Abstract
Folding free energy is an important biophysical characteristic of proteins that reflects the overall stability of the 3D structure of macromolecules. Changes in the amino acid sequence, naturally occurring or made in vitro, may affect the stability of the corresponding protein and thus could be associated with disease. Several approaches that predict the changes of the folding free energy caused by mutations have been proposed, but there is no method that is clearly superior to the others. The optimal goal is not only to accurately predict the folding free energy changes, but also to characterize the structural changes induced by mutations and the physical nature of the predicted folding free energy changes. Here we report a new method to predict the Single Amino Acid Folding free Energy Changes (SAAFEC) based on a knowledge-modified Molecular Mechanics Poisson-Boltzmann (MM/PBSA) approach. The method is comprised of two main components: a MM/PBSA component and a set of knowledge based terms delivered from a statistical study of the biophysical characteristics of proteins. The predictor utilizes a multiple linear regression model with weighted coefficients of various terms optimized against a set of experimental data. The aforementioned approach yields a correlation coefficient of 0.65 when benchmarked against 983 cases from 42 proteins in the ProTherm database. Availability: the webserver can be accessed via http://compbio.clemson.edu/SAAFEC/.
Highlights
Folding free energy is an important characteristic of proteins that is directly associated with the stability of the corresponding macromolecule
It is desirable to improve the accuracy of predictions and to provide additional information on the structural changes caused by mutation and the contribution of individual energy terms to the predicted folding free energy change [24,25]
We outline (a) the work done to find the optimal parameters for the MM/PBSA method; (b) the statistical analysis performed to find structural features that can be used as flags to predict if a mutation is supposed to cause large or small change of the folding free energy; and (c) the optimization of the weight coefficients
Summary
Folding free energy is an important characteristic of proteins that is directly associated with the stability of the corresponding macromolecule. Various approaches have been proposed to predict folding free energy changes due to missense mutations [5,14,15] These methods are grouped into two classes: structure based and sequence based. Like I-Mutant [16], utilize the amino acid sequence of proteins along with neural networks, support vector machines, and decision trees to predict changes in the folding free energy. While such methods can achieve high accuracy in discriminating disease-causing and harmless mutations, they do not predict structural changes caused by the mutation. It is desirable to improve the accuracy of predictions and to provide additional information on the structural changes caused by mutation and the contribution of individual energy terms to the predicted folding free energy change [24,25]
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