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A composite score for predicting errors in protein structure models
Author(s) -
Eramian David,
Shen Minyi,
Devos Damien,
Melo Francisco,
Sali Andrej,
MartiRenom Marc A.
Publication year - 2006
Publication title -
protein science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.353
H-Index - 175
eISSN - 1469-896X
pISSN - 0961-8368
DOI - 10.1110/ps.062095806
Subject(s) - support vector machine , statistical model , artificial intelligence , protein structure prediction , computer science , machine learning , pattern recognition (psychology) , protein structure , physics , nuclear magnetic resonance
Reliable prediction of model accuracy is an important unsolved problem in protein structure modeling. To address this problem, we studied 24 individual assessment scores, including physics‐based energy functions, statistical potentials, and machine learning–based scoring functions. Individual scores were also used to construct ∼85,000 composite scoring functions using support vector machine (SVM) regression. The scores were tested for their abilities to identify the most native‐like models from a set of 6000 comparative models of 20 representative protein structures. Each of the 20 targets was modeled using a template of <30% sequence identity, corresponding to challenging comparative modeling cases. The best SVM score outperformed all individual scores by decreasing the average RMSD difference between the model identified as the best of the set and the model with the lowest RMSD (ΔRMSD) from 0.63 Å to 0.45 Å, while having a higher Pearson correlation coefficient to RMSD ( r = 0.87) than any other tested score. The most accurate score is based on a combination of the DOPE non‐hydrogen atom statistical potential; surface, contact, and combined statistical potentials from MODPIPE; and two PSIPRED/DSSP scores. It was implemented in the SVMod program, which can now be applied to select the final model in various modeling problems, including fold assignment, target–template alignment, and loop modeling.