Empirical prediction of protein pK_avalues with residue mutation

Abstract

A fast, empirical method, Mut-pKa, is presented for predicting the pKa values of ionizable residues in proteins based on mutation. The method compares the effect of mutating each residue that may act as a hydrogen bond donor or acceptor for the ionizable residue. The energetic effect of each type of mutation, along with a desolvation measure and the overall background charge, is fit against pKa data for histidine and carboxyl residues. A total of 214 residues from 35 different proteins were used in the dataset. Using 11 parameters for each type of ionizable residue, a root mean squared error (RMSE) of 0.78 and 1.12 pH units were obtained for carboxyl and histidines residues, respectively, using leave one out cross validation (LOOCV). The results were particularly promising for buried residues, which had RMSE values of 0.99 and 1.13 for carboxyl and histidine residues, respectively. A number of desolvation measures were tested. The simplest measure, the number of atoms surrounding the residue, was found to work best. The effect of using dynamics was also studied using short molecular dynamics runs, followed by minimization of the structures. Mut-pKa has significantly fewer parameters than, but similar performance to, other empirical methods. Because of this and the LOOCV results, we believe the model is robust and that overfitting is not a problem.

Efficient parameterization of torsional terms for force fields

“…In this method, we propose to deal with these problems by taking an intermediate approach between purely sampling with dynamics and doing a constrained scan over each coordinate separately. This is done by sampling on the reduced PES of the dihedral angles of interest, using the Monte Carlo (MC) method to generate the ensemble, while minimizing all other degrees of freedom. To avoid the high cost of minimizing the system at each step, importance sampling is used.…”

Quantum Mechanics/Molecular Mechanics Restrained Electrostatic Potential Fitting

“…In particular, we study Asp26 on the same Thioredoxin protein structure as in ref . We study Asp residues on the traditional proteins, turkey lysozyme protein (PDB: ), and beta-cryptogein (PDB: ), both of which have been used in previous benchmarking p K a calculations. − …”