1
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Wang T, Jin X, Lu X, Min X, Ge S, Li S. Empirical validation of ProteinMPNN's efficiency in enhancing protein fitness. Front Genet 2024; 14:1347667. [PMID: 38274106 PMCID: PMC10808456 DOI: 10.3389/fgene.2023.1347667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 12/20/2023] [Indexed: 01/27/2024] Open
Abstract
Introduction: Protein engineering, which aims to improve the properties and functions of proteins, holds great research significance and application value. However, current models that predict the effects of amino acid substitutions often perform poorly when evaluated for precision. Recent research has shown that ProteinMPNN, a large-scale pre-training sequence design model based on protein structure, performs exceptionally well. It is capable of designing mutants with structures similar to the original protein. When applied to the field of protein engineering, the diverse designs for mutation positions generated by this model can be viewed as a more precise mutation range. Methods: We collected three biological experimental datasets and compared the design results of ProteinMPNN for wild-type proteins with the experimental datasets to verify the ability of ProteinMPNN in improving protein fitness. Results: The validation on biological experimental datasets shows that ProteinMPNN has the ability to design mutation types with higher fitness in single and multi-point mutations. We have verified the high accuracy of ProteinMPNN in protein engineering tasks from both positive and negative perspectives. Discussion: Our research indicates that using large-scale pre trained models to design protein mutants provides a new approach for protein engineering, providing strong support for guiding biological experiments and applications in biotechnology.
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Affiliation(s)
- Tianshu Wang
- School of Informatics, Institute of Artificial Intelligence, Xiamen University, Xiamen, China
- State Key Laboratory of Vaccines for Infectious Diseases, Xiamen University, Xiamen, China
- National Institute of Diagnostics and Vaccine Development in Infectious Diseases, Xiamen University, Xiamen, China
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Xiamen University, Xiamen, China
| | - Xiaocheng Jin
- State Key Laboratory of Vaccines for Infectious Diseases, Xiamen University, Xiamen, China
- National Institute of Diagnostics and Vaccine Development in Infectious Diseases, Xiamen University, Xiamen, China
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Xiamen University, Xiamen, China
- School of Public Health, Xiamen University, Xiamen, China
| | - Xiaoli Lu
- Information and Networking Center, Xiamen University, Xiamen, China
| | - Xiaoping Min
- School of Informatics, Institute of Artificial Intelligence, Xiamen University, Xiamen, China
- State Key Laboratory of Vaccines for Infectious Diseases, Xiamen University, Xiamen, China
- National Institute of Diagnostics and Vaccine Development in Infectious Diseases, Xiamen University, Xiamen, China
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Xiamen University, Xiamen, China
| | - Shengxiang Ge
- State Key Laboratory of Vaccines for Infectious Diseases, Xiamen University, Xiamen, China
- National Institute of Diagnostics and Vaccine Development in Infectious Diseases, Xiamen University, Xiamen, China
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Xiamen University, Xiamen, China
- School of Public Health, Xiamen University, Xiamen, China
| | - Shaowei Li
- State Key Laboratory of Vaccines for Infectious Diseases, Xiamen University, Xiamen, China
- National Institute of Diagnostics and Vaccine Development in Infectious Diseases, Xiamen University, Xiamen, China
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Xiamen University, Xiamen, China
- School of Public Health, Xiamen University, Xiamen, China
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2
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Rothfuss MT, Becht DC, Zeng B, McClelland LJ, Yates-Hansen C, Bowler BE. High-Accuracy Prediction of Stabilizing Surface Mutations to the Three-Helix Bundle, UBA(1), with EmCAST. J Am Chem Soc 2023; 145:22979-22992. [PMID: 37815921 PMCID: PMC10626973 DOI: 10.1021/jacs.3c04966] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/12/2023]
Abstract
The accurate modeling of energetic contributions to protein structure is a fundamental challenge in computational approaches to protein analysis and design. We describe a general computational method, EmCAST (empirical Cα stabilization), to score and optimize the sequence to the structure in proteins. The method relies on an empirical potential derived from the database of the Cα dihedral angle preferences for all possible four-residue sequences, using the data available in the Protein Data Bank. Our method produces stability predictions that naturally correlate one-to-one with the experimental results for solvent-exposed mutation sites. EmCAST predicted four mutations that increased the stability of a three-helix bundle, UBA(1), from 2.4 to 4.8 kcal/mol by optimizing residues in both helices and turns. For a set of eight variants, the predicted and experimental stabilizations correlate very well (R2 = 0.97) with a slope near 1 and with a 0.16 kcal/mol standard error for EmCAST predictions. Tests against literature data for the stability effects of surface-exposed mutations show that EmCAST outperforms the existing stability prediction methods. UBA(1) variants were crystallized to verify and analyze their structures at an atomic resolution. Thermodynamic and kinetic folding experiments were performed to determine the magnitude and mechanism of stabilization. Our method has the potential to enable the rapid, rational optimization of natural proteins, expand the analysis of the sequence/structure relationship, and supplement the existing protein design strategies.
