1
|
St-Jacques AD, Rodriguez JM, Eason MG, Foster SM, Khan ST, Damry AM, Goto NK, Thompson MC, Chica RA. Computational remodeling of an enzyme conformational landscape for altered substrate selectivity. Nat Commun 2023; 14:6058. [PMID: 37770431 PMCID: PMC10539519 DOI: 10.1038/s41467-023-41762-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Accepted: 09/13/2023] [Indexed: 09/30/2023] Open
Abstract
Structural plasticity of enzymes dictates their function. Yet, our ability to rationally remodel enzyme conformational landscapes to tailor catalytic properties remains limited. Here, we report a computational procedure for tuning conformational landscapes that is based on multistate design of hinge-mediated domain motions. Using this method, we redesign the conformational landscape of a natural aminotransferase to preferentially stabilize a less populated but reactive conformation and thereby increase catalytic efficiency with a non-native substrate, resulting in altered substrate selectivity. Steady-state kinetics of designed variants reveals activity increases with the non-native substrate of approximately 100-fold and selectivity switches of up to 1900-fold. Structural analyses by room-temperature X-ray crystallography and multitemperature nuclear magnetic resonance spectroscopy confirm that conformational equilibria favor the target conformation. Our computational approach opens the door to targeted alterations of conformational states and equilibria, which should facilitate the design of biocatalysts with customized activity and selectivity.
Collapse
Affiliation(s)
- Antony D St-Jacques
- Department of Chemistry and Biomolecular Sciences, University of Ottawa, Ottawa, ON, K1N 6N5, Canada
- Center for Catalysis Research and Innovation, University of Ottawa, Ottawa, ON, K1N 6N5, Canada
| | - Joshua M Rodriguez
- Department of Chemistry and Biochemistry, University of California, Merced, Merced, CA, 95343, USA
| | - Matthew G Eason
- Department of Chemistry and Biomolecular Sciences, University of Ottawa, Ottawa, ON, K1N 6N5, Canada
- Center for Catalysis Research and Innovation, University of Ottawa, Ottawa, ON, K1N 6N5, Canada
| | - Scott M Foster
- Department of Chemistry and Biomolecular Sciences, University of Ottawa, Ottawa, ON, K1N 6N5, Canada
- Center for Catalysis Research and Innovation, University of Ottawa, Ottawa, ON, K1N 6N5, Canada
| | - Safwat T Khan
- Department of Chemistry and Biomolecular Sciences, University of Ottawa, Ottawa, ON, K1N 6N5, Canada
- Center for Catalysis Research and Innovation, University of Ottawa, Ottawa, ON, K1N 6N5, Canada
| | - Adam M Damry
- Department of Chemistry and Biomolecular Sciences, University of Ottawa, Ottawa, ON, K1N 6N5, Canada
- Center for Catalysis Research and Innovation, University of Ottawa, Ottawa, ON, K1N 6N5, Canada
| | - Natalie K Goto
- Department of Chemistry and Biomolecular Sciences, University of Ottawa, Ottawa, ON, K1N 6N5, Canada
- Center for Catalysis Research and Innovation, University of Ottawa, Ottawa, ON, K1N 6N5, Canada
| | - Michael C Thompson
- Department of Chemistry and Biochemistry, University of California, Merced, Merced, CA, 95343, USA
| | - Roberto A Chica
- Department of Chemistry and Biomolecular Sciences, University of Ottawa, Ottawa, ON, K1N 6N5, Canada.
- Center for Catalysis Research and Innovation, University of Ottawa, Ottawa, ON, K1N 6N5, Canada.
| |
Collapse
|
2
|
Narkhede YB, Bhardwaj A, Motsa BB, Saxena R, Sharma T, Chapagain PP, Stahelin RV, Wiest O. Elucidating Residue-Level Determinants Affecting Dimerization of Ebola Virus Matrix Protein Using High-Throughput Site Saturation Mutagenesis and Biophysical Approaches. J Phys Chem B 2023; 127:6449-6461. [PMID: 37458567 DOI: 10.1021/acs.jpcb.3c01759] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/28/2023]
Abstract
The Ebola virus (EBOV) is a filamentous virus that acquires its lipid envelope from the plasma membrane of the host cell it infects. EBOV assembly and budding from the host cell plasma membrane are mediated by a peripheral protein, known as the matrix protein VP40. VP40 is a 326 amino acid protein with two domains that are loosely linked. The VP40 N-terminal domain (NTD) contains a hydrophobic α-helix, which mediates VP40 dimerization. The VP40 C-terminal domain has a cationic patch, which mediates interactions with anionic lipids and a hydrophobic region that mediates VP40 dimer-dimer interactions. The VP40 dimer is necessary for trafficking to the plasma membrane inner leaflet and interactions with anionic lipids to mediate the VP40 assembly and oligomerization. Despite significant structural information available on the VP40 dimer structure, little is known on how the VP40 dimer is stabilized and how residues outside the NTD hydrophobic portion of the α-helical dimer interface contribute to dimer stability. To better understand how VP40 dimer stability is maintained, we performed computational studies using per-residue energy decomposition and site saturation mutagenesis. These studies revealed a number of novel keystone residues for VP40 dimer stability just adjacent to the α-helical dimer interface as well as distant residues in the VP40 CTD that can stabilize the VP40 dimer form. Experimental studies with representative VP40 mutants in vitro and in cells were performed to test computational predictions that reveal residues that alter VP40 dimer stability. Taken together, these studies provide important biophysical insights into VP40 dimerization and may be useful in strategies to weaken or alter the VP40 dimer structure as a means of inhibiting the EBOV assembly.
Collapse
Affiliation(s)
- Yogesh B Narkhede
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, United States
| | - Atul Bhardwaj
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, United States
| | - Balindile B Motsa
- Department of Medicinal Chemistry & Molecular Pharmacology, Purdue Institute of Inflammation, Immunology, and Infectious Disease, Purdue University, West Lafayette, Indiana 47907, United States
| | - Roopashi Saxena
- Department of Medicinal Chemistry & Molecular Pharmacology, Purdue Institute of Inflammation, Immunology, and Infectious Disease, Purdue University, West Lafayette, Indiana 47907, United States
| | | | | | - Robert V Stahelin
- Department of Medicinal Chemistry & Molecular Pharmacology, Purdue Institute of Inflammation, Immunology, and Infectious Disease, Purdue University, West Lafayette, Indiana 47907, United States
| | - Olaf Wiest
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, United States
| |
Collapse
|
3
|
Wang P, Zhang J, Zhang S, Lu D, Zhu Y. Using High-Throughput Molecular Dynamics Simulation to Enhance the Computational Design of Kemp Elimination Enzymes. J Chem Inf Model 2023; 63:1323-1337. [PMID: 36782360 DOI: 10.1021/acs.jcim.3c00002] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/15/2023]
Abstract
Computational enzyme design has been successfully applied to identify new alternatives to natural enzymes for the biosynthesis of important compounds. However, the moderate catalytic activities of de novo designed enzymes indicate that the modeling accuracy of current computational enzyme design methods should be improved. Here, high-throughput molecular dynamics simulations were used to enhance computational enzyme design, thus allowing the identification of variants with higher activities in silico. Different time schemes of high-throughput molecular dynamics simulations were tested to identify the catalytic features of evolved Kemp eliminases. The 20 × 1 ns molecular dynamics simulation scheme was sufficiently accurate and computationally viable to screen the computationally designed massive variants of Kemp elimination enzymes. The developed hybrid computational strategy was used to redesign the most active Kemp eliminase, HG3.17, and five variants were generated and experimentally confirmed to afford higher catalytic efficiencies than that of HG3.17, with one double variant (D52Q/A53S) exhibiting a 55% increase. The hybrid computational enzyme design strategy is general and computationally economical, with which we anticipate the efficient creation of practical enzymes for industrial biocatalysis.
