Development of a prediction system for tail-anchored proteins

作者：Shigemitsu Shunsuke; Cao Wei; Terada Tohru; Shimizu Kentaro^*

来源：BMC Bioinformatics, 2016, 17(1): 378.

DOI：10.1186/s12859-016-1202-7

• TA proteins
• HMMs
• Membrane proteins
• Prediction
• Machine learning

Background: "Tail-anchored (TA) proteins" is a collective term for transmembrane proteins with a C-terminal transmembrane domain (TMD) and without an N-terminal signal sequence. TA proteins account for approximately 3-5 % of all transmembrane proteins that mediate membrane fusion, regulation of apoptosis, and vesicular transport. The combined use of TMD and signal sequence prediction tools is typically required to predict TA proteins.
Results: Here we developed a prediction system named TAPPM that predicted TA proteins solely from target amino acid sequences according to the knowledge of the sequence features of TMDs and the peripheral regions of TA proteins. Manually curated TA proteins were collected from published literature. We constructed hidden markov models of TA proteins as well as three different types of transmembrane proteins with similar structures and compared their likelihoods as TA proteins.
Conclusions: Using the HMM models, we achieved high prediction accuracy; area under the receiver operator curve values reaching 0.963. A command line tool written in Python is available at https://github.com/davecao/tappm_cli.

• 出版日期2016-9-15