communities in Native Cell Extracts by Machine Learning: A Structural Biologist's Perspective. Front Mol Biosci 2021; 8:660542. [PMID: 33937337 PMCID: PMC8082361 DOI: 10.3389/fmolb.2021.660542] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 03/18/2021] [Indexed: 11/13/2022] Open
Abstract
Native cell extracts hold great promise for understanding the molecular structure of ordered biological systems at high resolution. This is because higher-order biomolecular interactions, dubbed as protein communities, may be retained in their (near-)native state, in contrast to extensively purifying or artificially overexpressing the proteins of interest. The distinct machine-learning approaches are applied to discover protein-protein interactions within cell extracts, reconstruct dedicated biological networks, and report on protein community members from various organisms. Their validation is also important, e.g., by the cross-linking mass spectrometry or cell biology methods. In addition, the cell extracts are amenable to structural analysis by cryo-electron microscopy (cryo-EM), but due to their inherent complexity, sorting structural signatures of protein communities derived by cryo-EM comprises a formidable task. The application of image-processing workflows inspired by machine-learning techniques would provide improvements in distinguishing structural signatures, correlating proteomic and network data to structural signatures and subsequently reconstructed cryo-EM maps, and, ultimately, characterizing unidentified protein communities at high resolution. In this review article, we summarize recent literature in detecting protein communities from native cell extracts and identify the remaining challenges and opportunities. We argue that the progress in, and the integration of, machine learning, cryo-EM, and complementary structural proteomics approaches would provide the basis for a multi-scale molecular description of protein communities within native cell extracts.
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Affiliation(s)
- Fotis L. Kyrilis
- Interdisciplinary Research Center HALOmem, Charles Tanford Protein Center, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
- Institute of Biochemistry and Biotechnology, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
| | - Jaydeep Belapure
- Interdisciplinary Research Center HALOmem, Charles Tanford Protein Center, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
| | - Panagiotis L. Kastritis
- Interdisciplinary Research Center HALOmem, Charles Tanford Protein Center, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
- Institute of Biochemistry and Biotechnology, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
- Biozentrum, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
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362
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Chen Z, Kuenze G, Meiler J, Canessa CM. An arginine residue in the outer segment of hASIC1a TM1 affects both proton affinity and channel desensitization. J Gen Physiol 2021; 153:211986. [PMID: 33851970 PMCID: PMC8050794 DOI: 10.1085/jgp.202012802] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Revised: 12/14/2020] [Accepted: 03/11/2021] [Indexed: 12/25/2022] Open
Abstract
Acid-sensing ion channels (ASICs) respond to changes in pH in the central and peripheral nervous systems and participate in synaptic plasticity and pain perception. Understanding the proton-mediated gating mechanism remains elusive despite the of their structures in various conformational states. We report here that R64, an arginine located in the outer segment of the first transmembrane domain of all three isoforms of mammalian ASICs, markedly impacts the apparent proton affinity of activation and the degree of desensitization from the open and preopen states. Rosetta calculations of free energy changes predict that substitutions of R64 in hASIC1a by aromatic residues destabilize the closed conformation while stabilizing the open conformation. Accordingly, F64 enhances the efficacy of proton-mediated gating of hASIC1a, which increases the apparent pH50 and facilitates channel opening when only one or two subunits are activated. F64 also lengthens the duration of opening events, thus keeping channels open for extended periods of time and diminishing low pH-induced desensitization. Our results indicate that activation of a proton sensor(s) with pH50 equal to or greater than pH 7.2–7.1 opens F64hASIC1a, whereas it induces steady-state desensitization in wildtype channels due to the high energy of activation imposed by R64, which prevents opening of the pore. Together, these findings suggest that activation of a high-affinity proton-sensor(s) and a common gating mechanism may mediate the processes of activation and steady-state desensitization of hASIC1a.
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Affiliation(s)
- Zhuyuan Chen
- Department of Basic Sciences, Tsinghua University School of Medicine, Beijing, China
| | - Georg Kuenze
- Department of Chemistry, Vanderbilt University, Nashville, TN.,Center for Structural Biology, Vanderbilt University, Nashville, TN.,Institute for Drug Discovery, Leipzig University, Leipzig, Germany
| | - Jens Meiler
- Department of Chemistry, Vanderbilt University, Nashville, TN.,Center for Structural Biology, Vanderbilt University, Nashville, TN.,Department of Pharmacology, Vanderbilt University, Nashville, TN.,Institute for Drug Discovery, Leipzig University, Leipzig, Germany
| | - Cecilia M Canessa
- Department of Basic Sciences, Tsinghua University School of Medicine, Beijing, China.,Cellular and Molecular Physiology, Yale University, New Haven, CT
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363
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Patiño-Galindo JÁ, Filip I, Chowdhury R, Maranas CD, Sorger PK, AlQuraishi M, Rabadan R. Recombination and lineage-specific mutations linked to the emergence of SARS-CoV-2. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2021. [PMID: 32511304 PMCID: PMC7217262 DOI: 10.1101/2020.02.10.942748] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
The emergence of SARS-CoV-2 underscores the need to better understand the evolutionary processes that drive the emergence and adaptation of zoonotic viruses in humans. In the betacoronavirus genus, which also includes SARS-CoV and MERS-CoV, recombination frequently encompasses the Receptor Binding Domain (RBD) of the Spike protein, which, in turn, is responsible for viral binding to host cell receptors. Here, we find evidence of a recombination event in the RBD involving ancestral linages to both SARS-CoV and SARS-CoV-2. Although we cannot specify the recombinant nor the parental strains, likely due to the ancestry of the event and potential undersampling, our statistical analyses in the space of phylogenetic trees support such an ancestral recombination. Consequently, SARS-CoV and SARS-CoV-2 share an RBD sequence that includes two insertions (positions 432–436 and 460–472), as well as the variants 427N and 436Y. Both 427N and 436Y belong to a helix that interacts directly with the human ACE2 (hACE2) receptor. Reconstruction of ancestral states, combined with protein-binding affinity analyses using the physics-based trRosetta algorithm, reveal that the recombination event involving ancestral strains of SARS-CoV and SARS-CoV-2 led to an increased affinity for hACE2 binding, and that alleles 427N and 436Y significantly enhanced affinity as well. Structural modeling indicates that ancestors of SARS-CoV-2 may have acquired the ability to infect humans decades ago. The binding affinity with the human receptor was subsequently boosted in SARS-CoV and SARS-CoV-2 through further mutations in RBD. In sum, we report an ancestral recombination event affecting the RBD of both SARS-CoV and SARS-CoV-2 that was associated with an increased binding affinity to hACE2.
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Affiliation(s)
- Juan Ángel Patiño-Galindo
- Program for Mathemaical Genomics, Columbia University, New York, NY, USA.,Departments of Systems Biology and Biomedical Informatics, Columbia University, New York, NY, USA
| | - Ioan Filip
- Program for Mathemaical Genomics, Columbia University, New York, NY, USA.,Departments of Systems Biology and Biomedical Informatics, Columbia University, New York, NY, USA
| | - Ratul Chowdhury
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA.,Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
| | - Costas D Maranas
- Department of Chemical Engineering, The Pennsylvania State University University Park, PA, USA
| | - Peter K Sorger
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA.,Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
| | - Mohammed AlQuraishi
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA.,Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
| | - Raul Rabadan
- Program for Mathemaical Genomics, Columbia University, New York, NY, USA.,Departments of Systems Biology and Biomedical Informatics, Columbia University, New York, NY, USA
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364
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Divine R, Dang HV, Ueda G, Fallas JA, Vulovic I, Sheffler W, Saini S, Zhao YT, Raj IX, Morawski PA, Jennewein MF, Homad LJ, Wan YH, Tooley MR, Seeger F, Etemadi A, Fahning ML, Lazarovits J, Roederer A, Walls AC, Stewart L, Mazloomi M, King NP, Campbell DJ, McGuire AT, Stamatatos L, Ruohola-Baker H, Mathieu J, Veesler D, Baker D. Designed proteins assemble antibodies into modular nanocages. Science 2021; 372:eabd9994. [PMID: 33795432 PMCID: PMC8592034 DOI: 10.1126/science.abd9994] [Citation(s) in RCA: 83] [Impact Index Per Article: 27.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Revised: 11/23/2020] [Accepted: 02/10/2021] [Indexed: 12/11/2022]
Abstract
Multivalent display of receptor-engaging antibodies or ligands can enhance their activity. Instead of achieving multivalency by attachment to preexisting scaffolds, here we unite form and function by the computational design of nanocages in which one structural component is an antibody or Fc-ligand fusion and the second is a designed antibody-binding homo-oligomer that drives nanocage assembly. Structures of eight nanocages determined by electron microscopy spanning dihedral, tetrahedral, octahedral, and icosahedral architectures with 2, 6, 12, and 30 antibodies per nanocage, respectively, closely match the corresponding computational models. Antibody nanocages targeting cell surface receptors enhance signaling compared with free antibodies or Fc-fusions in death receptor 5 (DR5)-mediated apoptosis, angiopoietin-1 receptor (Tie2)-mediated angiogenesis, CD40 activation, and T cell proliferation. Nanocage assembly also increases severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pseudovirus neutralization by α-SARS-CoV-2 monoclonal antibodies and Fc-angiotensin-converting enzyme 2 (ACE2) fusion proteins.
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MESH Headings
- Angiopoietins/chemistry
- Angiopoietins/immunology
- Angiopoietins/metabolism
- Antibodies/chemistry
- Antibodies/immunology
- Antibodies, Monoclonal/chemistry
- Antibodies, Monoclonal/immunology
- Antibodies, Neutralizing/chemistry
- Antibodies, Neutralizing/immunology
- Antibodies, Viral/chemistry
- Antibodies, Viral/immunology
- B-Lymphocytes/immunology
- CD40 Antigens/chemistry
- CD40 Antigens/immunology
- CD40 Antigens/metabolism
- Cell Line, Tumor
- Cell Proliferation
- Computer Simulation
- Genes, Synthetic
- Humans
- Immunoglobulin Fc Fragments/chemistry
- Lymphocyte Activation
- Models, Molecular
- Nanostructures
- Protein Binding
- Protein Engineering
- Receptor, TIE-2/metabolism
- Receptors, TNF-Related Apoptosis-Inducing Ligand/immunology
- Receptors, TNF-Related Apoptosis-Inducing Ligand/metabolism
- SARS-CoV-2/immunology
- Signal Transduction
- T-Lymphocytes/immunology
- T-Lymphocytes/physiology
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Affiliation(s)
- Robby Divine
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Ha V Dang
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
| | - George Ueda
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Jorge A Fallas
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Ivan Vulovic
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - William Sheffler
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Shally Saini
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA 98109, USA
| | - Yan Ting Zhao
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA 98109, USA
- Oral Health Sciences, School of Dentistry, University of Washington, Seattle, WA 98195, USA
| | - Infencia Xavier Raj
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA 98109, USA
| | | | - Madeleine F Jennewein
- Vaccines and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98019, USA
| | - Leah J Homad
- Vaccines and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98019, USA
| | - Yu-Hsin Wan
- Vaccines and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98019, USA
| | - Marti R Tooley
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Franziska Seeger
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Ali Etemadi
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
- Medical Biotechnology Department, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | | | - James Lazarovits
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Alex Roederer
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Alexandra C Walls
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
| | - Lance Stewart
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Mohammadali Mazloomi
- Medical Biotechnology Department, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Neil P King
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | | | - Andrew T McGuire
- Vaccines and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98019, USA
- Department of Global Health, University of Washington, Seattle, WA 98195, USA
| | - Leonidas Stamatatos
- Vaccines and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98019, USA
- Department of Global Health, University of Washington, Seattle, WA 98195, USA
| | - Hannele Ruohola-Baker
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA 98109, USA
| | - Julie Mathieu
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA 98109, USA
- Department of Comparative Medicine, University of Washington, Seattle, WA 98195, USA
| | - David Veesler
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA.
