title: OSPREY Predicts Resistance Mutations Using Positive and Negative Computational Protein Design.
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.
authors.affiliation: Program in Computational Biology and Bioinformatics, Duke University, Durham, NC, 27708, USA.
authors.forename: Adegoke
authors.initials: A
authors.lastname: Ojewole
authors.affiliation: Program in Computational Biology and Bioinformatics, Duke University, Durham, NC, 27708, USA.
authors.forename: Anna
authors.initials: A
authors.lastname: Lowegard
authors.affiliation: Department of Computer Science, Duke University, Durham, NC, 27708, USA.
authors.forename: Pablo
authors.initials: P
authors.lastname: Gainza
authors.affiliation: Department of Pharmaceutical Sciences, University of Connecticut, Storrs, CT, 06269, USA.
authors.forename: Stephanie M
authors.initials: SM
authors.lastname: Reeve
authors.affiliation: Department of Computer Science, Duke University, Durham, NC, 27708, USA.|Vaccine Research Center, National Institute of Allergy and Infectious Diseases, Bethesda, MD, 20892, USA.
authors.forename: Ivelin
authors.initials: I
authors.lastname: Georgiev
authors.affiliation: Department of Pharmaceutical Sciences, University of Connecticut, Storrs, CT, 06269, USA.
authors.forename: Amy C
authors.initials: AC
authors.lastname: Anderson
authors.affiliation: Department of Computer Science, Duke University, Durham, NC, 27708, USA. brd+mimb15@cs.duke.edu.|Department of Biochemistry, Duke University, Durham, NC, 27708, USA. brd+mimb15@cs.duke.edu.|Department of Chemistry, Duke University, Durham, NC, 27708, USA. brd+mimb15@cs.duke.edu.
authors.forename: Bruce R
authors.initials: BR
authors.lastname: Donald
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D004351:Drug Resistance / Q000235:genetics*; D008958:Models, Molecular; D009154:Mutation; D010597:Pharmacogenetics / Q000379:methods; D011487:Protein Conformation; D015202:Protein Engineering / Q000379:methods*; D011506:Proteins / Q000737:chemistry* / Q000235:genetics*; D012984:Software; D064878:Web Browser
chemical_list: D011506:Proteins
keywords: Antibiotic resistance prediction; Computational protein design; OSPREY; Positive and negative design