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


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 .

Keywords: Optimum features; PSSM; Random forest; Stepwise selection.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Databases, Protein
  • Models, Molecular
  • Protein Domains
  • Protein Structure, Secondary
  • Proteins / chemistry*
  • Software*


  • Proteins