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA
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365
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Denish PR, Fenger JA, Powers R, Sigurdson GT, Grisanti L, Guggenheim KG, Laporte S, Li J, Kondo T, Magistrato A, Moloney MP, Riley M, Rusishvili M, Ahmadiani N, Baroni S, Dangles O, Giusti M, Collins TM, Didzbalis J, Yoshida K, Siegel JB, Robbins RJ. Discovery of a natural cyan blue: A unique food-sourced anthocyanin could replace synthetic brilliant blue. SCIENCE ADVANCES 2021; 7:7/15/eabe7871. [PMID: 33827818 PMCID: PMC8026139 DOI: 10.1126/sciadv.abe7871] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 02/18/2021] [Indexed: 05/11/2023]
Abstract
The color of food is critical to the food and beverage industries, as it influences many properties beyond eye-pleasing visuals including flavor, safety, and nutritional value. Blue is one of the rarest colors in nature's food palette-especially a cyan blue-giving scientists few sources for natural blue food colorants. Finding a natural cyan blue dye equivalent to FD&C Blue No. 1 remains an industry-wide challenge and the subject of several research programs worldwide. Computational simulations and large-array spectroscopic techniques were used to determine the 3D chemical structure, color expression, and stability of this previously uncharacterized cyan blue anthocyanin-based colorant. Synthetic biology and computational protein design tools were leveraged to develop an enzymatic transformation of red cabbage anthocyanins into the desired anthocyanin. More broadly, this research demonstrates the power of a multidisciplinary strategy to solve a long-standing challenge in the food industry.
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Affiliation(s)
- Pamela R Denish
- Biophysics Graduate Group, University of California, Davis, Davis, CA, USA
- Genome Center, University of California, Davis, Davis, CA 95616, USA
| | | | | | - Gregory T Sigurdson
- Department of Food Science and Technology, The Ohio State University, Columbus, OH 43210, USA
| | - Luca Grisanti
- Scuola Internazionale Superiore di Studi Avanzati, Trieste, Italy
- Division of Theoretical Physics, Institut Ruđer Bošković, Zagreb, Croatia
| | | | - Sara Laporte
- Scuola Internazionale Superiore di Studi Avanzati, Trieste, Italy
| | - Julia Li
- Mars Wrigley, Hackettstown, NJ 07840, USA
| | - Tadao Kondo
- Graduate School of Informatics, Nagoya University, Chikusa, Nagoya, Japan
| | - Alessandra Magistrato
- Consiglio Nazionale delle Ricerche, Istituto Officina dei Materiali, Scuola Internazionale Superiore di Studi Avanzati, Trieste, Italy
| | | | - Mary Riley
- Microbiology Graduate Group, University of California, Davis, Davis, CA 95616, USA
| | - Mariami Rusishvili
- Scuola Internazionale Superiore di Studi Avanzati, Trieste, Italy
- Pritzker School of Molecular Engineering, The University of Chicago, Chicago, IL, USA
| | - Neda Ahmadiani
- Department of Food Science and Technology, The Ohio State University, Columbus, OH 43210, USA
- Centre d'Innovació, Recerca I Transferència en Tecnologia dels Aliments, CERTA-UAB Tecnio Grup, XIA-UAB, Animal and Food Science Department, Universidad Autònoma de Barcelona, Bellaterra, Spain
| | - Stefano Baroni
- Scuola Internazionale Superiore di Studi Avanzati, Trieste, Italy
- Graduate School of Informatics, Nagoya University, Chikusa, Nagoya, Japan
| | | | - Monica Giusti
- Department of Food Science and Technology, The Ohio State University, Columbus, OH 43210, USA
| | | | - John Didzbalis
- Mars Advanced Research Institute, Mars, Incorporated, Hackettstown, NJ 07840, USA
| | - Kumi Yoshida
- Graduate School of Informatics, Nagoya University, Chikusa, Nagoya, Japan.
| | - Justin B Siegel
- Biophysics Graduate Group, University of California, Davis, Davis, CA, USA.
- Genome Center, University of California, Davis, Davis, CA 95616, USA
- Chemistry Department, University of California, Davis, Davis, CA 95616, USA
- Department of Biochemistry and Molecular Medicine, University of California, Davis, Sacramento, CA 95616, USA
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366
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Le KH, Adolf-Bryfogle J, Klima JC, Lyskov S, Labonte J, Bertolani S, Burman SSR, Leaver-Fay A, Weitzner B, Maguire J, Rangan R, Adrianowycz MA, Alford RF, Adal A, Nance ML, Wu Y, Willis J, Kulp DW, Das R, Dunbrack RL, Schief W, Kuhlman B, Siegel JB, Gray JJ. PyRosetta Jupyter Notebooks Teach Biomolecular Structure Prediction and Design. BIOPHYSICIST (ROCKVILLE, MD.) 2021; 2:108-122. [PMID: 35128343 DOI: 10.35459/tbp.2019.000147] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Biomolecular structure drives function, and computational capabilities have progressed such that the prediction and computational design of biomolecular structures is increasingly feasible. Because computational biophysics attracts students from many different backgrounds and with different levels of resources, teaching the subject can be challenging. One strategy to teach diverse learners is with interactive multimedia material that promotes self-paced, active learning. We have created a hands-on education strategy with a set of sixteen modules that teach topics in biomolecular structure and design, from fundamentals of conformational sampling and energy evaluation to applications like protein docking, antibody design, and RNA structure prediction. Our modules are based on PyRosetta, a Python library that encapsulates all computational modules and methods in the Rosetta software package. The workshop-style modules are implemented as Jupyter Notebooks that can be executed in the Google Colaboratory, allowing learners access with just a web browser. The digital format of Jupyter Notebooks allows us to embed images, molecular visualization movies, and interactive coding exercises. This multimodal approach may better reach students from different disciplines and experience levels as well as attract more researchers from smaller labs and cognate backgrounds to leverage PyRosetta in their science and engineering research. All materials are freely available at https://github.com/RosettaCommons/PyRosetta.notebooks.
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Affiliation(s)
- Kathy H Le
- T. C. Jenkins Department of Biophysics, Johns Hopkins University, Baltimore, Maryland, United States
| | - Jared Adolf-Bryfogle
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, California, United States
| | - Jason C Klima
- Institute for Protein Design, University of Washington, Seattle, Washington, United States.,Department of Biochemistry, University of Washington, Seattle, Washington, United States.,Lyell Immunopharma, Inc., Seattle, Washington, United States
| | - Sergey Lyskov
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States
| | - Jason Labonte
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States.,Department of Chemistry, Franklin & Marshall College, Lancaster, Pennsylvania, United States
| | - Steven Bertolani
- Department of Chemistry, Department of Biochemistry and Molecular Medicine, Genome Center, University of California, Davis, Davis, California, United States
| | - Shourya S Roy Burman
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States
| | - Andrew Leaver-Fay
- Department of Biochemistry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States
| | - Brian Weitzner
- Institute for Protein Design, University of Washington, Seattle, Washington, United States.,Department of Biochemistry, University of Washington, Seattle, Washington, United States.,Lyell Immunopharma, Inc., Seattle, Washington, United States
| | - Jack Maguire
- Program in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States
| | - Ramya Rangan
- Program in Biophysics, Stanford University, Stanford, California, United States
| | - Matt A Adrianowycz
- Program in Biophysics, Stanford University, Stanford, California, United States
| | - Rebecca F Alford
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States
| | - Aleexsan Adal
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States
| | - Morgan L Nance
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, Maryland, United States
| | - Yuanhan Wu
- Vaccine and Immunotherapy Center, Wistar Institute, Philadelphia, Pennsylvania, United States
| | - Jordan Willis
- RubrYc Therapeutics, San Ramon, California, United States
| | - Daniel W Kulp
- Vaccine and Immunotherapy Center, Wistar Institute, Philadelphia, Pennsylvania, United States
| | - Rhiju Das
- Program in Biophysics, Stanford University, Stanford, California, United States
| | | | - William Schief
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, California, United States
| | - Brian Kuhlman
- Department of Biochemistry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States.,Program in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States
| | - Justin B Siegel
- Department of Chemistry, Department of Biochemistry and Molecular Medicine, Genome Center, University of California, Davis, Davis, California, United States
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States.,Program in Molecular Biophysics, Johns Hopkins University, Baltimore, Maryland, United States
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367
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Marzolf DR, Seffernick JT, Lindert S. Protein Structure Prediction from NMR Hydrogen-Deuterium Exchange Data. J Chem Theory Comput 2021; 17:2619-2629. [PMID: 33780620 DOI: 10.1021/acs.jctc.1c00077] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Amide hydrogen-deuterium exchange (HDX) has long been used to determine regional flexibility and binding sites in proteins; however, the data are too sparse for full structural characterization. Experiments that measure HDX rates, such as HDX-NMR, have far higher throughput compared to structure determination via X-ray crystallography, cryo-EM, or a full suite of NMR experiments. Data from HDX-NMR experiments encode information on the protein structure, making HDX a prime candidate to be supplemented by computational algorithms for protein structure prediction. We have developed a methodology to incorporate HDX-NMR data into ab initio protein structure prediction using the Rosetta software framework to predict structures based on experimental agreement. To demonstrate the efficacy of our algorithm, we examined 38 proteins with HDX-NMR data available, comparing the predicted model with and without the incorporation of HDX data into scoring. The root-mean-square deviation (rmsd, a measure of the average atomic distance between superimposed models) of the predicted model improved by 1.42 Å on average after incorporating the HDX-NMR data into scoring. The average rmsd improvement for the proteins where the selected model rmsd changed after incorporating HDX data was 3.63 Å, including one improvement of more than 11 Å and seven proteins improving by greater than 4 Å, with 12/15 proteins improving overall. Additionally, for independent verification, two proteins that were not part of the original benchmark were scored including HDX data, with a dramatic improvement of the selected model rmsd of nearly 9 Å for one of the proteins. Moreover, we have developed a confidence metric allowing us to successfully identify near-native models in the absence of a native structure. Improvement in model selection with a strong confidence measure demonstrates that protein structure prediction with HDX-NMR is a powerful tool which can be performed with minimal additional computational strain and expense.
