1
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Colom MS, Vučinić J, Adolf‐Bryfogle J, Bowman JW, Verel S, Moczygemba I, Schiex T, Simoncini D, Bahl CD. Complete combinatorial mutational enumeration of a protein functional site enables sequence-landscape mapping and identifies highly-mutated variants that retain activity. Protein Sci 2024; 33:e5109. [PMID: 38989563 PMCID: PMC11237556 DOI: 10.1002/pro.5109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2024] [Revised: 05/20/2024] [Accepted: 06/25/2024] [Indexed: 07/12/2024]
Abstract
Understanding how proteins evolve under selective pressure is a longstanding challenge. The immensity of the search space has limited efforts to systematically evaluate the impact of multiple simultaneous mutations, so mutations have typically been assessed individually. However, epistasis, or the way in which mutations interact, prevents accurate prediction of combinatorial mutations based on measurements of individual mutations. Here, we use artificial intelligence to define the entire functional sequence landscape of a protein binding site in silico, and we call this approach Complete Combinatorial Mutational Enumeration (CCME). By leveraging CCME, we are able to construct a comprehensive map of the evolutionary connectivity within this functional sequence landscape. As a proof of concept, we applied CCME to the ACE2 binding site of the SARS-CoV-2 spike protein receptor binding domain. We selected representative variants from across the functional sequence landscape for testing in the laboratory. We identified variants that retained functionality to bind ACE2 despite changing over 40% of evaluated residue positions, and the variants now escape binding and neutralization by monoclonal antibodies. This work represents a crucial initial stride toward achieving precise predictions of pathogen evolution, opening avenues for proactive mitigation.
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Affiliation(s)
- Mireia Solà Colom
- Institute for Protein InnovationBostonMassachusettsUSA
- Division of Hematology/OncologyBoston Children's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Present address:
AI ProteinsBostonMassachusettsUSA
| | - Jelena Vučinić
- Université Fédérale de Toulouse, IRIT UMR 5505, ANITI, Université Toulouse CapitoleToulouseFrance
| | - Jared Adolf‐Bryfogle
- Institute for Protein InnovationBostonMassachusettsUSA
- Division of Hematology/OncologyBoston Children's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - James W. Bowman
- Institute for Protein InnovationBostonMassachusettsUSA
- Division of Hematology/OncologyBoston Children's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Present address:
AI ProteinsBostonMassachusettsUSA
| | | | - Isabelle Moczygemba
- Institute for Protein InnovationBostonMassachusettsUSA
- Division of Hematology/OncologyBoston Children's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Present address:
AI ProteinsBostonMassachusettsUSA
| | - Thomas Schiex
- MIAT, Université Fédérale de Toulouse, ANITI, INRAE UR 875ToulouseFrance
| | - David Simoncini
- Université Fédérale de Toulouse, IRIT UMR 5505, ANITI, Université Toulouse CapitoleToulouseFrance
| | - Christopher D. Bahl
- Institute for Protein InnovationBostonMassachusettsUSA
- Division of Hematology/OncologyBoston Children's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Present address:
AI ProteinsBostonMassachusettsUSA
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2
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Shao L, Ma J, Prelesnik JL, Zhou Y, Nguyen M, Zhao M, Jenekhe SA, Kalinin SV, Ferguson AL, Pfaendtner J, Mundy CJ, De Yoreo JJ, Baneyx F, Chen CL. Hierarchical Materials from High Information Content Macromolecular Building Blocks: Construction, Dynamic Interventions, and Prediction. Chem Rev 2022; 122:17397-17478. [PMID: 36260695 DOI: 10.1021/acs.chemrev.2c00220] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Hierarchical materials that exhibit order over multiple length scales are ubiquitous in nature. Because hierarchy gives rise to unique properties and functions, many have sought inspiration from nature when designing and fabricating hierarchical matter. More and more, however, nature's own high-information content building blocks, proteins, peptides, and peptidomimetics, are being coopted to build hierarchy because the information that determines structure, function, and interfacial interactions can be readily encoded in these versatile macromolecules. Here, we take stock of recent progress in the rational design and characterization of hierarchical materials produced from high-information content blocks with a focus on stimuli-responsive and "smart" architectures. We also review advances in the use of computational simulations and data-driven predictions to shed light on how the side chain chemistry and conformational flexibility of macromolecular blocks drive the emergence of order and the acquisition of hierarchy and also on how ionic, solvent, and surface effects influence the outcomes of assembly. Continued progress in the above areas will ultimately usher in an era where an understanding of designed interactions, surface effects, and solution conditions can be harnessed to achieve predictive materials synthesis across scale and drive emergent phenomena in the self-assembly and reconfiguration of high-information content building blocks.
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Affiliation(s)
- Li Shao
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Jinrong Ma
- Molecular Engineering and Sciences Institute, University of Washington, Seattle, Washington 98195, United States
| | - Jesse L Prelesnik
- Department of Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Yicheng Zhou
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Mary Nguyen
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States.,Department of Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Mingfei Zhao
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United States
| | - Samson A Jenekhe
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States.,Department of Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Sergei V Kalinin
- Department of Materials Science and Engineering, University of Tennessee, Knoxville, Tennessee 37996, United States
| | - Andrew L Ferguson
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United States
| | - Jim Pfaendtner
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States.,Materials Science and Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Christopher J Mundy
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States.,Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
| | - James J De Yoreo
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States.,Materials Science and Engineering, University of Washington, Seattle, Washington 98195, United States
| | - François Baneyx
- Molecular Engineering and Sciences Institute, University of Washington, Seattle, Washington 98195, United States.,Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Chun-Long Chen
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States.,Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
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3
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Bouchiba Y, Ruffini M, Schiex T, Barbe S. Computational Design of Miniprotein Binders. Methods Mol Biol 2022; 2405:361-382. [PMID: 35298822 DOI: 10.1007/978-1-0716-1855-4_17] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Miniprotein binders hold a great interest as a class of drugs that bridges the gap between monoclonal antibodies and small molecule drugs. Like monoclonal antibodies, they can be designed to bind to therapeutic targets with high affinity, but they are more stable and easier to produce and to administer. In this chapter, we present a structure-based computational generic approach for miniprotein inhibitor design. Specifically, we describe step-by-step the implementation of the approach for the design of miniprotein binders against the SARS-CoV-2 coronavirus, using available structural data on the SARS-CoV-2 spike receptor binding domain (RBD) in interaction with its native target, the human receptor ACE2. Structural data being increasingly accessible around many protein-protein interaction systems, this method might be applied to the design of miniprotein binders against numerous therapeutic targets. The computational pipeline exploits provable and deterministic artificial intelligence-based protein design methods, with some recent additions in terms of binding energy estimation, multistate design and diverse library generation.
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Affiliation(s)
- Younes Bouchiba
- TBI, Université de Toulouse, CNRS, INRAE, INSA, ANITI, Toulouse, France
| | - Manon Ruffini
- TBI, Université de Toulouse, CNRS, INRAE, INSA, ANITI, Toulouse, France
- Université Fédérale de Toulouse, ANITI, INRAE, UR 875, Toulouse, France
| | - Thomas Schiex
- Université Fédérale de Toulouse, ANITI, INRAE, UR 875, Toulouse, France
| | - Sophie Barbe
- TBI, Université de Toulouse, CNRS, INRAE, INSA, ANITI, Toulouse, France.
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4
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Opuu V, Mignon D, Simonson T. Knowledge-Based Unfolded State Model for Protein Design. Methods Mol Biol 2022; 2405:403-424. [PMID: 35298824 DOI: 10.1007/978-1-0716-1855-4_19] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The design of proteins and miniproteins is an important challenge. Designed variants should be stable, meaning the folded/unfolded free energy difference should be large enough. Thus, the unfolded state plays a central role. An extended peptide model is often used, where side chains interact with solvent and nearby backbone, but not each other. The unfolded energy is then a function of sequence composition only and can be empirically parametrized. If the space of sequences is explored with a Monte Carlo procedure, protein variants will be sampled according to a well-defined Boltzmann probability distribution. We can then choose unfolded model parameters to maximize the probability of sampling native-like sequences. This leads to a well-defined maximum likelihood framework. We present an iterative algorithm that follows the likelihood gradient. The method is presented in the context of our Proteus software, as a detailed downloadable tutorial. The unfolded model is combined with a folded model that uses molecular mechanics and a Generalized Born solvent. It was optimized for three PDZ domains and then used to redesign them. The sequences sampled are native-like and similar to a recent PDZ design study that was experimentally validated.