Collapse
Affiliation(s)
- Pengyu Wang
- College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China.,Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
| | - Jun Zhang
- Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
| | - Shengyu Zhang
- College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Diannan Lu
- Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
| | - Yushan Zhu
- College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| |
Collapse
|
4
|
Chen Y, Ming D, Zhu L, Huang H, Jiang L. Tailoring the Tag/Catcher System by Integrating Covalent Bonds and Noncovalent Interactions for Highly Efficient Protein Self-Assembly. Biomacromolecules 2022; 23:3936-3947. [PMID: 35998650 DOI: 10.1021/acs.biomac.2c00765] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Covalent bonds and noncovalent interactions play crucial roles in enzyme self-assembly. Here, we designed a Tag/Catcher system named NGTag/NGCatcher in which the Catcher is a highly charged protein that can bind proteins with positively charged tails and rapidly form a stable isopeptide bond with NGTag. In this study, we present a multienzyme strategy based on covalent bonds and noncovalent interactions. In vitro, mCherry, YFP, and GFP can form protein-rich three-dimensional networks based on NGCatcher, NGTag, and RK (Arginine/Lysine) tails, respectively. Furthermore, this technology was applied to improve lycopene production in Escherichia coli. Three key enzymes were involved in lycopene production variants from Deinococcus wulumuqiensis R12 of NGCatcher_CrtE, NGTag_Idi, and RKIspARK, where the multienzyme complexes were clearly observed in vivo and in vitro, and the lycopene production in vivo was 17.8-fold higher than that in the control group. The NGTag/NGCatcher system will provide new opportunities for in vivo and in vitro multienzyme catalysis.
Collapse
Affiliation(s)
- Yao Chen
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Food Science and Light Industry, Nanjing Tech University, Nanjing 211816, China.,College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Nanjing 211816, China
| | - Dengming Ming
- College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Nanjing 211816, China
| | - Liying Zhu
- School of Chemistry and Molecular Engineering, Nanjing Tech University, Nanjing 211816, China
| | - He Huang
- College of Pharmaceutical Science, Nanjing Tech University, Nanjing 211816, China.,School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing 210046, China
| | - Ling Jiang
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Food Science and Light Industry, Nanjing Tech University, Nanjing 211816, China
| |
Collapse
|
5
|
On the Rapid Calculation of Binding Affinities for Antigen and Antibody Design and Affinity Maturation Simulations. Antibodies (Basel) 2022; 11:antib11030051. [PMID: 35997345 PMCID: PMC9397028 DOI: 10.3390/antib11030051] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 07/23/2022] [Accepted: 08/01/2022] [Indexed: 02/05/2023] Open
Abstract
The accurate and efficient calculation of protein-protein binding affinities is an essential component in antibody and antigen design and optimization, and in computer modeling of antibody affinity maturation. Such calculations remain challenging despite advances in computer hardware and algorithms, primarily because proteins are flexible molecules, and thus, require explicit or implicit incorporation of multiple conformational states into the computational procedure. The astronomical size of the amino acid sequence space further compounds the challenge by requiring predictions to be computed within a short time so that many sequence variants can be tested. In this study, we compare three classes of methods for antibody/antigen (Ab/Ag) binding affinity calculations: (i) a method that relies on the physical separation of the Ab/Ag complex in equilibrium molecular dynamics (MD) simulations, (ii) a collection of 18 scoring functions that act on an ensemble of structures created using homology modeling software, and (iii) methods based on the molecular mechanics-generalized Born surface area (MM-GBSA) energy decomposition, in which the individual contributions of the energy terms are scaled to optimize agreement with the experiment. When applied to a set of 49 antibody mutations in two Ab/HIV gp120 complexes, all of the methods are found to have modest accuracy, with the highest Pearson correlations reaching about 0.6. In particular, the most computationally intensive method, i.e., MD simulation, did not outperform several scoring functions. The optimized energy decomposition methods provided marginally higher accuracy, but at the expense of requiring experimental data for parametrization. Within each method class, we examined the effect of the number of independent computational replicates, i.e., modeled structures or reinitialized MD simulations, on the prediction accuracy. We suggest using about ten modeled structures for scoring methods, and about five simulation replicates for MD simulations as a rule of thumb for obtaining reasonable convergence. We anticipate that our study will be a useful resource for practitioners working to incorporate binding affinity calculations within their protein design and optimization process.
Collapse
|
6
|
Ding W, Nakai K, Gong H. Protein design via deep learning. Brief Bioinform 2022; 23:bbac102. [PMID: 35348602 PMCID: PMC9116377 DOI: 10.1093/bib/bbac102] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 02/26/2022] [Accepted: 03/01/2022] [Indexed: 12/11/2022] Open
Abstract
Proteins with desired functions and properties are important in fields like nanotechnology and biomedicine. De novo protein design enables the production of previously unseen proteins from the ground up and is believed as a key point for handling real social challenges. Recent introduction of deep learning into design methods exhibits a transformative influence and is expected to represent a promising and exciting future direction. In this review, we retrospect the major aspects of current advances in deep-learning-based design procedures and illustrate their novelty in comparison with conventional knowledge-based approaches through noticeable cases. We not only describe deep learning developments in structure-based protein design and direct sequence design, but also highlight recent applications of deep reinforcement learning in protein design. The future perspectives on design goals, challenges and opportunities are also comprehensively discussed.
Collapse
Affiliation(s)
- Wenze Ding
- School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China
- School of Future Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China
- MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Beijing Advanced Innovation Center for Structural Biology, Tsinghua University, Beijing 100084, China
| | - Kenta Nakai
- Institute of Medical Science, the University of Tokyo, Tokyo 1088639, Japan
| | - Haipeng Gong
- MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Beijing Advanced Innovation Center for Structural Biology, Tsinghua University, Beijing 100084, China
| |
Collapse
|
7
|
Legault S, Fraser-Halberg DP, McAnelly RL, Eason MG, Thompson MC, Chica RA. Generation of bright monomeric red fluorescent proteins via computational design of enhanced chromophore packing. Chem Sci 2022; 13:1408-1418. [PMID: 35222925 PMCID: PMC8809391 DOI: 10.1039/d1sc05088e] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 01/07/2022] [Indexed: 12/11/2022] Open
Abstract
Red fluorescent proteins (RFPs) have found widespread application in chemical and biological research due to their longer emission wavelengths. Here, we use computational protein design to increase the quantum yield and thereby brightness of a dim monomeric RFP (mRojoA, quantum yield = 0.02) by optimizing chromophore packing with aliphatic residues, which we hypothesized would reduce torsional motions causing non-radiative decay. Experimental characterization of the top 10 designed sequences yielded mSandy1 (λ em = 609 nm, quantum yield = 0.26), a variant with equivalent brightness to mCherry, a widely used RFP. We next used directed evolution to further increase brightness, resulting in mSandy2 (λ em = 606 nm, quantum yield = 0.35), the brightest Discosoma sp. derived monomeric RFP with an emission maximum above 600 nm reported to date. Crystallographic analysis of mSandy2 showed that the chromophore p-hydroxybenzylidene moiety is sandwiched between the side chains of Leu63 and Ile197, a structural motif that has not previously been observed in RFPs, and confirms that aliphatic packing leads to chromophore rigidification. Our results demonstrate that computational protein design can be used to generate bright monomeric RFPs, which can serve as templates for the evolution of novel far-red fluorescent proteins.
Collapse
Affiliation(s)
- Sandrine Legault
- Department of Chemistry and Biomolecular Sciences, University of Ottawa 10 Marie-Curie Ottawa Ontario K1N 6N5 Canada
| | - Derek P Fraser-Halberg
- Department of Chemistry and Biomolecular Sciences, University of Ottawa 10 Marie-Curie Ottawa Ontario K1N 6N5 Canada
| | - Ralph L McAnelly
- Department of Chemistry and Biochemistry, University of California, Merced Merced California 95343 USA
| | - Matthew G Eason
- Department of Chemistry and Biomolecular Sciences, University of Ottawa 10 Marie-Curie Ottawa Ontario K1N 6N5 Canada
| | - Michael C Thompson
- Department of Chemistry and Biochemistry, University of California, Merced Merced California 95343 USA
| | - Roberto A Chica
- Department of Chemistry and Biomolecular Sciences, University of Ottawa 10 Marie-Curie Ottawa Ontario K1N 6N5 Canada
| |
Collapse
|
8
|
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.
