Protein design algorithms predict viable resistance to an experimental antifolate

Abstract

Methods to accurately predict potential drug target mutations in response to early-stage leads could drive the design of more resilient first generation drug candidates. In this study, a structurebased protein design algorithm (K* in the OSPREY suite) was used to prospectively identify single-nucleotide polymorphisms that confer resistance to an experimental inhibitor effective against dihydrofolate reductase (DHFR) from Staphylococcus aureus. Four of the top-ranked mutations in DHFR were found to be catalytically competent and resistant to the inhibitor. Selection of resistant bacteria in vitro reveals that two of the predicted mutations arise in the background of a compensatory mutation. Using enzyme kinetics, microbiology, and crystal structures of the complexes, we determined the fitness of the mutant enzymes and strains, the structural basis of resistance, and the compensatory relationship of the mutations. To our knowledge, this work illustrates the first application of protein design algorithms to prospectively predict viable resistance mutations that arise in bacteria under antibiotic pressure.drug resistance | antifolate | protein design | DHFR | MRSA

Algorithms for protein design

“…For example, Meiler and co-workers [76] recently showed that MSD resulted in better agreement with observed evolutionary sequence profiles of antibodies when compared with single state design. In another example [5, 6], Anderson, Donald and co-workers used MSD to predict active site mutations in a drug target that confer resistance to a drug while maintaining the natural function of the enzyme.…”

“…The first stage is selecting a target tertiary/quaternary protein fold that will be designed for a specific function. Often the selected fold is one that performs a similar function and can later be redesigned to a new one [5–9]. In other cases, a protein that has a completely different function is used as a scaffold and repurposed for a new one [10–12].…”

“…This latter stage has been historically referred to as protein design [28]. Many computational protein design protocols implement different variations of these two stages, and these have resulted in many successfully-engineered new proteins [5–19, 29–35]. Here we focus on protein design.…”

Computationally optimized deimmunization libraries yield highly mutated enzymes with low immunogenicity and enhanced activity

“…Multistate design is particularly challenging when designing positively for some criteria but negatively against others (33), requiring that the goals be treated separately instead of simply combined into a single goal. A variety of algorithmic approaches, based on explicit structural modeling (34-37), sequence potentials derived from the evolutionary record (15), and sequence potentials derived from structural modeling (38), have driven applications such as modifying protein interaction specificity (39)(40)(41), designing peptide inhibitors (42)(43)(44), altering substrate specificity (45)(46)(47), characterizing resistance mechanisms (48), and enabling a single protein to adopt multiple folds (49). We have focused on a Pareto optimization framework (50) as the basis for elucidating and explicitly optimizing trade-offs between criteria and thereby providing the recently well-discussed advantages of provable optimality guarantees, such as enabling more direct interpretation of experimental data according to the driving models without concern for algorithmic bias or sampling failure (17).…”

BWM*: A Novel, Provable, Ensemble-Based Dynamic Programming Algorithm for Sparse Approximations of Computational Protein Design

“…Several protein design algorithms have successfully predicted protein sequences that fold and bind the desired target in vitro (Frey et al, 2010;Roberts et al, 2012;Rudicell et al, 2014;Stevens et al, 2006;Georgiev et al, 2012;Georgiev and Donald, 2007;Georgiev et al, 2014;Donald, 2011), and even in vivo (Reeve et al, 2015;Roberts et al, 2012;Rudicell et al, 2014;Georgiev et al, 2012;Georgiev et al, 2014;Donald, 2011). However, protein design is NP-hard (Kingsford et al, 2005), making algorithms that guarantee optimality expensive for larger designs where many residues are allowed to mutate simultaneously.…”