Improvement of side-chain modeling in proteins with the self-consistent mean field theory method based on an analysis of the factors influencing prediction.
With the objective of improving side-chain conformation prediction, we have analyzed the influence of various factors on prediction by the Self-Consistent Mean Field Theory method, applied to a set of high resolution x-ray protein structure models. These factors may be classed as variations in the mean field optimization protocol, variations in the potential energy function, and variations in rotamer library completeness. We have developed an optimization protocol that consistently reached lower mean field conformational free energies than two other protocols. This protocol led to an important improvement in prediction. We observed a major improvement in prediction with two more detailed van der Waals parameter sets, which we found to be due mainly to the introduction of scaling of 1-4 interactions. In a comparison of two knowledge-based rotamer libraries of considerably different size, we observed an unexpected decrease in prediction with an increase in library completeness. However, when we introduced a torsion potential term in the potential energy function, we found an important increase in average prediction and in the prediction of almost all residue types with a more complete rotamer set. The two knowledge-based rotamer libraries now became equivalent in terms of average prediction. The results we obtained in an analysis of the effect of the introduction of an additional electrostatic term in the potential energy function were largely inconclusive. However, we found a small increase in average prediction for an electrostatic potential term with a fixed dielectric constant of 15. The combined effect of all the factors we analyzed in this study resulted in average prediction accuracies of 79.9% for X1, 68.1% for X1 + 2, and 1.590 A for global rms deviation (RMSD); the corresponding values for core residues were 88.2%, 78.6%, and 1.171 A. These values represent improvements in average prediction of 6.5% for X1, 9.1% for X1 + 2, and 0.163 A for global RMSD over the original conditions; the corresponding improvements in the core were 5.9%, 9.0%, and 0.180 A, respectively.
Mendes
J
J
Instituto de Tecnologia Química e Biológica, Universidade Nova de Lisboa, Oeiras, Portugal.
Soares
C M
CM
Carrondo
M A
MA
D000596:Amino Acids; D008956:Models, Chemical; D019151:Peptide Library; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry*; D012984:Software; D055672:Static Electricity; D013816:Thermodynamics
D000596:Amino Acids; D019151:Peptide Library; D011506:Proteins