Collapse
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.
| |
Collapse
|
9
|
Pereira JM, Vieira M, Santos SM. Step-by-step design of proteins for small molecule interaction: A review on recent milestones. Protein Sci 2021; 30:1502-1520. [PMID: 33934427 PMCID: PMC8284594 DOI: 10.1002/pro.4098] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 04/21/2021] [Accepted: 04/23/2021] [Indexed: 01/01/2023]
Abstract
Protein design is the field of synthetic biology that aims at developing de novo custom-made proteins and peptides for specific applications. Despite exploring an ambitious goal, recent computational advances in both hardware and software technologies have paved the way to high-throughput screening and detailed design of novel folds and improved functionalities. Modern advances in the field of protein design for small molecule targeting are described in this review, organized in a step-by-step fashion: from the conception of a new or upgraded active binding site, to scaffold design, sequence optimization, and experimental expression of the custom protein. In each step, contemporary examples are described, and state-of-the-art software is briefly explored.
Collapse
Affiliation(s)
- José M. Pereira
- CICECO & Departamento de QuímicaUniversidade de AveiroAveiroPortugal
| | - Maria Vieira
- CICECO & Departamento de QuímicaUniversidade de AveiroAveiroPortugal
| | - Sérgio M. Santos
- CICECO & Departamento de QuímicaUniversidade de AveiroAveiroPortugal
| |
Collapse
|
10
|
Marrero Diaz de Villegas R, Seki C, Mattion NM, König GA. Functional and in silico Characterization of Neutralizing Interactions Between Antibodies and the Foot-and-Mouth Disease Virus Immunodominant Antigenic Site. Front Vet Sci 2021; 8:554383. [PMID: 34026880 PMCID: PMC8137985 DOI: 10.3389/fvets.2021.554383] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Accepted: 02/19/2021] [Indexed: 12/04/2022] Open
Abstract
Molecular knowledge of virus–antibody interactions is essential for the development of better vaccines and for a timely assessment of the spread and severity of epidemics. For foot-and-mouth disease virus (FMDV) research, in particular, computational methods for antigen–antibody (Ag–Ab) interaction, and cross-antigenicity characterization and prediction are critical to design engineered vaccines with robust, long-lasting, and wider response against different strains. We integrated existing structural modeling and prediction algorithms to study the surface properties of FMDV Ags and Abs and their interaction. First, we explored four modeling and two Ag–Ab docking methods and implemented a computational pipeline based on a reference Ag–Ab structure for FMDV of serotype C, to be used as a source protocol for the study of unknown interaction pairs of Ag–Ab. Next, we obtained the variable region sequence of two monoclonal IgM and IgG antibodies that recognize and neutralize antigenic site A (AgSA) epitopes from South America serotype A FMDV and developed two peptide ELISAs for their fine epitope mapping. Then, we applied the previous Ag–Ab molecular structure modeling and docking protocol further scored by functional peptide ELISA data. This work highlights a possible different behavior in the immune response of IgG and IgM Ab isotypes. The present method yielded reliable Ab models with differential paratopes and Ag interaction topologies in concordance with their isotype classes. Moreover, it demonstrates the applicability of computational prediction techniques to the interaction phenomena between the FMDV immunodominant AgSA and Abs, and points out their potential utility as a metric for virus-related, massive Ab repertoire analysis or as a starting point for recombinant vaccine design.
Collapse
Affiliation(s)
- Ruben Marrero Diaz de Villegas
- Instituto de Agrobiotecnología y Biología Molecular, Instituto Nacional de Tecnología Agropecuaria, Consejo Nacional de Investigaciones Científicas y Tecnológicas, Buenos Aires, Argentina
| | - Cristina Seki
- Centro de Virología Animal, Consejo Nacional de Investigaciones Científicas y Tecnológicas, Universidad Abierta Interamericana, Buenos Aires, Argentina
| | - Nora M Mattion
- Centro de Virología Animal, Consejo Nacional de Investigaciones Científicas y Tecnológicas, Universidad Abierta Interamericana, Buenos Aires, Argentina
| | - Guido A König
- Instituto de Agrobiotecnología y Biología Molecular, Instituto Nacional de Tecnología Agropecuaria, Consejo Nacional de Investigaciones Científicas y Tecnológicas, Buenos Aires, Argentina
| |
Collapse
|
11
|
Pan X, Kortemme T. Recent advances in de novo protein design: Principles, methods, and applications. J Biol Chem 2021; 296:100558. [PMID: 33744284 PMCID: PMC8065224 DOI: 10.1016/j.jbc.2021.100558] [Citation(s) in RCA: 93] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 03/12/2021] [Accepted: 03/16/2021] [Indexed: 02/06/2023] Open
Abstract
The computational de novo protein design is increasingly applied to address a number of key challenges in biomedicine and biological engineering. Successes in expanding applications are driven by advances in design principles and methods over several decades. Here, we review recent innovations in major aspects of the de novo protein design and include how these advances were informed by principles of protein architecture and interactions derived from the wealth of structures in the Protein Data Bank. We describe developments in de novo generation of designable backbone structures, optimization of sequences, design scoring functions, and the design of the function. The advances not only highlight design goals reachable now but also point to the challenges and opportunities for the future of the field.
Collapse
Affiliation(s)
- Xingjie Pan
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, USA; UC Berkeley - UCSF Graduate Program in Bioengineering, University of California San Francisco, San Francisco, California, USA.
| | - Tanja Kortemme
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, USA; UC Berkeley - UCSF Graduate Program in Bioengineering, University of California San Francisco, San Francisco, California, USA; Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, California, USA.
| |
Collapse
|
12
|
Ensemble-based enzyme design can recapitulate the effects of laboratory directed evolution in silico. Nat Commun 2020; 11:4808. [PMID: 32968058 PMCID: PMC7511930 DOI: 10.1038/s41467-020-18619-x] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Accepted: 08/25/2020] [Indexed: 01/30/2023] Open
Abstract
The creation of artificial enzymes is a key objective of computational protein design. Although de novo enzymes have been successfully designed, these exhibit low catalytic efficiencies, requiring directed evolution to improve activity. Here, we use room-temperature X-ray crystallography to study changes in the conformational ensemble during evolution of the designed Kemp eliminase HG3 (kcat/KM 146 M−1s−1). We observe that catalytic residues are increasingly rigidified, the active site becomes better pre-organized, and its entrance is widened. Based on these observations, we engineer HG4, an efficient biocatalyst (kcat/KM 103,000 M−1s−1) containing key first and second-shell mutations found during evolution. HG4 structures reveal that its active site is pre-organized and rigidified for efficient catalysis. Our results show how directed evolution circumvents challenges inherent to enzyme design by shifting conformational ensembles to favor catalytically-productive sub-states, and suggest improvements to the design methodology that incorporate ensemble modeling of crystallographic data. Kemp eliminases are artificial enzymes that catalyze the concerted deprotonation and ring-opening of benzisoxazoles. Here, the authors use room-temperature X-ray crystallography to investigate changes to the conformational ensemble of the Kemp eliminase HG3 along a directed evolutionary trajectory, and develop an experimentally guided, ensemble-based computational enzyme design procedure.
Collapse
|
13
|
Glasgow AA, Huang YM, Mandell DJ, Thompson M, Ritterson R, Loshbaugh AL, Pellegrino J, Krivacic C, Pache RA, Barlow KA, Ollikainen N, Jeon D, Kelly MJS, Fraser JS, Kortemme T. Computational design of a modular protein sense-response system. Science 2020; 366:1024-1028. [PMID: 31754004 DOI: 10.1126/science.aax8780] [Citation(s) in RCA: 75] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Accepted: 10/07/2019] [Indexed: 12/28/2022]
Abstract
Sensing and responding to signals is a fundamental ability of living systems, but despite substantial progress in the computational design of new protein structures, there is no general approach for engineering arbitrary new protein sensors. Here, we describe a generalizable computational strategy for designing sensor-actuator proteins by building binding sites de novo into heterodimeric protein-protein interfaces and coupling ligand sensing to modular actuation through split reporters. Using this approach, we designed protein sensors that respond to farnesyl pyrophosphate, a metabolic intermediate in the production of valuable compounds. The sensors are functional in vitro and in cells, and the crystal structure of the engineered binding site closely matches the design model. Our computational design strategy opens broad avenues to link biological outputs to new signals.