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Affiliation(s)
- Daniel R Marzolf
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, Ohio 43210, United States
| | - Justin T Seffernick
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, Ohio 43210, United States
| | - Steffen Lindert
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, Ohio 43210, United States
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368
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Cao X, Tian P. Molecular free energy optimization on a computational graph. RSC Adv 2021; 11:12929-12937. [PMID: 35423805 PMCID: PMC8697515 DOI: 10.1039/d1ra01455b] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 03/26/2021] [Indexed: 11/21/2022] Open
Abstract
Free energy is arguably the most important property of molecular systems. Despite great progress in both its efficient estimation by scoring functions/potentials and more rigorous computation based on extensive sampling, we remain far from accurately predicting and manipulating biomolecular structures and their interactions. There are fundamental limitations, including accuracy of interaction description and difficulty of sampling in high dimensional space, to be tackled. Computational graph underlies major artificial intelligence platforms and is proven to facilitate training, optimization and learning. Combining autodifferentiation, coordinates transformation and generalized solvation free energy theory, we construct a computational graph infrastructure to realize seamless integration of fully trainable local free energy landscape with end to end differentiable iterative free energy optimization. This new framework drastically improves efficiency by replacing local sampling with differentiation. Its specific implementation in protein structure refinement achieves superb efficiency and competitive accuracy when compared with state of the art all-atom mainstream methods.
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Affiliation(s)
- Xiaoyong Cao
- School of Life Sciences, Jilin University Changchun 130012 China +86 431 85155287
| | - Pu Tian
- School of Life Sciences, Jilin University Changchun 130012 China +86 431 85155287
- School of Artificial Intelligence, Jilin University Changchun 130012 China
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369
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Muller YD, Nguyen DP, Ferreira LMR, Ho P, Raffin C, Valencia RVB, Congrave-Wilson Z, Roth TL, Eyquem J, Van Gool F, Marson A, Perez L, Wells JA, Bluestone JA, Tang Q. The CD28-Transmembrane Domain Mediates Chimeric Antigen Receptor Heterodimerization With CD28. Front Immunol 2021; 12:639818. [PMID: 33833759 PMCID: PMC8021955 DOI: 10.3389/fimmu.2021.639818] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 02/08/2021] [Indexed: 12/12/2022] Open
Abstract
Anti-CD19 chimeric antigen receptor (CD19-CAR)-engineered T cells are approved therapeutics for malignancies. The impact of the hinge domain (HD) and the transmembrane domain (TMD) between the extracellular antigen-targeting CARs and the intracellular signaling modalities of CARs has not been systemically studied. In this study, a series of 19-CARs differing only by their HD (CD8, CD28, or IgG4) and TMD (CD8 or CD28) was generated. CARs containing a CD28-TMD, but not a CD8-TMD, formed heterodimers with the endogenous CD28 in human T cells, as shown by co-immunoprecipitation and CAR-dependent proliferation of anti-CD28 stimulation. This dimerization was dependent on polar amino acids in the CD28-TMD and was more efficient with CARs containing CD28 or CD8 HD than IgG4-HD. The CD28-CAR heterodimers did not respond to CD80 and CD86 stimulation but had a significantly reduced CD28 cell-surface expression. These data unveiled a fundamental difference between CD28-TMD and CD8-TMD and indicated that CD28-TMD can modulate CAR T-cell activities by engaging endogenous partners.
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Affiliation(s)
- Yannick D Muller
- Department of Surgery, University of California, San Francisco, San Francisco, CA, United States.,Diabetes Center, University of California, San Francisco, San Francisco, CA, United States.,Department of Medicine, Service Immunologie et Allergie, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - Duy P Nguyen
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, United States
| | - Leonardo M R Ferreira
- Department of Surgery, University of California, San Francisco, San Francisco, CA, United States.,Diabetes Center, University of California, San Francisco, San Francisco, CA, United States.,Sean N. Parker Autoimmune Research Laboratory, University of California, San Francisco, San Francisco, CA, United States
| | - Patrick Ho
- Department of Surgery, University of California, San Francisco, San Francisco, CA, United States.,Diabetes Center, University of California, San Francisco, San Francisco, CA, United States.,Sean N. Parker Autoimmune Research Laboratory, University of California, San Francisco, San Francisco, CA, United States
| | - Caroline Raffin
- Diabetes Center, University of California, San Francisco, San Francisco, CA, United States.,Sean N. Parker Autoimmune Research Laboratory, University of California, San Francisco, San Francisco, CA, United States
| | | | - Zion Congrave-Wilson
- Department of Surgery, University of California, San Francisco, San Francisco, CA, United States
| | - Theodore L Roth
- Diabetes Center, University of California, San Francisco, San Francisco, CA, United States.,Department of Medicine, University of California, San Francisco, San Francisco, CA, United States
| | - Justin Eyquem
- Sean N. Parker Autoimmune Research Laboratory, University of California, San Francisco, San Francisco, CA, United States
| | - Frederic Van Gool
- Diabetes Center, University of California, San Francisco, San Francisco, CA, United States.,Sean N. Parker Autoimmune Research Laboratory, University of California, San Francisco, San Francisco, CA, United States
| | - Alexander Marson
- Diabetes Center, University of California, San Francisco, San Francisco, CA, United States.,Department of Medicine, University of California, San Francisco, San Francisco, CA, United States.,Gladstone-UCSF Institute of Genomic Immunology, Gladstone Institutes, San Francisco, CA, United States
| | - Laurent Perez
- Department of Medicine, Service Immunologie et Allergie, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - James A Wells
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, United States.,Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, United States
| | - Jeffrey A Bluestone
- Diabetes Center, University of California, San Francisco, San Francisco, CA, United States.,Sean N. Parker Autoimmune Research Laboratory, University of California, San Francisco, San Francisco, CA, United States
| | - Qizhi Tang
- Department of Surgery, University of California, San Francisco, San Francisco, CA, United States.,Diabetes Center, University of California, San Francisco, San Francisco, CA, United States
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370
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Ribeiro J, Dupaigne P, Petrillo C, Ducrot C, Duquenne C, Veaute X, Saintomé C, Busso D, Guerois R, Martini E, Livera G. The meiosis-specific MEIOB-SPATA22 complex cooperates with RPA to form a compacted mixed MEIOB/SPATA22/RPA/ssDNA complex. DNA Repair (Amst) 2021; 102:103097. [PMID: 33812231 DOI: 10.1016/j.dnarep.2021.103097] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Revised: 03/05/2021] [Accepted: 03/08/2021] [Indexed: 12/30/2022]
Abstract
During meiosis, programmed double-strand breaks are repaired by homologous recombination (HR) to form crossovers that are essential to homologous chromosome segregation. Single-stranded DNA (ssDNA) containing intermediates are key features of HR, which must be highly regulated. RPA, the ubiquitous ssDNA binding complex, was thought to play similar roles during mitotic and meiotic HR until the recent discovery of MEIOB and its partner, SPATA22, two essential meiosis-specific proteins. Here, we show that like MEIOB, SPATA22 resembles RPA subunits and binds ssDNA. We studied the physical and functional interactions existing between MEIOB, SPATA22, and RPA, and show that MEIOB and SPATA22 interact with the preformed RPA complex through their interacting domain and condense RPA-coated ssDNA in vitro. In meiotic cells, we show that MEIOB and SPATA22 modify the immunodetection of the two large subunits of RPA. Given these results, we propose that MEIOB-SPATA22 and RPA form a functional ssDNA-interacting complex to satisfy meiotic HR requirements by providing specific properties to the ssDNA.
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Affiliation(s)
- Jonathan Ribeiro
- Laboratory of Development of the Gonads, UMR E008 Genetic Stability Stem Cells and Radiations, Université de Paris, Université Paris Saclay, CEA, F-92265, Fontenay aux Roses, France
| | - Pauline Dupaigne
- Laboratoire de Microscopie Moléculaire et Cellulaire, UMR 8126, Interactions Moléculaires et Cancer, CNRS, Université Paris Sud, Institut de Cancérologie Gustave Roussy, Villejuif, France
| | - Cynthia Petrillo
- Laboratory of Development of the Gonads, UMR E008 Genetic Stability Stem Cells and Radiations, Université de Paris, Université Paris Saclay, CEA, F-92265, Fontenay aux Roses, France
| | - Cécile Ducrot
- Laboratory of Development of the Gonads, UMR E008 Genetic Stability Stem Cells and Radiations, Université de Paris, Université Paris Saclay, CEA, F-92265, Fontenay aux Roses, France
| | - Clotilde Duquenne
- Laboratory of Development of the Gonads, UMR E008 Genetic Stability Stem Cells and Radiations, Université de Paris, Université Paris Saclay, CEA, F-92265, Fontenay aux Roses, France
| | - Xavier Veaute
- CIGEx, UMRE008 Stabilité Génétique Cellules Souches et Radiations, Université de Paris, Université Paris-Saclay, CEA, Inserm, U1274, F-92260, Fontenay-aux-Roses, France
| | - Carole Saintomé
- MNHN, CNRS UMR 7196, INSERM U1154, Sorbonne Universités, 75231, Paris, France
| | - Didier Busso
- CIGEx, UMRE008 Stabilité Génétique Cellules Souches et Radiations, Université de Paris, Université Paris-Saclay, CEA, Inserm, U1274, F-92260, Fontenay-aux-Roses, France
| | - Raphaël Guerois
- CNRS I2BC UMR 9198, iBiTec-S, SB²SM CEA SACLAY, 91191, Gif sur Yvette, France
| | - Emmanuelle Martini
- Laboratory of Development of the Gonads, UMR E008 Genetic Stability Stem Cells and Radiations, Université de Paris, Université Paris Saclay, CEA, F-92265, Fontenay aux Roses, France.
| | - Gabriel Livera
- Laboratory of Development of the Gonads, UMR E008 Genetic Stability Stem Cells and Radiations, Université de Paris, Université Paris Saclay, CEA, F-92265, Fontenay aux Roses, France
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371
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Gidley F, Parmeggiani F. Repeat proteins: designing new shapes and functions for solenoid folds. Curr Opin Struct Biol 2021; 68:208-214. [PMID: 33721772 DOI: 10.1016/j.sbi.2021.02.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Revised: 01/31/2021] [Accepted: 02/01/2021] [Indexed: 10/21/2022]
Abstract
The modular nature of repeat proteins has inspired the design of regular and completely novel sequences and structures. Research in the past years has provided a broad set of design approaches and new repeat proteins that have found applications in molecular recognition, taking advantage of the natural ability of some of these families to bind proteins, peptides and nucleic acids. Here, we provide an overview on the recent trends in design of repeat proteins, particularly solenoid folds, and their applications. By exploiting the intrinsic modularity of repeats, new architectures have been designed that combine different types of repeat, are easily scalable by changing the number of repeats and can be quickly generated by using existing modular building blocks.
