Quality Assessment of Predicted Protein Models Using Energies Calculated by the Fragment Molecular Orbital Method. Issue 2 (4th February 2015)

• Record Type: Journal Article
• Authors: Simoncini, David; Nakata, Hiroya; Ogata, Koji; Nakamura, Shinichiro; Zhang, Kam YJ
• Is Part Of: Molecular informatics. Volume 34:Issue 2/3(2015:Feb.)
• Page Start: 97
• Page End: 104
• Publication Date: 2015-02-04
• Subjects: Structure prediction -- Quality assessment -- Model selection -- Fragment molecular orbital -- Pair interaction energy; Cheminformatics -- Periodicals; QSAR (Biochemistry) -- Periodicals; Structure-activity relationships (Biochemistry) -- Periodicals; Drugs -- Structure-activity relationships -- Periodicals; 615.19
• DOI: 10.1002/minf.201400108
• Languages: English
• ISSNs: 1868-1743

Abstract

Abstract: Protein structure prediction directly from sequences is a very challenging problem in computational biology. One of the most successful approaches employs stochastic conformational sampling to search an empirically derived energy function landscape for the global energy minimum state. Due to the errors in the empirically derived energy function, the lowest energy conformation may not be the best model. We have evaluated the use of energy calculated by the fragment molecular orbital method (FMO energy) to assess the quality of predicted models and its ability to identify the best model among an ensemble of predicted models. The fragment molecular orbital method implemented in GAMESS was used to calculate the FMO energy of predicted models. When tested on eight protein targets, we found that the model ranking based on FMO energies is better than that based on empirically derived energies when there is sufficient diversity among these models. This model diversity can be estimated prior to the FMO energy calculations. Our result demonstrates that the FMO energy calculated by the fragment molecular orbital method is a practical and promising measure for the assessment of protein model quality and the selection of the best protein model among many generated. Abstract :