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Affiliation(s)
- Michael T. Rothfuss
- Department of Chemistry and Biochemistry, University of Montana, Missoula, MT 59812, United States
| | - Dustin C. Becht
- Department of Chemistry and Biochemistry, University of Montana, Missoula, MT 59812, United States
| | - Baisen Zeng
- Center for Biomolecular Structure and Dynamics, University of Montana, Missoula, MT 59812, United States
| | - Levi J. McClelland
- Center for Biomolecular Structure and Dynamics, University of Montana, Missoula, MT 59812, United States
- Division of Biological Sciences, University of Montana, Missoula, MT 59812, United States
| | - Cindee Yates-Hansen
- Center for Biomolecular Structure and Dynamics, University of Montana, Missoula, MT 59812, United States
| | - Bruce E. Bowler
- Department of Chemistry and Biochemistry, University of Montana, Missoula, MT 59812, United States
- Center for Biomolecular Structure and Dynamics, University of Montana, Missoula, MT 59812, United States
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3
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Kunka A, Marques SM, Havlasek M, Vasina M, Velatova N, Cengelova L, Kovar D, Damborsky J, Marek M, Bednar D, Prokop Z. Advancing Enzyme's Stability and Catalytic Efficiency through Synergy of Force-Field Calculations, Evolutionary Analysis, and Machine Learning. ACS Catal 2023; 13:12506-12518. [PMID: 37822856 PMCID: PMC10563018 DOI: 10.1021/acscatal.3c02575] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 08/24/2023] [Indexed: 10/13/2023]
Abstract
Thermostability is an essential requirement for the use of enzymes in the bioindustry. Here, we compare different protein stabilization strategies using a challenging target, a stable haloalkane dehalogenase DhaA115. We observe better performance of automated stabilization platforms FireProt and PROSS in designing multiple-point mutations over the introduction of disulfide bonds and strengthening the intra- and the inter-domain contacts by in silico saturation mutagenesis. We reveal that the performance of automated stabilization platforms was still compromised due to the introduction of some destabilizing mutations. Notably, we show that their prediction accuracy can be improved by applying manual curation or machine learning for the removal of potentially destabilizing mutations, yielding highly stable haloalkane dehalogenases with enhanced catalytic properties. A comparison of crystallographic structures revealed that current stabilization rounds were not accompanied by large backbone re-arrangements previously observed during the engineering stability of DhaA115. Stabilization was achieved by improving local contacts including protein-water interactions. Our study provides guidance for further improvement of automated structure-based computational tools for protein stabilization.
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Affiliation(s)
- Antonin Kunka
- Loschmidt
Laboratories, Department of Experimental Biology and RECETOX, Faculty
of Science, Masaryk University, Brno 601 77, Czech Republic
- International
Clinical Research Center, St. Anne’s University Hospital, Brno 601 77, Czech Republic
| | - Sérgio M. Marques
- Loschmidt
Laboratories, Department of Experimental Biology and RECETOX, Faculty
of Science, Masaryk University, Brno 601 77, Czech Republic
- International
Clinical Research Center, St. Anne’s University Hospital, Brno 601 77, Czech Republic
| | - Martin Havlasek
- Loschmidt
Laboratories, Department of Experimental Biology and RECETOX, Faculty
of Science, Masaryk University, Brno 601 77, Czech Republic
| | - Michal Vasina
- Loschmidt
Laboratories, Department of Experimental Biology and RECETOX, Faculty
of Science, Masaryk University, Brno 601 77, Czech Republic
- International
Clinical Research Center, St. Anne’s University Hospital, Brno 601 77, Czech Republic
| | - Nikola Velatova
- Loschmidt
Laboratories, Department of Experimental Biology and RECETOX, Faculty
of Science, Masaryk University, Brno 601 77, Czech Republic
| | - Lucia Cengelova
- Loschmidt
Laboratories, Department of Experimental Biology and RECETOX, Faculty
of Science, Masaryk University, Brno 601 77, Czech Republic
| | - David Kovar
- Loschmidt
Laboratories, Department of Experimental Biology and RECETOX, Faculty
of Science, Masaryk University, Brno 601 77, Czech Republic
- International
Clinical Research Center, St. Anne’s University Hospital, Brno 601 77, Czech Republic
| | - Jiri Damborsky
- Loschmidt
Laboratories, Department of Experimental Biology and RECETOX, Faculty
of Science, Masaryk University, Brno 601 77, Czech Republic
- International
Clinical Research Center, St. Anne’s University Hospital, Brno 601 77, Czech Republic
| | - Martin Marek
- Loschmidt
Laboratories, Department of Experimental Biology and RECETOX, Faculty
of Science, Masaryk University, Brno 601 77, Czech Republic
- International
Clinical Research Center, St. Anne’s University Hospital, Brno 601 77, Czech Republic
| | - David Bednar
- Loschmidt
Laboratories, Department of Experimental Biology and RECETOX, Faculty
of Science, Masaryk University, Brno 601 77, Czech Republic
- International
Clinical Research Center, St. Anne’s University Hospital, Brno 601 77, Czech Republic
| | - Zbynek Prokop
- Loschmidt
Laboratories, Department of Experimental Biology and RECETOX, Faculty
of Science, Masaryk University, Brno 601 77, Czech Republic
- International
Clinical Research Center, St. Anne’s University Hospital, Brno 601 77, Czech Republic
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4
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Li W, Bano F, Khan A, Wei DQ, Alshammari A, Xu B, Wang Y. Inhibition of cMYC-MAX transcription factors hetero-dimerization with structurally engineered omoMYC to downregulate oncogenic pathways in renal carcinoma. Comput Biol Med 2023; 164:107257. [PMID: 37527610 DOI: 10.1016/j.compbiomed.2023.107257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 06/27/2023] [Accepted: 07/08/2023] [Indexed: 08/03/2023]
Abstract
In the current study, we employed, structural informatics, and molecular simulation-based methods to engineer OmoMyc, a c-Myc dominant negative protein, to design novel mutants that could abrogate the c-MYC-MAX complex in Renal Carcinoma (RC). Among the total 472 mutations, only six mutations A61Q, Q64E, Q64K, N77R, Q64E-N77R, and Q64K-N77R were reported to increase the binding affinity and subjected to subsequent analysis such as protein-protein docking. The docking results revealed that the predicted mutants improve the functionality of the OmoMyc by not only increasing the binding affinity but also vdW and electrostatic energy in each complex that consequently increase the binding of the engineered OmoMyc by establishing extra hydrogen bonds, salt-bridges, and non-bonded contacts. Molecular simulation revealed a more stable behavior by the mutant complexes in contrast to the native OmoMyc however structural perturbations were reported in the regions, DBD (DNA-binding domain), loop region, and minor deviations at CTD (C terminal domain). Moreover, the hydrogen bonding and binding free energy results further validated the promising activity of our predicted mutants of OmoMyc. The results for TBE (total binding energy) revealed that the for each complex the TBE was calculated to be -87.88 ± 0.16 kcal/mol (WT OmoMyc-MAX), -91.89 ± 0.21 kcal/mol (A61Q OmoMyc-MAX), -91.55 ± 0.20 kcal/mol (Q64E OmoMyc-MAX), -95.17 ± 0.24 kcal/mol (Q64K OmoMyc-MAX), -96.49 ± 0.22 kcal/mol (N77R OmoMyc-MAX), -97.76 ± 0.22 kcal/mol (Q64E-N77R OmoMyc-MAX), and -95.31 ± 0.20 kcal/mol (Q64K-N77R OmoMyc-MAX) respectively. The results for TBE revealed promising results that allow the mutants to competitively inhibit the c-Myc-MAX complex more swiftly. Additionally, the internal motion and energy landscape were altered. These findings provide important insights into the potential of the mutants of OmoMyc as a therapeutic candidate for cancer treatment, particularly renal carcinoma, and could pave the way for the development of more effective clinical versions of OmoMyc.