Collapse
Affiliation(s)
- Anum A Glasgow
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Yao-Ming Huang
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Daniel J Mandell
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA.,Bioinformatics Graduate Program, University of California San Francisco, San Francisco, CA, USA
| | - Michael Thompson
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Ryan Ritterson
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Amanda L Loshbaugh
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA.,Biophysics Graduate Program, University of California San Francisco, San Francisco, CA, USA
| | - Jenna Pellegrino
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA.,Biophysics Graduate Program, University of California San Francisco, San Francisco, CA, USA
| | - Cody Krivacic
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA.,UC Berkeley-UCSF Graduate Program in Bioengineering, University of California San Francisco, San Francisco, CA, USA
| | - Roland A Pache
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Kyle A Barlow
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA.,Bioinformatics Graduate Program, 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.,Bioinformatics Graduate Program, University of California San Francisco, San Francisco, CA, USA
| | - Deborah Jeon
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Mark J S Kelly
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, USA
| | - James S Fraser
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA.,Biophysics Graduate Program, University of California San Francisco, San Francisco, CA, USA.,Quantitative Biosciences Institute, University of California San Francisco, San Francisco, CA, USA
| | - Tanja Kortemme
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA. .,Bioinformatics Graduate Program, University of California San Francisco, San Francisco, CA, USA.,Biophysics Graduate Program, University of California San Francisco, San Francisco, CA, USA.,UC Berkeley-UCSF Graduate Program in Bioengineering, University of California San Francisco, San Francisco, CA, USA.,Quantitative Biosciences Institute, University of California San Francisco, San Francisco, CA, USA.,Chan Zuckerberg Biohub, San Francisco, CA, USA
| |
Collapse
|
14
|
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.
Collapse
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
| |
Collapse
|
15
|
Towards functional de novo designed proteins. Curr Opin Chem Biol 2019; 52:102-111. [DOI: 10.1016/j.cbpa.2019.06.011] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Revised: 04/25/2019] [Accepted: 06/06/2019] [Indexed: 12/31/2022]
|
16
|
Loshbaugh AL, Kortemme T. Comparison of Rosetta flexible-backbone computational protein design methods on binding interactions. Proteins 2019; 88:206-226. [PMID: 31344278 DOI: 10.1002/prot.25790] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2019] [Revised: 07/15/2019] [Accepted: 07/19/2019] [Indexed: 01/03/2023]
Abstract
Computational design of binding sites in proteins remains difficult, in part due to limitations in our current ability to sample backbone conformations that enable precise and accurate geometric positioning of side chains during sequence design. Here we present a benchmark framework for comparison between flexible-backbone design methods applied to binding interactions. We quantify the ability of different flexible backbone design methods in the widely used protein design software Rosetta to recapitulate observed protein sequence profiles assumed to represent functional protein/protein and protein/small molecule binding interactions. The CoupledMoves method, which combines backbone flexibility and sequence exploration into a single acceptance step during the sampling trajectory, better recapitulates observed sequence profiles than the BackrubEnsemble and FastDesign methods, which separate backbone flexibility and sequence design into separate acceptance steps during the sampling trajectory. Flexible-backbone design with the CoupledMoves method is a powerful strategy for reducing sequence space to generate targeted libraries for experimental screening and selection.
Collapse
Affiliation(s)
- Amanda L Loshbaugh
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California.,Biophysics Graduate Program, University of California San Francisco, San Francisco, California
| | - Tanja Kortemme
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California.,Biophysics Graduate Program, University of California San Francisco, San Francisco, California.,Quantitative Biosciences Institute, University of California San Francisco, San Francisco, California.,Chan Zuckerberg Biohub, San Francisco, California
| |
Collapse
|
17
|
Computational Modeling of Designed Ankyrin Repeat Protein Complexes with Their Targets. J Mol Biol 2019; 431:2852-2868. [DOI: 10.1016/j.jmb.2019.05.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2019] [Revised: 05/03/2019] [Accepted: 05/03/2019] [Indexed: 01/24/2023]
|
18
|
Xiong P, Hu X, Huang B, Zhang J, Chen Q, Liu H. Increasing the efficiency and accuracy of the ABACUS protein sequence design method. Bioinformatics 2019; 36:136-144. [DOI: 10.1093/bioinformatics/btz515] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2018] [Revised: 05/29/2019] [Accepted: 06/21/2019] [Indexed: 11/13/2022] Open
Abstract
Abstract
Motivation
The ABACUS (a backbone-based amino acid usage survey) method uses unique statistical energy functions to carry out protein sequence design. Although some of its results have been experimentally verified, its accuracy remains improvable because several important components of the method have not been specifically optimized for sequence design or in contexts of other parts of the method. The computational efficiency also needs to be improved to support interactive online applications or the consideration of a large number of alternative backbone structures.
Results
We derived a model to measure solvent accessibility with larger mutual information with residue types than previous models, optimized a set of rotamers which can approximate the sidechain atomic positions more accurately, and devised an empirical function to treat inter-atomic packing with parameters fitted to native structures and optimized in consistence with the rotamer set. Energy calculations have been accelerated by interpolation between pre-determined representative points in high-dimensional structural feature spaces. Sidechain repacking tests showed that ABACUS2 can accurately reproduce the conformation of native sidechains. In sequence design tests, the native residue type recovery rate reached 37.7%, exceeding the value of 32.7% for ABACUS1. Applying ABACUS2 to designed sequences on three native backbones produced proteins shown to be well-folded by experiments.
Availability and implementation
The ABACUS2 sequence design server can be visited at http://biocomp.ustc.edu.cn/servers/abacus-design.php.
Supplementary information
Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Peng Xiong
- School of Life Sciences, Hefei, Anhui 230026, China
| | - Xiuhong Hu
- School of Life Sciences, Hefei, Anhui 230026, China
| | - Bin Huang
- School of Life Sciences, Hefei, Anhui 230026, China
| | - Jiahai Zhang
- School of Life Sciences, Hefei, Anhui 230026, China
| | - Quan Chen
- School of Life Sciences, Hefei, Anhui 230026, China
| | - Haiyan Liu
- School of Life Sciences, Hefei, Anhui 230026, China
- Hefei National Laboratory for Physical Sciences at the Microscale, Hefei, Anhui 230026, China
- School of Data Science, University of Sciences and Technology of China, Hefei, Anhui 230026, China
| |
Collapse
|
19
|
St-Jacques AD, Eyahpaise MÈC, Chica RA. Computational Design of Multisubstrate Enzyme Specificity. ACS Catal 2019. [DOI: 10.1021/acscatal.9b01464] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Antony D. St-Jacques
- Department of Chemistry and Biomolecular Sciences, University of Ottawa, Ottawa, Ontario K1N 6N5, Canada
| | - Marie-Ève C. Eyahpaise
- Department of Chemistry and Biomolecular Sciences, University of Ottawa, Ottawa, Ontario K1N 6N5, Canada
| | - Roberto A. Chica
- Department of Chemistry and Biomolecular Sciences, University of Ottawa, Ottawa, Ontario K1N 6N5, Canada
| |
Collapse
|
20
|
Frappier V, Jenson JM, Zhou J, Grigoryan G, Keating AE. Tertiary Structural Motif Sequence Statistics Enable Facile Prediction and Design of Peptides that Bind Anti-apoptotic Bfl-1 and Mcl-1. Structure 2019; 27:606-617.e5. [PMID: 30773399 PMCID: PMC6447450 DOI: 10.1016/j.str.2019.01.008] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2018] [Revised: 12/20/2018] [Accepted: 01/18/2019] [Indexed: 12/25/2022]
Abstract
Understanding the relationship between protein sequence and structure well enough to design new proteins with desired functions is a longstanding goal in protein science. Here, we show that recurring tertiary structural motifs (TERMs) in the PDB provide rich information for protein-peptide interaction prediction and design. TERM statistics can be used to predict peptide binding energies for Bcl-2 family proteins as accurately as widely used structure-based tools. Furthermore, design using TERM energies (dTERMen) rapidly and reliably generates high-affinity peptide binders of anti-apoptotic proteins Bfl-1 and Mcl-1 with just 15%-38% sequence identity to any known native Bcl-2 family protein ligand. High-resolution structures of four designed peptides bound to their targets provide opportunities to analyze the strengths and limitations of the computational design method. Our results support dTERMen as a powerful approach that can complement existing tools for protein engineering.
