title: Filling-in void and sparse regions in protein sequence space by protein-like artificial sequences enables remarkable enhancement in remote homology detection capability.
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 three-dimensional 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 analyze their ability to link proteins in homology searches, we used 3024 queries to search two databases, one containing only natural sequences and another one additionally containing 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.
authors: IISc Mathematics Initiative, Indian Institute of Science, Bangalore 560 012, India.
authors: Richa
authors: R
authors: Mudgal
authors: National Centre for Biological Sciences, University of Agricultural Sciences Gandhi Krishi Vignan Kendra Campus, Bangalore 560 065, India.
authors: Ramanathan
authors: R
authors: Sowdhamini
authors: Department of Biochemistry, Indian Institute of Science, Bangalore 560 012, India.
authors: Nagasuma
authors: N
authors: Chandra
authors: Molecular Biophysics Unit, Indian Institute of Science, Bangalore 560 012, India. Electronic address: ns@mbu.iisc.ernet.in.
authors: Narayanaswamy
authors: N
authors: Srinivasan
authors: Department of Biochemistry, Indian Institute of Science, Bangalore 560 012, India.
authors: Sankaran
authors: S
authors: Sandhya
mesh_terms: D000595:Amino Acid Sequence; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D030562:Databases, Protein; D063208:Methionyl Aminopeptidases / Q000737:chemistry; D008969:Molecular Sequence Data; D054467:Phospholipases A2 / Q000737:chemistry; D017510:Protein Folding; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry*; D020539:Sequence Analysis, Protein / Q000379:methods*; D017386:Sequence Homology, Amino Acid
chemical_list: D011506:Proteins; D054467:Phospholipases A2; D063208:Methionyl Aminopeptidases
keywords: HMM; PSSM; hidden Markov model; in silico protein design; position-specific scoring matrix; protein evolution; remote homology detection; sequence analysis