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Affiliation(s)
- Wenfeng Li
- Department of Urology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Farah Bano
- Nawaz Sharif Medical College, Gujrat, Punjab, Pakistan.
| | - Abbas Khan
- Engineering Research Center of Cell & Therapeutic Antibody, School of Pharmacy, Shanghai Jiao Tong University, Shanghai, 200240, China; Department of Bioinformatics and Biological Statistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, PR China
| | - Dong-Qing Wei
- Department of Bioinformatics and Biological Statistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, PR China; Peng Cheng National Laboratory, Shenzhen, Guangdong, 518055, China
| | - Abdulrahman Alshammari
- Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, Riyadh, 11451, Saudi Arabia.
| | - Bin Xu
- Department of Urology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Yanjing Wang
- Engineering Research Center of Cell & Therapeutic Antibody, School of Pharmacy, Shanghai Jiao Tong University, Shanghai, 200240, China.
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Tsuboyama K, Dauparas J, Chen J, Laine E, Mohseni Behbahani Y, Weinstein JJ, Mangan NM, Ovchinnikov S, Rocklin GJ. Mega-scale experimental analysis of protein folding stability in biology and design. Nature 2023; 620:434-444. [PMID: 37468638 PMCID: PMC10412457 DOI: 10.1038/s41586-023-06328-6] [Citation(s) in RCA: 43] [Impact Index Per Article: 43.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 06/14/2023] [Indexed: 07/21/2023]
Abstract
Advances in DNA sequencing and machine learning are providing insights into protein sequences and structures on an enormous scale1. However, the energetics driving folding are invisible in these structures and remain largely unknown2. The hidden thermodynamics of folding can drive disease3,4, shape protein evolution5-7 and guide protein engineering8-10, and new approaches are needed to reveal these thermodynamics for every sequence and structure. Here we present cDNA display proteolysis, a method for measuring thermodynamic folding stability for up to 900,000 protein domains in a one-week experiment. From 1.8 million measurements in total, we curated a set of around 776,000 high-quality folding stabilities covering all single amino acid variants and selected double mutants of 331 natural and 148 de novo designed protein domains 40-72 amino acids in length. Using this extensive dataset, we quantified (1) environmental factors influencing amino acid fitness, (2) thermodynamic couplings (including unexpected interactions) between protein sites, and (3) the global divergence between evolutionary amino acid usage and protein folding stability. We also examined how our approach could identify stability determinants in designed proteins and evaluate design methods. The cDNA display proteolysis method is fast, accurate and uniquely scalable, and promises to reveal the quantitative rules for how amino acid sequences encode folding stability.
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Affiliation(s)
- Kotaro Tsuboyama
- Department of Pharmacology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Center for Synthetic Biology, Northwestern University, Evanston, IL, USA
- PRESTO, Japan Science and Technology Agency, Tokyo, Japan
- Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
| | - Justas Dauparas
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Jonathan Chen
- Department of Pharmacology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Center for Synthetic Biology, Northwestern University, Evanston, IL, USA
- McCormick School of Engineering, Northwestern University, Evanston, IL, USA
| | - Elodie Laine
- Sorbonne Université, CNRS, IBPS, Laboratory of Computational and Quantitative Biology (LCQB), UMR 7238, Paris, France
| | - Yasser Mohseni Behbahani
- Sorbonne Université, CNRS, IBPS, Laboratory of Computational and Quantitative Biology (LCQB), UMR 7238, Paris, France
| | - Jonathan J Weinstein
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Niall M Mangan
- Center for Synthetic Biology, Northwestern University, Evanston, IL, USA
- Department of Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, IL, USA
| | - Sergey Ovchinnikov
- John Harvard Distinguished Science Fellowship Program, Harvard University, Cambridge, MA, USA
| | - Gabriel J Rocklin
- Department of Pharmacology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
- Center for Synthetic Biology, Northwestern University, Evanston, IL, USA.
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6
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Ferreira-Martins AJ, Castaldoni R, Alencar BM, Ferreira MV, Nogueira T, Rios RA, Lopes TJS. Full-scale network analysis reveals properties of the FV protein structure organization. Sci Rep 2023; 13:9546. [PMID: 37308572 DOI: 10.1038/s41598-023-36528-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 06/05/2023] [Indexed: 06/14/2023] Open
Abstract
Blood coagulation is a vital process for humans and other species. Following an injury to a blood vessel, a cascade of molecular signals is transmitted, inhibiting and activating more than a dozen coagulation factors and resulting in the formation of a fibrin clot that ceases the bleeding. In this process, the Coagulation factor V (FV) is a master regulator, coordinating critical steps of this process. Mutations to this factor result in spontaneous bleeding episodes and prolonged hemorrhage after trauma or surgery. Although the role of FV is well characterized, it is unclear how single-point mutations affect its structure. In this study, to understand the effect of mutations, we created a detailed network map of this protein, where each node is a residue, and two residues are connected if they are in close proximity in the three-dimensional structure. Overall, we analyzed 63 point-mutations from patients and identified common patterns underlying FV deficient phenotypes. We used structural and evolutionary patterns as input to machine learning algorithms to anticipate the effects of mutations and anticipated FV-deficiency with fair accuracy. Together, our results demonstrate how clinical features, genetic data and in silico analysis are converging to enhance treatment and diagnosis of coagulation disorders.