Collapse
Affiliation(s)
- Vincent Frappier
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Justin M Jenson
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Jianfu Zhou
- Department of Computer Science, Dartmouth College, Hanover, NH 03755, USA
| | - Gevorg Grigoryan
- Department of Computer Science, Dartmouth College, Hanover, NH 03755, USA; Institute for Quantitative Biomedical Sciences, Dartmouth College, Hanover, NH 03755, USA; Department of Biological Sciences, Dartmouth College, Hanover, NH 03755, USA.
| | - Amy E Keating
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Koch Center for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
| |
Collapse
|
21
|
Multistate design of influenza antibodies improves affinity and breadth against seasonal viruses. Proc Natl Acad Sci U S A 2019; 116:1597-1602. [PMID: 30642961 PMCID: PMC6358683 DOI: 10.1073/pnas.1806004116] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
Influenza is an annual threat to global public health, in part because of constant antigenic drift that facilitates evasion of the antibody response. Rapid changes in the influenza HA protein make it difficult for an antibody to achieve broad activity against different virus subtypes. We developed a computational method that can optimize an antibody sequence to be robust against seasonal variation. As a proof of concept, we tested this method by redesigning a known antibody against a set of diverse HA antigens and showed that the variant redesigned antibodies have improved activity against the virus panel, as predicted. This work shows that computational design can improve naturally occurring antibodies for recognition of different virus strains. Influenza is a yearly threat to global public health. Rapid changes in influenza surface proteins resulting from antigenic drift and shift events make it difficult to readily identify antibodies with broadly neutralizing activity against different influenza subtypes with high frequency, specifically antibodies targeting the receptor binding domain (RBD) on influenza HA protein. We developed an optimized computational design method that is able to optimize an antibody for recognition of large panels of antigens. To demonstrate the utility of this multistate design method, we used it to redesign an antiinfluenza antibody against a large panel of more than 500 seasonal HA antigens of the H1 subtype. As a proof of concept, we tested this method on a variety of known antiinfluenza antibodies and identified those that could be improved computationally. We generated redesigned variants of antibody C05 to the HA RBD and experimentally characterized variants that exhibited improved breadth and affinity against our panel. C05 mutants exhibited improved affinity for three of the subtypes used in design by stabilizing the CDRH3 loop and creating favorable electrostatic interactions with the antigen. These mutants possess increased breadth and affinity of binding while maintaining high-affinity binding to existing targets, surpassing a major limitation up to this point.
Collapse
|
22
|
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.
Collapse
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.
| |
Collapse
|
23
|
Sevy AM, Panda S, Crowe JE, Meiler J, Vorobeychik Y. Integrating linear optimization with structural modeling to increase HIV neutralization breadth. PLoS Comput Biol 2018; 14:e1005999. [PMID: 29451898 PMCID: PMC5833279 DOI: 10.1371/journal.pcbi.1005999] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2017] [Revised: 03/01/2018] [Accepted: 01/24/2018] [Indexed: 11/18/2022] Open
Abstract
Computational protein design has been successful in modeling fixed backbone proteins in a single conformation. However, when modeling large ensembles of flexible proteins, current methods in protein design have been insufficient. Large barriers in the energy landscape are difficult to traverse while redesigning a protein sequence, and as a result current design methods only sample a fraction of available sequence space. We propose a new computational approach that combines traditional structure-based modeling using the Rosetta software suite with machine learning and integer linear programming to overcome limitations in the Rosetta sampling methods. We demonstrate the effectiveness of this method, which we call BROAD, by benchmarking the performance on increasing predicted breadth of anti-HIV antibodies. We use this novel method to increase predicted breadth of naturally-occurring antibody VRC23 against a panel of 180 divergent HIV viral strains and achieve 100% predicted binding against the panel. In addition, we compare the performance of this method to state-of-the-art multistate design in Rosetta and show that we can outperform the existing method significantly. We further demonstrate that sequences recovered by this method recover known binding motifs of broadly neutralizing anti-HIV antibodies. Finally, our approach is general and can be extended easily to other protein systems. Although our modeled antibodies were not tested in vitro, we predict that these variants would have greatly increased breadth compared to the wild-type antibody. In this article, we report a new approach for protein design, which combines traditional structural modeling with machine learning and integer programming. Using this method, we are able to design antibodies that are predicted to bind large panels of antigenically diverse HIV variants. The combination of methods from these fields allows us to surpass protein design limitations that have been seen up to this point. We predict that if we tested these modified antibodies against HIV variants they would have greater neutralization breadth than any antibodies seen to this point.
Collapse
Affiliation(s)
- Alexander M. Sevy
- Center for Structural Biology, Vanderbilt University, Nashville, TN, United States of America
| | - Swetasudha Panda
- Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, United States of America
| | - James E. Crowe
- Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, TN, United States of America
| | - Jens Meiler
- Center for Structural Biology, Vanderbilt University, Nashville, TN, United States of America
- Department of Chemistry, Vanderbilt University, Nashville, TN, United States of America
| | - Yevgeniy Vorobeychik
- Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, United States of America
- * E-mail:
| |
Collapse
|
24
|
Barlow KA, Ó Conchúir S, Thompson S, Suresh P, Lucas JE, Heinonen M, Kortemme T. Flex ddG: Rosetta Ensemble-Based Estimation of Changes in Protein-Protein Binding Affinity upon Mutation. J Phys Chem B 2018; 122:5389-5399. [PMID: 29401388 DOI: 10.1021/acs.jpcb.7b11367] [Citation(s) in RCA: 141] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Computationally modeling changes in binding free energies upon mutation (interface ΔΔ G) allows large-scale prediction and perturbation of protein-protein interactions. Additionally, methods that consider and sample relevant conformational plasticity should be able to achieve higher prediction accuracy over methods that do not. To test this hypothesis, we developed a method within the Rosetta macromolecular modeling suite (flex ddG) that samples conformational diversity using "backrub" to generate an ensemble of models and then applies torsion minimization, side chain repacking, and averaging across this ensemble to estimate interface ΔΔ G values. We tested our method on a curated benchmark set of 1240 mutants, and found the method outperformed existing methods that sampled conformational space to a lesser degree. We observed considerable improvements with flex ddG over existing methods on the subset of small side chain to large side chain mutations, as well as for multiple simultaneous non-alanine mutations, stabilizing mutations, and mutations in antibody-antigen interfaces. Finally, we applied a generalized additive model (GAM) approach to the Rosetta energy function; the resulting nonlinear reweighting model improved the agreement with experimentally determined interface ΔΔ G values but also highlighted the necessity of future energy function improvements.
