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Random Forest-Based Protein Model Quality Assessment (RFMQA) Using Structural Features and Potential Energy Terms

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  • Balachandran Manavalan
  • Juyong Lee
  • Jooyoung Lee

Abstract

Recently, predicting proteins three-dimensional (3D) structure from its sequence information has made a significant progress due to the advances in computational techniques and the growth of experimental structures. However, selecting good models from a structural model pool is an important and challenging task in protein structure prediction. In this study, we present the first application of random forest based model quality assessment (RFMQA) to rank protein models using its structural features and knowledge-based potential energy terms. The method predicts a relative score of a model by using its secondary structure, solvent accessibility and knowledge-based potential energy terms. We trained and tested the RFMQA method on CASP8 and CASP9 targets using 5-fold cross-validation. The correlation coefficient between the TM-score of the model selected by RFMQA (TMRF) and the best server model (TMbest) is 0.945. We benchmarked our method on recent CASP10 targets by using CASP8 and 9 server models as a training set. The correlation coefficient and average difference between TMRF and TMbest over 95 CASP10 targets are 0.984 and 0.0385, respectively. The test results show that our method works better in selecting top models when compared with other top performing methods. RFMQA is available for download from http://lee.kias.re.kr/RFMQA/RFMQA_eval.tar.gz.

Suggested Citation

  • Balachandran Manavalan & Juyong Lee & Jooyoung Lee, 2014. "Random Forest-Based Protein Model Quality Assessment (RFMQA) Using Structural Features and Potential Energy Terms," PLOS ONE, Public Library of Science, vol. 9(9), pages 1-11, September.
    • Handle: RePEc:plo:pone00:0106542
    DOI: 10.1371/journal.pone.0106542
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    References listed on IDEAS

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    1. Juyong Lee & Jooyoung Lee, 2013. "Hidden Information Revealed by Optimal Community Structure from a Protein-Complex Bipartite Network Improves Protein Function Prediction," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-11, April.
    2. Yunqi Li & Jianwen Fang, 2012. "PROTS-RF: A Robust Model for Predicting Mutation-Induced Protein Stability Changes," PLOS ONE, Public Library of Science, vol. 7(10), pages 1-9, October.
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    Cited by:

    1. Rin Sato & Takashi Ishida, 2019. "Protein model accuracy estimation based on local structure quality assessment using 3D convolutional neural network," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-15, September.
    2. Clare E West & Saulo H P de Oliveira & Charlotte M Deane, 2019. "RFQAmodel: Random Forest Quality Assessment to identify a predicted protein structure in the correct fold," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-16, October.

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