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Affiliation(s)
| | | | - Brenno M Alencar
- Institute of Computing, Federal University of Bahia, Salvador, Brazil
| | - Marcos V Ferreira
- Institute of Computing, Federal University of Bahia, Salvador, Brazil
| | - Tatiane Nogueira
- Institute of Computing, Federal University of Bahia, Salvador, Brazil
| | - Ricardo A Rios
- Institute of Computing, Federal University of Bahia, Salvador, Brazil
| | - Tiago J S Lopes
- Center for Regenerative Medicine, National Centre for Child Health and Development Research Institute, 2-10-1 Okura, Setagaya, Tokyo, 157-8535, Japan.
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7
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Ferreira MV, Nogueira T, Rios RA, Lopes TJS. A graph-based machine learning framework identifies critical properties of FVIII that lead to hemophilia A. FRONTIERS IN BIOINFORMATICS 2023; 3:1152039. [PMID: 37235045 PMCID: PMC10206133 DOI: 10.3389/fbinf.2023.1152039] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 04/10/2023] [Indexed: 05/28/2023] Open
Abstract
Introduction: Blood coagulation is an essential process to cease bleeding in humans and other species. This mechanism is characterized by a molecular cascade of more than a dozen components activated after an injury to a blood vessel. In this process, the coagulation factor VIII (FVIII) is a master regulator, enhancing the activity of other components by thousands of times. In this sense, it is unsurprising that even single amino acid substitutions result in hemophilia A (HA)-a disease marked by uncontrolled bleeding and that leaves patients at permanent risk of hemorrhagic complications. Methods: Despite recent advances in the diagnosis and treatment of HA, the precise role of each residue of the FVIII protein remains unclear. In this study, we developed a graph-based machine learning framework that explores in detail the network formed by the residues of the FVIII protein, where each residue is a node, and two nodes are connected if they are in close proximity on the FVIII 3D structure. Results: Using this system, we identified the properties that lead to severe and mild forms of the disease. Finally, in an effort to advance the development of novel recombinant therapeutic FVIII proteins, we adapted our framework to predict the activity and expression of more than 300 in vitro alanine mutations, once more observing a close agreement between the in silico and the in vitro results. Discussion: Together, the results derived from this study demonstrate how graph-based classifiers can leverage the diagnostic and treatment of a rare disease.
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Affiliation(s)
| | - Tatiane Nogueira
- Institute of Computing, Federal University of Bahia, Salvador, Brazil
| | - Ricardo A. Rios
- Institute of Computing, Federal University of Bahia, Salvador, Brazil
| | - Tiago J. S. Lopes
- Center for Regenerative Medicine, National Center for Child Health and Development Research Institute, Tokyo, Japan
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Vasina M, Kovar D, Damborsky J, Ding Y, Yang T, deMello A, Mazurenko S, Stavrakis S, Prokop Z. In-depth analysis of biocatalysts by microfluidics: An emerging source of data for machine learning. Biotechnol Adv 2023; 66:108171. [PMID: 37150331 DOI: 10.1016/j.biotechadv.2023.108171] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 05/04/2023] [Accepted: 05/04/2023] [Indexed: 05/09/2023]
Abstract
Nowadays, the vastly increasing demand for novel biotechnological products is supported by the continuous development of biocatalytic applications which provide sustainable green alternatives to chemical processes. The success of a biocatalytic application is critically dependent on how quickly we can identify and characterize enzyme variants fitting the conditions of industrial processes. While miniaturization and parallelization have dramatically increased the throughput of next-generation sequencing systems, the subsequent characterization of the obtained candidates is still a limiting process in identifying the desired biocatalysts. Only a few commercial microfluidic systems for enzyme analysis are currently available, and the transformation of numerous published prototypes into commercial platforms is still to be streamlined. This review presents the state-of-the-art, recent trends, and perspectives in applying microfluidic tools in the functional and structural analysis of biocatalysts. We discuss the advantages and disadvantages of available technologies, their reproducibility and robustness, and readiness for routine laboratory use. We also highlight the unexplored potential of microfluidics to leverage the power of machine learning for biocatalyst development.
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Affiliation(s)
- Michal Vasina
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, 602 00 Brno, Czech Republic; International Clinical Research Centre, St. Anne's University Hospital, 656 91 Brno, Czech Republic
| | - David Kovar
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, 602 00 Brno, Czech Republic; International Clinical Research Centre, St. Anne's University Hospital, 656 91 Brno, Czech Republic
| | - Jiri Damborsky
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, 602 00 Brno, Czech Republic; International Clinical Research Centre, St. Anne's University Hospital, 656 91 Brno, Czech Republic
| | - Yun Ding
- Institute for Chemical and Bioengineering, ETH Zürich, 8093 Zürich, Switzerland
| | - Tianjin Yang
- Institute for Chemical and Bioengineering, ETH Zürich, 8093 Zürich, Switzerland; Department of Biochemistry, University of Zurich, 8057 Zurich, Switzerland
| | - Andrew deMello
- Institute for Chemical and Bioengineering, ETH Zürich, 8093 Zürich, Switzerland
| | - Stanislav Mazurenko
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, 602 00 Brno, Czech Republic; International Clinical Research Centre, St. Anne's University Hospital, 656 91 Brno, Czech Republic.
| | - Stavros Stavrakis
- Institute for Chemical and Bioengineering, ETH Zürich, 8093 Zürich, Switzerland.
| | - Zbynek Prokop
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, 602 00 Brno, Czech Republic; International Clinical Research Centre, St. Anne's University Hospital, 656 91 Brno, Czech Republic.
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9
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Rosace A, Bennett A, Oeller M, Mortensen MM, Sakhnini L, Lorenzen N, Poulsen C, Sormanni P. Automated optimisation of solubility and conformational stability of antibodies and proteins. Nat Commun 2023; 14:1937. [PMID: 37024501 PMCID: PMC10079162 DOI: 10.1038/s41467-023-37668-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Accepted: 03/24/2023] [Indexed: 04/08/2023] Open
Abstract
Biologics, such as antibodies and enzymes, are crucial in research, biotechnology, diagnostics, and therapeutics. Often, biologics with suitable functionality are discovered, but their development is impeded by developability issues. Stability and solubility are key biophysical traits underpinning developability potential, as they determine aggregation, correlate with production yield and poly-specificity, and are essential to access parenteral and oral delivery. While advances for the optimisation of individual traits have been made, the co-optimization of multiple traits remains highly problematic and time-consuming, as mutations that improve one property often negatively impact others. In this work, we introduce a fully automated computational strategy for the simultaneous optimisation of conformational stability and solubility, which we experimentally validate on six antibodies, including two approved therapeutics. Our results on 42 designs demonstrate that the computational procedure is highly effective at improving developability potential, while not affecting antigen-binding. We make the method available as a webserver at www-cohsoftware.ch.cam.ac.uk.
