Predicting the Effect of Amino Acid Single-Point Mutations on Protein Stability—Large-Scale Validation of MD-Based Relative Free Energy Calculations. Issue 7 (7th April 2017) - Steinbrecher, Thomas; Zhu, Chongkai; Wang, Lingle; Abel, Robert; Negron, Christopher; Pearlman, David; Feyfant, Eric; Duan, Jianxin; Sherman, Woody
Abstract: The stability of folded proteins is critical to their biological function and for the efficacy of protein therapeutics. Predicting the energetic effects of protein mutations can improve our fundamental understanding of structural biology, the molecular basis of diseases, and possible routes to addressing those diseases with biological drugs. Identifying the effect of single amino acid point mutations on the thermodynamic equilibrium between the folded and unfolded states of a protein can pinpoint residues of critical importance that should be avoided in the process of improving other properties (affinity, solubility, viscosity, etc.) and suggest changes at other positions for increasing stability in protein engineering. Multiple computational tools have been developed for in silico predictions of protein stability in recent years, ranging from sequence-based empirical approaches to rigorous physics-based free energy methods. In this work, we show that FEP+, which is a free energy perturbation method based on all-atom molecular dynamics simulations, can provide accurate thermal stability predictions for a wide range of biologically relevant systems. Significantly, the FEP+ approach, while originally developed for relative binding free energies of small molecules to proteins and not specifically fitted for protein stability calculations, performs well compared to other methods that were fitted specifically to predict protein stability. Here, we present the broadest validation of a rigorous free energy-based approach applied to protein stability reported to date: 700 + single-point mutations spanning 10 different protein targets. Across the entire data set, we correctly classify the mutations as stabilizing or destabilizing in 84% of the cases, and obtain statistically significant predictions as compared with experiment [average error of 1.6 kcal/mol and coefficient of determination ( R 2 ) of 0.40]. This study demonstrates, for the first time in a large-scale validation, that rigorous free energy calculations can be used to predict changes in protein stability from point mutations without parameterization or system-specific customization, although further improvements should be possible with additional sampling and a better representation of the unfolded state of the protein. Here, we describe the FEP+ method as applied to protein stability calculations, summarize the large-scale retrospective validation results, and discuss limitations of the method, along with future directions for further improvements. Graphical abstract: Highlights: Free energy calculations on protein stability predictions are conducted. The physics-based FEP+ approach outperforms empirical methods. Predictions show a coefficient of determination of 0.4 and 1.6 kcal/mol average error. Predictions have an 84% chance of correctly classifying a mutation.