Assigning biological function using hidden signatures in cystine-stabilized peptide sequences.

ABSTRACT: Cystine-stabilized peptides have great utility as they naturally block ion channels, inhibit acetylcholine receptors, or inactivate microbes. However, only a tiny fraction of these peptides has been characterized. Exploration for novel peptides most efficiently starts with the identification of candidates from genome sequence data. Unfortunately, though cystine-stabilized peptides have shared structures, they have low DNA sequence similarity, restricting the utility of BLAST and even more powerful sequence alignment-based annotation algorithms, such as PSI-BLAST and HMMER. In contrast, a supervised machine learning approach may improve discovery and function assignment of these peptides. To this end, we employed our previously described m-NGSG algorithm, which utilizes hidden signatures em

SUBMITTER: Islam SMA 

PROVIDER: S-EPMC5998126 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature