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HMMSTRTM: A hidden Markov model for local structure prediction in globular and membrane associated proteins

Benavides, T. L.; Bystroff, C.

2023-02-10 bioinformatics
10.1101/2023.02.08.527695 bioRxiv
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MotivationWe present HMMSTRTM, a Hidden Markov Model (HMM) that is useful for predicting topology of trans-membrane (TM) proteins. HMMSTRTM provides additional prediction categories of TM regions provided by the PDBTM corpus such as transmembrane beta sheets, coils, and reentrant loops. ResultsHMMSTRTM is competitive with existing TM protein topology predictors like TMHMM, it correctly predicts at least half the residues in 96.18% of all transmembrane helices in a cross validation dataset. AvailabilityModel architecture, source code, and supplementary figures are made available on github: github.com/TiburonB/HMMSTRTM. Contactbystrc@rpi.edu

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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.