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PLoS Comput Biol. 2015 Aug 7;11(8):e1004419. doi: 10.1371/journal.pcbi.1004419. eCollection 2015 Aug.

Modularity of Protein Folds as a Tool for Template-Free Modeling of Structures.

Author information

1
Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, New York, New York, United States of America.

Abstract

Predicting the three-dimensional structure of proteins from their amino acid sequences remains a challenging problem in molecular biology. While the current structural coverage of proteins is almost exclusively provided by template-based techniques, the modeling of the rest of the protein sequences increasingly require template-free methods. However, template-free modeling methods are much less reliable and are usually applicable for smaller proteins, leaving much space for improvement. We present here a novel computational method that uses a library of supersecondary structure fragments, known as Smotifs, to model protein structures. The library of Smotifs has saturated over time, providing a theoretical foundation for efficient modeling. The method relies on weak sequence signals from remotely related protein structures to create a library of Smotif fragments specific to the target protein sequence. This Smotif library is exploited in a fragment assembly protocol to sample decoys, which are assessed by a composite scoring function. Since the Smotif fragments are larger in size compared to the ones used in other fragment-based methods, the proposed modeling algorithm, SmotifTF, can employ an exhaustive sampling during decoy assembly. SmotifTF successfully predicts the overall fold of the target proteins in about 50% of the test cases and performs competitively when compared to other state of the art prediction methods, especially when sequence signal to remote homologs is diminishing. Smotif-based modeling is complementary to current prediction methods and provides a promising direction in addressing the structure prediction problem, especially when targeting larger proteins for modeling.

PMID:
26252221
PMCID:
PMC4529212
DOI:
10.1371/journal.pcbi.1004419
[Indexed for MEDLINE]
Free PMC Article

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