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Elsevier, Journal of Molecular Biology, 4(426), p. 962-979, 2014

DOI: 10.1016/j.jmb.2013.11.026

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Filling-in Void and Sparse Regions in Protein Sequence Space by Protein-Like Artificial Sequences Enables Remarkable Enhancement in Remote Homology Detection Capability

This paper is available in a repository.
This paper is available in a repository.

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Abstract

Protein functional annotation relies on the identification of accurate relationships, sequence divergence being a key factor. This is especially evident when distant protein relationships are demonstrated only with 3-D structures. To address this challenge, we describe a computational approach to purposefully bridge gaps between related protein families through directed design of protein-like 'linker' sequences. For this we represented SCOP domain families, integrated with sequence homologues, as multiple profiles and performed HMM-HMM alignments between related domain families. Where convincing alignments were achieved, we applied a roulette wheel-based method to design 3,611,010 protein-like sequences corresponding to 374 SCOP folds. To analyse their ability to link proteins in homology searches, we used 3,024 queries to search two databases, one containing only natural sequences, and another which additionally contained designed sequences. Our results showed that augmented database searches showed up to 30% improvement in fold coverage for over 74% of the folds with 52 folds achieving all theoretically possible connections. Although sequences could not be designed between some families, the availability of designed sequences between other families within the fold established the sequence continuum to demonstrate 373 difficult relationships. Ultimately, as a practical and realistic extension, we demonstrate that such protein-like sequences can be "plugged-into" routine and generic sequence database searches to empower not only remote homology detection but also fold recognition. Our richly statistically supported findings show that complementary searches in both databases will increase the effectiveness of sequence-based searches in recognizing all homologues sharing a common fold.