Biology

Prof. N. Srinivasan
Indian Institute of Science, Bangalore

First clue to function of a protein of known sequence is often obtained on the basis of its evolutionary relationship with another protein of known functional properties. Therefore our ability to recognize functions of proteins at the genomic scale relies on our ability to identify related proteins solely from their amino acid sequences. Despite development of many sensitive methods to detect distantly related proteins many relationships are missed and they become evident only after their 3-D structures are determined. We approached this challenge differently by developing a computational approach to purposefully bridge gaps between related protein families through directed design of protein-like ‘linker’ sequences. For this we represented protein domain families of known 3-D structure, 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 protein 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. 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. Indeed, application of the proposed protocol has helped identification of homologues of domains of unknown function (DUF) enabling structure and function recognition for DUFs.