Amino Acids

, Volume 49, Issue 10, pp 1773–1785 | Cite as

Protein binding hot spots prediction from sequence only by a new ensemble learning method

  • Shan-Shan Hu
  • Peng ChenEmail author
  • Bing Wang
  • Jinyan Li
Original Article


Hot spots are interfacial core areas of binding proteins, which have been applied as targets in drug design. Experimental methods are costly in both time and expense to locate hot spot areas. Recently, in-silicon computational methods have been widely used for hot spot prediction through sequence or structure characterization. As the structural information of proteins is not always solved, and thus hot spot identification from amino acid sequences only is more useful for real-life applications. This work proposes a new sequence-based model that combines physicochemical features with the relative accessible surface area of amino acid sequences for hot spot prediction. The model consists of 83 classifiers involving the IBk (Instance-based k means) algorithm, where instances are encoded by important properties extracted from a total of 544 properties in the AAindex1 (Amino Acid Index) database. Then top-performance classifiers are selected to form an ensemble by a majority voting technique. The ensemble classifier outperforms the state-of-the-art computational methods, yielding an F1 score of 0.80 on the benchmark binding interface database (BID) test set.Availability:


Hot spot residue Ensemble system IBk 



This work was supported by the National Natural Science Foundation of China (Nos. 61672035, 61300058, 61472282, 61271098 and 61374181).

Author contributions

SH and PC conceived the study; SH participated in the experimental design; SH and PC carried it out and drafted the manuscript. All authors revised the manuscript critically. JL and PC approved the final manuscript.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no competing interests.

Ethical statement

The authors declare that their manuscript complies to the Ethical Rules applicable for this journal.

Supplementary material

726_2017_2474_MOESM1_ESM.pdf (6 kb)
Supplementary material 1 (pdf 6 KB)
726_2017_2474_MOESM2_ESM.pdf (6 kb)
Supplementary material 2 (pdf 6 KB)


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Copyright information

© Springer-Verlag GmbH Austria 2017

Authors and Affiliations

  • Shan-Shan Hu
    • 1
    • 2
  • Peng Chen
    • 1
    • 2
    • 4
    Email author
  • Bing Wang
    • 3
  • Jinyan Li
    • 4
  1. 1.School of Computer Science and TechnologyAnhui UniversityHefeiChina
  2. 2.Institute of Health SciencesAnhui UniversityHefeiChina
  3. 3.School of Electrical and Information EngineeringAnhui University of TechnologyMa′anshanChina
  4. 4.Advanced Analytics Institute and Centre for Health TechnologiesUniversity of TechnologyBroadwayAustralia

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