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Affiliation(s)
- Angelo Rosace
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield road, CB2 1EW, Cambridge, UK
- Master in Bioinformatics for Health Sciences, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain
- Institute for Research in Biomedicine (IRB), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Anja Bennett
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield road, CB2 1EW, Cambridge, UK
- Department of Mammalian Expression, Global Research Technologies, Novo Nordisk A/S, Novo Nordisk Park 1, 2760, Måløv, Denmark
- BRIC, Faculty of Health and Medical Sciences, University of Copenhagen, Ole Maaløes Vej 5, 2200, Copenhagen, Denmark
| | - Marc Oeller
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield road, CB2 1EW, Cambridge, UK
| | - Mie M Mortensen
- Department of Purification Technologies, Global Research Technologies, Novo Nordisk A/S, Novo Nordisk Park 1, 2760, Måløv, Denmark
- Faculty of Engineering and Science, Department of Biotechnology, Chemistry and Environmental Engineering, University of Aalborg, Fredrik Bajers Vej 7H, 9220, Aalborg, Denmark
| | - Laila Sakhnini
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield road, CB2 1EW, Cambridge, UK
- Department of Biophysics and Injectable Formulation 2, Global Research Technologies, Novo Nordisk A/S, Måløv, 2760, Denmark
| | - Nikolai Lorenzen
- Department of Biophysics and Injectable Formulation 2, Global Research Technologies, Novo Nordisk A/S, Måløv, 2760, Denmark
| | - Christian Poulsen
- Department of Mammalian Expression, Global Research Technologies, Novo Nordisk A/S, Novo Nordisk Park 1, 2760, Måløv, Denmark
| | - Pietro Sormanni
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield road, CB2 1EW, Cambridge, UK.
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10
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Kasavajhala K, Simmerling C. Exploring the Transferability of Replica Exchange Structure Reservoirs to Accelerate Generation of Ensembles for Alternate Hamiltonians or Protein Mutations. J Chem Theory Comput 2023; 19:1931-1944. [PMID: 36861842 PMCID: PMC10658647 DOI: 10.1021/acs.jctc.3c00005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/03/2023]
Abstract
Generating precise ensembles is commonly a prerequisite to understand the energetics of biological processes using Molecular Dynamics (MD) simulations. Previously, we have shown how unweighted reservoirs built from high temperature MD simulations can accelerate convergence of Boltzmann-weighted ensembles by at least 10× with the Reservoir Replica Exchange MD (RREMD) method. Therefore, in this work, we explore whether an unweighted structure reservoir generated with one Hamiltonian (solute force field plus solvent model) can be reused to quickly generate accurately weighted ensembles for Hamiltonians other than the one that was used to generate the reservoir. We also extended this methodology to rapidly estimate the effects of mutations on peptide stability by using a reservoir of diverse structures obtained from wild-type simulations. These results suggest that structures generated via fast methods such as coarse-grained models or structures predicted by Rosetta or deep learning approaches could be integrated into a reservoir to accelerate generation of ensembles using more accurate representations.
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Affiliation(s)
- Koushik Kasavajhala
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States
| | - Carlos Simmerling
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States
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11
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Anderson DM, Jayanthi LP, Gosavi S, Meiering EM. Engineering the kinetic stability of a β-trefoil protein by tuning its topological complexity. Front Mol Biosci 2023; 10:1021733. [PMID: 36845544 PMCID: PMC9945329 DOI: 10.3389/fmolb.2023.1021733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 01/02/2023] [Indexed: 02/11/2023] Open
Abstract
Kinetic stability, defined as the rate of protein unfolding, is central to determining the functional lifetime of proteins, both in nature and in wide-ranging medical and biotechnological applications. Further, high kinetic stability is generally correlated with high resistance against chemical and thermal denaturation, as well as proteolytic degradation. Despite its significance, specific mechanisms governing kinetic stability remain largely unknown, and few studies address the rational design of kinetic stability. Here, we describe a method for designing protein kinetic stability that uses protein long-range order, absolute contact order, and simulated free energy barriers of unfolding to quantitatively analyze and predict unfolding kinetics. We analyze two β-trefoil proteins: hisactophilin, a quasi-three-fold symmetric natural protein with moderate stability, and ThreeFoil, a designed three-fold symmetric protein with extremely high kinetic stability. The quantitative analysis identifies marked differences in long-range interactions across the protein hydrophobic cores that partially account for the differences in kinetic stability. Swapping the core interactions of ThreeFoil into hisactophilin increases kinetic stability with close agreement between predicted and experimentally measured unfolding rates. These results demonstrate the predictive power of readily applied measures of protein topology for altering kinetic stability and recommend core engineering as a tractable target for rationally designing kinetic stability that may be widely applicable.
