Average assignment method for predicting the stability of protein mutants

Abstract

Prediction of protein stability upon amino acid substitutions is an important problem in molecular biology and it will be helpful for designing stable mutants. In this work, we have analyzed the stability of protein mutants using three different data sets of 1791, 1396, and 2204 mutants, respectively, for thermal stability (DeltaTm), free energy change due to thermal (DeltaDeltaG), and denaturant denaturations (DeltaDeltaGH2O), obtained from the ProTherm database. We have classified the mutants into 380 possible substitutions and assigned the stability of each mutant using the information obtained with similar type of mutations. We observed that this assignment could distinguish the stabilizing and destabilizing mutants to an accuracy of 70-80% at different measures of stability. Further, we have classified the mutants based on secondary structure and solvent accessibility (ASA) and observed that the classification significantly improved the accuracy of prediction. The classification of mutants based on helix, strand, and coil distinguished the stabilizing/destabilizing mutants at an average accuracy of 82% and the correlation is 0.56; information about the location of residues at the interior, partially buried, and surface regions of a protein correctly identified the stabilizing/destabilizing residues at an average accuracy of 81% and the correlation is 0.59. The nine subclassifications based on three secondary structures and solvent accessibilities improved the accuracy of assigning stabilizing/destabilizing mutants to an accuracy of 84-89% for the three data sets. Further, the present method is able to predict the free energy change (DeltaDeltaG) upon mutations within a deviation of 0.64 kcal/mol. We suggest that this method could be used for predicting the stability of protein mutants.

In silico comparative characterization of pharmacogenomic missense variants

“…It has been shown previously that structural features [25] and amino acid parameters [13] can be used for the prediction of stability changes. To our best knowledge, evolutionary features have been used only in our previous work [15].…”

“…It has been shown previously that stability changes prediction can be guided by observing structural properties describing the secondary structure and accessible surface area of the mutated residue [25]. However, structural information is not available in the case of sequence-based prediction of stability changes.…”

A Model for Protein Sequence Evolution Based on Selective Pressure for Protein Stability: Application to Hemoglobins

“…In laboratory studies a significant proportion of random substitutions lead to reduced protein stability4–6 supporting the concept that much selection may act on genes encoding unstable proteins. Selection and mutation rate determine stationary frequencies for amino acids at any given site.…”

“…One alternative approach to predict site stationary frequencies for proteins is to use information derived from the protein structure 18. Stationary frequencies might also be calculated using mutational/biophysical-based approaches1,19 that permit calculation of the contribution of each residue to protein stability with accuracy 4,20,21. Alternative approaches include decision-tree models and methods relying on phylogeny to predict the effects of mutations 22,23…”

Protein folding: Then and now

“…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].…”