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J Comput Aided Mol Des. 2017 Feb;31(2):237-244. doi: 10.1007/s10822-016-9999-8. Epub 2016 Dec 27.

Fast H-DROP: A thirty times accelerated version of H-DROP for interactive SVM-based prediction of helical domain linkers.

Author information

1
Department of Biotechnology and Life Science, Tokyo University of Agriculture and Technology, 12-24-16 Nakamachi, Koganei-shi, Tokyo, 184-8588, Japan.
2
Department of Physiology, Graduate school of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan.
3
Department of Biotechnology and Life Science, Tokyo University of Agriculture and Technology, 12-24-16 Nakamachi, Koganei-shi, Tokyo, 184-8588, Japan. ykuroda@cc.tuat.ac.jp.

Abstract

Efficient and rapid prediction of domain regions from amino acid sequence information alone is often required for swift structural and functional characterization of large multi-domain proteins. Here we introduce Fast H-DROP, a thirty times accelerated version of our previously reported H-DROP (Helical Domain linker pRediction using OPtimal features), which is unique in specifically predicting helical domain linkers (boundaries). Fast H-DROP, analogously to H-DROP, uses optimum features selected from a set of 3000 ones by combining a random forest and a stepwise feature selection protocol. We reduced the computational time from 8.5 min per sequence in H-DROP to 14 s per sequence in Fast H-DROP on an 8 Xeon processor Linux server by using SWISS-PROT instead of Genbank non-redundant (nr) database for generating the PSSMs. The sensitivity and precision of Fast H-DROP assessed by cross-validation were 33.7 and 36.2%, which were merely ~2% lower than that of H-DROP. The reduced computational time of Fast H-DROP, without affecting prediction performances, makes it more interactive and user-friendly. Fast H-DROP and H-DROP are freely available from http://domserv.lab.tuat.ac.jp/ .

KEYWORDS:

Optimum features; PSSM; Random forest; Stepwise selection

PMID:
28028736
DOI:
10.1007/s10822-016-9999-8
[Indexed for MEDLINE]

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