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Affiliation(s)
- Vaitea Opuu
- Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole Polytechnique, Palaiseau, France
| | - David Mignon
- Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole Polytechnique, Palaiseau, France
| | - Thomas Simonson
- Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole Polytechnique, Palaiseau, France.
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5
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Defresne M, Barbe S, Schiex T. Protein Design with Deep Learning. Int J Mol Sci 2021; 22:11741. [PMID: 34769173 PMCID: PMC8584038 DOI: 10.3390/ijms222111741] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 10/23/2021] [Accepted: 10/26/2021] [Indexed: 12/21/2022] Open
Abstract
Computational Protein Design (CPD) has produced impressive results for engineering new proteins, resulting in a wide variety of applications. In the past few years, various efforts have aimed at replacing or improving existing design methods using Deep Learning technology to leverage the amount of publicly available protein data. Deep Learning (DL) is a very powerful tool to extract patterns from raw data, provided that data are formatted as mathematical objects and the architecture processing them is well suited to the targeted problem. In the case of protein data, specific representations are needed for both the amino acid sequence and the protein structure in order to capture respectively 1D and 3D information. As no consensus has been reached about the most suitable representations, this review describes the representations used so far, discusses their strengths and weaknesses, and details their associated DL architecture for design and related tasks.
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Affiliation(s)
- Marianne Defresne
- Toulouse Biotechnology Institute, Université de Toulouse, CNRS, INRAE, INSA, ANITI, 31077 Toulouse, France; (M.D.); (S.B.)
- Université Fédérale de Toulouse, ANITI, INRAE, UR 875, 31326 Toulouse, France
| | - Sophie Barbe
- Toulouse Biotechnology Institute, Université de Toulouse, CNRS, INRAE, INSA, ANITI, 31077 Toulouse, France; (M.D.); (S.B.)
| | - Thomas Schiex
- Université Fédérale de Toulouse, ANITI, INRAE, UR 875, 31326 Toulouse, France
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6
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Beuvin F, de Givry S, Schiex T, Verel S, Simoncini D. Iterated local search with partition crossover for computational protein design. Proteins 2021; 89:1522-1529. [PMID: 34228826 DOI: 10.1002/prot.26174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Accepted: 05/25/2021] [Indexed: 11/06/2022]
Abstract
Structure-based computational protein design (CPD) refers to the problem of finding a sequence of amino acids which folds into a specific desired protein structure, and possibly fulfills some targeted biochemical properties. Recent studies point out the particularly rugged CPD energy landscape, suggesting that local search optimization methods should be designed and tuned to easily escape local minima attraction basins. In this article, we analyze the performance and search dynamics of an iterated local search (ILS) algorithm enhanced with partition crossover. Our algorithm, PILS, quickly finds local minima and escapes their basins of attraction by solution perturbation. Additionally, the partition crossover operator exploits the structure of the residue interaction graph in order to efficiently mix solutions and find new unexplored basins. Our results on a benchmark of 30 proteins of various topology and size show that PILS consistently finds lower energy solutions compared to Rosetta fixbb and a classic ILS, and that the corresponding sequences are mostly closer to the native.
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Affiliation(s)
- François Beuvin
- IRIT UMR 5505-CNRS, Université de Toulouse I Capitole, Toulouse, France.,Artificial and Natural Intelligence Toulouse Institute, ANITI, Toulouse, France
| | - Simon de Givry
- Artificial and Natural Intelligence Toulouse Institute, ANITI, Toulouse, France.,MIAT, Université de Toulouse, INRAE, UR 875, Toulouse, France
| | - Thomas Schiex
- Artificial and Natural Intelligence Toulouse Institute, ANITI, Toulouse, France.,MIAT, Université de Toulouse, INRAE, UR 875, Toulouse, France
| | | | - David Simoncini
- IRIT UMR 5505-CNRS, Université de Toulouse I Capitole, Toulouse, France.,Artificial and Natural Intelligence Toulouse Institute, ANITI, Toulouse, France
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7
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Guaranteed Diversity and Optimality in Cost Function Network Based Computational Protein Design Methods. ALGORITHMS 2021. [DOI: 10.3390/a14060168] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Proteins are the main active molecules of life. Although natural proteins play many roles, as enzymes or antibodies for example, there is a need to go beyond the repertoire of natural proteins to produce engineered proteins that precisely meet application requirements, in terms of function, stability, activity or other protein capacities. Computational Protein Design aims at designing new proteins from first principles, using full-atom molecular models. However, the size and complexity of proteins require approximations to make them amenable to energetic optimization queries. These approximations make the design process less reliable, and a provable optimal solution may fail. In practice, expensive libraries of solutions are therefore generated and tested. In this paper, we explore the idea of generating libraries of provably diverse low-energy solutions by extending cost function network algorithms with dedicated automaton-based diversity constraints on a large set of realistic full protein redesign problems. We observe that it is possible to generate provably diverse libraries in reasonable time and that the produced libraries do enhance the Native Sequence Recovery, a traditional measure of design methods reliability.
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8
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Bouchiba Y, Cortés J, Schiex T, Barbe S. Molecular flexibility in computational protein design: an algorithmic perspective. Protein Eng Des Sel 2021; 34:6271252. [PMID: 33959778 DOI: 10.1093/protein/gzab011] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 03/12/2021] [Accepted: 03/29/2021] [Indexed: 12/19/2022] Open
Abstract
Computational protein design (CPD) is a powerful technique for engineering new proteins, with both great fundamental implications and diverse practical interests. However, the approximations usually made for computational efficiency, using a single fixed backbone and a discrete set of side chain rotamers, tend to produce rigid and hyper-stable folds that may lack functionality. These approximations contrast with the demonstrated importance of molecular flexibility and motions in a wide range of protein functions. The integration of backbone flexibility and multiple conformational states in CPD, in order to relieve the inaccuracies resulting from these simplifications and to improve design reliability, are attracting increased attention. However, the greatly increased search space that needs to be explored in these extensions defines extremely challenging computational problems. In this review, we outline the principles of CPD and discuss recent effort in algorithmic developments for incorporating molecular flexibility in the design process.
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Affiliation(s)
- Younes Bouchiba
- Toulouse Biotechnology Institute, TBI, CNRS, INRAE, INSA, ANITI, Toulouse 31400, France.,Laboratoire d'Analyse et d'Architecture des Systèmes, LAAS CNRS, Université de Toulouse, CNRS, Toulouse 31400, France
| | - Juan Cortés
- Laboratoire d'Analyse et d'Architecture des Systèmes, LAAS CNRS, Université de Toulouse, CNRS, Toulouse 31400, France
| | - Thomas Schiex
- Université de Toulouse, ANITI, INRAE, UR MIAT, F-31320, Castanet-Tolosan, France
| | - Sophie Barbe
- Toulouse Biotechnology Institute, TBI, CNRS, INRAE, INSA, ANITI, Toulouse 31400, France
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9
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Mylemans B, Voet AR, Tame JR. The Taming of the Screw: the natural and artificial development of β-propeller proteins. Curr Opin Struct Biol 2020; 68:48-54. [PMID: 33373773 DOI: 10.1016/j.sbi.2020.11.009] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 11/09/2020] [Accepted: 11/27/2020] [Indexed: 12/17/2022]
Abstract
Many proteins are found to possess repeated structural elements, which hint at ancient evolutionary origins and ongoing evolutionary processes. β-propeller proteins are a large family of such proteins, and a popular focus of structural analysis. This review highlights recent work to understand how they arose, and how they have developed into one of the most successful of all protein folds.
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Affiliation(s)
- Bram Mylemans
- Laboraotry for biomolecular modelling and design, KU Leuven, Celestijnenlaan 200G, 3001 Leuven, Belgium
| | - Arnout Rd Voet
- Protein Design Laboratory, Graduate School of Medical Life Science, Yokohama City University, Suehiro 1-7-29, Tsurumi, Yokohama 230-0045, Japan
| | - Jeremy Rh Tame
- Protein Design Laboratory, Graduate School of Medical Life Science, Yokohama City University, Suehiro 1-7-29, Tsurumi, Yokohama 230-0045, Japan.
