In quest of an empirical potential for protein structure prediction.
Key to successful protein structure prediction is a potential that recognizes the native state from misfolded structures. Recent advances in empirical potentials based on known protein structures include improved reference states for assessing random interactions, sidechain-orientation-dependent pair potentials, potentials for describing secondary or supersecondary structural preferences and, most importantly, optimization protocols that sculpt the energy landscape to enhance the correlation between native-like features and the energy. Improved clustering algorithms that select native-like structures on the basis of cluster density also resulted in greater prediction accuracy. For template-based modeling, these advances allowed improvement in predicted structures relative to their initial template alignments over a wide range of target-template homology. This represents significant progress and suggests applications to proteome-scale structure prediction.
Skolnick
Jeffrey
J
Center of Excellence in Bioinformatics, University at Buffalo, 901 Washington Street, Buffalo, NY 14203, USA. skolnick@buffalo.edu
D000465:Algorithms; D011487:Protein Conformation; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry*
D011506:Proteins