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Determination of protein folding kinetic types using sequence and predicted secondary structure and solvent accessibility

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Abstract

Proteins fold through a two-state (TS), with no visible intermediates, or a multi-state (MS), via at least one intermediate, process. We analyze sequence-derived factors that determine folding types by introducing a novel sequence-based folding type predictor called FOKIT. This method implements a logistic regression model with six input features which hybridize information concerning amino acid composition and predicted secondary structure and solvent accessibility. FOKIT provides predictions with average Matthews correlation coefficient (MCC) between 0.58 and 0.91 measured using out-of-sample tests on four benchmark datasets. These results are shown to be competitive or better than results of four modern predictors. We also show that FOKIT outperforms these methods when predicting chains that share low similarity with the chains used to build the model, which is an important advantage given the limited number of annotated chains. We demonstrate that inclusion of solvent accessibility helps in discrimination of the folding kinetic types and that three of the features constitute statistically significant markers that differentiate TS and MS folders. We found that the increased content of exposed Trp and buried Leu are indicative of the MS folding, which implies that the exposure/burial of certain hydrophobic residues may play important role in the formation of the folding intermediates. Our conclusions are supported by two case studies.

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Acknowledgments

The authors would like to thank Emidio Capriotti, Michael Gromiha, Ji-Tao Huang, and Bin-Guang Ma for providing their datasets. This work was supported in part by the National Natural Science Foundation of China (Grant No. 61003187), Zhejiang Provincial Natural Science Foundation of China (Grant No. Y1090688), and NSERC Canada.

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Authors and Affiliations

  1. School of Computer Science and Information Engineering, Zhejiang Gongshang University, Hangzhou, People’s Republic of China

    Hua Zhang

  2. College of Mathematical Science and LPMC, Nankai University, Tianjin, People’s Republic of China

    Hua Zhang, Tuo Zhang, Jianzhao Gao, Jishou Ruan & Shiyi Shen

  3. Department of Electrical and Computer Engineering, University of Alberta, ECERF (9107 116 Street), Edmonton, AB, T6G 2V4, Canada

    Hua Zhang, Tuo Zhang & Lukasz Kurgan

  4. Indiana University School of Informatics, Indiana University-Purdue University, Indianapolis, IN, USA

    Tuo Zhang

  5. Chern Institute of Mathematics, Tianjin, People’s Republic of China

    Jishou Ruan & Shiyi Shen

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  1. Hua Zhang
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  2. Tuo Zhang
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Correspondence to Lukasz Kurgan.

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Zhang, H., Zhang, T., Gao, J. et al. Determination of protein folding kinetic types using sequence and predicted secondary structure and solvent accessibility. Amino Acids 42, 271–283 (2012). https://doi.org/10.1007/s00726-010-0805-y

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