Advertisement

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

Abstract

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: http://www2.ahu.edu.cn/pchen/web/HotspotEC.htm.

Keywords

Hot spot residue Ensemble system IBk 

Notes

Acknowledgements

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)

References

  1. Aha DW, Kibler D, Albert MK (1991) Instance-Based Learning Algorithms. Machine Learning. 6(1):37–66Google Scholar
  2. Bogan AA, Thorn KS (1998 Jul) Anatomy of hot spots in protein interfaces. J Mol Biol. 280:1–9CrossRefPubMedGoogle Scholar
  3. Brenke R, Kozakov D, Chuang GY, Beglov D, Hall D, Landon MR, et al. Fragment-based identification of druggable ’hot spots’ of proteins using Fourier domain correlation techniques. Bioinformatics (Oxford, England). 2009;25:621–7Google Scholar
  4. Chen R, Chen W, Yang S, Wu D, Wang Y, Tian Y et al (2011) Rigorous assessment and integration of the sequence and structure based features to predict hot spots. BMC Bioinformatics. 12:311–311CrossRefGoogle Scholar
  5. Chothia C, Janin J (1975) Principles of proteinprotein recognition. Nature. 256(5520):705Google Scholar
  6. Clackson T, Wells JA (1995 Jan) A hot spot of binding energy in a hormone-receptor interface. Science (New York, NY) 267:383–6CrossRefGoogle Scholar
  7. Chen P, Li J, Wong L, Kuwahara H, Huang JZ, Gao X. Accurate prediction of hot spot residues through physicochemical characteristics of amino acid sequences. Proteins. 2013 Aug;81(8):1351–1362. Available from: http://dx.doi.org/10.1002/prot.24278Google Scholar
  8. Chou KC (2001 May) Prediction of protein cellular attributes using pseudo-amino acid composition. Proteins. 43:246–55CrossRefPubMedGoogle Scholar
  9. Ki Cho (2009 May) Kim D, Lee D. A feature-based approach to modeling protein-protein interaction hot spots. Nucleic acids research. 37:2672–87CrossRefPubMedPubMedCentralGoogle Scholar
  10. Darnell SJ, Page D, Mitchell JC (2007 Sep) An automated decision-tree approach to predicting protein interaction hot spots. Proteins. 68:813–23CrossRefPubMedGoogle Scholar
  11. Darnell SJ, LeGault L, Mitchell JC (2008 Jul) KFC Server: interactive forecasting of protein interaction hot spots. Nucleic acids research. 36:W265–9CrossRefPubMedPubMedCentralGoogle Scholar
  12. DeLano WL (2002 Feb) Unraveling hot spots in binding interfaces: progress and challenges. Current opinion in structural biology. 12:14–20CrossRefPubMedGoogle Scholar
  13. Emsley J, Knight CG, Farndale RW, Barnes MJ, Liddington RC (2000 Mar) Structural basis of collagen recognition by integrin alpha2beta1. Cell. 101:47–56CrossRefPubMedGoogle Scholar
  14. Fasman GD, Sober HA, et al. Handbook of biochemistry and molecular biology. vol. 1. CRC press, Cleveland; 1977Google Scholar
  15. Fernandezrecio J (2011) Prediction of protein binding sites and hot spots. Wiley Interdisciplinary Reviews: Computational Molecular Science. 1(5):680–698CrossRefGoogle Scholar
  16. Fischer TB, Arunachalam KV, Bailey D, Mangual V, Bakhru S, Russo R et al (2003) The binding interface database (BID): a compilation of amino acid hot spots in protein interfaces. Bioinformatics. 19(11):1453–1454CrossRefPubMedGoogle Scholar
  17. Di Giulio M (2005 Feb) A comparison of proteins from Pyrococcus furiosus and Pyrococcus abyssi: barophily in the physicochemical properties of amino acids and in the genetic code. Gene. 346:1–6CrossRefPubMedGoogle Scholar
  18. Guerois R, Nielsen JE, Serrano L (2002 Jul) Predicting changes in the stability of proteins and protein complexes: a study of more than 1000 mutations. Journal of molecular biology. 320:369–87CrossRefPubMedGoogle Scholar
  19. Kawashima S, Kanehisa M (2000 Jan) AAindex: amino acid index database. Nucleic acids research. 28:374CrossRefPubMedPubMedCentralGoogle Scholar
  20. Kawashima S, Pokarowski P, Pokarowska M, Kolinski A, Katayama T, Kanehisa M, AAindex: amino acid index database, progress report 2008. Nucleic Acids Res. (2008) Jan; 36(Database issue):D202–D205. Available from. doi: 10.1093/nar/gkm998 CrossRefPubMedGoogle Scholar
  21. Kim DE, Chivian D, Baker D (2004 Jul) Protein structure prediction and analysis using the Robetta server. Nucleic acids research. 32:W526–31CrossRefPubMedPubMedCentralGoogle Scholar
  22. Kortemme T, Baker D (2002 Oct) A simple physical model for binding energy hot spots in protein-protein complexes. Proceedings of the National Academy of Sciences of the United States of America. 99:14116–21CrossRefPubMedPubMedCentralGoogle Scholar
  23. Kortemme T, Kim DE, Baker D. Computational alanine scanning of protein-protein interfaces. Science’s STKE : signal transduction knowledge environment. 2004 Feb;2004:pl2Google Scholar