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Affiliation(s)
| | - Lakshmi P. Jayanthi
- Simons Centre for the Study of Living Machines, National Centre for Biological Sciences, Tata Institute of Fundamental Research, Bangalore, India
| | - Shachi Gosavi
- Simons Centre for the Study of Living Machines, National Centre for Biological Sciences, Tata Institute of Fundamental Research, Bangalore, India
| | - Elizabeth M. Meiering
- Department of Chemistry, University of Waterloo, Waterloo, ON, Canada,*Correspondence: Elizabeth M. Meiering,
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12
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Lihan M, Lupyan D, Oehme D. Target-template relationships in protein structure prediction and their effect on the accuracy of thermostability calculations. Protein Sci 2023; 32:e4557. [PMID: 36573828 PMCID: PMC9878467 DOI: 10.1002/pro.4557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 12/22/2022] [Accepted: 12/23/2022] [Indexed: 12/28/2022]
Abstract
Improving protein thermostability has been a labor- and time-consuming process in industrial applications of protein engineering. Advances in computational approaches have facilitated the development of more efficient strategies to allow the prioritization of stabilizing mutants. Among these is FEP+, a free energy perturbation implementation that uses a thoroughly tested physics-based method to achieve unparalleled accuracy in predicting changes in protein thermostability. To gauge the applicability of FEP+ to situations where crystal structures are unavailable, here we have applied the FEP+ approach to homology models of 12 different proteins covering 316 mutations. By comparing predictions obtained with homology models to those obtained using crystal structures, we have identified that local rather than global sequence conservation between target and template sequence is a determining factor in the accuracy of predictions. By excluding mutation sites with low local sequence identity (<40%) to a template structure, we have obtained predictions with comparable performance to crystal structures (R2 of 0.67 and 0.63 and an RMSE of 1.20 and 1.16 kcal/mol for crystal structure and homology model predictions, respectively) for identifying stabilizing mutations when incorporating residue scanning into a cascade screening strategy. Additionally, we identify and discuss inherent limitations in sequence alignments and homology modeling protocols that translate into the poor FEP+ performance of a few select examples. Overall, our retrospective study provides detailed guidelines for the application of the FEP+ approach using homology models for protein thermostability predictions, which will greatly extend this approach to studies that were previously limited by structure availability.
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Affiliation(s)
- Muyun Lihan
- NIH Center for Macromolecular Modeling and Bioinformatics, Beckman Institute for Advanced Science and Technology, and Center for Biophysics and Quantitative BiologyUniversity of Illinois Urbana‐ChampaignUrbanaIllinoisUSA
- Schrödinger Inc.CambridgeMassachusettsUSA
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13
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Lopes TJS, Rios RA, Rios TN, Alencar BM, Ferreira MV, Morishita E. Computational analyses reveal fundamental properties of the AT structure related to thrombosis. BIOINFORMATICS ADVANCES 2022; 3:vbac098. [PMID: 36698764 PMCID: PMC9838315 DOI: 10.1093/bioadv/vbac098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 11/28/2022] [Accepted: 12/22/2022] [Indexed: 12/25/2022]
Abstract
Summary Blood coagulation is a vital process for humans and other species. Following an injury to a blood vessel, a cascade of molecular signals is transmitted, inhibiting and activating more than a dozen coagulation factors and resulting in the formation of a fibrin clot that ceases the bleeding. In this process, antithrombin (AT), encoded by the SERPINC1 gene is a key player regulating the clotting activity and ensuring that it stops at the right time. In this sense, mutations to this factor often result in thrombosis-the excessive coagulation that leads to the potentially fatal formation of blood clots that obstruct veins. Although this process is well known, it is still unclear why even single residue substitutions to AT lead to drastically different phenotypes. In this study, to understand the effect of mutations throughout the AT structure, we created a detailed network map of this protein, where each node is an amino acid, and two amino acids are connected if they are in close proximity in the three-dimensional structure. With this simple and intuitive representation and a machine-learning framework trained using genetic information from more than 130 patients, we found that different types of thrombosis have emerging patterns that are readily identifiable. Together, these results demonstrate how clinical features, genetic data and in silico analysis are converging to enhance the diagnosis and treatment of coagulation disorders. Supplementary information Supplementary data are available at Bioinformatics Advances online.
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Affiliation(s)
| | - Ricardo A Rios
- Institute of Computing, Federal University of Bahia, Salvador 40170-110, Brazil
| | - Tatiane N Rios
- Institute of Computing, Federal University of Bahia, Salvador 40170-110, Brazil
| | - Brenno M Alencar
- Institute of Computing, Federal University of Bahia, Salvador 40170-110, Brazil
| | - Marcos V Ferreira
- Institute of Computing, Federal University of Bahia, Salvador 40170-110, Brazil
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14
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Dissecting the stability determinants of a challenging de novo protein fold using massively parallel design and experimentation. Proc Natl Acad Sci U S A 2022; 119:e2122676119. [PMID: 36191185 PMCID: PMC9564214 DOI: 10.1073/pnas.2122676119] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
Designing entirely new protein structures remains challenging because we do not fully understand the biophysical determinants of folding stability. Yet, some protein folds are easier to design than others. Previous work identified the 43-residue ɑββɑ fold as especially challenging: The best designs had only a 2% success rate, compared to 39 to 87% success for other simple folds [G. J. Rocklin et al., Science 357, 168-175 (2017)]. This suggested the ɑββɑ fold would be a useful model system for gaining a deeper understanding of folding stability determinants and for testing new protein design methods. Here, we designed over 10,000 new ɑββɑ proteins and found over 3,000 of them to fold into stable structures using a high-throughput protease-based assay. NMR, hydrogen-deuterium exchange, circular dichroism, deep mutational scanning, and scrambled sequence control experiments indicated that our stable designs fold into their designed ɑββɑ structures with exceptional stability for their small size. Our large dataset enabled us to quantify the influence of universal stability determinants including nonpolar burial, helix capping, and buried unsatisfied polar atoms, as well as stability determinants unique to the ɑββɑ topology. Our work demonstrates how large-scale design and test cycles can solve challenging design problems while illuminating the biophysical determinants of folding.
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15
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Abstract
In-cell structural biology aims at extracting structural information about proteins or nucleic acids in their native, cellular environment. This emerging field holds great promise and is already providing new facts and outlooks of interest at both fundamental and applied levels. NMR spectroscopy has important contributions on this stage: It brings information on a broad variety of nuclei at the atomic scale, which ensures its great versatility and uniqueness. Here, we detail the methods, the fundamental knowledge, and the applications in biomedical engineering related to in-cell structural biology by NMR. We finally propose a brief overview of the main other techniques in the field (EPR, smFRET, cryo-ET, etc.) to draw some advisable developments for in-cell NMR. In the era of large-scale screenings and deep learning, both accurate and qualitative experimental evidence are as essential as ever to understand the interior life of cells. In-cell structural biology by NMR spectroscopy can generate such a knowledge, and it does so at the atomic scale. This review is meant to deliver comprehensive but accessible information, with advanced technical details and reflections on the methods, the nature of the results, and the future of the field.
