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Erschienen in:
A Variable Neighborhood Search Approach for the Capacitated m-Ring-Star Problem Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Prediction of Hot Spots Based on Physicochemical Features and Relative Accessible Surface Area of Amino Acid Sequence

verfasst von : ShanShan Hu, Peng Chen, Jun Zhang, Bing Wang

Erschienen in: Intelligent Computing Theories and Application

Verlag: Springer International Publishing

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Abstract

Hot spot is dominant for understanding the mechanism of protein-protein interactions and can be applied as a target to drug design. Since experimental methods are costly and time-consuming, computational methods are prevalently applied as an useful tool in hot spot prediction through sequence or structure information. Here, we propose a new sequence-based model that combines physicochemical features with relative accessible surface area of amino acid sequence. The model consists of 83 classifiers involving IBk algorithm, where instances for one classifier are encoded by corresponding property extracted from 544 properties in AAindex1 database. Then several top performance classifiers with respect to F1 score are selected to be an ensemble by majority voting technique. The model outperforms other state-of-the-art computational methods, yields a F1 score of 0.80 on BID test set.

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Vorheriges Kapitel Application of Machine Learning-Based Classification to Genomic Selection and Performance Improvement
Nächstes Kapitel Identification of Hot Regions in Protein-Protein Interactions Based on Detecting Local Community Structure
Literatur
1.
Chothia, C., Janin, J.: Principles of protein-protein recognition. Nature 256(5520), 705–708 (1975)CrossRef
2.
Bogan, A.A., Thorn, K.S.: Anatomy of hot spots in protein interfaces. J. Mol. Biol. 280(1), 1–9 (1998)CrossRef
3.
Brenke, R., Kozakov, D., Chuang, G.Y., Beglov, D., Hall, D., Landon, M.R., Mattos, C., Vajda, S.: Fragment-based identification of druggable ‘hot spots’ of proteins using Fourier domain correlation techniques. Bioinformatics 25(5), 621–627 (2009)CrossRef
4.
Wells, J.A.: Systematic mutational analyses of protein-protein interfaces. Methods Enzymol. 202, 390–411 (1991)CrossRef
5.
DeLano, W.L.: Unraveling hot spots in binding interfaces: progress and challenges. Curr. Opin. Struct. Biol. 12(1), 14–20 (2002)CrossRef
6.
Kortemme, T., Baker, D.: A simple physical model for binding energy hot spots in protein-protein complexes. Proc. Nat. Acad. Sci. U.S.A. 99(22), 14116–14121 (2002)CrossRef
7.
Guerois, R., Nielsen, J.E., Serrano, L.: Predicting changes in the stability of proteins and protein complexes: a study of more than 1000 mutations. J. Mol. Biol. 320(2), 369–387 (2002)CrossRef
8.
Darnell, S.J., Page, D., Mitchell, J.C.: An automated decision-tree approach to predicting protein interaction hot spots. Proteins 68(4), 813–823 (2007)CrossRef
9.
Shingate, P., Manoharan, M., Sukhwa, A., Sowdhamini, R.: ECMIS: computational approach for the identification of hotspots at protein-protein interfaces. BMC Bioinformatics 15, 303 (2014)CrossRef
10.
Wang, L., Zhang, W., Gao, Q., Xiong, C.: Prediction of hot spots in protein interfaces using extreme learning machines with the information of spatial neighbour residues. IET Syst. Biol. 8(4), 184–190 (2014)CrossRef
11.
Kawashima, S., Pokarowski, P., Pokarowska, M., Kolinski, A., Katayama, T., Kanehisa, M.: AAindex: amino acid index database, progress report 2008. Nucleic Acids Res. (Database Issue) 36, D202–205 (2008)CrossRef
12.
Aha, D., Kibler, D., Albert, M.: Instance-based learning algorithms. Mach. Learn. 6(1), 37–66 (1991)
13.
Thorn, K.S., Bogan, A.A.: ASEdb: a database of alanine mutations and their effects on the free energy of binding in protein interactions. Bioinformatics 17(3), 284–285 (2001)CrossRef
14.
Fischer, T.B., Arunachalam, K.V., Bailey, D., Mangual, V., Bakhru, S., Russo, R., Huang, D., Paczkowski, M., Lalchandani, V., Ramachandra, C.: The binding interface database (BID): a compilation of amino acid hot spots in protein interfaces. Bioinformatics 19(11), 1453–1454 (2003)CrossRef
15.
Chen, P., Li, J., Wong, L., Kuwahara, H., Huang, J.Z., Gao, X.: Accurate prediction of hot spot residues through physicochemical characteristics of amino acid sequences. Proteins 81(8), 1351–1362 (2013)CrossRef
16.
Chou, K.C.: Prediction of protein cellular attributes using pseudo-amino acid composition. Proteins 43(3), 246–255 (2001)CrossRef
17.
Liu, B., Wang, S., Wang, X.: DNA binding protein identification by combining pseudo amino acid composition and profile-based protein representation. Sci. Rep. 5, 15479 (2015)CrossRef
18.
Tang, H., Chen, W., Lin, H.: Identification of immunoglobulins using chou’s pseudo amino acid composition with feature selection technique. Mol. BioSyst. 12(4), 1269–1275 (2016)CrossRef
19.
Shen, H.B., Chou, K.C.: PseAAC: a flexible web server for generating various kinds of protein pseudo amino acid composition. Anal. Biochem. 373(2), 386–388 (2008)CrossRef
20.
Martins, J.M., Ramos, R.M., Pimenta, A.C., Moreira, I.S.: Solvent-accessible surface area: how well can be applied to hot-spot detection? Proteins 82(3), 479–490 (2014)CrossRef
21.
Chen, R., Chen, W., Yang, S., Wu, D., Wang, Y., Tian, Y., Shi, Y.: Rigorous assessment and integration of the sequence and structure based features to predict hot spots. BMC Bioinformatics 12, 311 (2011)CrossRef
22.
Petersen, B., Petersen, T.N., Andersen, P., Nielsen, M., Lundegaard, C.: A generic method for assignment of reliability scores applied to solvent accessibility predictions. BMC Struct. Biol. 9, 51 (2009)CrossRef
23.
Darnell, S.J., LeGault, L., Mitchell, J.C.: KFC server: interactive forecasting of protein interaction hot spots. Nucleic Acids Res. (Web Server Issue). 36, W265–269 (2008)CrossRef
24.
Ofran, Y., Rost, B.: ISIS: interaction sites identified from sequence. Bioinformatics 23(2), E13–E16 (2007)CrossRef
25.
Tuncbag, N., Gursoy, A., Keskin, O.: Identification of computational hot spots in protein interfaces: combining solvent accessibility and inter-residue potentials improves the accuracy. Bioinformatics 25(12), 1513–1520 (2009)CrossRef
Metadaten
Titel
Prediction of Hot Spots Based on Physicochemical Features and Relative Accessible Surface Area of Amino Acid Sequence
verfasst von
ShanShan Hu
Peng Chen
Jun Zhang
Bing Wang
Copyright-Jahr
2016
Buch
Intelligent Computing Theories and Application

Print ISBN: 978-3-319-42290-9

Electronic ISBN: 978-3-319-42291-6

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-42291-6_42

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