  24. Li J, Liu Q. ’Double water exclusion’: a hypothesis refining the O-ring theory for the hot spots at protein interfa. Bioinformatics (Oxford, England). 2009 25:743–50CrossRefGoogle Scholar
  25. Li Z, Wong L, Li J (2011) DBAC: a simple prediction method for protein binding hot spots based on burial levels and deeply buried atomic contacts. BMC systems biology. 5(Suppl 1):S5CrossRefGoogle Scholar
  26. Martins JM, Ramos RM, Pimenta AC, Moreira IS (2014 Mar) Solvent-accessible surface area: How well can be applied to hot-spot detection? Proteins. 82:479–90CrossRefPubMedGoogle Scholar
  27. Moal IH, Fernandezrecio J (2012) SKEMPI: A Structural Kinetic and Energetic database of Mutant Protein Interactions and its use in empirical models. Bioinformatics. 28(20):2600–2607CrossRefPubMedGoogle Scholar
  28. Moreira IS, Fernandes PA, Ramos MJ (2007 Sep) Hot spots-a review of the protein-protein interface determinant amino-acid residues. Proteins. 68:803–12CrossRefPubMedGoogle Scholar
  29. Naderi-Manesh H, Sadeghi M, Arab S, Moosavi Movahedi AA (2001 Mar) Prediction of protein surface accessibility with information theory. Proteins. 42:452–9CrossRefPubMedGoogle Scholar
  30. Ofran Y, Rost B. ISIS: interaction sites identified from sequence. Bioinformatics (Oxford, England). 2007 Jan;23:e13–6Google Scholar
  31. Ofran Y, Rost B (2007 Jul) Protein-protein interaction hotspots carved into sequences. PLoS computational biology. 3:e119CrossRefPubMedPubMedCentralGoogle Scholar
  32. Shen HB, Chou KC (2008 Feb) PseAAC: a flexible web server for generating various kinds of protein pseudo amino acid composition. Analytical biochemistry. 373:386–8CrossRefPubMedGoogle Scholar
  33. Shingate P, Sukhwal A, Sowdhamini R (2014) ECMIS: computational approach for the identification of hotspots at protein-protein interfaces. BMC Bioinformatics. 15(1):303CrossRefGoogle Scholar
  34. Sueki M, Lee S, Powers SP, Denton JB, Konishi Y, Scheraga HA (1984) Helix-coil stability constants for the naturally occurring amino acids in water. XXII. Histidine parameters from poly[(hydroxybutyl) glutamine-co-l-histidine]. Macromolecules. 17:148–155CrossRefGoogle Scholar
  35. Thorn KS, Bogan AA (2001) ASEdb: a database of alanine mutations and their effects on the free energy of binding in protein interactions. Bioinformatics. 17(3):284–285CrossRefPubMedGoogle Scholar
  36. Tuncbag N, Keskin O, Gursoy A (2010 Jul) HotPoint: hot spot prediction server for protein interfaces. Nucleic acids research. 38:W402–6CrossRefPubMedPubMedCentralGoogle Scholar
  37. Tuncbag N, Gursoy A, Keskin O (2009) Identification of computational hot spots in protein interfaces: combining solvent accessibility and inter-residue potentials improves the accuracy. Bioinformatics. 25(12):1513–1520CrossRefPubMedGoogle Scholar
  38. Wang L, Liu Z, Zhang X, Chen L (2012) Prediction of hot spots in protein interfaces using a random forest model with hybrid features. Protein Engineering Design & Selection. 25(3):119–126CrossRefGoogle Scholar
  39. Wang L, Zhang W, Gao Q, Xiong C (2014) Prediction of hot spots in protein interfaces using extreme learning machines with the information of spatial neighbour residues. Iet Systems Biology. 8(4):184–190CrossRefPubMedGoogle Scholar
  40. Wells JA (1991) Systematic mutational analyses of protein-protein interfaces. Methods in enzymology. 202:390–411CrossRefPubMedGoogle Scholar
  41. Xia J, Zhao X, Song J, Huang D (2010) APIS: accurate prediction of hot spots in protein interfaces by combining protrusion index with solvent accessibility. BMC Bioinformatics. 11:174CrossRefGoogle Scholar
  42. Xu B, Wei X, Deng L, Guan J, Zhou S (2012) A semi-supervised boosting SVM for predicting hot spots at protein-protein interfaces. BMC systems biology. 6(Suppl 2):S6CrossRefPubMedPubMedCentralGoogle Scholar
  43. Ye L, Kuang Q, Jiang L, Luo J, Jiang Y, Ding Z et al (2014) Prediction of hot spots residues in proteinprotein interface using network feature and microenvironment feature. Chemometrics and Intelligent Laboratory Systems. 131:16–21CrossRefGoogle Scholar
  44. Zhu X, Mitchell JC (2011 Sep) KFC2: a knowledge-based hot spot prediction method based on interface solvation, atomic density, and plasticity features. Proteins. 79:2671–83CrossRefPubMedGoogle Scholar
  45. Zwahlen C, Li SC, Kay LE, Pawson T, Forman-Kay JD (2000 Apr) Multiple modes of peptide recognition by the PTB domain of the cell fate determinant Numb. The EMBO journal. 19:1505–15CrossRefPubMedPubMedCentralGoogle Scholar

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

Personalised recommendations