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Affiliation(s)
- Francois-Xavier Theillet
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198 Gif-sur-Yvette, France
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16
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Lemay-St-Denis C, Doucet N, Pelletier JN. Integrating dynamics into enzyme engineering. Protein Eng Des Sel 2022; 35:6842866. [PMID: 36416215 DOI: 10.1093/protein/gzac015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 11/02/2022] [Accepted: 11/06/2022] [Indexed: 11/24/2022] Open
Abstract
Enzyme engineering has become a widely adopted practice in research labs and industry. In parallel, the past decades have seen tremendous strides in characterizing the dynamics of proteins, using a growing array of methodologies. Importantly, links have been established between the dynamics of proteins and their function. Characterizing the dynamics of an enzyme prior to, and following, its engineering is beginning to inform on the potential of 'dynamic engineering', i.e. the rational modification of protein dynamics to alter enzyme function. Here we examine the state of knowledge at the intersection of enzyme engineering and protein dynamics, describe current challenges and highlight pioneering work in the nascent area of dynamic engineering.
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Affiliation(s)
- Claudèle Lemay-St-Denis
- PROTEO, The Québec Network for Research on Protein, Function, Engineering and Applications, Quebec, QC, Canada
- CGCC, Center in Green Chemistry and Catalysis, Montreal, QC, Canada
- Department of Biochemistry and Molecular Medicine, Université de Montréal, Montreal, QC, Canada
| | - Nicolas Doucet
- PROTEO, The Québec Network for Research on Protein, Function, Engineering and Applications, Quebec, QC, Canada
- Centre Armand-Frappier Santé Biotechnologie, Institut National de la Recherche Scientifique (INRS), Université du Québec, Laval, QC, Canada
| | - Joelle N Pelletier
- PROTEO, The Québec Network for Research on Protein, Function, Engineering and Applications, Quebec, QC, Canada
- CGCC, Center in Green Chemistry and Catalysis, Montreal, QC, Canada
- Department of Biochemistry and Molecular Medicine, Université de Montréal, Montreal, QC, Canada
- Chemistry Department, Université de Montréal, Montreal, QC, Canada
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17
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Sankara Narayanan P, Runthala A. Accurate computational evolution of proteins and its dependence on deep learning and machine learning strategies. BIOCATAL BIOTRANSFOR 2022. [DOI: 10.1080/10242422.2022.2030317] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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18
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Sorokina I, Mushegian AR, Koonin EV. Is Protein Folding a Thermodynamically Unfavorable, Active, Energy-Dependent Process? Int J Mol Sci 2022; 23:521. [PMID: 35008947 PMCID: PMC8745595 DOI: 10.3390/ijms23010521] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 12/30/2021] [Accepted: 12/31/2021] [Indexed: 02/04/2023] Open
Abstract
The prevailing current view of protein folding is the thermodynamic hypothesis, under which the native folded conformation of a protein corresponds to the global minimum of Gibbs free energy G. We question this concept and show that the empirical evidence behind the thermodynamic hypothesis of folding is far from strong. Furthermore, physical theory-based approaches to the prediction of protein folds and their folding pathways so far have invariably failed except for some very small proteins, despite decades of intensive theory development and the enormous increase of computer power. The recent spectacular successes in protein structure prediction owe to evolutionary modeling of amino acid sequence substitutions enhanced by deep learning methods, but even these breakthroughs provide no information on the protein folding mechanisms and pathways. We discuss an alternative view of protein folding, under which the native state of most proteins does not occupy the global free energy minimum, but rather, a local minimum on a fluctuating free energy landscape. We further argue that ΔG of folding is likely to be positive for the majority of proteins, which therefore fold into their native conformations only through interactions with the energy-dependent molecular machinery of living cells, in particular, the translation system and chaperones. Accordingly, protein folding should be modeled as it occurs in vivo, that is, as a non-equilibrium, active, energy-dependent process.
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Affiliation(s)
| | - Arcady R. Mushegian
- Division of Molecular and Cellular Biosciences, National Science Foundation, Alexandria, VA 22314, USA;
- Clare Hall College, University of Cambridge, Cambridge CB3 9AL, UK
| | - Eugene V. Koonin
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
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19
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Accurate Prediction of Protein Thermodynamic Stability Changes upon Residue Mutation using Free Energy Perturbation. J Mol Biol 2021; 434:167375. [PMID: 34826524 DOI: 10.1016/j.jmb.2021.167375] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 11/05/2021] [Accepted: 11/17/2021] [Indexed: 01/17/2023]
Abstract
This work describes the application of a physics-based computational approach to predict the relative thermodynamic stability of protein variants, and evaluates the quantitative accuracy of those predictions compared to experimental data obtained from a diverse set of protein systems assayed at variable pH conditions. Physical stability is a key determinant of the clinical and commercial success of biological therapeutics, vaccines, diagnostics, enzymes and other protein-based products. Although experimental techniques for measuring the impact of amino acid residue mutation on the stability of proteins exist, they tend to be time consuming and costly, hence the need for accurate prediction methods. In contrast to many of the commonly available computational methods for stability prediction, the Free Energy Perturbation approach applied in this paper explicitly accounts for solvent effects and samples conformational dynamics using a rigorous molecular dynamics simulation process. On the entire validation dataset, consisting of 328 single point mutations spread across 14 distinct protein structures, our results show good overall correlation with experiment with an R2 of 0.65 and a low mean unsigned error of 0.95 kcal/mol. Application of the FEP approach in conjunction with experimental assessment techniques offers opportunities to lower the time and expense of product development and reduce the risk of costly late-stage failures.