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Affiliation(s)
- Frances Gidley
- School of Chemistry, School of Biochemistry, Bristol Biodesign Institute, University of Bristol, United Kingdom
| | - Fabio Parmeggiani
- School of Chemistry, School of Biochemistry, Bristol Biodesign Institute, University of Bristol, United Kingdom.
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372
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Schoeder CT, 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: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [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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373
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Mahajan SP, Srinivasan Y, Labonte JW, DeLisa MP, Gray JJ. Structural basis for peptide substrate specificities of glycosyltransferase GalNAc-T2. ACS Catal 2021; 11:2977-2991. [PMID: 34322281 DOI: 10.1021/acscatal.0c04609] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
The polypeptide N-acetylgalactosaminyl transferase (GalNAc-T) enzyme family initiates O-linked mucin-type glycosylation. The family constitutes 20 isoenzymes in humans. GalNAc-Ts exhibit both redundancy and finely tuned specificity for a wide range of peptide substrates. In this work, we deciphered the sequence and structural motifs that determine the peptide substrate preferences for the GalNAc-T2 isoform. Our approach involved sampling and characterization of peptide-enzyme conformations obtained from Rosetta Monte Carlo-minimization-based flexible docking. We computationally scanned 19 amino acid residues at positions -1 and +1 of an eight-residue peptide substrate, which comprised a dataset of 361 (19x19) peptides with previously characterized experimental GalNAc-T2 glycosylation efficiencies. The calculations recapitulated experimental specificity data, successfully discriminating between glycosylatable and non-glycosylatable peptides with a probability of 96.5% (ROC-AUC score), a balanced accuracy of 85.5% and a false positive rate of 7.3%. The glycosylatable peptide substrates viz. peptides with proline, serine, threonine, and alanine at the -1 position of the peptide preferentially exhibited cognate sequon-like conformations. The preference for specific residues at the -1 position of the peptide was regulated by enzyme residues R362, K363, Q364, H365 and W331, which modulate the pocket size and specific enzyme-peptide interactions. For the +1 position of the peptide, enzyme residues K281 and K363 formed gating interactions with aromatics and glutamines at the +1 position of the peptide, leading to modes of peptide-binding sub-optimal for catalysis. Overall, our work revealed enzyme features that lead to the finely tuned specificity observed for a broad range of peptide substrates for the GalNAc-T2 enzyme. We anticipate that the key sequence and structural motifs can be extended to analyze specificities of other isoforms of the GalNAc-T family and can be used to guide design of variants with tailored specificity.
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Affiliation(s)
- Sai Pooja Mahajan
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Yashes Srinivasan
- Department of Bioengineering, University of California—Los Angeles, Los Angeles, California 90095, United States
| | - Jason W. Labonte
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
- Department of Chemistry, Franklin & Marshall College, Lancaster, Pennsylvania 17604, United States
| | - Matthew P. DeLisa
- Robert Frederick Smith School of Chemical and Biomolecular Engineering, Department of Microbiology, and Nancy E. and Peter C. Meinig School of Biomedical Engineering, Biochemistry, Molecular and Cell Biology, Cornell University, Ithaca, New York 14853, United States
| | - Jeffrey J. Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, Maryland 21224, United States
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374
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375
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Bai N, Miller SA, Andrianov GV, Yates M, Kirubakaran P, Karanicolas J. Rationalizing PROTAC-Mediated Ternary Complex Formation Using Rosetta. J Chem Inf Model 2021; 61:1368-1382. [PMID: 33625214 DOI: 10.1021/acs.jcim.0c01451] [Citation(s) in RCA: 55] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Proteolysis-targeting chimaeras (PROTACs) are molecules that combine a target-binding warhead with an E3 ligase-recruiting moiety; by drawing the target protein into a ternary complex with the E3 ligase, PROTACs induce target protein degradation. While PROTACs hold exciting potential as chemical probes and as therapeutic agents, development of a PROTAC typically requires synthesis of numerous analogs to thoroughly explore variations on the chemical linker; without extensive trial and error, it is unclear how to link the two protein-recruiting moieties to promote formation of a productive ternary complex. Here, we describe a structure-based computational method for evaluating the suitability of a given linker for ternary complex formation. Our method uses Rosetta to dock the protein components and then builds the PROTAC from its component fragments into each binding mode; complete models of the ternary complex are then refined. We apply this approach to retrospectively evaluate multiple PROTACs from the literature, spanning diverse target proteins. We find that modeling ternary complex formation is sufficient to explain both activity and selectivity reported for these PROTACs, implying that other cellular factors are not key determinants of activity in these cases. We further find that interpreting PROTAC activity is best approached using an ensemble of structures of the ternary complex rather than a single static conformation and that members of a structurally conserved protein family can be recruited by the same PROTAC through vastly different binding modes. To encourage adoption of these methods and promote further analyses, we disseminate both the computational methods and the models of ternary complexes.
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Affiliation(s)
- Nan Bai
- Program in Molecular Therapeutics, Fox Chase Cancer Center, Philadelphia, Pennsylvania 19111, United States.,Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas 66045, United States
| | - Sven A Miller
- Program in Molecular Therapeutics, Fox Chase Cancer Center, Philadelphia, Pennsylvania 19111, United States
| | - Grigorii V Andrianov
- Program in Molecular Therapeutics, Fox Chase Cancer Center, Philadelphia, Pennsylvania 19111, United States.,Institute of Fundamental Medicine and Biology, Kazan Federal University, Kazan 420008, Russia
| | - Max Yates
- Program in Molecular Therapeutics, Fox Chase Cancer Center, Philadelphia, Pennsylvania 19111, United States
| | - Palani Kirubakaran
- Program in Molecular Therapeutics, Fox Chase Cancer Center, Philadelphia, Pennsylvania 19111, United States
| | - John Karanicolas
- Program in Molecular Therapeutics, Fox Chase Cancer Center, Philadelphia, Pennsylvania 19111, United States
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376
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Sun L, Li P, Ju X, Rao J, Huang W, Ren L, Zhang S, Xiong T, Xu K, Zhou X, Gong M, Miska E, Ding Q, Wang J, Zhang QC. In vivo structural characterization of the SARS-CoV-2 RNA genome identifies host proteins vulnerable to repurposed drugs. Cell 2021; 184:1865-1883.e20. [PMID: 33636127 PMCID: PMC7871767 DOI: 10.1016/j.cell.2021.02.008] [Citation(s) in RCA: 121] [Impact Index Per Article: 40.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 10/01/2020] [Accepted: 02/02/2021] [Indexed: 01/10/2023]
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of the ongoing coronavirus disease 2019 (COVID-19) pandemic. Understanding of the RNA virus and its interactions with host proteins could improve therapeutic interventions for COVID-19. By using icSHAPE, we determined the structural landscape of SARS-CoV-2 RNA in infected human cells and from refolded RNAs, as well as the regulatory untranslated regions of SARS-CoV-2 and six other coronaviruses. We validated several structural elements predicted in silico and discovered structural features that affect the translation and abundance of subgenomic viral RNAs in cells. The structural data informed a deep-learning tool to predict 42 host proteins that bind to SARS-CoV-2 RNA. Strikingly, antisense oligonucleotides targeting the structural elements and FDA-approved drugs inhibiting the SARS-CoV-2 RNA binding proteins dramatically reduced SARS-CoV-2 infection in cells derived from human liver and lung tumors. Our findings thus shed light on coronavirus and reveal multiple candidate therapeutics for COVID-19 treatment.
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Affiliation(s)
- Lei Sun
- MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China; Tsinghua-Peking Center for Life Sciences, Beijing 100084, China
| | - Pan Li
- MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China; Tsinghua-Peking Center for Life Sciences, Beijing 100084, China
| | - Xiaohui Ju
- Center for Infectious Disease Research, School of Medicine, Tsinghua University, Beijing 100084, China
| | - Jian Rao
- NHC Key Laboratory of Systems Biology of Pathogens and Christophe Mérieux Laboratory, Institute of Pathogen Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
| | - Wenze Huang
- MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China; Tsinghua-Peking Center for Life Sciences, Beijing 100084, China
| | - Lili Ren
- NHC Key Laboratory of Systems Biology of Pathogens and Christophe Mérieux Laboratory, Institute of Pathogen Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China; Key Laboratory of Respiratory Disease Pathogenomics, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - Shaojun Zhang
- MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China; Tsinghua-Peking Center for Life Sciences, Beijing 100084, China
| | - Tuanlin Xiong
- MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China; Tsinghua-Peking Center for Life Sciences, Beijing 100084, China
| | - Kui Xu
- MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China; Tsinghua-Peking Center for Life Sciences, Beijing 100084, China
| | - Xiaolin Zhou
- MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China; Tsinghua-Peking Center for Life Sciences, Beijing 100084, China
| | - Mingli Gong
- Center for Infectious Disease Research, School of Medicine, Tsinghua University, Beijing 100084, China
| | - Eric Miska
- Wellcome Trust/Cancer Research UK Gurdon Institute, Department of Genetics, University of Cambridge, Cambridge CB2 1QN, UK; Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, CB10 1SA, UK
| | - Qiang Ding
- Center for Infectious Disease Research, School of Medicine, Tsinghua University, Beijing 100084, China.
| | - Jianwei Wang
- NHC Key Laboratory of Systems Biology of Pathogens and Christophe Mérieux Laboratory, Institute of Pathogen Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China; Key Laboratory of Respiratory Disease Pathogenomics, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China.
| | - Qiangfeng Cliff Zhang
- MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China; Tsinghua-Peking Center for Life Sciences, Beijing 100084, China.
