Journal
PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
Volume 80, Issue 1, Pages 111-125Publisher
WILEY
DOI: 10.1002/prot.23168
Keywords
LIE; protein stability; G prediction; PLOP; AGBNP; free-energy
Categories
Funding
- NIH [5 T90 DK070135, GM30580]
Ask authors/readers for more resources
The coupling of protein energetics and sequence changes is a critical aspect of computational protein design, as well as for the understanding of protein evolution, human disease, and drug resistance. To study the molecular basis for this coupling, computational tools must be sufficiently accurate and computationally inexpensive enough to handle large amounts of sequence data. We have developed a computational approach based on the linear interaction energy (LIE) approximation to predict the changes in the free-energy of the native state induced by a single mutation. This approach was applied to a set of 822 mutations in 10 proteins which resulted in an average unsigned error of 0.82 kcal/mol and a correlation coefficient of 0.72 between the calculated and experimental Delta Delta G values. The method is able to accurately identify destabilizing hot spot mutations; however, it has difficulty in distinguishing between stabilizing and destabilizing mutations because of the distribution of stability changes for the set of mutations used to parameterize the model. In addition, the model also performs quite well in initial tests on a small set of double mutations. On the basis of these promising results, we can begin to examine the relationship between protein stability and fitness, correlated mutations, and drug resistance. Proteins 2012; (C) 2011 Wiley Periodicals, Inc.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available