Active machine learning for transmembrane helix prediction.

BMC Bioinformatics
Hatice U OsmanbeyogluMadhavi K Ganapathiraju

Abstract

About 30% of genes code for membrane proteins, which are involved in a wide variety of crucial biological functions. Despite their importance, experimentally determined structures correspond to only about 1.7% of protein structures deposited in the Protein Data Bank due to the difficulty in crystallizing membrane proteins. Algorithms that can identify proteins whose high-resolution structure can aid in predicting the structure of many previously unresolved proteins are therefore of potentially high value. Active machine learning is a supervised machine learning approach which is suitable for this domain where there are a large number of sequences but only very few have known corresponding structures. In essence, active learning seeks to identify proteins whose structure, if revealed experimentally, is maximally predictive of others. An active learning approach is presented for selection of a minimal set of proteins whose structures can aid in the determination of transmembrane helices for the remaining proteins. TMpro, an algorithm for high accuracy TM helix prediction we previously developed, is coupled with active learning. We show that with a well-designed selection procedure, high accuracy can be achieved with only few prot...Continue Reading

References

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Citations

Aug 23, 2012·IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society·Yifan FuAhmed K Elmagarmid
Oct 14, 2016·Cellular and Molecular Life Sciences : CMLS·Xin MengRobert C Ford
Apr 6, 2013·Briefings in Bioinformatics·Dick de RidderMarcel J T Reinders
Apr 22, 2020·Nature Communications·Olivier BorkowskiJean-Loup Faulon

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Methods Mentioned

BETA
interaction prediction

Software Mentioned

TMpro
TMHMM
active
PDBTM
MPtopo
MATLAB
SOM

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