Abstract

A complete understanding of the relationships between protein structure and stability remains an open problem. Much of our insight comes from laborious experimental analyses that perturb structure via directed mutation. The glycolytic enzyme lysozyme is among the most well characterized proteins under this paradigm, due to its abundance and ease of manipulation. To speed up such analyses, efficient computational models that can accurately predict mutation effects are needed. We employ a minimal Distance Constraint Model (mDCM) to predict the stability of a series of lysozyme mutants (specifically, human wild-type C-type lysozyme and 14 point mutations). With three phenomenological parameters that characterize microscopic interactions, the mDCM parameters are determined by obtaining the least squares error between predicted and experimental heat capacity curves. The mutants are chemically and structurally diverse, but have been experimentally characterized under nearly identical thermodynamic conditions (pH, ionic strength, etc.). The parameters found from best fits to heat capacity curves for one or more lysozyme structures are subsequently used to predict the heat capacity on the remaining. We simulate a typical experimental situation, where prediction of relative stabilities in an untested mutated structure is based on known results as they accumulate. From the statistical significance of these simulations, we establish that the mDCM is a viable predictor for relative stability of protein mutants. Remarkably, using parameters from any single fitting yields an average percent error of 4.3%. Across the dataset, the mDCM reproduces experimental trends sufficiently well (R = 0.64) to be of practical value to experimentalists when making decisions about which mutations to invest time and funds for characterization.

Keywords: Lysozyme, mutation, melting point, protein stability, heat capacity, distance constraint model, Human C-Type Lysozyme Mutants, Tanford-Kirk, Poisson-Boltzmann theories, point mutation stability, Simulated Annealing, Best-fit Parameters, Mutant Stability, total H-bond enthalpy, average H-bond en-thalpy, Average Prediction Accuracy