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377
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Hasan M, Khakzad H, Happonen L, Sundin A, Unge J, Mueller U, Malmström J, Westergren-Thorsson G, Malmström L, Ellervik U, Malmström A, Tykesson E. The structure of human dermatan sulfate epimerase 1 emphasizes the importance of C5-epimerization of glucuronic acid in higher organisms. Chem Sci 2021; 12:1869-1885. [PMID: 33815739 PMCID: PMC8006597 DOI: 10.1039/d0sc05971d] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 12/04/2020] [Indexed: 01/21/2023] Open
Abstract
Dermatan sulfate epimerase 1 (DS-epi1, EC 5.1.3.19) catalyzes the conversion of d-glucuronic acid to l-iduronic acid on the polymer level, a key step in the biosynthesis of the glycosaminoglycan dermatan sulfate. Here, we present the first crystal structure of the catalytic domains of DS-epi1, solved at 2.4 Å resolution, as well as a model of the full-length luminal protein obtained by a combination of macromolecular crystallography and targeted cross-linking mass spectrometry. Based on docking studies and molecular dynamics simulations of the protein structure and a chondroitin substrate, we suggest a novel mechanism of DS-epi1, involving a His/double-Tyr motif. Our work uncovers detailed information about the domain architecture, active site, metal-coordinating center and pattern of N-glycosylation of the protein. Additionally, the structure of DS-epi1 reveals a high structural similarity to proteins from several families of bacterial polysaccharide lyases. DS-epi1 is of great importance in a range of diseases, and the structure provides a necessary starting point for design of active site inhibitors.
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Affiliation(s)
- Mahmudul Hasan
- Department of Biochemistry and Structural Biology , Lund University , Lund , Sweden
| | - Hamed Khakzad
- Equipe Signalisation Calcique et Infections Microbiennes , Ecole Normale Supérieure Paris-Saclay , 91190 Gif-sur-Yvette , France
- Institut National de la Santé et de la Recherche Médicale U1282 , 91190 Gif-sur-Yvette , France
| | - Lotta Happonen
- Department of Clinical Sciences , Lund University , Lund , Sweden
| | - Anders Sundin
- Department of Chemistry , Lund University , Lund , Sweden
| | - Johan Unge
- Department of Biological Chemistry , University of California Los Angeles , Los Angeles , CA 90095 , USA
| | - Uwe Mueller
- Macromolecular Crystallography Group , Helmholtz-Zentrum-Berlin für Materialien und Energie , Albert-Einstein Str. 15 , 12489 Berlin , Germany
| | - Johan Malmström
- Department of Clinical Sciences , Lund University , Lund , Sweden
| | | | - Lars Malmström
- Department of Clinical Sciences , Lund University , Lund , Sweden
| | - Ulf Ellervik
- Department of Chemistry , Lund University , Lund , Sweden
| | - Anders Malmström
- Department of Experimental Medical Science , Lund University , Lund , Sweden .
| | - Emil Tykesson
- Department of Experimental Medical Science , Lund University , Lund , Sweden .
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378
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Guest JD, Vreven T, Zhou J, Moal I, Jeliazkov JR, Gray JJ, Weng Z, Pierce BG. An expanded benchmark for antibody-antigen docking and affinity prediction reveals insights into antibody recognition determinants. Structure 2021; 29:606-621.e5. [PMID: 33539768 DOI: 10.1016/j.str.2021.01.005] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2020] [Revised: 11/15/2020] [Accepted: 01/11/2021] [Indexed: 01/04/2023]
Abstract
Accurate predictive modeling of antibody-antigen complex structures and structure-based antibody design remain major challenges in computational biology, with implications for biotherapeutics, immunity, and vaccines. Through a systematic search for high-resolution structures of antibody-antigen complexes and unbound antibody and antigen structures, in conjunction with identification of experimentally determined binding affinities, we have assembled a non-redundant set of test cases for antibody-antigen docking and affinity prediction. This benchmark more than doubles the number of antibody-antigen complexes and corresponding affinities available in our previous benchmarks, providing an unprecedented view of the determinants of antibody recognition and insights into molecular flexibility. Initial assessments of docking and affinity prediction tools highlight the challenges posed by this diverse set of cases, which includes camelid nanobodies, therapeutic monoclonal antibodies, and broadly neutralizing antibodies targeting viral glycoproteins. This dataset will enable development of advanced predictive modeling and design methods for this therapeutically relevant class of protein-protein interactions.
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Affiliation(s)
- Johnathan D Guest
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD 20850, USA; Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD 20742, USA
| | - Thom Vreven
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA
| | - Jing Zhou
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Iain Moal
- Computational Sciences, GlaxoSmithKline Research and Development, Stevenage SG1 2NY, UK
| | - Jeliazko R Jeliazkov
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA; Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Zhiping Weng
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA.
| | - Brian G Pierce
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD 20850, USA; Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD 20742, USA.
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379
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Reetz MT. A breakthrough in protein engineering of a glycosyltransferase. GREEN SYNTHESIS AND CATALYSIS 2021. [DOI: 10.1016/j.gresc.2021.01.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
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380
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Abstract
While native proteins cover diverse structural spaces and achieve various biological events, not many of them can directly serve human needs. One reason is that the native proteins usually contain idiosyncrasies evolved for their native functions but disfavoring engineering requirements. To overcome this issue, one strategy is to create de novo proteins which are designed to possess improved stability, high environmental tolerance, and enhanced engineering potential. Compared to other protein engineering strategies, in silico design of de novo proteins has significantly expanded the protein structural and sequence spaces, reduced wet lab workload, and incorporated engineered features in a guided and efficient manner. In the Baker laboratory we have been applying a design pipeline that uses the blueprint builder to design different folds of de novo proteins, and have successfully obtained libraries of de novo proteins with improved stability and engineering potential. In this article, we will use the design of de novo β-barrel proteins as an example to describe the principles and basic procedures of the blueprint builder-based design pipeline. © 2020 Wiley Periodicals LLC. Basic Protocol 1: The construction of blueprints Alternate Protocol: Build blueprints based on existing protein .pdb files Basic Protocol 2: De novo protein design pipeline using the blueprint builder.
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Affiliation(s)
- Linna An
- Institute for Protein Design, University of Washington, Seattle, Washington
| | - Gyu Rie Lee
- Institute for Protein Design, University of Washington, Seattle, Washington
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381
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Strokach A, Lu TY, Kim PM. ELASPIC2 (EL2): Combining Contextualized Language Models and Graph Neural Networks to Predict Effects of Mutations. J Mol Biol 2021; 433:166810. [PMID: 33450251 DOI: 10.1016/j.jmb.2021.166810] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Revised: 12/19/2020] [Accepted: 01/03/2021] [Indexed: 12/21/2022]
Abstract
The ELASPIC web server allows users to evaluate the effect of mutations on protein folding and protein-protein interaction on a proteome-wide scale. It uses homology models of proteins and protein-protein interactions, which have been precalculated for several proteomes, and machine learning models, which integrate structural information with sequence conservation scores, in order to make its predictions. Since the original publication of the ELASPIC web server, several advances have motivated a revisiting of the problem of mutation effect prediction. First, progress in neural network architectures and self-supervised pre-trained has resulted in models which provide more informative embeddings of protein sequence and structure than those used by the original version of ELASPIC. Second, the amount of training data has increased several-fold, largely driven by advances in deep mutation scanning and other multiplexed assays of variant effect. Here, we describe two machine learning models which leverage the recent advances in order to achieve superior accuracy in predicting the effect of mutation on protein folding and protein-protein interaction. The models incorporate features generated using pre-trained transformer- and graph convolution-based neural networks, and are trained to optimize a ranking objective function, which permits the use of heterogeneous training data. The outputs from the new models have been incorporated into the ELASPIC web server, available at http://elaspic.kimlab.org.
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Affiliation(s)
- Alexey Strokach
- Department of Computer Science, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Tian Yu Lu
- Department of Computer Science, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Philip M Kim
- Department of Computer Science, University of Toronto, Toronto, ON M5S 3E1, Canada; Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1, Canada.
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382
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Biehn SE, Lindert S. Accurate protein structure prediction with hydroxyl radical protein footprinting data. Nat Commun 2021; 12:341. [PMID: 33436604 PMCID: PMC7804018 DOI: 10.1038/s41467-020-20549-7] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Accepted: 12/08/2020] [Indexed: 01/10/2023] Open
Abstract
Hydroxyl radical protein footprinting (HRPF) in combination with mass spectrometry reveals the relative solvent exposure of labeled residues within a protein, thereby providing insight into protein tertiary structure. HRPF labels nineteen residues with varying degrees of reliability and reactivity. Here, we are presenting a dynamics-driven HRPF-guided algorithm for protein structure prediction. In a benchmark test of our algorithm, usage of the dynamics data in a score term resulted in notable improvement of the root-mean-square deviations of the lowest-scoring ab initio models and improved the funnel-like metric Pnear for all benchmark proteins. We identified models with accurate atomic detail for three of the four benchmark proteins. This work suggests that HRPF data along with side chain dynamics sampled by a Rosetta mover ensemble can be used to accurately predict protein structure.
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Affiliation(s)
- Sarah E Biehn
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, OH, 43210, USA
| | - Steffen Lindert
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, OH, 43210, USA.
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383
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Khakzad H, Happonen L, Tran Van Nhieu G, Malmström J, Malmström L. In vivo Cross-Linking MS of the Complement System MAC Assembled on Live Gram-Positive Bacteria. Front Genet 2021; 11:612475. [PMID: 33488677 PMCID: PMC7820895 DOI: 10.3389/fgene.2020.612475] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 11/24/2020] [Indexed: 11/27/2022] Open
Abstract
Protein–protein interactions are central in many biological processes, but they are challenging to characterize, especially in complex samples. Protein cross-linking combined with mass spectrometry (MS) and computational modeling is gaining increased recognition as a viable tool in protein interaction studies. Here, we provide insights into the structure of the multicomponent human complement system membrane attack complex (MAC) using in vivo cross-linking MS combined with computational macromolecular modeling. We developed an affinity procedure followed by chemical cross-linking on human blood plasma using live Streptococcus pyogenes to enrich for native MAC associated with the bacterial surface. In this highly complex sample, we identified over 100 cross-linked lysine–lysine pairs between different MAC components that enabled us to present a quaternary model of the assembled MAC in its native environment. Demonstrating the validity of our approach, this MAC model is supported by existing X-ray crystallographic and electron cryo-microscopic models. This approach allows the study of protein–protein interactions in native environment mimicking their natural milieu. Its high potential in assisting and refining data interpretation in electron cryo-tomographic experiments will be discussed.