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10
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Leman JK, Weitzner BD, Lewis SM, Adolf-Bryfogle J, Alam N, Alford RF, Aprahamian M, Baker D, Barlow KA, Barth P, Basanta B, Bender BJ, Blacklock K, Bonet J, Boyken SE, Bradley P, Bystroff C, Conway P, Cooper S, Correia BE, Coventry B, Das R, De Jong RM, DiMaio F, Dsilva L, Dunbrack R, Ford AS, Frenz B, Fu DY, Geniesse C, Goldschmidt L, Gowthaman R, Gray JJ, Gront D, Guffy S, Horowitz S, Huang PS, Huber T, Jacobs TM, Jeliazkov JR, Johnson DK, Kappel K, Karanicolas J, Khakzad H, Khar KR, Khare SD, Khatib F, Khramushin A, King IC, Kleffner R, Koepnick B, Kortemme T, Kuenze G, Kuhlman B, Kuroda D, Labonte JW, Lai JK, Lapidoth G, Leaver-Fay A, Lindert S, Linsky T, London N, Lubin JH, Lyskov S, Maguire J, Malmström L, Marcos E, Marcu O, Marze NA, Meiler J, Moretti R, Mulligan VK, Nerli S, Norn C, Ó'Conchúir S, Ollikainen N, Ovchinnikov S, Pacella MS, Pan X, Park H, Pavlovicz RE, Pethe M, Pierce BG, Pilla KB, Raveh B, Renfrew PD, Burman SSR, Rubenstein A, Sauer MF, Scheck A, Schief W, Schueler-Furman O, Sedan Y, Sevy AM, Sgourakis NG, Shi L, Siegel JB, Silva DA, Smith S, Song Y, Stein A, Szegedy M, Teets FD, Thyme SB, Wang RYR, Watkins A, Zimmerman L, Bonneau R. Macromolecular modeling and design in Rosetta: recent methods and frameworks. Nat Methods 2020; 17:665-680. [PMID: 32483333 PMCID: PMC7603796 DOI: 10.1038/s41592-020-0848-2] [Citation(s) in RCA: 429] [Impact Index Per Article: 107.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Accepted: 04/22/2020] [Indexed: 12/12/2022]
Abstract
The Rosetta software for macromolecular modeling, docking and design is extensively used in laboratories worldwide. During two decades of development by a community of laboratories at more than 60 institutions, Rosetta has been continuously refactored and extended. Its advantages are its performance and interoperability between broad modeling capabilities. Here we review tools developed in the last 5 years, including over 80 methods. We discuss improvements to the score function, user interfaces and usability. Rosetta is available at http://www.rosettacommons.org.
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Affiliation(s)
- Julia Koehler Leman
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA.
- Department of Biology, New York University, New York, New York, USA.
| | - Brian D Weitzner
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Lyell Immunopharma Inc., Seattle, WA, USA
| | - Steven M Lewis
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Biochemistry, Duke University, Durham, NC, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Jared Adolf-Bryfogle
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, USA
| | - Nawsad Alam
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Rebecca F Alford
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Melanie Aprahamian
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, OH, USA
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Kyle A Barlow
- Graduate Program in Bioinformatics, University of California San Francisco, San Francisco, CA, USA
| | - Patrick Barth
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Baylor College of Medicine, Department of Pharmacology, Houston, TX, USA
| | - Benjamin Basanta
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Biological Physics Structure and Design PhD Program, University of Washington, Seattle, WA, USA
| | - Brian J Bender
- Department of Pharmacology, Vanderbilt University, Nashville, TN, USA
| | - Kristin Blacklock
- Institute of Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Jaume Bonet
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Scott E Boyken
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Lyell Immunopharma Inc., Seattle, WA, USA
| | - Phil Bradley
- Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Chris Bystroff
- Department of Biological Sciences, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Patrick Conway
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Seth Cooper
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Bruno E Correia
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Brian Coventry
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Rhiju Das
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Frank DiMaio
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Lorna Dsilva
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Roland Dunbrack
- Institute for Cancer Research, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Alexander S Ford
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Brandon Frenz
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Darwin Y Fu
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
| | - Caleb Geniesse
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Ragul Gowthaman
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, USA
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD, USA
| | - Dominik Gront
- Faculty of Chemistry, Biological and Chemical Research Centre, University of Warsaw, Warsaw, Poland
| | - Sharon Guffy
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Scott Horowitz
- Department of Chemistry & Biochemistry, University of Denver, Denver, CO, USA
- The Knoebel Institute for Healthy Aging, University of Denver, Denver, CO, USA
| | - Po-Ssu Huang
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Thomas Huber
- Research School of Chemistry, Australian National University, Canberra, Australian Capital Territory, Australia
| | - Tim M Jacobs
- Program in Bioinformatics and Computational Biology, Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - David K Johnson
- Center for Computational Biology, University of Kansas, Lawrence, KS, USA
| | - Kalli Kappel
- Biophysics Program, Stanford University, Stanford, CA, USA
| | - John Karanicolas
- Institute for Cancer Research, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Hamed Khakzad
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Institute for Computational Science, University of Zurich, Zurich, Switzerland
- S3IT, University of Zurich, Zurich, Switzerland
| | - Karen R Khar
- Cyrus Biotechnology, Seattle, WA, USA
- Center for Computational Biology, University of Kansas, Lawrence, KS, USA
| | - Sagar D Khare
- Institute of Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Department of Chemistry and Chemical Biology, The State University of New Jersey, Piscataway, NJ, USA
- Center for Integrative Proteomics Research, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Computational Biology and Molecular Biophysics Program, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Firas Khatib
- Department of Computer and Information Science, University of Massachusetts Dartmouth, Dartmouth, MA, USA
| | - Alisa Khramushin
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Indigo C King
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Robert Kleffner
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Brian Koepnick
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Tanja Kortemme
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Georg Kuenze
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA
| | - Brian Kuhlman
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Daisuke Kuroda
- Medical Device Development and Regulation Research Center, School of Engineering, University of Tokyo, Tokyo, Japan
- Department of Bioengineering, School of Engineering, University of Tokyo, Tokyo, Japan
| | - Jason W Labonte
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Chemistry, Franklin & Marshall College, Lancaster, PA, USA
| | - Jason K Lai
- Baylor College of Medicine, Department of Pharmacology, Houston, TX, USA
| | - Gideon Lapidoth
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Andrew Leaver-Fay
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Steffen Lindert
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, OH, USA
| | - Thomas Linsky
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Nir London
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Joseph H Lubin
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Sergey Lyskov
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jack Maguire
- Program in Bioinformatics and Computational Biology, Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Lars Malmström
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Institute for Computational Science, University of Zurich, Zurich, Switzerland
- S3IT, University of Zurich, Zurich, Switzerland
- Division of Infection Medicine, Department of Clinical Sciences Lund, Faculty of Medicine, Lund University, Lund, Sweden
| | - Enrique Marcos
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Research in Biomedicine Barcelona, The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Orly Marcu
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Nicholas A Marze
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jens Meiler
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA
- Departments of Chemistry, Pharmacology and Biomedical Informatics, Vanderbilt University, Nashville, TN, USA
- Institute for Chemical Biology, Vanderbilt University, Nashville, TN, USA
| | - Rocco Moretti
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
| | - Vikram Khipple Mulligan
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Santrupti Nerli
- Department of Computer Science, University of California Santa Cruz, Santa Cruz, CA, USA
| | - Christoffer Norn
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Shane Ó'Conchúir
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Noah Ollikainen
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Sergey Ovchinnikov
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Molecular and Cellular Biology Program, University of Washington, Seattle, WA, USA
| | - Michael S Pacella
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Xingjie Pan
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Hahnbeom Park
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Ryan E Pavlovicz
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Manasi Pethe
- Department of Chemistry and Chemical Biology, The State University of New Jersey, Piscataway, NJ, USA
- Center for Integrative Proteomics Research, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Brian G Pierce
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, USA
| | - Kala Bharath Pilla
- Research School of Chemistry, Australian National University, Canberra, Australian Capital Territory, Australia
| | - Barak Raveh
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - P Douglas Renfrew
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA
| | - Shourya S Roy Burman
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Aliza Rubenstein
- Institute of Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Computational Biology and Molecular Biophysics Program, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Marion F Sauer
- Chemical and Physical Biology Program, Vanderbilt Vaccine Center, Vanderbilt University, Nashville, TN, USA
| | - Andreas Scheck
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - William Schief
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, USA
| | - Ora Schueler-Furman
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Yuval Sedan
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Alexander M Sevy
- Chemical and Physical Biology Program, Vanderbilt Vaccine Center, Vanderbilt University, Nashville, TN, USA
| | - Nikolaos G Sgourakis
- Department of Chemistry and Biochemistry, University of California Santa Cruz, Santa Cruz, CA, USA
| | - Lei Shi
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Justin B Siegel
- Department of Chemistry, University of California, Davis, Davis, CA, USA
- Department of Biochemistry and Molecular Medicine, University of California, Davis, Davis, California, USA
- Genome Center, University of California, Davis, Davis, CA, USA
| | | | - Shannon Smith
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
| | - Yifan Song
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Amelie Stein
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Maria Szegedy
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Frank D Teets
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Summer B Thyme
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Ray Yu-Ruei Wang
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Andrew Watkins
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
| | - Lior Zimmerman
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Richard Bonneau
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA.