Collapse
Affiliation(s)
- Kyle A Barlow
- Graduate Program in Bioinformatics , University of California San Francisco , San Francisco , California , United States of America
| | - Shane Ó Conchúir
- California Institute for Quantitative Biosciences , University of California San Francisco , San Francisco , California , United States of America.,Department of Bioengineering and Therapeutic Sciences , University of California San Francisco , San Francisco , California , United States of America
| | - Samuel Thompson
- Graduate Program in Biophysics , University of California San Francisco , San Francisco , California , United States of America
| | - Pooja Suresh
- Graduate Program in Biophysics , University of California San Francisco , San Francisco , California , United States of America
| | - James E Lucas
- Graduate Program in Bioengineering , University of California San Francisco , San Francisco , California , United States of America
| | - Markus Heinonen
- Department of Computer Science , Aalto University , Espoo , Finland.,Helsinki Institute for Information Technology (HIIT) , Helsinki , Finland
| | - Tanja Kortemme
- Graduate Program in Bioinformatics , University of California San Francisco , San Francisco , California , United States of America.,California Institute for Quantitative Biosciences , University of California San Francisco , San Francisco , California , United States of America.,Department of Bioengineering and Therapeutic Sciences , University of California San Francisco , San Francisco , California , United States of America.,Graduate Program in Biophysics , University of California San Francisco , San Francisco , California , United States of America.,Graduate Program in Bioengineering , University of California San Francisco , San Francisco , California , United States of America.,Chan Zuckerberg Biohub , San Francisco , California 94158 , United States
| |
Collapse
|
25
|
Rational design of proteins that exchange on functional timescales. Nat Chem Biol 2017; 13:1280-1285. [DOI: 10.1038/nchembio.2503] [Citation(s) in RCA: 64] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2017] [Accepted: 09/18/2017] [Indexed: 12/12/2022]
|
26
|
Broom A, Jacobi Z, Trainor K, Meiering EM. Computational tools help improve protein stability but with a solubility tradeoff. J Biol Chem 2017; 292:14349-14361. [PMID: 28710274 DOI: 10.1074/jbc.m117.784165] [Citation(s) in RCA: 77] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2017] [Revised: 07/11/2017] [Indexed: 01/18/2023] Open
Abstract
Accurately predicting changes in protein stability upon amino acid substitution is a much sought after goal. Destabilizing mutations are often implicated in disease, whereas stabilizing mutations are of great value for industrial and therapeutic biotechnology. Increasing protein stability is an especially challenging task, with random substitution yielding stabilizing mutations in only ∼2% of cases. To overcome this bottleneck, computational tools that aim to predict the effect of mutations have been developed; however, achieving accuracy and consistency remains challenging. Here, we combined 11 freely available tools into a meta-predictor (meieringlab.uwaterloo.ca/stabilitypredict/). Validation against ∼600 experimental mutations indicated that our meta-predictor has improved performance over any of the individual tools. The meta-predictor was then used to recommend 10 mutations in a previously designed protein of moderate thermodynamic stability, ThreeFoil. Experimental characterization showed that four mutations increased protein stability and could be amplified through ThreeFoil's structural symmetry to yield several multiple mutants with >2-kcal/mol stabilization. By avoiding residues within functional ties, we could maintain ThreeFoil's glycan-binding capacity. Despite successfully achieving substantial stabilization, however, almost all mutations decreased protein solubility, the most common cause of protein design failure. Examination of the 600-mutation data set revealed that stabilizing mutations on the protein surface tend to increase hydrophobicity and that the individual tools favor this approach to gain stability. Thus, whereas currently available tools can increase protein stability and combining them into a meta-predictor yields enhanced reliability, improvements to the potentials/force fields underlying these tools are needed to avoid gaining protein stability at the cost of solubility.
Collapse
Affiliation(s)
- Aron Broom
- From the Department of Chemistry, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada
| | - Zachary Jacobi
- From the Department of Chemistry, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada
| | - Kyle Trainor
- From the Department of Chemistry, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada
| | | |
Collapse
|
27
|
Löffler P, Schmitz S, Hupfeld E, Sterner R, Merkl R. Rosetta:MSF: a modular framework for multi-state computational protein design. PLoS Comput Biol 2017; 13:e1005600. [PMID: 28604768 PMCID: PMC5484525 DOI: 10.1371/journal.pcbi.1005600] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2017] [Revised: 06/26/2017] [Accepted: 05/27/2017] [Indexed: 12/20/2022] Open
Abstract
Computational protein design (CPD) is a powerful technique to engineer existing proteins or to design novel ones that display desired properties. Rosetta is a software suite including algorithms for computational modeling and analysis of protein structures and offers many elaborate protocols created to solve highly specific tasks of protein engineering. Most of Rosetta’s protocols optimize sequences based on a single conformation (i. e. design state). However, challenging CPD objectives like multi-specificity design or the concurrent consideration of positive and negative design goals demand the simultaneous assessment of multiple states. This is why we have developed the multi-state framework MSF that facilitates the implementation of Rosetta’s single-state protocols in a multi-state environment and made available two frequently used protocols. Utilizing MSF, we demonstrated for one of these protocols that multi-state design yields a 15% higher performance than single-state design on a ligand-binding benchmark consisting of structural conformations. With this protocol, we designed de novo nine retro-aldolases on a conformational ensemble deduced from a (βα)8-barrel protein. All variants displayed measurable catalytic activity, testifying to a high success rate for this concept of multi-state enzyme design. Protein engineering, i. e. the targeted modification or design of proteins has tremendous potential for medical and industrial applications. One generally applicable strategy for protein engineering is rational protein design: based on detailed knowledge of structure and function, computer programs like Rosetta propose the sequence of a protein possessing the desired properties. So far, most computer protocols have used rigid structures for design, which is a simplification because a protein’s structure is more accurately specified by a conformational ensemble. We have now implemented a framework for computational protein design that allows certain design protocols of Rosetta to make use of multiple design states like structural ensembles. An in silico assessment simulating ligand-binding design showed that this new approach generates more reliably native-like sequences than a single-state approach. As a proof-of-concept, we introduced de novo retro-aldolase activity into a scaffold protein and characterized nine variants experimentally, all of which were catalytically active.
Collapse
Affiliation(s)
- Patrick Löffler
- Institute of Biophysics and Physical Biochemistry, University of Regensburg, Regensburg, Germany
| | - Samuel Schmitz
- Institute of Biophysics and Physical Biochemistry, University of Regensburg, Regensburg, Germany
| | - Enrico Hupfeld
- Institute of Biophysics and Physical Biochemistry, University of Regensburg, Regensburg, Germany
| | - Reinhard Sterner
- Institute of Biophysics and Physical Biochemistry, University of Regensburg, Regensburg, Germany
| | - Rainer Merkl
- Institute of Biophysics and Physical Biochemistry, University of Regensburg, Regensburg, Germany
- * E-mail:
| |
Collapse
|
28
|
Frappier V, Chartier M, Najmanovich R. Applications of Normal Mode Analysis Methods in Computational Protein Design. Methods Mol Biol 2017; 1529:203-214. [PMID: 27914052 DOI: 10.1007/978-1-4939-6637-0_9] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Recent advances in coarse-grained normal mode analysis methods make possible the large-scale prediction of the effect of mutations on protein stability and dynamics as well as the generation of biologically relevant conformational ensembles. Given the interplay between flexibility and enzymatic activity, the combined analysis of stability and dynamics using the Elastic Network Contact Model (ENCoM) method has ample applications in protein engineering in industrial and medical applications such as in computational antibody design. Here, we present a detailed tutorial on how to perform such calculations using ENCoM.
Collapse
Affiliation(s)
- Vincent Frappier
- Department of Biology, Massachusetts Institute of Technology, 77 Massachusetts avenue, Cambridge, MA, 02139, USA
- Faculty of Medicine and Health Sciences, Department of Biochemistry, University of Sherbrooke, 3001, 12 Av., NordSherbrooke, QCJ1H 5N4, Canada
| | - Matthieu Chartier
- Faculty of Medicine and Health Sciences, Department of Biochemistry, University of Sherbrooke, 3001, 12 Av., NordSherbrooke, QCJ1H 5N4, Canada
| | - Rafael Najmanovich
- Department of Pharmacology and Physiology, Faculty of Medicine, Université de Montreal, Montreal, H3C 3J7, QC, Canada.