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Affiliation(s)
- Hamed Khakzad
- Equipe Signalisation Calcique et Infections Microbiennes, Ecole Normale Supérieure Paris-Saclay, Gif-sur-Yvette, France.,Institut National de la Santé et de la Recherche Médicale U1282, Gif-sur-Yvette, France
| | - Lotta Happonen
- Faculty of Medicine, Department of Clinical Sciences, Division of Infection Medicine, Lund University, Lund, Sweden
| | - Guy Tran Van Nhieu
- Equipe Signalisation Calcique et Infections Microbiennes, Ecole Normale Supérieure Paris-Saclay, Gif-sur-Yvette, France.,Institut National de la Santé et de la Recherche Médicale U1282, Gif-sur-Yvette, France
| | - Johan Malmström
- Faculty of Medicine, Department of Clinical Sciences, Division of Infection Medicine, Lund University, Lund, Sweden
| | - Lars Malmström
- Faculty of Medicine, Department of Clinical Sciences, Division of Infection Medicine, Lund University, Lund, Sweden
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384
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Structural determination of Streptococcus pyogenes M1 protein interactions with human immunoglobulin G using integrative structural biology. PLoS Comput Biol 2021; 17:e1008169. [PMID: 33411763 PMCID: PMC7817036 DOI: 10.1371/journal.pcbi.1008169] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Revised: 01/20/2021] [Accepted: 11/24/2020] [Indexed: 01/31/2023] Open
Abstract
Streptococcus pyogenes (Group A streptococcus; GAS) is an important human pathogen responsible for mild to severe, life-threatening infections. GAS expresses a wide range of virulence factors, including the M family proteins. The M proteins allow the bacteria to evade parts of the human immune defenses by triggering the formation of a dense coat of plasma proteins surrounding the bacteria, including IgGs. However, the molecular level details of the M1-IgG interaction have remained unclear. Here, we characterized the structure and dynamics of this interaction interface in human plasma on the surface of live bacteria using integrative structural biology, combining cross-linking mass spectrometry and molecular dynamics (MD) simulations. We show that the primary interaction is formed between the S-domain of M1 and the conserved IgG Fc-domain. In addition, we show evidence for a so far uncharacterized interaction between the A-domain and the IgG Fc-domain. Both these interactions mimic the protein G-IgG interface of group C and G streptococcus. These findings underline a conserved scavenging mechanism used by GAS surface proteins that block the IgG-receptor (FcγR) to inhibit phagocytic killing. We additionally show that we can capture Fab-bound IgGs in a complex background and identify XLs between the constant region of the Fab-domain and certain regions of the M1 protein engaged in the Fab-mediated binding. Our results elucidate the M1-IgG interaction network involved in inhibition of phagocytosis and reveal important M1 peptides that can be further investigated as future vaccine targets. Streptococcus pyogenes is a human specific pathogen causing both mild and invasive infections. It employs sophisticated mechanisms to evade and circumvent parts of the host’s immune defenses, in part via its major surface associated virulence factor, the family of M proteins. Of these, the M1 protein is the most prevalent serotype. The M1 protein creates a dense coat-like structure with multiple host proteins on the bacterial surface to disguise itself from opsonizing antibodies. It specifically interacts in a non-immune way with human immunoglobulin G (IgG) Fc-domains to disarm their receptor binding site. The molecular level details of this interaction have not been characterized. Here, we describe these interactions from minimally perturbed samples of human plasma adsorbed onto living bacteria using an integrative structural biology approach including cross-linking mass spectrometry, molecular modeling, and molecular dynamics simulations. We identify two distinct M1-peptides that bind IgGs and reveal the stability of these interactions. We show that both peptides block the Fc-receptor binding sites through capturing IgGs via their Fc-domains. These results highlight the importance of describing novel pathogen-derived peptides mediating host immune evasion as potential vaccine targets in future studies.
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385
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Dybowski R. Artificial Intelligence in Medicine: Biochemical 3D Modeling and Drug Discovery. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_318-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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386
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Siniavin AE, Streltsova MA, Nikiforova MA, Kudryavtsev DS, Grinkina SD, Gushchin VA, Mozhaeva VA, Starkov VG, Osipov AV, Lummis SCR, Tsetlin VI, Utkin YN. Snake venom phospholipase A 2s exhibit strong virucidal activity against SARS-CoV-2 and inhibit the viral spike glycoprotein interaction with ACE2. Cell Mol Life Sci 2021; 78:7777-7794. [PMID: 34714362 PMCID: PMC8554752 DOI: 10.1007/s00018-021-03985-6] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 09/17/2021] [Accepted: 10/14/2021] [Indexed: 01/08/2023]
Abstract
The COVID-19 pandemic caused by SARS-CoV-2 requires new treatments both to alleviate the symptoms and to prevent the spread of this disease. Previous studies demonstrated good antiviral and virucidal activity of phospholipase A2s (PLA2s) from snake venoms against viruses from different families but there was no data for coronaviruses. Here we show that PLA2s from snake venoms protect Vero E6 cells against SARS-CoV-2 cytopathic effects. PLA2s showed low cytotoxicity to Vero E6 cells with some activity at micromolar concentrations, but strong antiviral activity at nanomolar concentrations. Dimeric PLA2 from the viper Vipera nikolskii and its subunits manifested especially potent virucidal effects, which were related to their phospholipolytic activity, and inhibited cell-cell fusion mediated by the SARS-CoV-2 spike glycoprotein. Moreover, PLA2s interfered with binding both of an antibody against ACE2 and of the receptor-binding domain of the glycoprotein S to 293T/ACE2 cells. This is the first demonstration of a detrimental effect of PLA2s on β-coronaviruses. Thus, snake PLA2s are promising for the development of antiviral drugs that target the viral envelope, and could also prove to be useful tools to study the interaction of viruses with host cells.
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Affiliation(s)
- Andrei E. Siniavin
- grid.4886.20000 0001 2192 9124Department of Molecular Neuroimmune Signalling, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia ,N.F. Gamaleya National Research Center for Epidemiology and Microbiology, Ivanovsky Institute of Virology, Ministry of Health of the Russian Federation, Moscow, Russia
| | - Maria A. Streltsova
- grid.4886.20000 0001 2192 9124Department of Immunology, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia
| | - Maria A. Nikiforova
- N.F. Gamaleya National Research Center for Epidemiology and Microbiology, Ivanovsky Institute of Virology, Ministry of Health of the Russian Federation, Moscow, Russia
| | - Denis S. Kudryavtsev
- grid.4886.20000 0001 2192 9124Department of Molecular Neuroimmune Signalling, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia
| | - Svetlana D. Grinkina
- N.F. Gamaleya National Research Center for Epidemiology and Microbiology, Ivanovsky Institute of Virology, Ministry of Health of the Russian Federation, Moscow, Russia
| | - Vladimir A. Gushchin
- N.F. Gamaleya National Research Center for Epidemiology and Microbiology, Ivanovsky Institute of Virology, Ministry of Health of the Russian Federation, Moscow, Russia
| | - Vera A. Mozhaeva
- grid.4886.20000 0001 2192 9124Department of Molecular Neuroimmune Signalling, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia ,grid.4886.20000 0001 2192 9124Prokhorov General Physics Institute, Russian Academy of Sciences, Moscow, Russia
| | - Vladislav G. Starkov
- grid.4886.20000 0001 2192 9124Department of Molecular Neuroimmune Signalling, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia
| | - Alexey V. Osipov
- grid.4886.20000 0001 2192 9124Department of Molecular Neuroimmune Signalling, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia
| | - Sarah C. R. Lummis
- grid.5335.00000000121885934Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - Victor I. Tsetlin
- grid.4886.20000 0001 2192 9124Department of Molecular Neuroimmune Signalling, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia
| | - Yuri N. Utkin
- grid.4886.20000 0001 2192 9124Department of Molecular Neuroimmune Signalling, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia
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387
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Perotti M, Marcandalli J, Demurtas D, Sallusto F, Perez L. Rationally designed Human Cytomegalovirus gB nanoparticle vaccine with improved immunogenicity. PLoS Pathog 2020; 16:e1009169. [PMID: 33370407 PMCID: PMC7794029 DOI: 10.1371/journal.ppat.1009169] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Revised: 01/08/2021] [Accepted: 11/16/2020] [Indexed: 12/15/2022] Open
Abstract
Human cytomegalovirus (HCMV) is the primary viral cause of congenital birth defects and causes significant morbidity and mortality in immune-suppressed transplant recipients. Despite considerable efforts in vaccine development, HCMV infection still represents an unmet clinical need. In recent phase II trials, a MF59-adjuvanted gB vaccine showed only modest efficacy in preventing infection. These findings might be attributed to low level of antibodies (Abs) with a neutralizing activity induced by this vaccine. Here, we analyzed the immunogenicity of each gB antigenic domain (AD) and demonstrated that domain I of gB (AD5) is the main target of HCMV neutralizing antibodies. Furthermore, we designed, characterized and evaluated immunogenic responses to two different nanoparticles displaying a trimeric AD5 antigen. We showed that mice immunization with nanoparticles induces sera neutralization titers up to 100-fold higher compared to those obtained with the gB extracellular domain (gBECD). Collectively, these results illustrate with a medically relevant example the advantages of using a general approach combining antigen discovery, protein engineering and scaffold presentation for modern development of subunit vaccines against complex pathogens.
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Affiliation(s)
- Michela Perotti
- Institute for Research in Biomedicine, Università della Svizzera italiana, faculty of Biomedical Sciences, Bellinzona, Switzerland.,Institute of Microbiology, ETH Zürich, Zürich, Switzerland
| | - Jessica Marcandalli
- Institute for Research in Biomedicine, Università della Svizzera italiana, faculty of Biomedical Sciences, Bellinzona, Switzerland
| | - Davide Demurtas
- BioEM Facility, School of Life Sciences, Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland
| | - Federica Sallusto
- Institute for Research in Biomedicine, Università della Svizzera italiana, faculty of Biomedical Sciences, Bellinzona, Switzerland.,Institute of Microbiology, ETH Zürich, Zürich, Switzerland
| | - Laurent Perez
- Institute for Research in Biomedicine, Università della Svizzera italiana, faculty of Biomedical Sciences, Bellinzona, Switzerland.,University of Lausanne (UNIL), Lausanne University Hospital (CHUV), Department of Medicine, Division of Immunology and Allergy, Center for Human Immunology (CHIL), Lausanne, Switzerland
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388
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Zou J, Yin J, Fang L, Yang M, Wang T, Wu W, Bellucci MA, Zhang P. Computational Prediction of Mutational Effects on SARS-CoV-2 Binding by Relative Free Energy Calculations. J Chem Inf Model 2020; 60:5794-5802. [PMID: 32786709 PMCID: PMC7460864 DOI: 10.1021/acs.jcim.0c00679] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Indexed: 11/29/2022]
Abstract
The ability of coronaviruses to infect humans is invariably associated with their binding strengths to human receptor proteins. Both SARS-CoV-2, initially named 2019-nCoV, and SARS-CoV were reported to utilize angiotensin-converting enzyme 2 (ACE2) as an entry receptor in human cells. To better understand the interplay between SARS-CoV-2 and ACE2, we performed computational alanine scanning mutagenesis on the "hotspot" residues at protein-protein interfaces using relative free energy calculations. Our data suggest that the mutations in SARS-CoV-2 lead to a greater binding affinity relative to SARS-CoV. In addition, our free energy calculations provide insight into the infectious ability of viruses on a physical basis and also provide useful information for the design of antiviral drugs.