- Department of Biology, New York University, New York, New York, USA.
- Department of Computer Science, New York University, New York, NY, USA.
- Center for Data Science, New York University, New York, NY, USA.
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11
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Lowegard AU, Frenkel MS, Holt GT, Jou JD, Ojewole AA, Donald BR. Novel, provable algorithms for efficient ensemble-based computational protein design and their application to the redesign of the c-Raf-RBD:KRas protein-protein interface. PLoS Comput Biol 2020; 16:e1007447. [PMID: 32511232 PMCID: PMC7329130 DOI: 10.1371/journal.pcbi.1007447] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Revised: 07/01/2020] [Accepted: 05/13/2020] [Indexed: 11/25/2022] Open
Abstract
The K* algorithm provably approximates partition functions for a set of states (e.g., protein, ligand, and protein-ligand complex) to a user-specified accuracy ε. Often, reaching an ε-approximation for a particular set of partition functions takes a prohibitive amount of time and space. To alleviate some of this cost, we introduce two new algorithms into the osprey suite for protein design: fries, a Fast Removal of Inadequately Energied Sequences, and EWAK*, an Energy Window Approximation to K*. fries pre-processes the sequence space to limit a design to only the most stable, energetically favorable sequence possibilities. EWAK* then takes this pruned sequence space as input and, using a user-specified energy window, calculates K* scores using the lowest energy conformations. We expect fries/EWAK* to be most useful in cases where there are many unstable sequences in the design sequence space and when users are satisfied with enumerating the low-energy ensemble of conformations. In combination, these algorithms provably retain calculational accuracy while limiting the input sequence space and the conformations included in each partition function calculation to only the most energetically favorable, effectively reducing runtime while still enriching for desirable sequences. This combined approach led to significant speed-ups compared to the previous state-of-the-art multi-sequence algorithm, BBK*, while maintaining its efficiency and accuracy, which we show across 40 different protein systems and a total of 2,826 protein design problems. Additionally, as a proof of concept, we used these new algorithms to redesign the protein-protein interface (PPI) of the c-Raf-RBD:KRas complex. The Ras-binding domain of the protein kinase c-Raf (c-Raf-RBD) is the tightest known binder of KRas, a protein implicated in difficult-to-treat cancers. fries/EWAK* accurately retrospectively predicted the effect of 41 different sets of mutations in the PPI of the c-Raf-RBD:KRas complex. Notably, these mutations include mutations whose effect had previously been incorrectly predicted using other computational methods. Next, we used fries/EWAK* for prospective design and discovered a novel point mutation that improves binding of c-Raf-RBD to KRas in its active, GTP-bound state (KRasGTP). We combined this new mutation with two previously reported mutations (which were highly-ranked by osprey) to create a new variant of c-Raf-RBD, c-Raf-RBD(RKY). fries/EWAK* in osprey computationally predicted that this new variant binds even more tightly than the previous best-binding variant, c-Raf-RBD(RK). We measured the binding affinity of c-Raf-RBD(RKY) using a bio-layer interferometry (BLI) assay, and found that this new variant exhibits single-digit nanomolar affinity for KRasGTP, confirming the computational predictions made with fries/EWAK*. This new variant binds roughly five times more tightly than the previous best known binder and roughly 36 times more tightly than the design starting point (wild-type c-Raf-RBD). This study steps through the advancement and development of computational protein design by presenting theory, new algorithms, accurate retrospective designs, new prospective designs, and biochemical validation. Computational structure-based protein design is an innovative tool for redesigning proteins to introduce a particular or novel function. One such function is improving the binding of one protein to another, which can increase our understanding of important protein systems. Herein we introduce two novel, provable algorithms, fries and EWAK*, for more efficient computational structure-based protein design as well as their application to the redesign of the c-Raf-RBD:KRas protein-protein interface. These new algorithms speed-up computational structure-based protein design while maintaining accurate calculations, allowing for larger, previously infeasible protein designs. Additionally, using fries and EWAK* within the osprey suite, we designed the tightest known binder of KRas, a heavily studied cancer target that interacts with a number of different proteins. This previously undiscovered variant of a KRas-binding domain, c-Raf-RBD, has potential to serve as a tool to further probe the protein-protein interface of KRas with its effectors and its discovery alone emphasizes the potential for more successful applications of computational structure-based protein design.
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Affiliation(s)
- Anna U. Lowegard
- Program in Computational Biology and Bioinformatics, Duke University Medical Center, Durham, North Carolina, United States of America
- Department of Computer Science, Duke University, Durham, North Carolina, United States of America
| | - Marcel S. Frenkel
- Department of Biochemistry, Duke University Medical Center, Durham, North Carolina, United States of America
| | - Graham T. Holt
- Program in Computational Biology and Bioinformatics, Duke University Medical Center, Durham, North Carolina, United States of America
- Department of Computer Science, Duke University, Durham, North Carolina, United States of America
| | - Jonathan D. Jou
- Department of Computer Science, Duke University, Durham, North Carolina, United States of America
| | - Adegoke A. Ojewole
- Program in Computational Biology and Bioinformatics, Duke University Medical Center, Durham, North Carolina, United States of America
- Department of Computer Science, Duke University, Durham, North Carolina, United States of America
| | - Bruce R. Donald
- Department of Computer Science, Duke University, Durham, North Carolina, United States of America
- Department of Biochemistry, Duke University Medical Center, Durham, North Carolina, United States of America
- * E-mail:
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12
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Surpeta B, Sequeiros-Borja CE, Brezovsky J. Dynamics, a Powerful Component of Current and Future in Silico Approaches for Protein Design and Engineering. Int J Mol Sci 2020; 21:E2713. [PMID: 32295283 PMCID: PMC7215530 DOI: 10.3390/ijms21082713] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Revised: 04/10/2020] [Accepted: 04/12/2020] [Indexed: 12/13/2022] Open
Abstract
Computational prediction has become an indispensable aid in the processes of engineering and designing proteins for various biotechnological applications. With the tremendous progress in more powerful computer hardware and more efficient algorithms, some of in silico tools and methods have started to apply the more realistic description of proteins as their conformational ensembles, making protein dynamics an integral part of their prediction workflows. To help protein engineers to harness benefits of considering dynamics in their designs, we surveyed new tools developed for analyses of conformational ensembles in order to select engineering hotspots and design mutations. Next, we discussed the collective evolution towards more flexible protein design methods, including ensemble-based approaches, knowledge-assisted methods, and provable algorithms. Finally, we highlighted apparent challenges that current approaches are facing and provided our perspectives on their further development.
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Affiliation(s)
- Bartłomiej Surpeta
- Laboratory of Biomolecular Interactions and Transport, Department of Gene Expression, Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University, Uniwersytetu Poznanskiego 6, 61-614 Poznan, Poland; (B.S.); (C.E.S.-B.)
- International Institute of Molecular and Cell Biology in Warsaw, Ks Trojdena 4, 02-109 Warsaw, Poland
| | - Carlos Eduardo Sequeiros-Borja
- Laboratory of Biomolecular Interactions and Transport, Department of Gene Expression, Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University, Uniwersytetu Poznanskiego 6, 61-614 Poznan, Poland; (B.S.); (C.E.S.-B.)
- International Institute of Molecular and Cell Biology in Warsaw, Ks Trojdena 4, 02-109 Warsaw, Poland
| | - Jan Brezovsky
- Laboratory of Biomolecular Interactions and Transport, Department of Gene Expression, Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University, Uniwersytetu Poznanskiego 6, 61-614 Poznan, Poland; (B.S.); (C.E.S.-B.)
