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Ojewole A, Lowegard A, Gainza P, Reeve SM, Georgiev I, Anderson AC, Donald BR. OSPREY Predicts Resistance Mutations Using Positive and Negative Computational Protein Design. Methods Mol Biol 2017; 1529:291-306. [PMID: 27914058 PMCID: PMC5192561 DOI: 10.1007/978-1-4939-6637-0_15] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Drug resistance in protein targets is an increasingly common phenomenon that reduces the efficacy of both existing and new antibiotics. However, knowledge of future resistance mutations during pre-clinical phases of drug development would enable the design of novel antibiotics that are robust against not only known resistant mutants, but also against those that have not yet been clinically observed. Computational structure-based protein design (CSPD) is a transformative field that enables the prediction of protein sequences with desired biochemical properties such as binding affinity and specificity to a target. The use of CSPD to predict previously unseen resistance mutations represents one of the frontiers of computational protein design. In a recent study (Reeve et al. Proc Natl Acad Sci U S A 112(3):749-754, 2015), we used our OSPREY (Open Source Protein REdesign for You) suite of CSPD algorithms to prospectively predict resistance mutations that arise in the active site of the dihydrofolate reductase enzyme from methicillin-resistant Staphylococcus aureus (SaDHFR) in response to selective pressure from an experimental competitive inhibitor. We demonstrated that our top predicted candidates are indeed viable resistant mutants. Since that study, we have significantly enhanced the capabilities of OSPREY with not only improved modeling of backbone flexibility, but also efficient multi-state design, fast sparse approximations, partitioned continuous rotamers for more accurate energy bounds, and a computationally efficient representation of molecular-mechanics and quantum-mechanical energy functions. Here, using SaDHFR as an example, we present a protocol for resistance prediction using the latest version of OSPREY. Specifically, we show how to use a combination of positive and negative design to predict active site escape mutations that maintain the enzyme's catalytic function but selectively ablate binding of an inhibitor.
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Affiliation(s)
- Adegoke Ojewole
- Program in Computational Biology and Bioinformatics, Duke University, Durham, NC, 27708, USA
| | - Anna Lowegard
- Program in Computational Biology and Bioinformatics, Duke University, Durham, NC, 27708, USA
| | - Pablo Gainza
- Department of Computer Science, Duke University, Durham, NC, 27708, USA
| | - Stephanie M Reeve
- Department of Pharmaceutical Sciences, University of Connecticut, Storrs, CT, 06269, USA
| | - Ivelin Georgiev
- Department of Computer Science, Duke University, Durham, NC, 27708, USA
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, Bethesda, MD, 20892, USA
| | - Amy C Anderson
- Department of Pharmaceutical Sciences, University of Connecticut, Storrs, CT, 06269, USA
| | - Bruce R Donald
- Department of Computer Science, Duke University, Durham, NC, 27708, USA.
- Department of Biochemistry, Duke University, Durham, NC, 27708, USA.
- Department of Chemistry, Duke University, Durham, NC, 27708, USA.
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Simoncini D, Allouche D, de Givry S, Delmas C, Barbe S, Schiex T. Guaranteed Discrete Energy Optimization on Large Protein Design Problems. J Chem Theory Comput 2015; 11:5980-9. [DOI: 10.1021/acs.jctc.5b00594] [Citation(s) in RCA: 49] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
| | - David Allouche
- INRA MIAT, UR 875, Castanet-Tolosan, 31326 Cedex, France
| | - Simon de Givry
- INRA MIAT, UR 875, Castanet-Tolosan, 31326 Cedex, France
| | - Céline Delmas
- INRA MIAT, UR 875, Castanet-Tolosan, 31326 Cedex, France
| | - Sophie Barbe
- Université de Toulouse; INSA, UPS, INP; LISBP, 135 Avenue de Rangueil, F-31077 Toulouse, France
- CNRS, UMR5504, F-31400 Toulouse, France
- INRA, UMR792 Ingénierie des Systèmes Biologiques et des Procédés, F-31400 Toulouse, France
| | - Thomas Schiex
- INRA MIAT, UR 875, Castanet-Tolosan, 31326 Cedex, France
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Roberts KE, Gainza P, Hallen MA, Donald BR. Fast gap-free enumeration of conformations and sequences for protein design. Proteins 2015; 83:1859-1877. [PMID: 26235965 DOI: 10.1002/prot.24870] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2015] [Revised: 07/14/2015] [Accepted: 07/21/2015] [Indexed: 12/12/2022]
Abstract
Despite significant successes in structure-based computational protein design in recent years, protein design algorithms must be improved to increase the biological accuracy of new designs. Protein design algorithms search through an exponential number of protein conformations, protein ensembles, and amino acid sequences in an attempt to find globally optimal structures with a desired biological function. To improve the biological accuracy of protein designs, it is necessary to increase both the amount of protein flexibility allowed during the search and the overall size of the design, while guaranteeing that the lowest-energy structures and sequences are found. DEE/A*-based algorithms are the most prevalent provable algorithms in the field of protein design and can provably enumerate a gap-free list of low-energy protein conformations, which is necessary for ensemble-based algorithms that predict protein binding. We present two classes of algorithmic improvements to the A* algorithm that greatly increase the efficiency of A*. First, we analyze the effect of ordering the expansion of mutable residue positions within the A* tree and present a dynamic residue ordering that reduces the number of A* nodes that must be visited during the search. Second, we propose new methods to improve the conformational bounds used to estimate the energies of partial conformations during the A* search. The residue ordering techniques and improved bounds can be combined for additional increases in A* efficiency. Our enhancements enable all A*-based methods to more fully search protein conformation space, which will ultimately improve the accuracy of complex biomedically relevant designs.
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Affiliation(s)
- Kyle E Roberts
- Department of Computer Science, Duke University, Durham, NC
| | - Pablo Gainza
- Department of Computer Science, Duke University, Durham, NC
| | - Mark A Hallen
- Department of Computer Science, Duke University, Durham, NC
| | - Bruce R Donald
- Department of Computer Science, Duke University, Durham, NC.,Department of Biochemistry, Duke University Medical Center, Durham, NC.,Department of Chemistry, Duke University, Durham, NC
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Jou JD, Jain S, Georgiev I, Donald BR. BWM*: A Novel, Provable, Ensemble-Based Dynamic Programming Algorithm for Sparse Approximations of Computational Protein Design. LECTURE NOTES IN COMPUTER SCIENCE 2015. [DOI: 10.1007/978-3-319-16706-0_16] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
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