- Faculty of Medicine and Health Sciences, Department of Biochemistry, University of Sherbrooke, 3001, 12 Av., NordSherbrooke, QCJ1H 5N4, Canada.
| |
Collapse
|
29
|
Abstract
The ability of computational protein design (CPD) to identify protein sequences possessing desired characteristics in vast sequence spaces makes it a highly valuable tool in the protein engineering toolbox. CPD calculations are typically performed using a single-state design (SSD) approach in which amino-acid sequences are optimized on a single protein structure. Although SSD has been successfully applied to the design of numerous protein functions and folds, the approach can lead to the incorrect rejection of desirable sequences because of the combined use of a fixed protein backbone template and a set of rigid rotamers. This fixed backbone approximation can be addressed by using multistate design (MSD) with backbone ensembles. MSD improves the quality of predicted sequences by using ensembles approximating conformational flexibility as input templates instead of a single fixed protein structure. In this chapter, we present a step-by-step guide to the implementation and analysis of MSD calculations with backbone ensembles. Specifically, we describe ensemble generation with the PertMin protocol, execution of MSD calculations for recapitulation of Streptococcal protein G domain β1 mutant stability, and analysis of computational predictions by sequence binning. Furthermore, we provide a comparison between MSD and SSD calculation results and discuss the benefits of multistate approaches to CPD.
Collapse
|
30
|
Romero-Rivera A, Garcia-Borràs M, Osuna S. Computational tools for the evaluation of laboratory-engineered biocatalysts. Chem Commun (Camb) 2016; 53:284-297. [PMID: 27812570 PMCID: PMC5310519 DOI: 10.1039/c6cc06055b] [Citation(s) in RCA: 71] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2016] [Accepted: 09/06/2016] [Indexed: 12/18/2022]
Abstract
Biocatalysis is based on the application of natural catalysts for new purposes, for which enzymes were not designed. Although the first examples of biocatalysis were reported more than a century ago, biocatalysis was revolutionized after the discovery of an in vitro version of Darwinian evolution called Directed Evolution (DE). Despite the recent advances in the field, major challenges remain to be addressed. Currently, the best experimental approach consists of creating multiple mutations simultaneously while limiting the choices using statistical methods. Still, tens of thousands of variants need to be tested experimentally, and little information is available on how these mutations lead to enhanced enzyme proficiency. This review aims to provide a brief description of the available computational techniques to unveil the molecular basis of improved catalysis achieved by DE. An overview of the strengths and weaknesses of current computational strategies is explored with some recent representative examples. The understanding of how this powerful technique is able to obtain highly active variants is important for the future development of more robust computational methods to predict amino-acid changes needed for activity.
Collapse
Affiliation(s)
- Adrian Romero-Rivera
- Institut de Química Computacional i Catàlisi and Departament de Química Universitat de Girona, Campus Montilivi, 17071 Girona, Catalonia, Spain.
| | - Marc Garcia-Borràs
- Department of Chemistry and Biochemistry, University of California, 607 Charles E. Young Drive, Los Angeles, California 90095, USA
| | - Sílvia Osuna
- Institut de Química Computacional i Catàlisi and Departament de Química Universitat de Girona, Campus Montilivi, 17071 Girona, Catalonia, Spain.
| |
Collapse
|
31
|
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.
Collapse
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
| |
Collapse
|
32
|
Rosenfeld L, Heyne M, Shifman JM, Papo N. Protein Engineering by Combined Computational and In Vitro Evolution Approaches. Trends Biochem Sci 2016; 41:421-433. [PMID: 27061494 DOI: 10.1016/j.tibs.2016.03.002] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2016] [Revised: 02/29/2016] [Accepted: 03/09/2016] [Indexed: 12/30/2022]
Abstract
Two alternative strategies are commonly used to study protein-protein interactions (PPIs) and to engineer protein-based inhibitors. In one approach, binders are selected experimentally from combinatorial libraries of protein mutants that are displayed on a cell surface. In the other approach, computational modeling is used to explore an astronomically large number of protein sequences to select a small number of sequences for experimental testing. While both approaches have some limitations, their combination produces superior results in various protein engineering applications. Such applications include the design of novel binders and inhibitors, the enhancement of affinity and specificity, and the mapping of binding epitopes. The combination of these approaches also aids in the understanding of the specificity profiles of various PPIs.
Collapse
Affiliation(s)
- Lior Rosenfeld
- Department of Biotechnology Engineering and the National Institute of Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Michael Heyne
- Department of Biotechnology Engineering and the National Institute of Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva, Israel; Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Julia M Shifman
- Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel.
| | - Niv Papo
- Department of Biotechnology Engineering and the National Institute of Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
| |
Collapse
|
33
|
Leaver-Fay A, Froning KJ, Atwell S, Aldaz H, Pustilnik A, Lu F, Huang F, Yuan R, Hassanali S, Chamberlain AK, Fitchett JR, Demarest SJ, Kuhlman B. Computationally Designed Bispecific Antibodies using Negative State Repertoires. Structure 2016; 24:641-651. [PMID: 26996964 DOI: 10.1016/j.str.2016.02.013] [Citation(s) in RCA: 49] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2015] [Revised: 02/04/2016] [Accepted: 02/17/2016] [Indexed: 12/27/2022]
Abstract
A challenge in the structure-based design of specificity is modeling the negative states, i.e., the complexes that you do not want to form. This is a difficult problem because mutations predicted to destabilize the negative state might be accommodated by small conformational rearrangements. To overcome this challenge, we employ an iterative strategy that cycles between sequence design and protein docking in order to build up an ensemble of alternative negative state conformations for use in specificity prediction. We have applied our technique to the design of heterodimeric CH3 interfaces in the Fc region of antibodies. Combining computationally and rationally designed mutations produced unique designs with heterodimer purities greater than 90%. Asymmetric Fc crystallization was able to resolve the interface mutations; the heterodimer structures confirmed that the interfaces formed as designed. With these CH3 mutations, and those made at the heavy-/light-chain interface, we demonstrate one-step synthesis of four fully IgG-bispecific antibodies.
Collapse
Affiliation(s)
- Andrew Leaver-Fay
- Department of Biochemistry, University of North Carolina at Chapel Hill, 120 Mason Farm Road, Campus Box 7260, Chapel Hill, NC 27599, USA
| | - Karen J Froning
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, Suite 200, San Diego, CA 92121, USA
| | - Shane Atwell
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, Suite 200, San Diego, CA 92121, USA
| | - Hector Aldaz
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, Suite 200, San Diego, CA 92121, USA
| | - Anna Pustilnik
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, Suite 200, San Diego, CA 92121, USA
| | - Frances Lu
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, Suite 200, San Diego, CA 92121, USA
| | - Flora Huang
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, Suite 200, San Diego, CA 92121, USA
| | - Richard Yuan
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, Suite 200, San Diego, CA 92121, USA
| | - Saleema Hassanali
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, Suite 200, San Diego, CA 92121, USA
| | - Aaron K Chamberlain
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, Suite 200, San Diego, CA 92121, USA
| | - Jonathan R Fitchett
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, Suite 200, San Diego, CA 92121, USA
| | - Stephen J Demarest
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, Suite 200, San Diego, CA 92121, USA.
| | - Brian Kuhlman
- Department of Biochemistry, University of North Carolina at Chapel Hill, 120 Mason Farm Road, Campus Box 7260, Chapel Hill, NC 27599, USA; Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, 101 Manning Drive, Chapel Hill, NC 27514, USA.
| |
Collapse
|
34
|
Pottel J, Moitessier N. Single-Point Mutation with a Rotamer Library Toolkit: Toward Protein Engineering. J Chem Inf Model 2015; 55:2657-71. [PMID: 26623941 DOI: 10.1021/acs.jcim.5b00525] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Protein engineers have long been hard at work to harness biocatalysts as a natural source of regio-, stereo-, and chemoselectivity in order to carry out chemistry (reactions and/or substrates) not previously achieved with these enzymes. The extreme labor demands and exponential number of mutation combinations have induced computational advances in this domain. The first step in our virtual approach is to predict the correct conformations upon mutation of residues (i.e., rebuilding side chains). For this purpose, we opted for a combination of molecular mechanics and statistical data. In this work, we have developed automated computational tools to extract protein structural information and created conformational libraries for each amino acid dependent on a variable number of parameters (e.g., resolution, flexibility, secondary structure). We have also developed the necessary tool to apply the mutation and optimize the conformation accordingly. For side-chain conformation prediction, we obtained overall average root-mean-square deviations (RMSDs) of 0.91 and 1.01 Å for the 18 flexible natural amino acids within two distinct sets of over 3000 and 1500 side-chain residues, respectively. The commonly used dihedral angle differences were also evaluated and performed worse than the state of the art. These two metrics are also compared. Furthermore, we generated a family-specific library for kinases that produced an average 2% lower RMSD upon side-chain reconstruction and a residue-specific library that yielded a 17% improvement. Ultimately, since our protein engineering outlook involves using our docking software, Fitted/Impacts, we applied our mutation protocol to a benchmarked data set for self- and cross-docking. Our side-chain reconstruction does not hinder our docking software, demonstrating differences in pose prediction accuracy of approximately 2% (RMSD cutoff metric) for a set of over 200 protein/ligand structures. Similarly, when docking to a set of over 100 kinases, side-chain reconstruction (using both general and biased conformation libraries) had minimal detriment to the docking accuracy.