- International Institute of Molecular and Cell Biology in Warsaw, Ks Trojdena 4, 02-109 Warsaw, Poland
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13
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Ouali A, Allouche D, de Givry S, Loudni S, Lebbah Y, Loukil L, Boizumault P. Variable neighborhood search for graphical model energy minimization. ARTIF INTELL 2020. [DOI: 10.1016/j.artint.2019.103194] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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14
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Jou JD, Holt GT, Lowegard AU, Donald BR. Minimization-Aware Recursive K*: A Novel, Provable Algorithm that Accelerates Ensemble-Based Protein Design and Provably Approximates the Energy Landscape. J Comput Biol 2019; 27:550-564. [PMID: 31855059 DOI: 10.1089/cmb.2019.0315] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Protein design algorithms that model continuous sidechain flexibility and conformational ensembles better approximate the in vitro and in vivo behavior of proteins. The previous state of the art, iMinDEE-A*-K*, computes provable ɛ-approximations to partition functions of protein states (e.g., bound vs. unbound) by computing provable, admissible pairwise-minimized energy lower bounds on protein conformations, and using the A* enumeration algorithm to return a gap-free list of lowest-energy conformations. iMinDEE-A*-K* runs in time sublinear in the number of conformations, but can be trapped in loosely-bounded, low-energy conformational wells containing many conformations with highly similar energies. That is, iMinDEE-A*-K* is unable to exploit the correlation between protein conformation and energy: similar conformations often have similar energy. We introduce two new concepts that exploit this correlation: Minimization-Aware Enumeration and Recursive K*. We combine these two insights into a novel algorithm, Minimization-Aware Recursive K* (MARK*), which tightens bounds not on single conformations, but instead on distinct regions of the conformation space. We compare the performance of iMinDEE-A*-K* versus MARK* by running the Branch and Bound over K* (BBK*) algorithm, which provably returns sequences in order of decreasing K* score, using either iMinDEE-A*-K* or MARK* to approximate partition functions. We show on 200 design problems that MARK* not only enumerates and minimizes vastly fewer conformations than the previous state of the art, but also runs up to 2 orders of magnitude faster. Finally, we show that MARK* not only efficiently approximates the partition function, but also provably approximates the energy landscape. To our knowledge, MARK* is the first algorithm to do so. We use MARK* to analyze the change in energy landscape of the bound and unbound states of an HIV-1 capsid protein C-terminal domain in complex with a camelid VHH, and measure the change in conformational entropy induced by binding. Thus, MARK* both accelerates existing designs and offers new capabilities not possible with previous algorithms.
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Affiliation(s)
- Jonathan D Jou
- Department of Computer Science, Duke University, Durham, North Carolina
| | - Graham T Holt
- Department of Computer Science, Duke University, Durham, North Carolina.,Computational Biology and Bioinformatics Program, Duke University, Durham, North Carolina
| | - Anna U Lowegard
- Department of Computer Science, Duke University, Durham, North Carolina.,Computational Biology and Bioinformatics Program, Duke University, Durham, North Carolina
| | - Bruce R Donald
- Department of Computer Science, Duke University, Durham, North Carolina.,Department of Biochemistry, Duke University Medical Center, Durham, North Carolina.,Department of Chemistry, Duke University, Durham, North Carolina
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15
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Holt GT, Jou JD, Gill NP, Lowegard AU, Martin JW, Madden DR, Donald BR. Computational Analysis of Energy Landscapes Reveals Dynamic Features That Contribute to Binding of Inhibitors to CFTR-Associated Ligand. J Phys Chem B 2019; 123:10441-10455. [PMID: 31697075 DOI: 10.1021/acs.jpcb.9b07278] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
The CFTR-associated ligand PDZ domain (CALP) binds to the cystic fibrosis transmembrane conductance regulator (CFTR) and mediates lysosomal degradation of mature CFTR. Inhibition of this interaction has been explored as a therapeutic avenue for cystic fibrosis. Previously, we reported the ensemble-based computational design of a novel peptide inhibitor of CALP, which resulted in the most binding-efficient inhibitor to date. This inhibitor, kCAL01, was designed using osprey and evinced significant biological activity in in vitro cell-based assays. Here, we report a crystal structure of kCAL01 bound to CALP and compare structural features against iCAL36, a previously developed inhibitor of CALP. We compute side-chain energy landscapes for each structure to not only enable approximation of binding thermodynamics but also reveal ensemble features that contribute to the comparatively efficient binding of kCAL01. Finally, we compare the previously reported design ensemble for kCAL01 vs the new crystal structure and show that, despite small differences between the design model and crystal structure, significant biophysical features that enhance inhibitor binding are captured in the design ensemble. This suggests not only that ensemble-based design captured thermodynamically significant features observed in vitro, but also that a design eschewing ensembles would miss the kCAL01 sequence entirely.
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Affiliation(s)
- Graham T Holt
- Department of Computer Science , Duke University , Durham , North Carolina 27708 , United States.,Program in Computational Biology and Bioinformatics , Duke University , Durham , North Carolina 27708 , United States
| | - Jonathan D Jou
- Department of Computer Science , Duke University , Durham , North Carolina 27708 , United States
| | - Nicholas P Gill
- Department of Biochemistry & Cell Biology , Geisel School of Medicine at Dartmouth , Hanover , New Hampshire 03755 , United States
| | - Anna U Lowegard
- Department of Computer Science , Duke University , Durham , North Carolina 27708 , United States.,Program in Computational Biology and Bioinformatics , Duke University , Durham , North Carolina 27708 , United States
| | - Jeffrey W Martin
- Department of Computer Science , Duke University , Durham , North Carolina 27708 , United States
| | - Dean R Madden
- Department of Biochemistry & Cell Biology , Geisel School of Medicine at Dartmouth , Hanover , New Hampshire 03755 , United States
| | - Bruce R Donald
- Department of Computer Science , Duke University , Durham , North Carolina 27708 , United States.,Department of Biochemistry , Duke University , Durham , North Carolina 27710 , United States.,Department of Chemistry , Duke University , Durham , North Carolina 27710 , United States
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16
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HALLEN MARKA, DONALD BRUCER. Protein Design by Provable Algorithms. COMMUNICATIONS OF THE ACM 2019; 62:76-84. [PMID: 31607753 PMCID: PMC6788629 DOI: 10.1145/3338124] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Protein design algorithms can leverage provable guarantees of accuracy to provide new insights and unique optimized molecules.
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Affiliation(s)
- MARK A. HALLEN
- Research assistant professor at the Toyota Technological Institute at Chicago, IL, USA
| | - BRUCE R. DONALD
- James B. Duke Professor of Computer Science at Duke University, as well as a
professor of chemistry and biochemistry in the Duke University Medical
Center, Durham, NC, USA
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17
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Vucinic J, Simoncini D, Ruffini M, Barbe S, Schiex T. Positive multistate protein design. Bioinformatics 2019; 36:122-130. [DOI: 10.1093/bioinformatics/btz497] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Revised: 05/20/2019] [Accepted: 06/11/2019] [Indexed: 11/12/2022] Open
Abstract
Abstract
Motivation
Structure-based computational protein design (CPD) plays a critical role in advancing the field of protein engineering. Using an all-atom energy function, CPD tries to identify amino acid sequences that fold into a target structure and ultimately perform a desired function. The usual approach considers a single rigid backbone as a target, which ignores backbone flexibility. Multistate design (MSD) allows instead to consider several backbone states simultaneously, defining challenging computational problems.
Results
We introduce efficient reductions of positive MSD problems to Cost Function Networks with two different fitness definitions and implement them in the Pompd (Positive Multistate Protein design) software. Pompd is able to identify guaranteed optimal sequences of positive multistate full protein redesign problems and exhaustively enumerate suboptimal sequences close to the MSD optimum. Applied to nuclear magnetic resonance and back-rubbed X-ray structures, we observe that the average energy fitness provides the best sequence recovery. Our method outperforms state-of-the-art guaranteed computational design approaches by orders of magnitudes and can solve MSD problems with sizes previously unreachable with guaranteed algorithms.
Availability and implementation
https://forgemia.inra.fr/thomas.schiex/pompd as documented Open Source.
