Combining evolutionary and structural information for local protein structure prediction.
We study the effects of various factors in representing and combining evolutionary and structural information for local protein structural prediction based on fragment selection. We prepare databases of fragments from a set of non-redundant protein domains. For each fragment, evolutionary information is derived from homologous sequences and represented as estimated effective counts and frequencies of amino acids (evolutionary frequencies) at each position. Position-specific amino acid preferences called structural frequencies are derived from statistical analysis of discrete local structural environments in database structures. Our method for local structure prediction is based on ranking and selecting database fragments that are most similar to a target fragment. Using secondary structure type as a local structural property, we test our method in a number of settings. The major findings are: (1) the COMPASS-type scoring function for fragment similarity comparison gives better prediction accuracy than three other tested scoring functions for profile-profile comparison. We show that the COMPASS-type scoring function can be derived both in the probabilistic framework and in the framework of statistical potentials. (2) Using the evolutionary frequencies of database fragments gives better prediction accuracy than using structural frequencies. (3) Finer definition of local environments, such as including more side-chain solvent accessibility classes and considering the backbone conformations of neighboring residues, gives increasingly better prediction accuracy using structural frequencies. (4) Combining evolutionary and structural frequencies of database fragments, either in a linear fashion or using a pseudocount mixture formula, results in improvement of prediction accuracy. Combination at the log-odds score level is not as effective as combination at the frequency level. This suggests that there might be better ways of combining sequence and structural information than the commonly used linear combination of log-odds scores. Our method of fragment selection and frequency combination gives reasonable results of secondary structure prediction tested on 56 CASP5 targets (average SOV score 0.77), suggesting that it is a valid method for local protein structure prediction. Mixture of predicted structural frequencies and evolutionary frequencies improve the quality of local profile-to-profile alignment by COMPASS.
Department of Biochemistry, University of Texas Southwestern Medical Center, Dallas, Texas 75390-9050, USA.
Jimin
J
Pei
Nick V
NV
Grishin
D000465:Algorithms; D020169:Caspases / Q000737:chemistry; D019295:Computational Biology / Q000379:methods; D003546:Cysteine Endopeptidases / Q000737:chemistry; D030562:Databases, Protein; D019143:Evolution, Molecular; D010446:Peptide Fragments / Q000737:chemistry / Q000235:genetics; D011237:Predictive Value of Tests; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry* / Q000235:genetics*; D012995:Solubility; D012997:Solvents / Q000737:chemistry
D010446:Peptide Fragments; D011506:Proteins; D012997:Solvents; C099217:CASP5 protein, human; D020169:Caspases; D003546:Cysteine Endopeptidases