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20
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Zhao VY, Rodrigues JV, Lozovsky ER, Hartl DL, Shakhnovich EI. Switching an active site helix in dihydrofolate reductase reveals limits to subdomain modularity. Biophys J 2021; 120:4738-4750. [PMID: 34571014 PMCID: PMC8595743 DOI: 10.1016/j.bpj.2021.09.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 09/14/2021] [Accepted: 09/22/2021] [Indexed: 11/23/2022] Open
Abstract
To what degree are individual structural elements within proteins modular such that similar structures from unrelated proteins can be interchanged? We study subdomain modularity by creating 20 chimeras of an enzyme, Escherichia coli dihydrofolate reductase (DHFR), in which a catalytically important, 10-residue α-helical sequence is replaced by α-helical sequences from a diverse set of proteins. The chimeras stably fold but have a range of diminished thermal stabilities and catalytic activities. Evolutionary coupling analysis indicates that the residues of this α-helix are under selection pressure to maintain catalytic activity in DHFR. Reversion to phenylalanine at key position 31 was found to partially restore catalytic activity, which could be explained by evolutionary coupling values. We performed molecular dynamics simulations using replica exchange with solute tempering. Chimeras with low catalytic activity exhibit nonhelical conformations that block the binding site and disrupt the positioning of the catalytically essential residue D27. Simulation observables and in vitro measurements of thermal stability and substrate-binding affinity are strongly correlated. Several E. coli strains with chromosomally integrated chimeric DHFRs can grow, with growth rates that follow predictions from a kinetic flux model that depends on the intracellular abundance and catalytic activity of DHFR. Our findings show that although α-helices are not universally substitutable, the molecular and fitness effects of modular segments can be predicted by the biophysical compatibility of the replacement segment.
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Affiliation(s)
- Victor Y Zhao
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts
| | - João V Rodrigues
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts
| | - Elena R Lozovsky
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts
| | - Daniel L Hartl
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts
| | - Eugene I Shakhnovich
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts.
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21
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Sternke M, Tripp KW, Barrick D. Surface residues and non-additive interactions stabilize a consensus homeodomain protein. Biophys J 2021; 120:5267-5278. [PMID: 34757081 DOI: 10.1016/j.bpj.2021.10.035] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 09/01/2021] [Accepted: 10/26/2021] [Indexed: 11/26/2022] Open
Abstract
Despite the widely reported success of consensus design in producing highly stabilized proteins, little is known about the physical mechanisms underlying this stabilization. Here we explore the potential sources of stabilization by performing a systematic analysis of the 29 substitutions that we previously found to collectively stabilize a consensus homeodomain compared to an extant homeodomain. By separately introducing groups of consensus substitutions that alter or preserve charge state, occur at varying degrees of residue burial, and occur at positions of varying degrees of conservation, we determine the extent to which these three features contribute to the consensus stability enhancement. Surprisingly, we find that the largest total contribution to stability comes from consensus substitutions on the protein surface and that the largest per-substitution contributions come from substitutions that maintain charge state. This finding suggests that although consensus proteins are often enriched in charged residues, consensus stabilization does not result primarily from interactions involving charged residues. Although consensus substitutions at strongly conserved positions also contribute disproportionately to stabilization, significant stabilization is also contributed from substitutions at weakly conserved positions. Furthermore, we find that identical consensus substitutions show larger stabilizing effects when introduced into the consensus background than when introduced into an extant homeodomain, indicating that synergistic, stabilizing interactions among the consensus residues contribute to consensus stability enhancement of the homeodomain. By measuring DNA binding affinity for the same set of variants, we find that although consensus design of the homeodomain increases both affinity and folding stability, it does so using a largely non-overlapping set of substitutions.
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Affiliation(s)
- Matt Sternke
- The T.C. Jenkins Department of Biophysics, Johns Hopkins University, 3400 N. Charles St., Baltimore, MD 21218 USA
| | - Katherine W Tripp
- The T.C. Jenkins Department of Biophysics, Johns Hopkins University, 3400 N. Charles St., Baltimore, MD 21218 USA
| | - Doug Barrick
- The T.C. Jenkins Department of Biophysics, Johns Hopkins University, 3400 N. Charles St., Baltimore, MD 21218 USA.
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22
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Liu R, Wang J, Xiong P, Chen Q, Liu H. De novo sequence redesign of a functional Ras-binding domain globally inverted the surface charge distribution and led to extreme thermostability. Biotechnol Bioeng 2021; 118:2031-2042. [PMID: 33590881 DOI: 10.1002/bit.27716] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 02/05/2021] [Accepted: 02/14/2021] [Indexed: 11/05/2022]
Abstract
To acquire extremely thermostable proteins of given functions is challenging for conventional protein engineering. Here we applied ABACUS, a statistical energy function we developed for de novo amino acid sequence design, to globally redesign a Ras-binding domain (RBD), and obtained an extremely thermostable RBD that unfolds reversibly at above 110°C, the redesigned RBD experimentally confirmed to have expected structure and Ras-binding interface. Directed evolution of the redesigned RBD improved its Ras-binding affinity to the native protein level without excessive loss of thermostability. The designed amino acid substitutions were mostly at the protein surface. For many substitutions, strong epistasis or significantly differentiated effects on thermostability in the native sequence context relative to the redesigned sequence context were observed, suggesting the globally redesigned sequence to be unreachable through combining beneficial mutations of the native sequence. Further analyses revealed that by replacing 38 of a total of 48 non-interfacial surface residues at once, ABACUS redesign was able to globally "invert" the protein's charge distribution pattern in an optimized way. Our study demonstrates that computational protein design provides powerful new tools to solve challenging protein engineering problems.
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Affiliation(s)
- Ruicun Liu
- School of Life Sciences, University of Science and Technology of China, Hefei, Anhui, China
| | - Jichao Wang
- School of Life Sciences, University of Science and Technology of China, Hefei, Anhui, China
| | - Peng Xiong
- School of Life Sciences, University of Science and Technology of China, Hefei, Anhui, China
| | - Quan Chen
- School of Life Sciences, University of Science and Technology of China, Hefei, Anhui, China.,Hefei National Laboratory for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei, Anhui, China
| | - Haiyan Liu
- School of Life Sciences, University of Science and Technology of China, Hefei, Anhui, China.,Hefei National Laboratory for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei, Anhui, China.,School of Data Science, University of Science and Technology of China, Hefei, Anhui, China
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23
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Mazurenko S. Predicting protein stability and solubility changes upon mutations: data perspective. ChemCatChem 2020. [DOI: 10.1002/cctc.202000933] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
- Stanislav Mazurenko
- Loschmidt Laboratories Department of Experimental Biology and RECETOX Faculty of Science Masaryk University Zerotinovo nam. 617/9 601 77 Brno Czech Republic
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