Supplementary information
Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jelena Vucinic
- LISBP, Université de Toulouse, CNRS, INRA, INSA, 31400 Toulouse, France
- MIAT, Université de Toulouse, INRA, 31326 Castanet-Tolosan Cedex, France
| | - David Simoncini
- LISBP, Université de Toulouse, CNRS, INRA, INSA, 31400 Toulouse, France
- IRIT UMR 5505-CNRS, Université de Toulouse, 31042 Cedex 9, France
| | - Manon Ruffini
- LISBP, Université de Toulouse, CNRS, INRA, INSA, 31400 Toulouse, France
- MIAT, Université de Toulouse, INRA, 31326 Castanet-Tolosan Cedex, France
| | - Sophie Barbe
- LISBP, Université de Toulouse, CNRS, INRA, INSA, 31400 Toulouse, France
| | - Thomas Schiex
- MIAT, Université de Toulouse, INRA, 31326 Castanet-Tolosan Cedex, France
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18
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Noguchi H, Addy C, Simoncini D, Wouters S, Mylemans B, Van Meervelt L, Schiex T, Zhang KYJ, Tame JRH, Voet ARD. Computational design of symmetrical eight-bladed β-propeller proteins. IUCRJ 2019; 6:46-55. [PMID: 30713702 PMCID: PMC6327176 DOI: 10.1107/s205225251801480x] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/23/2018] [Accepted: 10/19/2018] [Indexed: 05/04/2023]
Abstract
β-Propeller proteins form one of the largest families of protein structures, with a pseudo-symmetrical fold made up of subdomains called blades. They are not only abundant but are also involved in a wide variety of cellular processes, often by acting as a platform for the assembly of protein complexes. WD40 proteins are a subfamily of propeller proteins with no intrinsic enzymatic activity, but their stable, modular architecture and versatile surface have allowed evolution to adapt them to many vital roles. By computationally reverse-engineering the duplication, fusion and diversification events in the evolutionary history of a WD40 protein, a perfectly symmetrical homologue called Tako8 was made. If two or four blades of Tako8 are expressed as single polypeptides, they do not self-assemble to complete the eight-bladed architecture, which may be owing to the closely spaced negative charges inside the ring. A different computational approach was employed to redesign Tako8 to create Ika8, a fourfold-symmetrical protein in which neighbouring blades carry compensating charges. Ika2 and Ika4, carrying two or four blades per subunit, respectively, were found to assemble spontaneously into a complete eight-bladed ring in solution. These artificial eight-bladed rings may find applications in bionanotechnology and as models to study the folding and evolution of WD40 proteins.
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Affiliation(s)
- Hiroki Noguchi
- Laboratory of Biomolecular Modelling and Design, Department of Chemistry, KU Leuven, Celestijnenlaan 200G, 3001 Leuven, Belgium
| | - Christine Addy
- Graduate School of Medical Life Science, Yokohama City University, 1-7-29 Suehiro, Yokohama, Kanagawa 230-0045, Japan
| | - David Simoncini
- MIAT, Université de Toulouse, INRA, Castanet-Tolosan, France
| | - Staf Wouters
- Laboratory of Biomolecular Modelling and Design, Department of Chemistry, KU Leuven, Celestijnenlaan 200G, 3001 Leuven, Belgium
| | - Bram Mylemans
- Laboratory of Biomolecular Modelling and Design, Department of Chemistry, KU Leuven, Celestijnenlaan 200G, 3001 Leuven, Belgium
| | - Luc Van Meervelt
- Laboratory of Biomolecular Architecture, Department of Chemistry, KU Leuven, Celestijnenlaan 200F, 3001 Leuven, Belgium
| | - Thomas Schiex
- MIAT, Université de Toulouse, INRA, Castanet-Tolosan, France
| | - Kam Y. J. Zhang
- Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Yokohama, Kanagawa 230-0045, Japan
| | - Jeremy R. H. Tame
- Graduate School of Medical Life Science, Yokohama City University, 1-7-29 Suehiro, Yokohama, Kanagawa 230-0045, Japan
| | - Arnout R. D. Voet
- Laboratory of Biomolecular Modelling and Design, Department of Chemistry, KU Leuven, Celestijnenlaan 200G, 3001 Leuven, Belgium
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19
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Simoncini D, Zhang KYJ, Schiex T, Barbe S. A structural homology approach for computational protein design with flexible backbone. Bioinformatics 2018; 35:2418-2426. [DOI: 10.1093/bioinformatics/bty975] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2018] [Revised: 11/01/2018] [Accepted: 11/28/2018] [Indexed: 01/09/2023] Open
Abstract
Abstract
Motivation
Structure-based Computational Protein design (CPD) plays a critical role in advancing the field of protein engineering. Using an all-atom energy function, CPD tries to identify amino acid sequences that fold into a target structure and ultimately perform a desired function. Energy functions remain however imperfect and injecting relevant information from known structures in the design process should lead to improved designs.
Results
We introduce Shades, a data-driven CPD method that exploits local structural environments in known protein structures together with energy to guide sequence design, while sampling side-chain and backbone conformations to accommodate mutations. Shades (Structural Homology Algorithm for protein DESign), is based on customized libraries of non-contiguous in-contact amino acid residue motifs. We have tested Shades on a public benchmark of 40 proteins selected from different protein families. When excluding homologous proteins, Shades achieved a protein sequence recovery of 30% and a protein sequence similarity of 46% on average, compared with the PFAM protein family of the target protein. When homologous structures were added, the wild-type sequence recovery rate achieved 93%.
Availability and implementation
Shades source code is available at https://bitbucket.org/satsumaimo/shades as a patch for Rosetta 3.8 with a curated protein structure database and ITEM library creation software.
Supplementary information
Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- David Simoncini
- Laboratoire d'Ingénierie des Systèmes Biologiques et des Procédés, LISBP, Université de Toulouse, CNRS, INRA, INSA, F Toulouse cedex 04, France
- Institut de recherche en informatique de Toulouse, IRIT, UMR 5505-CNRS, Université de Toulouse, Cedex 9, France
| | - Kam Y J Zhang
- Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, Yokohama, Kanagawa, Japan
| | - Thomas Schiex
- Institut de recherche en informatique de Toulouse, UMR 5505-CNRS, Université de Toulouse, Cedex 9, France
| | - Sophie Barbe
- Laboratoire d'Ingénierie des Systèmes Biologiques et des Procédés, LISBP, Université de Toulouse, CNRS, INRA, INSA, F Toulouse cedex 04, France
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20
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Hallen MA. PLUG (Pruning of Local Unrealistic Geometries) removes restrictions on biophysical modeling for protein design. Proteins 2018; 87:62-73. [PMID: 30378699 DOI: 10.1002/prot.25623] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2018] [Revised: 10/10/2018] [Accepted: 10/16/2018] [Indexed: 12/29/2022]
Abstract
Protein design algorithms must search an enormous conformational space to identify favorable conformations. As a result, those that perform this search with guarantees of accuracy generally start with a conformational pruning step, such as dead-end elimination (DEE). However, the mathematical assumptions of DEE-based pruning algorithms have up to now severely restricted the biophysical model that can feasibly be used in protein design. To lift these restrictions, I propose to prune local unrealistic geometries (PLUG) using a linear programming-based method. PLUG's biophysical model consists only of well-known lower bounds on interatomic distances. PLUG is intended as preprocessing for energy-based protein design calculations, whose biophysical model need not support DEE pruning. Based on 96 test cases, PLUG is at least as effective at pruning as DEE for larger protein designs-the type that most require pruning. When combined with the LUTE protein design algorithm, PLUG greatly facilitates designs that account for continuous entropy, large multistate designs with continuous flexibility, and designs with extensive continuous backbone flexibility and advanced nonpairwise energy functions. Many of these designs are tractable only with PLUG, either for empirical reasons (LUTE's machine learning step achieves an accurate fit only after PLUG pruning), or for theoretical reasons (many energy functions are fundamentally incompatible with DEE).
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Affiliation(s)
- Mark A Hallen
- Toyota Technological Institute at Chicago, Chicago, Illinois
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21
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Charpentier A, Mignon D, Barbe S, Cortes J, Schiex T, Simonson T, Allouche D. Variable Neighborhood Search with Cost Function Networks To Solve Large Computational Protein Design Problems. J Chem Inf Model 2018; 59:127-136. [DOI: 10.1021/acs.jcim.8b00510] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
| | - David Mignon
- Laboratoire de Biochimie (CNRS UMR 7654), École Polytechnique, 91128 Palaiseau, France
| | - Sophie Barbe
- Laboratoire d’Ingénierie des Systèmes Biologiques et Procédés, LISBP, Université de Toulouse, CNRS, INRA, INSA, 31077 Toulouse, France
| | - Juan Cortes
- LAAS-CNRS, Université de Toulouse, CNRS, 31400 Toulouse, France
| | - Thomas Schiex
- MIAT, Université de Toulouse, INRA, 31326 Castanet-Tolosan, France
| | - Thomas Simonson
- Laboratoire de Biochimie (CNRS UMR 7654), École Polytechnique, 91128 Palaiseau, France
| | - David Allouche
- MIAT, Université de Toulouse, INRA, 31326 Castanet-Tolosan, France
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22
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Lechner H, Ferruz N, Höcker B. Strategies for designing non-natural enzymes and binders. Curr Opin Chem Biol 2018; 47:67-76. [PMID: 30248579 DOI: 10.1016/j.cbpa.2018.07.022] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2018] [Revised: 07/16/2018] [Accepted: 07/17/2018] [Indexed: 12/20/2022]
Abstract
The design of tailor-made enzymes is a major goal in biochemical research that can result in wide-range applications and will lead to a better understanding of how proteins fold and function. In this review we highlight recent advances in enzyme and small molecule binder design. A focus is placed on novel strategies for the design of scaffolds, developments in computational methods, and recent applications of these techniques on receptors, sensors, and enzymes. Further, the integration of computational and experimental methodologies is discussed. The outlined examples of designed enzymes and binders for various purposes highlight the importance of this topic and underline the need for tailor-made proteins.
