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

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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/.

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Acknowledgements

This work was supported by the Japan Society for the Promotion of Science (JSPS) postdoctoral fellowship to TR.

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Author notes

  1. Teppei Ebina

    Present address: Department of Physiology, Graduate school of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan

Authors and Affiliations

  1. Department of Biotechnology and Life Science, Tokyo University of Agriculture and Technology, 12-24-16 Nakamachi, Koganei-shi, Tokyo, 184-8588, Japan

    Tambi Richa, Soichiro Ide, Ryosuke Suzuki, Teppei Ebina & Yutaka Kuroda

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  1. Tambi Richa
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Correspondence to Yutaka Kuroda.

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Richa, T., Ide, S., Suzuki, R. et al. Fast H-DROP: A thirty times accelerated version of H-DROP for interactive SVM-based prediction of helical domain linkers. J Comput Aided Mol Des 31, 237–244 (2017). https://doi.org/10.1007/s10822-016-9999-8

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