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.
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Aha DW, Kibler D, Albert MK (1991) Instance-Based Learning Algorithms. Machine Learning. 6(1):37–66
Bogan AA, Thorn KS (1998 Jul) Anatomy of hot spots in protein interfaces. J Mol Biol. 280:1–9
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;25:621–7
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. 12:311–311
Chothia C, Janin J (1975) Principles of proteinprotein recognition. Nature. 256(5520):705
Clackson T, Wells JA (1995 Jan) A hot spot of binding energy in a hormone-receptor interface. Science (New York, NY) 267:383–6
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.24278
Chou KC (2001 May) Prediction of protein cellular attributes using pseudo-amino acid composition. Proteins. 43:246–55
Ki Cho (2009 May) Kim D, Lee D. A feature-based approach to modeling protein-protein interaction hot spots. Nucleic acids research. 37:2672–87
Darnell SJ, Page D, Mitchell JC (2007 Sep) An automated decision-tree approach to predicting protein interaction hot spots. Proteins. 68:813–23
Darnell SJ, LeGault L, Mitchell JC (2008 Jul) KFC Server: interactive forecasting of protein interaction hot spots. Nucleic acids research. 36:W265–9
DeLano WL (2002 Feb) Unraveling hot spots in binding interfaces: progress and challenges. Current opinion in structural biology. 12:14–20
Emsley J, Knight CG, Farndale RW, Barnes MJ, Liddington RC (2000 Mar) Structural basis of collagen recognition by integrin alpha2beta1. Cell. 101:47–56
Fasman GD, Sober HA, et al. Handbook of biochemistry and molecular biology. vol. 1. CRC press, Cleveland; 1977
Fernandezrecio J (2011) Prediction of protein binding sites and hot spots. Wiley Interdisciplinary Reviews: Computational Molecular Science. 1(5):680–698
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):1453–1454
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. 346:1–6
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. 320:369–87
Kawashima S, Kanehisa M (2000 Jan) AAindex: amino acid index database. Nucleic acids research. 28:374
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
Kim DE, Chivian D, Baker D (2004 Jul) Protein structure prediction and analysis using the Robetta server. Nucleic acids research. 32:W526–31
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. 99:14116–21
Kortemme T, Kim DE, Baker D. Computational alanine scanning of protein-protein interfaces. Science’s STKE : signal transduction knowledge environment. 2004 Feb;2004:pl2
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 25:743–50
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):S5
Martins JM, Ramos RM, Pimenta AC, Moreira IS (2014 Mar) Solvent-accessible surface area: How well can be applied to hot-spot detection? Proteins. 82:479–90
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):2600–2607
Moreira IS, Fernandes PA, Ramos MJ (2007 Sep) Hot spots-a review of the protein-protein interface determinant amino-acid residues. Proteins. 68:803–12
Naderi-Manesh H, Sadeghi M, Arab S, Moosavi Movahedi AA (2001 Mar) Prediction of protein surface accessibility with information theory. Proteins. 42:452–9
Ofran Y, Rost B. ISIS: interaction sites identified from sequence. Bioinformatics (Oxford, England). 2007 Jan;23:e13–6
Ofran Y, Rost B (2007 Jul) Protein-protein interaction hotspots carved into sequences. PLoS computational biology. 3:e119
Shen HB, Chou KC (2008 Feb) PseAAC: a flexible web server for generating various kinds of protein pseudo amino acid composition. Analytical biochemistry. 373:386–8
Shingate P, Sukhwal A, Sowdhamini R (2014) ECMIS: computational approach for the identification of hotspots at protein-protein interfaces. BMC Bioinformatics. 15(1):303
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. 17:148–155
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):284–285
Tuncbag N, Keskin O, Gursoy A (2010 Jul) HotPoint: hot spot prediction server for protein interfaces. Nucleic acids research. 38:W402–6
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):1513–1520
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):119–126
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):184–190
Wells JA (1991) Systematic mutational analyses of protein-protein interfaces. Methods in enzymology. 202:390–411
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. 11:174
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):S6
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. 131:16–21
Zhu X, Mitchell JC (2011 Sep) KFC2: a knowledge-based hot spot prediction method based on interface solvation, atomic density, and plasticity features. Proteins. 79:2671–83
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. 19:1505–15
This work was supported by the National Natural Science Foundation of China (Nos. 61672035, 61300058, 61472282, 61271098 and 61374181).
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The authors declare that they have no competing interests.
The authors declare that their manuscript complies to the Ethical Rules applicable for this journal.
Handling Editor: L. Taher.
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Hu, S., Chen, P., Wang, B. et al. Protein binding hot spots prediction from sequence only by a new ensemble learning method. Amino Acids 49, 1773–1785 (2017). https://doi.org/10.1007/s00726-017-2474-6
- Hot spot residue
- Ensemble system