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Affiliation(s)
- Horst Lechner
- Department of Biochemistry, University of Bayreuth, 95447 Bayreuth, Germany
| | - Noelia Ferruz
- Department of Biochemistry, University of Bayreuth, 95447 Bayreuth, Germany
| | - Birte Höcker
- Department of Biochemistry, University of Bayreuth, 95447 Bayreuth, Germany.
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23
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Villa F, Panel N, Chen X, Simonson T. Adaptive landscape flattening in amino acid sequence space for the computational design of protein:peptide binding. J Chem Phys 2018; 149:072302. [PMID: 30134674 DOI: 10.1063/1.5022249] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
For the high throughput design of protein:peptide binding, one must explore a vast space of amino acid sequences in search of low binding free energies. This complex problem is usually addressed with either simple heuristic scoring or expensive sequence enumeration schemes. Far more efficient than enumeration is a recent Monte Carlo approach that adaptively flattens the energy landscape in sequence space of the unbound peptide and provides formally exact binding free energy differences. The method allows the binding free energy to be used directly as the design criterion. We propose several improvements that allow still more efficient sampling and can address larger design problems. They include the use of Replica Exchange Monte Carlo and landscape flattening for both the unbound and bound peptides. We used the method to design peptides that bind to the PDZ domain of the Tiam1 signaling protein and could serve as inhibitors of its activity. Four peptide positions were allowed to mutate freely. Almost 75 000 peptide variants were processed in two simulations of 109 steps each that used 1 CPU hour on a desktop machine. 96% of the theoretical sequence space was sampled. The relative binding free energies agreed qualitatively with values from experiment. The sampled sequences agreed qualitatively with an experimental library of Tiam1-binding peptides. The main assumption limiting accuracy is the fixed backbone approximation, which could be alleviated in future work by using increased computational resources and multi-backbone designs.
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Affiliation(s)
- Francesco Villa
- Laboratoire de Biochimie (CNRS UMR7654), Ecole Polytechnique, Palaiseau, France
| | - Nicolas Panel
- Laboratoire de Biochimie (CNRS UMR7654), Ecole Polytechnique, Palaiseau, France
| | - Xingyu Chen
- Laboratoire de Biochimie (CNRS UMR7654), Ecole Polytechnique, Palaiseau, France
| | - Thomas Simonson
- Laboratoire de Biochimie (CNRS UMR7654), Ecole Polytechnique, Palaiseau, France
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24
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Abstract
Motivation Multistate protein design addresses real-world challenges, such as multi-specificity design and backbone flexibility, by considering both positive and negative protein states with an ensemble of substates for each. It also presents an enormous challenge to exact algorithms that guarantee the optimal solutions and enable a direct test of mechanistic hypotheses behind models. However, efficient exact algorithms are lacking for multistate protein design. Results We have developed an efficient exact algorithm called interconnected cost function networks (iCFN) for multistate protein design. Its generic formulation allows for a wide array of applications such as stability, affinity and specificity designs while addressing concerns such as global flexibility of protein backbones. iCFN treats each substate design as a weighted constraint satisfaction problem (WCSP) modeled through a CFN; and it solves the coupled WCSPs using novel bounds and a depth-first branch-and-bound search over a tree structure of sequences, substates, and conformations. When iCFN is applied to specificity design of a T-cell receptor, a problem of unprecedented size to exact methods, it drastically reduces search space and running time to make the problem tractable. Moreover, iCFN generates experimentally-agreeing receptor designs with improved accuracy compared with state-of-the-art methods, highlights the importance of modeling backbone flexibility in protein design, and reveals molecular mechanisms underlying binding specificity. Availability and implementation https://shen-lab.github.io/software/iCFN. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Mostafa Karimi
- Department of Electrical and Computer Engineering and TEES-AgriLife Center for Bioinformatics and Genomic Systems Engineering, Texas A&M University, College Station, USA
| | - Yang Shen
- Department of Electrical and Computer Engineering and TEES-AgriLife Center for Bioinformatics and Genomic Systems Engineering, Texas A&M University, College Station, USA
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25
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Hallen MA, Donald BR. CATS (Coordinates of Atoms by Taylor Series): protein design with backbone flexibility in all locally feasible directions. Bioinformatics 2018; 33:i5-i12. [PMID: 28882005 PMCID: PMC5870559 DOI: 10.1093/bioinformatics/btx277] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Motivation When proteins mutate or bind to ligands, their backbones often move significantly, especially in loop regions. Computational protein design algorithms must model these motions in order to accurately optimize protein stability and binding affinity. However, methods for backbone conformational search in design have been much more limited than for sidechain conformational search. This is especially true for combinatorial protein design algorithms, which aim to search a large sequence space efficiently and thus cannot rely on temporal simulation of each candidate sequence. Results We alleviate this difficulty with a new parameterization of backbone conformational space, which represents all degrees of freedom of a specified segment of protein chain that maintain valid bonding geometry (by maintaining the original bond lengths and angles and ω dihedrals). In order to search this space, we present an efficient algorithm, CATS, for computing atomic coordinates as a function of our new continuous backbone internal coordinates. CATS generalizes the iMinDEE and EPIC protein design algorithms, which model continuous flexibility in sidechain dihedrals, to model continuous, appropriately localized flexibility in the backbone dihedrals ϕ and ψ as well. We show using 81 test cases based on 29 different protein structures that CATS finds sequences and conformations that are significantly lower in energy than methods with less or no backbone flexibility do. In particular, we show that CATS can model the viability of an antibody mutation known experimentally to increase affinity, but that appears sterically infeasible when modeled with less or no backbone flexibility. Availability and implementation Our code is available as free software at https://github.com/donaldlab/OSPREY_refactor. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Mark A Hallen
- Department of Computer Science, Duke University, Durham, NC, USA.,Toyota Technological Institute at Chicago, Chicago, IL, USA
| | - Bruce R Donald
- Department of Computer Science, Duke University, Durham, NC, USA.,Department of Chemistry, Duke University, Durham, NC, USA.,Department of Biochemistry, Duke University Medical Center, Durham, NC, USA
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26
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Ojewole AA, Jou JD, Fowler VG, Donald BR. BBK* (Branch and Bound Over K*): A Provable and Efficient Ensemble-Based Protein Design Algorithm to Optimize Stability and Binding Affinity Over Large Sequence Spaces. J Comput Biol 2018; 25:726-739. [PMID: 29641249 DOI: 10.1089/cmb.2017.0267] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023] Open
Abstract
Computational protein design (CPD) algorithms that compute binding affinity, Ka, search for sequences with an energetically favorable free energy of binding. Recent work shows that three principles improve the biological accuracy of CPD: ensemble-based design, continuous flexibility of backbone and side-chain conformations, and provable guarantees of accuracy with respect to the input. However, previous methods that use all three design principles are single-sequence (SS) algorithms, which are very costly: linear in the number of sequences and thus exponential in the number of simultaneously mutable residues. To address this computational challenge, we introduce BBK*, a new CPD algorithm whose key innovation is the multisequence (MS) bound: BBK* efficiently computes a single provable upper bound to approximate Ka for a combinatorial number of sequences, and avoids SS computation for all provably suboptimal sequences. Thus, to our knowledge, BBK* is the first provable, ensemble-based CPD algorithm to run in time sublinear in the number of sequences. Computational experiments on 204 protein design problems show that BBK* finds the tightest binding sequences while approximating Ka for up to 105-fold fewer sequences than the previous state-of-the-art algorithms, which require exhaustive enumeration of sequences. Furthermore, for 51 protein-ligand design problems, BBK* provably approximates Ka up to 1982-fold faster than the previous state-of-the-art iMinDEE/[Formula: see text]/[Formula: see text] algorithm. Therefore, BBK* not only accelerates protein designs that are possible with previous provable algorithms, but also efficiently performs designs that are too large for previous methods.
