Abstract
Membrane proteins plays significant role in living cells. Transmembrane proteins are estimated to constitute approximately 30% of proteins at genomic scale. It has been a difficult task to develop specific alignment tools for transmembrane proteins due to limited number of experimentally validated protein structures. Alignment tools based on homology modeling provide fairly good result by recapitulating 70–80% residues in reference alignment provided all input sequences should have known template structures. However, homology modeling tools took substantial amount of time, thus aligning large numbers of sequences becomes computationally demanding. Here we present TM-Aligner, a new tool for transmembrane protein sequence alignment. TM-Aligner is based on Wu-Manber and dynamic string matching algorithm which has significantly improved its accuracy and speed of multiple sequence alignment. We compared TM-Aligner with prevailing other popular tools and performed benchmarking using three separate reference sets, BaliBASE3.0 reference set7 of alpha-helical transmembrane proteins, structure based alignment of transmembrane proteins from Pfam database and structure alignment from GPCRDB. Benchmarking against reference datasets indicated that TM-Aligner is more advanced method having least turnaround time with significant improvements over the most accurate methods such as PROMALS, MAFFT, TM-Coffee, Kalign, ClustalW, Muscle and PRALINE. TM-Aligner is freely available through http://lms.snu.edu.in/TM-Aligner/.
Highlights
Transmembrane proteins or integral proteins are known for the variety of role they play inside the cellular system like communication, metabolism and regulation
Total Column score (TC) were not considered for scoring purpose because this score did not reflect the biological correctness of alignments
We have shown how 2D structure prediction and string matching algorithms can increase alignment quality for transmembrane proteins
Summary
Transmembrane proteins or integral proteins are known for the variety of role they play inside the cellular system like communication, metabolism and regulation. Unknown structure of a target sequence is modeled on a known (template) structure of a distantly-related protein, in order to gain insights into membrane protein function Such studies rely on methods for detecting relationships between two proteins, by subsequently, aligning their protein sequences. Despite availability of homology based tools for multiple sequence alignment of transmembrane proteins, it is likely that a significant number of transmembrane regions remain undetected or unaligned because of limitations of the available methods like number of input sequences, turnaround time and dependency on structures. As biological membrane proteins have a transmembrane between cytoplasmic and non-cytoplasmic regions, so even at low sequence similarity, accurate alignment is possible by dividing the sequence into different regions and aligning them separately These alignments are stitched together precisely so that transmembrane regions were not disrupted and important residues within protein family are conserved throughout the alignment process. TMHMM16 was used and alignments were made using dynamic programming and Wu-Manber string matching algorithm[17] to stitch different regions together
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