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Affiliation(s)
- Junjie Zou
- Shenzhen Jingtai
Technology Co., Ltd. (XtalPi), 4F, No. 9 Hualian
Industrial Zone, Dalang Street, Longhua District, Shenzhen,
China, 518000
| | - Jian Yin
- Shenzhen Jingtai
Technology Co., Ltd. (XtalPi), 4F, No. 9 Hualian
Industrial Zone, Dalang Street, Longhua District, Shenzhen,
China, 518000
| | - Lei Fang
- Shenzhen Jingtai
Technology Co., Ltd. (XtalPi), 4F, No. 9 Hualian
Industrial Zone, Dalang Street, Longhua District, Shenzhen,
China, 518000
| | - Mingjun Yang
- Shenzhen Jingtai
Technology Co., Ltd. (XtalPi), 4F, No. 9 Hualian
Industrial Zone, Dalang Street, Longhua District, Shenzhen,
China, 518000
| | - Tianyuan Wang
- XtalPi−AI Research
Center (XARC), 9F, Tower A, Dongsheng Building,
No.8, Zhongguancun East Road, Haidian District, Beijing,
China, 100083
| | - Weikun Wu
- XtalPi−AI Research
Center (XARC), 9F, Tower A, Dongsheng Building,
No.8, Zhongguancun East Road, Haidian District, Beijing,
China, 100083
| | - Michael A. Bellucci
- XtalPi, 245
Main Street, 11th Floor, Cambridge, Massachusetts 02142,
United States
| | - Peiyu Zhang
- Shenzhen Jingtai
Technology Co., Ltd. (XtalPi), 4F, No. 9 Hualian
Industrial Zone, Dalang Street, Longhua District, Shenzhen,
China, 518000
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389
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Seffernick JT, Lindert S. Hybrid methods for combined experimental and computational determination of protein structure. J Chem Phys 2020; 153:240901. [PMID: 33380110 PMCID: PMC7773420 DOI: 10.1063/5.0026025] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Accepted: 11/10/2020] [Indexed: 02/04/2023] Open
Abstract
Knowledge of protein structure is paramount to the understanding of biological function, developing new therapeutics, and making detailed mechanistic hypotheses. Therefore, methods to accurately elucidate three-dimensional structures of proteins are in high demand. While there are a few experimental techniques that can routinely provide high-resolution structures, such as x-ray crystallography, nuclear magnetic resonance (NMR), and cryo-EM, which have been developed to determine the structures of proteins, these techniques each have shortcomings and thus cannot be used in all cases. However, additionally, a large number of experimental techniques that provide some structural information, but not enough to assign atomic positions with high certainty have been developed. These methods offer sparse experimental data, which can also be noisy and inaccurate in some instances. In cases where it is not possible to determine the structure of a protein experimentally, computational structure prediction methods can be used as an alternative. Although computational methods can be performed without any experimental data in a large number of studies, inclusion of sparse experimental data into these prediction methods has yielded significant improvement. In this Perspective, we cover many of the successes of integrative modeling, computational modeling with experimental data, specifically for protein folding, protein-protein docking, and molecular dynamics simulations. We describe methods that incorporate sparse data from cryo-EM, NMR, mass spectrometry, electron paramagnetic resonance, small-angle x-ray scattering, Förster resonance energy transfer, and genetic sequence covariation. Finally, we highlight some of the major challenges in the field as well as possible future directions.
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Affiliation(s)
- Justin T. Seffernick
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, Ohio 43210, USA
| | - Steffen Lindert
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, Ohio 43210, USA
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390
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Heiliger J, Matzel T, Çetiner EC, Schwalbe H, Kuenze G, Corzilius B. Site-specific dynamic nuclear polarization in a Gd(III)-labeled protein. Phys Chem Chem Phys 2020; 22:25455-25466. [PMID: 33103678 DOI: 10.1039/d0cp05021k] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Dynamic nuclear polarization (DNP) of a biomolecule tagged with a polarizing agent has the potential to not only increase NMR sensitivity but also to provide specificity towards the tagging site. Although the general concept has been often discussed, the observation of true site-specific DNP and its dependence on the electron-nuclear distance has been elusive. Here, we demonstrate site-specific DNP in a uniformly isotope-labeled ubiquitin. By recombinant expression of three different ubiquitin point mutants (F4C, A28C, and G75C) post-translationally modified with a Gd3+-chelator tag, localized metal-ion DNP of 13C and 15N is investigated. Effects counteracting the site-specificity of DNP such as nuclear spin-lattice relaxation and proton-driven spin diffusion have been attenuated by perdeuteration of the protein. Particularly for 15N, large DNP enhancement factors on the order of 100 and above as well as localized effects within side-chain resonances differently distributed over the protein are observed. By analyzing the experimental DNP built-up dynamics combined with structural modeling of Gd3+-tags in ubiquitin supported by paramagnetic relaxation enhancement (PRE) in solution, we provide, for the first time, quantitative information on the distance dependence of the initial DNP transfer. We show that the direct 15N DNP transfer rate indeed linearly depends on the square of the hyperfine interaction between the electron and the nucleus following Fermi's golden rule, however, below a certain distance cutoff paramagnetic signal bleaching may dramatically skew the correlation.
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Affiliation(s)
- Jörg Heiliger
- Institute of Physical and Theoretical Chemistry, Goethe University Frankfurt, Max-von-Laue-Str. 7, 60438 Frankfurt am Main, Germany
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391
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Heckmann CM, Paradisi F. Looking Back: A Short History of the Discovery of Enzymes and How They Became Powerful Chemical Tools. ChemCatChem 2020; 12:6082-6102. [PMID: 33381242 PMCID: PMC7756376 DOI: 10.1002/cctc.202001107] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Revised: 09/02/2020] [Indexed: 12/20/2022]
Abstract
Enzymatic approaches to challenges in chemical synthesis are increasingly popular and very attractive to industry given their green nature and high efficiency compared to traditional methods. In this historical review we highlight the developments across several fields that were necessary to create the modern field of biocatalysis, with enzyme engineering and directed evolution at its core. We exemplify the modular, incremental, and highly unpredictable nature of scientific discovery, driven by curiosity, and showcase the resulting examples of cutting-edge enzymatic applications in industry.
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Affiliation(s)
- Christian M Heckmann
- School of Chemistry University of Nottingham University Park Nottingham NG7 2RD UK
| | - Francesca Paradisi
- School of Chemistry University of Nottingham University Park Nottingham NG7 2RD UK
- Department of Chemistry and Biochemistry University of Bern Freiestrasse 3 3012 Bern Switzerland
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392
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Adolf-Bryfogle J, Teets FD, Bahl CD. Toward complete rational control over protein structure and function through computational design. Curr Opin Struct Biol 2020; 66:170-177. [PMID: 33276237 DOI: 10.1016/j.sbi.2020.10.015] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 10/08/2020] [Accepted: 10/19/2020] [Indexed: 11/28/2022]
Abstract
The grand challenge of protein design is a general method for producing a polypeptide with arbitrary functionality, conformation, and biochemical properties. To that end, a wide variety of methods have been developed for the improvement of native proteins, the design of ideal proteins de novo, and the redesign of suboptimal proteins with better-performing substructures. These methods employ informatic comparisons of function-structure-sequence relationships as well as knowledge-based evaluation of protein properties to narrow the immense protein sequence search space down to an enumerable and often manually evaluable set of structures that meet specified criteria. While arbitrary manipulation of protein-protein interfaces and molecular catalysis remains an unsolved problem, and no protein shape or behavior manipulation algorithm is universally applicable, the promising results thus far are a strong indicator that a general approach to the arbitrary manipulation of polypeptides is within reach.
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Affiliation(s)
- Jared Adolf-Bryfogle
- Institute for Protein Innovation, Boston, MA 02115, USA; Division of Hematology/Oncology, Boston Children's Hospital, Boston, MA, USA; Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Frank D Teets
- Institute for Protein Innovation, Boston, MA 02115, USA; Division of Hematology/Oncology, Boston Children's Hospital, Boston, MA, USA; Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Christopher D Bahl
- Institute for Protein Innovation, Boston, MA 02115, USA; Division of Hematology/Oncology, Boston Children's Hospital, Boston, MA, USA; Department of Pediatrics, Harvard Medical School, Boston, MA, USA.
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393
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Rapid and accurate determination of atomistic RNA dynamic ensemble models using NMR and structure prediction. Nat Commun 2020; 11:5531. [PMID: 33139729 PMCID: PMC7608651 DOI: 10.1038/s41467-020-19371-y] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2020] [Accepted: 10/07/2020] [Indexed: 11/08/2022] Open
Abstract
Biomolecules form dynamic ensembles of many inter-converting conformations which are key for understanding how they fold and function. However, determining ensembles is challenging because the information required to specify atomic structures for thousands of conformations far exceeds that of experimental measurements. We addressed this data gap and dramatically simplified and accelerated RNA ensemble determination by using structure prediction tools that leverage the growing database of RNA structures to generate a conformation library. Refinement of this library with NMR residual dipolar couplings provided an atomistic ensemble model for HIV-1 TAR, and the model accuracy was independently supported by comparisons to quantum-mechanical calculations of NMR chemical shifts, comparison to a crystal structure of a substate, and through designed ensemble redistribution via atomic mutagenesis. Applications to TAR bulge variants and more complex tertiary RNAs support the generality of this approach and the potential to make the determination of atomic-resolution RNA ensembles routine. Determining dynamic ensembles of biomolecules is still challenging. Here the authors present an approach for rapid RNA ensemble determination that combines RNA structure prediction tools and NMR residual dipolar coupling data and use it to determine atomistic ensemble models for a variety of RNAs.