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Affiliation(s)
- Adegoke A Ojewole
- 1 Department of Computer Science, Duke University , Durham, North Carolina.,2 Computational Biology and Bioinformatics Program, Duke University , Durham, North Carolina
| | - Jonathan D Jou
- 1 Department of Computer Science, Duke University , Durham, North Carolina
| | - Vance G Fowler
- 3 Division of Infectious Diseases, Duke University Medical Center , Durham, North Carolina
| | - Bruce R Donald
- 1 Department of Computer Science, Duke University , Durham, North Carolina.,4 Department of Biochemistry, Duke University Medical Center , Durham North Carolina
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27
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Viricel C, de Givry S, Schiex T, Barbe S. Cost function network-based design of protein–protein interactions: predicting changes in binding affinity. Bioinformatics 2018; 34:2581-2589. [DOI: 10.1093/bioinformatics/bty092] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2017] [Accepted: 02/16/2018] [Indexed: 11/14/2022] Open
Affiliation(s)
- Clément Viricel
- Laboratoire d’Ingénierie des Systèmes Biologiques et des Procédés, Université de Toulouse, CNRS, INRA, INSA, Toulouse, France
- Unité de Mathématiques et Informatique Appliquées de Toulouse, INRA, Castanet Tolosan cedex, France
| | - Simon de Givry
- Unité de Mathématiques et Informatique Appliquées de Toulouse, INRA, Castanet Tolosan cedex, France
| | - Thomas Schiex
- Unité de Mathématiques et Informatique Appliquées de Toulouse, INRA, Castanet Tolosan cedex, France
| | - Sophie Barbe
- Laboratoire d’Ingénierie des Systèmes Biologiques et des Procédés, Université de Toulouse, CNRS, INRA, INSA, Toulouse, France
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28
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Hallen MA, Jou JD, Donald BR. LUTE (Local Unpruned Tuple Expansion): Accurate Continuously Flexible Protein Design with General Energy Functions and Rigid Rotamer-Like Efficiency. J Comput Biol 2016; 24:536-546. [PMID: 27681371 DOI: 10.1089/cmb.2016.0136] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
Most protein design algorithms search over discrete conformations and an energy function that is residue-pairwise, that is, a sum of terms that depend on the sequence and conformation of at most two residues. Although modeling of continuous flexibility and of non-residue-pairwise energies significantly increases the accuracy of protein design, previous methods to model these phenomena add a significant asymptotic cost to design calculations. We now remove this cost by modeling continuous flexibility and non-residue-pairwise energies in a form suitable for direct input to highly efficient, discrete combinatorial optimization algorithms such as DEE/A* or branch-width minimization. Our novel algorithm performs a local unpruned tuple expansion (LUTE), which can efficiently represent both continuous flexibility and general, possibly nonpairwise energy functions to an arbitrary level of accuracy using a discrete energy matrix. We show using 47 design calculation test cases that LUTE provides a dramatic speedup in both single-state and multistate continuously flexible designs.
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Affiliation(s)
- Mark A Hallen
- 1 Department of Computer Science, Levine Science Research Center, Duke University , Durham, North Carolina
| | - Jonathan D Jou
- 1 Department of Computer Science, Levine Science Research Center, Duke University , Durham, North Carolina
| | - Bruce R Donald
- 1 Department of Computer Science, Levine Science Research Center, Duke University , Durham, North Carolina.,2 Department of Chemistry, Duke University , Durham, North Carolina.,3 Department of Biochemistry, Duke University Medical Center , Durham, North Carolina
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29
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Allouche D, Bessiere C, Boizumault P, de Givry S, Gutierrez P, Lee JH, Leung KL, Loudni S, Métivier JP, Schiex T, Wu Y. Tractability-preserving transformations of global cost functions. ARTIF INTELL 2016. [DOI: 10.1016/j.artint.2016.06.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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30
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Gainza P, Nisonoff HM, Donald BR. Algorithms for protein design. Curr Opin Struct Biol 2016; 39:16-26. [PMID: 27086078 PMCID: PMC5065368 DOI: 10.1016/j.sbi.2016.03.006] [Citation(s) in RCA: 58] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2015] [Revised: 03/15/2016] [Accepted: 03/22/2016] [Indexed: 02/05/2023]
Abstract
Computational structure-based protein design programs are becoming an increasingly important tool in molecular biology. These programs compute protein sequences that are predicted to fold to a target structure and perform a desired function. The success of a program's predictions largely relies on two components: first, the input biophysical model, and second, the algorithm that computes the best sequence(s) and structure(s) according to the biophysical model. Improving both the model and the algorithm in tandem is essential to improving the success rate of current programs, and here we review recent developments in algorithms for protein design, emphasizing how novel algorithms enable the use of more accurate biophysical models. We conclude with a list of algorithmic challenges in computational protein design that we believe will be especially important for the design of therapeutic proteins and protein assemblies.
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Affiliation(s)
- Pablo Gainza
- Department of Computer Science, Duke University, Durham, NC, United States
| | - Hunter M Nisonoff
- Department of Computer Science, Duke University, Durham, NC, United States
| | - Bruce R Donald
- Department of Computer Science, Duke University, Durham, NC, United States; Department of Biochemistry, Duke University Medical Center, Durham, NC, United States; Department of Chemistry, Duke University, Durham, NC, United States.
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31
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Liu H, Chen Q. Computational protein design for given backbone: recent progresses in general method-related aspects. Curr Opin Struct Biol 2016; 39:89-95. [PMID: 27348345 DOI: 10.1016/j.sbi.2016.06.013] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2016] [Revised: 05/18/2016] [Accepted: 06/15/2016] [Indexed: 10/21/2022]
Abstract
To achieve high success rate in protein design requires a reliable sequence design method to find amino acid sequences that stably fold into a desired backbone structure. This problem is addressed by computational protein design through the approach of energy minimization. Here we review recent method progresses related to improving the accuracy of this approach. First, the quality of the energy model is a key factor. Second, with structure sensitive energy functions, whether and how backbone flexibility is considered can have large effects on design accuracy, although usually only small adjustments of the backbone structure itself are involved. Third, the effective accuracy of design results can be boosted by post-processing a small number of designed sequences with complementary models that may not be efficient enough for full sequence optimization. Finally, computational method development will benefit greatly from increasingly efficient experimental approaches that can be applied to obtain extensive feedbacks.
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Affiliation(s)
- Haiyan Liu
- School of Life Sciences, University of Science and Technology of China, China; Hefei National Laboratory for Physical Sciences at the Microscales, China; Collaborative Innovation Center of Chemistry for Life Sciences, Hefei, Anhui 230027, China; Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui 230031, China.
| | - Quan Chen
- School of Life Sciences, University of Science and Technology of China, China
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32
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Mignon D, Simonson T. Comparing three stochastic search algorithms for computational protein design: Monte Carlo, replica exchange Monte Carlo, and a multistart, steepest-descent heuristic. J Comput Chem 2016; 37:1781-93. [PMID: 27197555 DOI: 10.1002/jcc.24393] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2015] [Revised: 02/26/2016] [Accepted: 03/27/2016] [Indexed: 01/11/2023]
Abstract
Computational protein design depends on an energy function and an algorithm to search the sequence/conformation space. We compare three stochastic search algorithms: a heuristic, Monte Carlo (MC), and a Replica Exchange Monte Carlo method (REMC). The heuristic performs a steepest-descent minimization starting from thousands of random starting points. The methods are applied to nine test proteins from three structural families, with a fixed backbone structure, a molecular mechanics energy function, and with 1, 5, 10, 20, 30, or all amino acids allowed to mutate. Results are compared to an exact, "Cost Function Network" method that identifies the global minimum energy conformation (GMEC) in favorable cases. The designed sequences accurately reproduce experimental sequences in the hydrophobic core. The heuristic and REMC agree closely and reproduce the GMEC when it is known, with a few exceptions. Plain MC performs well for most cases, occasionally departing from the GMEC by 3-4 kcal/mol. With REMC, the diversity of the sequences sampled agrees with exact enumeration where the latter is possible: up to 2 kcal/mol above the GMEC. Beyond, room temperature replicas sample sequences up to 10 kcal/mol above the GMEC, providing thermal averages and a solution to the inverse protein folding problem. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- David Mignon
- Laboratoire De Biochimie (UMR CNRS 7654), Department Of Biology, Ecole Polytechnique, Palaiseau, France
| | - Thomas Simonson
- Laboratoire De Biochimie (UMR CNRS 7654), Department Of Biology, Ecole Polytechnique, Palaiseau, France
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