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Free Energy Estimates of All-Atom Protein Structures Using Generalized Belief Propagation

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Research in Computational Molecular Biology (RECOMB 2007)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 4453))

Abstract

We present a technique for approximating the free energy of protein structures using Generalized Belief Propagation (GBP). The accuracy and utility of these estimates are then demonstrated in two different application domains. First, we show that the entropy component of our free energy estimates can be useful in distinguishing native protein structures from decoys — structures with similar internal energy to that of the native structure, but otherwise incorrect. Our method is able to correctly identify the native fold from among a set of decoys with 87.5% accuracy over a total of 48 different immunoglobin folds. The remaining 12.5% of native structures are ranked among the top 4 of all structures. Second, we show that our estimates of ΔΔ G upon mutation upon mutation for three different data sets have linear correlations between 0.63-0.70 with experimental values and statistically significant p-values. Together, these results suggests that GBP is an effective means for computing free energy in all-atom models of protein structures. GBP is also efficient, taking a few minutes to run on a typical sized protein, further suggesting that GBP may be an attractive alternative to more costly molecular dynamic simulations for some tasks.

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Authors and Affiliations

  1. Computer Science Department, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15217,  

    Hetunandan Kamisetty, Eric P. Xing & Christopher J. Langmead

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  1. Hetunandan Kamisetty
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  2. Eric P. Xing
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  3. Christopher J. Langmead
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Terry Speed Haiyan Huang

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Kamisetty, H., Xing, E.P., Langmead, C.J. (2007). Free Energy Estimates of All-Atom Protein Structures Using Generalized Belief Propagation. In: Speed, T., Huang, H. (eds) Research in Computational Molecular Biology. RECOMB 2007. Lecture Notes in Computer Science(), vol 4453. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71681-5_26

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  • DOI: https://doi.org/10.1007/978-3-540-71681-5_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-71680-8

  • Online ISBN: 978-3-540-71681-5

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