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394
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Yeh CT, Obendorf L, Parmeggiani F. Elfin UI: A Graphical Interface for Protein Design With Modular Building Blocks. Front Bioeng Biotechnol 2020; 8:568318. [PMID: 33195130 PMCID: PMC7644802 DOI: 10.3389/fbioe.2020.568318] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Accepted: 10/02/2020] [Indexed: 02/01/2023] Open
Abstract
Molecular models have enabled understanding of biological structures and functions and allowed design of novel macro-molecules. Graphical user interfaces (GUIs) in molecular modeling are generally focused on atomic representations, but, especially for proteins, do not usually address designs of complex and large architectures, from nanometers to microns. Therefore, we have developed Elfin UI as a Blender add-on for the interactive design of large protein architectures with custom shapes. Elfin UI relies on compatible building blocks to design single- and multiple-chain protein structures. The software can be used: (1) as an interactive environment to explore building blocks combinations; and (2) as a computer aided design (CAD) tool to define target shapes that guide automated design. Elfin UI allows users to rapidly build new protein shapes, without the need to focus on amino acid sequence, and aims to make design of proteins and protein-based materials intuitive and accessible to researchers and members of the general public with limited expertise in protein engineering.
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Affiliation(s)
- Chun-Ting Yeh
- School of Chemistry and School of Biochemistry, University of Bristol, Bristol, United Kingdom
| | - Leon Obendorf
- School of Chemistry and School of Biochemistry, University of Bristol, Bristol, United Kingdom.,Institute of Chemistry and Biochemistry, Freie Universität Berlin, Berlin, Germany
| | - Fabio Parmeggiani
- School of Chemistry and School of Biochemistry, University of Bristol, Bristol, United Kingdom.,Bristol Biodesign Institute and BrisSynBio, a BBSRC/EPSRC Synthetic Biology Research Centre, University of Bristol, Bristol, United Kingdom
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395
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Phongsavanh X, Al-Qatabi N, Shaban MS, Khoder-Agha F, El Asri M, Comisso M, Guérois R, Mirande M. How HIV-1 Integrase Associates with Human Mitochondrial Lysyl-tRNA Synthetase. Viruses 2020; 12:v12101202. [PMID: 33096929 PMCID: PMC7589778 DOI: 10.3390/v12101202] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 10/14/2020] [Accepted: 10/20/2020] [Indexed: 01/13/2023] Open
Abstract
Replication of human immunodeficiency virus type 1 (HIV-1) requires the packaging of tRNALys,3 from the host cell into the new viral particles. The GagPol viral polyprotein precursor associates with mitochondrial lysyl-tRNA synthetase (mLysRS) in a complex with tRNALys, an essential step to initiate reverse transcription in the virions. The C-terminal integrase moiety of GagPol is essential for its association with mLysRS. We show that integrases from HIV-1 and HIV-2 bind mLysRS with the same efficiency. In this work, we have undertaken to probe the three-dimensional (3D) architecture of the complex of integrase with mLysRS. We first established that the C-terminal domain (CTD) of integrase is the major interacting domain with mLysRS. Using the pBpa-photo crosslinking approach, inter-protein cross-links were observed involving amino acid residues located at the surface of the catalytic domain of mLysRS and of the CTD of integrase. In parallel, using molecular docking simulation, a single structural model of complex was found to outscore other alternative conformations. Consistent with crosslinking experiments, this structural model was further probed experimentally. Five compensatory mutations in the two partners were successfully designed which supports the validity of the model. The complex highlights that binding of integrase could stabilize the tRNALys:mLysRS interaction.
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396
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Scherbinina SI, Toukach PV. Three-Dimensional Structures of Carbohydrates and Where to Find Them. Int J Mol Sci 2020; 21:E7702. [PMID: 33081008 PMCID: PMC7593929 DOI: 10.3390/ijms21207702] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Revised: 10/15/2020] [Accepted: 10/16/2020] [Indexed: 02/06/2023] Open
Abstract
Analysis and systematization of accumulated data on carbohydrate structural diversity is a subject of great interest for structural glycobiology. Despite being a challenging task, development of computational methods for efficient treatment and management of spatial (3D) structural features of carbohydrates breaks new ground in modern glycoscience. This review is dedicated to approaches of chemo- and glyco-informatics towards 3D structural data generation, deposition and processing in regard to carbohydrates and their derivatives. Databases, molecular modeling and experimental data validation services, and structure visualization facilities developed for last five years are reviewed.
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Affiliation(s)
- Sofya I. Scherbinina
- N.D. Zelinsky Institute of Organic Chemistry, Russian Academy of Science, Leninsky prospect 47, 119991 Moscow, Russia
- Higher Chemical College, D. Mendeleev University of Chemical Technology of Russia, Miusskaya Square 9, 125047 Moscow, Russia
| | - Philip V. Toukach
- N.D. Zelinsky Institute of Organic Chemistry, Russian Academy of Science, Leninsky prospect 47, 119991 Moscow, Russia
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397
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398
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Basanta B, Bick MJ, Bera AK, Norn C, Chow CM, Carter LP, Goreshnik I, Dimaio F, Baker D. An enumerative algorithm for de novo design of proteins with diverse pocket structures. Proc Natl Acad Sci U S A 2020; 117:22135-22145. [PMID: 32839327 PMCID: PMC7486743 DOI: 10.1073/pnas.2005412117] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
To create new enzymes and biosensors from scratch, precise control over the structure of small-molecule binding sites is of paramount importance, but systematically designing arbitrary protein pocket shapes and sizes remains an outstanding challenge. Using the NTF2-like structural superfamily as a model system, we developed an enumerative algorithm for creating a virtually unlimited number of de novo proteins supporting diverse pocket structures. The enumerative algorithm was tested and refined through feedback from two rounds of large-scale experimental testing, involving in total the assembly of synthetic genes encoding 7,896 designs and assessment of their stability on yeast cell surface, detailed biophysical characterization of 64 designs, and crystal structures of 5 designs. The refined algorithm generates proteins that remain folded at high temperatures and exhibit more pocket diversity than naturally occurring NTF2-like proteins. We expect this approach to transform the design of small-molecule sensors and enzymes by enabling the creation of binding and active site geometries much more optimal for specific design challenges than is accessible by repurposing the limited number of naturally occurring NTF2-like proteins.
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Affiliation(s)
- Benjamin Basanta
- Institute for Protein Design, University of Washington, Seattle, WA 98195
- Biochemistry Department, School of Medicine, University of Washington, Seattle, WA 98195
| | - Matthew J Bick
- Institute for Protein Design, University of Washington, Seattle, WA 98195
- Biochemistry Department, School of Medicine, University of Washington, Seattle, WA 98195
| | - Asim K Bera
- Institute for Protein Design, University of Washington, Seattle, WA 98195
- Biochemistry Department, School of Medicine, University of Washington, Seattle, WA 98195
| | - Christoffer Norn
- Institute for Protein Design, University of Washington, Seattle, WA 98195
- Biochemistry Department, School of Medicine, University of Washington, Seattle, WA 98195
| | - Cameron M Chow
- Institute for Protein Design, University of Washington, Seattle, WA 98195
- Biochemistry Department, School of Medicine, University of Washington, Seattle, WA 98195
| | - Lauren P Carter
- Institute for Protein Design, University of Washington, Seattle, WA 98195
- Biochemistry Department, School of Medicine, University of Washington, Seattle, WA 98195
| | - Inna Goreshnik
- Institute for Protein Design, University of Washington, Seattle, WA 98195
- Biochemistry Department, School of Medicine, University of Washington, Seattle, WA 98195
| | - Frank Dimaio
- Institute for Protein Design, University of Washington, Seattle, WA 98195
- Biochemistry Department, School of Medicine, University of Washington, Seattle, WA 98195
| | - David Baker
- Institute for Protein Design, University of Washington, Seattle, WA 98195;
- Biochemistry Department, School of Medicine, University of Washington, Seattle, WA 98195
- Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195
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399
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Teajaroen W, Phimwapi S, Daduang J, Klaynongsruang S, Tipmanee V, Daduang S. A Role of Newly Found Auxiliary Site in Phospholipase A 1 from Thai Banded Tiger Wasp ( Vespa affinis) in Its Enzymatic Enhancement: In Silico Homology Modeling and Molecular Dynamics Insights. Toxins (Basel) 2020; 12:toxins12080510. [PMID: 32784438 PMCID: PMC7472737 DOI: 10.3390/toxins12080510] [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: 06/30/2020] [Revised: 08/06/2020] [Accepted: 08/07/2020] [Indexed: 12/18/2022] Open
Abstract
Phospholipase A1 from Thai banded tiger wasp (Vespa affinis) venom also known as Ves a 1 plays an essential role in fatal vespid allergy. Ves a 1 becomes an important therapeutic target for toxin remedy. However, established Ves a 1 structure or a mechanism of Ves a 1 function were not well documented. This circumstance has prevented efficient design of a potential phospholipase A1 inhibitor. In our study, we successfully recruited homology modeling and molecular dynamic (MD) simulation to model Ves a 1 three-dimensional structure. The Ves a 1 structure along with dynamic behaviors were visualized and explained. In addition, we performed molecular docking of Ves a 1 with 1,2-Dimyristoyl-sn-glycero-3-phosphorylcholine (DMPC) lipid to assess a possible lipid binding site. Interestingly, molecular docking predicted another lipid binding region apart from its corresponding catalytic site, suggesting an auxiliary role of the alternative site at the Ves a 1 surface. The new molecular mechanism related to the surface lipid binding site (auxiliary site) provided better understanding of how phospholipase A1 structure facilitates its enzymatic function. This auxiliary site, conserved among Hymenoptera species as well as some mammalian lipases, could be a guide for interaction-based design of a novel phospholipase A1 inhibitor.
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Affiliation(s)
- Withan Teajaroen
- Biomedical Sciences Program, Graduate School of Khon Kaen University, Khon Kaen 40002, Thailand;
| | | | - Jureerut Daduang
- Centre for Research and Development of Medical Diagnostic Laboratories, Faculty of Associated Medical Sciences, Khon Kaen University, Khon Kaen 40002, Thailand;
| | - Sompong Klaynongsruang
- Protein and Proteomics Research Center for Commercial and Industrial Purposes (ProCCI), Khon Kaen 40002, Thailand;
| | - Varomyalin Tipmanee
- EZ-Mol-Design Laboratory and Department of Biomedical Sciences, Faculty of Medicine, Prince of Songkla University, Songkhla 90110, Thailand
- Correspondence: (V.T.); (S.D.); Tel.: +66-74-45-1180 (V.T.); +66-43-34-2911 (S.D.)
| | - Sakda Daduang
- Division of Pharmacognosy and Toxicology, Faculty of Pharmaceutical Sciences, Khon Kaen University, Khon Kaen 40002, Thailand
- Correspondence: (V.T.); (S.D.); Tel.: +66-74-45-1180 (V.T.); +66-43-34-2911 (S.D.)
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