Different derivations of knowledge-based potentials and analysis of their robustness and context-dependent predictive power.
The possibility of defining effective potentials from known protein structures, which are sufficiently accurate to be used for protein-structure-prediction purposes, is investigated. Three types of distance potentials and three types of backbone torsion potentials are defined, based on propensities of amino acid pairs to be separated by a given spatial distance or to be associated to a backbone torsion angle domain. Their differences reside in the way the physical correlations between the amino acids and the conformational states are extracted from the bulk interactions due to the presence of many residues in a protein. For the distance potentials, a physical meaning can be associated to the different definitions, given that some of the potentials favor hydrophobic interactions and others favor interactions between oppositely charged residues. The performance of the different torsion and distance potentials in structure prediction procedures, in particular native-fold recognition and evaluation of protein stability changes upon point mutations, is analyzed. It appears to differ according to the specific proteins and protein environments. In particular, one of the distance potentials performs better than the others for membrane proteins and in protein regions involving charged residues, but less well in other protein regions. Furthermore, the dependence of the potentials on the characteristics of the proteins from which they are derived is analyzed. It is shown that the dependence of the potentials on the length, amino acid composition and secondary-structure content of the proteins from the dataset is either very limited or rather strong, according to the type of potential. The results obtained suggest that the main problem limiting the performance of database-derived potentials is their lack of universality: each potential describes with satisfactory accuracy only the interactions present in certain protein environments.
Rooman
M
M
Ingénierie Biomoléculaire, Service de Chimie Organique, Université Libre de Bruxelles, Brussels, Belgium. mrooman@ulb.ac.be
Gilis
D
D
D000465:Algorithms; D000596:Amino Acids / Q000737:chemistry; D019992:Databases as Topic; D009154:Mutation / Q000235:genetics; D011487:Protein Conformation; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry*
D000596:Amino Acids; D011506:Proteins