Protein Structure Classification Based on Conserved Hydrophobic Residues
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
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The problem of finding folding nuclei (a set of native contacts that play an important role in folding) along with identifying folding pathways (a time-ordered sequence of folding events) of proteins is one of the most important problems in protein chemistry. Here we propose a novel and simple approach to address this problem as follows: given the topology of the native state, identify native contacts that form folding nuclei based on a graph-theoretical approach that considers effective contact order (effective loop closure) as its objective function. Motivation: A number of computational methods for the prediction of folding nuclei already exists in the literature, but most of them rely on restrictive assumptions about the nature of nuclei or the process of folding. Our motivation is to develop a simple, efficient and robust algorithm to find an ensemble of pathways with the lowest effective contact order and to identify contacts that are crucial for folding. Results: Our approach is different from the previously used methods in that it uses efficient graph algorithms and does not formulate restrictive assumptions about folding nuclei. Our predictions provide more details concerning the protein folding pathway than most other methods in the literature. We demonstrate the success of our approach by predicting folding nuclei for a dataset of proteins for which experimental kinetic data is available. We show that our method compares favourably with other methods in the literature and that its results agree with experimental results. Availability: The executable for the proposed algorithm is available at http://www.cs.ubc.ca/~oshmygel/foldingnuclei.html Contact: oshmygel@cs.ubc.ca