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Open Access Highly Accessed Research article

Directionality in protein fold prediction

Jonathan J Ellis1, Fabien PE Huard1, Charlotte M Deane2, Sheenal Srivastava1 and Graham R Wood1*

Author Affiliations

1 Department of Statistics, Macquarie University, Sydney, NSW 2109, Australia

2 Department of Statistics, Oxford University, 1 South Parks Road, Oxford OX1 3TG, UK

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BMC Bioinformatics 2010, 11:172  doi:10.1186/1471-2105-11-172

Published: 7 April 2010

Abstract

Background

Ever since the ground-breaking work of Anfinsen et al. in which a denatured protein was found to refold to its native state, it has been frequently stated by the protein fold prediction community that all the information required for protein folding lies in the amino acid sequence. Recent in vitro experiments and in silico computational studies, however, have shown that cotranslation may affect the folding pathway of some proteins, especially those of ancient folds. In this paper aspects of cotranslational folding have been incorporated into a protein structure prediction algorithm by adapting the Rosetta program to fold proteins as the nascent chain elongates. This makes it possible to conduct a pairwise comparison of folding accuracy, by comparing folds created sequentially from each end of the protein.

Results

A single main result emerged: in 94% of proteins analyzed, following the sense of translation, from N-terminus to C-terminus, produced better predictions than following the reverse sense of translation, from the C-terminus to N-terminus. Two secondary results emerged. First, this superiority of N-terminus to C-terminus folding was more marked for proteins showing stronger evidence of cotranslation and second, an algorithm following the sense of translation produced predictions comparable to, and occasionally better than, Rosetta.

Conclusions

There is a directionality effect in protein fold prediction. At present, prediction methods appear to be too noisy to take advantage of this effect; as techniques refine, it may be possible to draw benefit from a sequential approach to protein fold prediction.