J Chem Inf Model - Building a knowledge-based statistical potential by capturing high-order inter-residue interactions and its applications in protein secondary structure assessment.

Tópicos

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Resumo

The rapidly increasing number of protein crystal structures available in the Protein Data Bank (PDB) has naturally made statistical analyses feasible in studying complex high-order inter-residue correlations. In this paper, we report a context-based secondary structure potential (CSSP) for assessing the quality of predicted protein secondary structures generated by various prediction servers. CSSP is a sequence-position-specific knowledge-based potential generated based on the potentials of mean force approach, where high-order inter-residue interactions are taken into consideration. The CSSP potential is effective in identifying secondary structure predictions with good quality. In 56% of the targets in the CB513 benchmark, the optimal CSSP potential is able to recognize the native secondary structure or a prediction with Q3 accuracy higher than 90% as best scored in the predicted secondary structures generated by 10 popularly used secondary structure prediction servers. In more than 80% of the CB513 targets, the predicted secondary structures with the lowest CSSP potential values yield higher than 80% Q3 accuracy. Similar performance of CSSP is found on the CASP9 targets as well. Moreover, our computational results also show that the CSSP potential using triplets outperforms the CSSP potential using doublets and is currently better than the CSSP potential using quartets.

Resumo Limpo

rapid increas number protein crystal structur avail protein data bank pdb natur made statist analys feasibl studi complex highord interresidu correl paper report contextbas secondari structur potenti cssp assess qualiti predict protein secondari structur generat various predict server cssp sequencepositionspecif knowledgebas potenti generat base potenti mean forc approach highord interresidu interact taken consider cssp potenti effect identifi secondari structur predict good qualiti target cb benchmark optim cssp potenti abl recogn nativ secondari structur predict q accuraci higher best score predict secondari structur generat popular use secondari structur predict server cb target predict secondari structur lowest cssp potenti valu yield higher q accuraci similar perform cssp found casp target well moreov comput result also show cssp potenti use triplet outperform cssp potenti use doublet current better cssp potenti use quartet

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