Large‐scale prediction of protein geometry and stability changes for arbitrary single point mutations

Abstract

We have developed a method to both predict the geometry and the relative stability of point mutants that may be used for arbitrary mutations. The geometry optimization procedure was first tested on a new benchmark of 2141 ordered pairs of X-ray crystal structures of proteins that differ by a single point mutation, the largest data set to date. An empirical energy function, which includes terms representing the energy contributions of the folded and denatured proteins and uses the predicted mutant side chain conformation, was fit to a training set consisting of half of a diverse set of 1816 experimental stability values for single point mutations in 81 different proteins. The data included a substantial number of small to large residue mutations not considered by previous prediction studies. After removing 22 (approximately 2%) outliers, the stability calculation gave a standard deviation of 1.08 kcal/mol with a correlation coefficient of 0.82. The prediction method was then tested on the remaining half of the experimental data, giving a standard deviation of 1.10 kcal/mol and covariance of 0.66 for 97% of the test set. A regression fit of the energy function to a subset of 137 mutants, for which both native and mutant structures were available, gave a prediction error comparable to that for the complete training set with predicted side chain conformations. We found that about half of the variation is due to conformation-independent residue contributions. Finally, a fit to the experimental stability data using these residue parameters exclusively suggests guidelines for improving protein stability in the absence of detailed structure information.

The interactome: Predicting the protein-protein interactions in cells

“…Pairs of sequences homologous to a known interacting pair are then scored for how well they preserve the atomic contacts at the interaction interface [153][154][155]. Up to now, all the computational algorithms have only used single machine learning methods for the analysis and prediction of protein-protein interactions [156][157][158][159][160], or the statistical analysis of interacting patches of protein surfaces [75,149,161,162]. Our experience clearly supports the idea that each machine learning algorithm performs better for selected types of training data [163,164].…”

Average assignment method for predicting the stability of protein mutants

“…Several methods have been proposed for predicting protein stability changes upon mutations. These methods are based on detailed atomic models coupled with semiempirical force fields [1][2][3][4] ; a mean force field approach based on protein main-chain characteristics 5 ; simplified energy criteria 6,7 ; self-consistent ensemble optimization 8 ; molecular modeling; dynamics and quantum chemical calculations 9,10 ; an empirical method that takes into account of the free energy between the denatured state and compact native state 11,12 ; structural environment-dependent amino acid substitution tables 13,14 ; knowledge-based potentials [15][16][17] ; amino acid properties 18 ; and neural network. 19 The analysis on protein stability upon mutations requires a good and reliable source of data.…”

Protein folding: Then and now

“…Khatun et al [105] utilized contact potentials to predict ΔΔG of three sets of 303, 658 and 1356 mutants and their prediction correlations varied between 0.45 to 0.78. Bordner and Abagyan [106] used a combination of physical energy terms, statistical energy terms and a structural descriptor with weight factors scaled to experimental data for ΔΔG predictions, and found a correlation of 0.59 on 908 test mutants. Saraboji et al classified the available thermal denaturing data on mutations according to substitution types, secondary structures and the area of solvent accessibilities, and used the average value from each category for the prediction and obtained a correlation of 0.64 [107].…”