Collapse
Affiliation(s)
- Joshua Pottel
- Department of Chemistry, McGill University , 801 Sherbrooke Street West, Montreal, QC, Canada H3A 0B8
| | - Nicolas Moitessier
- Department of Chemistry, McGill University , 801 Sherbrooke Street West, Montreal, QC, Canada H3A 0B8
| |
Collapse
|
35
|
Prediction of Stable Globular Proteins Using Negative Design with Non-native Backbone Ensembles. Structure 2015; 23:2011-21. [DOI: 10.1016/j.str.2015.07.021] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2015] [Revised: 07/26/2015] [Accepted: 07/29/2015] [Indexed: 11/21/2022]
|
36
|
Sevy AM, Jacobs TM, Crowe JE, Meiler J. Design of Protein Multi-specificity Using an Independent Sequence Search Reduces the Barrier to Low Energy Sequences. PLoS Comput Biol 2015; 11:e1004300. [PMID: 26147100 PMCID: PMC4493036 DOI: 10.1371/journal.pcbi.1004300] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2015] [Accepted: 04/27/2015] [Indexed: 11/18/2022] Open
Abstract
Computational protein design has found great success in engineering proteins for thermodynamic stability, binding specificity, or enzymatic activity in a 'single state' design (SSD) paradigm. Multi-specificity design (MSD), on the other hand, involves considering the stability of multiple protein states simultaneously. We have developed a novel MSD algorithm, which we refer to as REstrained CONvergence in multi-specificity design (RECON). The algorithm allows each state to adopt its own sequence throughout the design process rather than enforcing a single sequence on all states. Convergence to a single sequence is encouraged through an incrementally increasing convergence restraint for corresponding positions. Compared to MSD algorithms that enforce (constrain) an identical sequence on all states the energy landscape is simplified, which accelerates the search drastically. As a result, RECON can readily be used in simulations with a flexible protein backbone. We have benchmarked RECON on two design tasks. First, we designed antibodies derived from a common germline gene against their diverse targets to assess recovery of the germline, polyspecific sequence. Second, we design "promiscuous", polyspecific proteins against all binding partners and measure recovery of the native sequence. We show that RECON is able to efficiently recover native-like, biologically relevant sequences in this diverse set of protein complexes.
Collapse
Affiliation(s)
- Alexander M. Sevy
- Center for Structural Biology, Vanderbilt University, Nashville, Tennessee, United States of America
| | - Tim M. Jacobs
- Department of Biochemistry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - James E. Crowe
- Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Jens Meiler
- Center for Structural Biology, Vanderbilt University, Nashville, Tennessee, United States of America
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee, United States of America
| |
Collapse
|
37
|
Davey JA, Chica RA. Optimization of rotamers prior to template minimization improves stability predictions made by computational protein design. Protein Sci 2015; 24:545-60. [PMID: 25492709 DOI: 10.1002/pro.2618] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2014] [Accepted: 12/04/2014] [Indexed: 11/07/2022]
Abstract
Computational protein design (CPD) predictions are highly dependent on the structure of the input template used. However, it is unclear how small differences in template geometry translate to large differences in stability prediction accuracy. Herein, we explored how structural changes to the input template affect the outcome of stability predictions by CPD. To do this, we prepared alternate templates by Rotamer Optimization followed by energy Minimization (ROM) and used them to recapitulate the stability of 84 protein G domain β1 mutant sequences. In the ROM process, side-chain rotamers for wild-type (WT) or mutant sequences are optimized on crystal or nuclear magnetic resonance (NMR) structures prior to template minimization, resulting in alternate structures termed ROM templates. We show that use of ROM templates prepared from sequences known to be stable results predominantly in improved prediction accuracy compared to using the minimized crystal or NMR structures. Conversely, ROM templates prepared from sequences that are less stable than the WT reduce prediction accuracy by increasing the number of false positives. These observed changes in prediction outcomes are attributed to differences in side-chain contacts made by rotamers in ROM templates. Finally, we show that ROM templates prepared from sequences that are unfolded or that adopt a nonnative fold result in the selective enrichment of sequences that are also unfolded or that adopt a nonnative fold, respectively. Our results demonstrate the existence of a rotamer bias caused by the input template that can be harnessed to skew predictions toward sequences displaying desired characteristics.
Collapse
Affiliation(s)
- James A Davey
- Department of Chemistry, University of Ottawa, Ottawa, Ontario, Canada, K1N 6N5
| | | |
Collapse
|
38
|
Lanouette S, Davey JA, Elisma F, Ning Z, Figeys D, Chica RA, Couture JF. Discovery of substrates for a SET domain lysine methyltransferase predicted by multistate computational protein design. Structure 2014; 23:206-215. [PMID: 25533488 DOI: 10.1016/j.str.2014.11.004] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2014] [Revised: 11/04/2014] [Accepted: 11/05/2014] [Indexed: 01/01/2023]
Abstract
Characterization of lysine methylation has proven challenging despite its importance in biological processes such as gene transcription, protein turnover, and cytoskeletal organization. In contrast to other key posttranslational modifications, current proteomics techniques have thus far shown limited success at characterizing methyl-lysine residues across the cellular landscape. To complement current biochemical characterization methods, we developed a multistate computational protein design procedure to probe the substrate specificity of the protein lysine methyltransferase SMYD2. Modeling of substrate-bound SMYD2 identified residues important for substrate recognition and predicted amino acids necessary for methylation. Peptide- and protein- based substrate libraries confirmed that SMYD2 activity is dictated by the motif [LFM]-1-K(∗)-[AFYMSHRK]+1-[LYK]+2 around the target lysine K(∗). Comprehensive motif-based searches and mutational analysis further established four additional substrates of SMYD2. Our methodology paves the way to systematically predict and validate posttranslational modification sites while simultaneously pairing them with their associated enzymes.
Collapse
Affiliation(s)
- Sylvain Lanouette
- Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology and Immunology, University of Ottawa, 451 Smyth Road, Ottawa, ON K1H 8M5, Canada
| | - James A Davey
- Department of Chemistry, University of Ottawa, Ottawa, ON, K1N 6N5, Canada; Centre for Catalysis Research and Innovation, University of Ottawa, Ottawa, ON, K1N 6N5, Canada
| | - Fred Elisma
- Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology and Immunology, University of Ottawa, 451 Smyth Road, Ottawa, ON K1H 8M5, Canada
| | - Zhibin Ning
- Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology and Immunology, University of Ottawa, 451 Smyth Road, Ottawa, ON K1H 8M5, Canada
| | - Daniel Figeys
- Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology and Immunology, University of Ottawa, 451 Smyth Road, Ottawa, ON K1H 8M5, Canada; Department of Chemistry, University of Ottawa, Ottawa, ON, K1N 6N5, Canada
| | - Roberto A Chica
- Department of Chemistry, University of Ottawa, Ottawa, ON, K1N 6N5, Canada; Centre for Catalysis Research and Innovation, University of Ottawa, Ottawa, ON, K1N 6N5, Canada.
| | - Jean-François Couture
- Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology and Immunology, University of Ottawa, 451 Smyth Road, Ottawa, ON K1H 8M5, Canada; Centre for Catalysis Research and Innovation, University of Ottawa, Ottawa, ON, K1N 6N5, Canada.
| |
Collapse
|