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Open Access Research article

Predicting protein folding pathways at the mesoscopic level based on native interactions between secondary structure elements

Qingwu Yang1 and Sing-Hoi Sze12*

Author Affiliations

1 Department of Computer Science, Texas A&M University, College Station, TX 77843, USA

2 Department of Biochemistry & Biophysics, Texas A&M University, College Station, TX 77843, USA

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BMC Bioinformatics 2008, 9:320  doi:10.1186/1471-2105-9-320

Published: 23 July 2008

Abstract

Background

Since experimental determination of protein folding pathways remains difficult, computational techniques are often used to simulate protein folding. Most current techniques to predict protein folding pathways are computationally intensive and are suitable only for small proteins.

Results

By assuming that the native structure of a protein is known and representing each intermediate conformation as a collection of fully folded structures in which each of them contains a set of interacting secondary structure elements, we show that it is possible to significantly reduce the conformation space while still being able to predict the most energetically favorable folding pathway of large proteins with hundreds of residues at the mesoscopic level, including the pig muscle phosphoglycerate kinase with 416 residues. The model is detailed enough to distinguish between different folding pathways of structurally very similar proteins, including the streptococcal protein G and the peptostreptococcal protein L. The model is also able to recognize the differences between the folding pathways of protein G and its two structurally similar variants NuG1 and NuG2, which are even harder to distinguish. We show that this strategy can produce accurate predictions on many other proteins with experimentally determined intermediate folding states.

Conclusion

Our technique is efficient enough to predict folding pathways for both large and small proteins at the mesoscopic level. Such a strategy is often the only feasible choice for large proteins. A software program implementing this strategy (SSFold) is available at http://faculty.cs.tamu.edu/shsze/ssfold webcite.