Prediction of GTP interacting residues, dipeptides and tripeptides in a protein from its evolutionary information

Background Guanosine triphosphate (GTP)-binding proteins play an important role in regulation of G-protein. Thus prediction of GTP interacting residues in a protein is one of the major challenges in the field of the computational biology. In this study, an attempt has been made to develop a computat...

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Veröffentlicht in:BMC bioinformatics Jg. 11; H. 1; S. 301
Hauptverfasser: Chauhan, Jagat S, Mishra, Nitish K, Raghava, Gajendra PS
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London BioMed Central 03.06.2010
BioMed Central Ltd
BMC
Schlagworte:
Algorithms
Applications software
Binding Sites
Bioinformatics
Biomedical and Life Sciences
Computational biology
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Databases, Protein
Dipeptides - chemistry
Dipeptides - metabolism
Evolution, Molecular
G proteins
Guanosine Triphosphate - chemistry
Guanosine Triphosphate - metabolism
Life Sciences
Microarrays
Oligopeptides - chemistry
Oligopeptides - metabolism
Physiological aspects
Protein-protein interactions
Proteins - chemistry
Proteins - metabolism
Research Article
Sequence analysis (applications)
Sequence Analysis, Protein
India
Support Vector Machine Model
Evolutionary Information
Support Vector Machine
Negative Pattern
Matthews Correlation Coefficient
ISSN:1471-2105, 1471-2105
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Zusammenfassung:Background Guanosine triphosphate (GTP)-binding proteins play an important role in regulation of G-protein. Thus prediction of GTP interacting residues in a protein is one of the major challenges in the field of the computational biology. In this study, an attempt has been made to develop a computational method for predicting GTP interacting residues in a protein with high accuracy (Acc), precision (Prec) and recall (Rc). Result All the models developed in this study have been trained and tested on a non-redundant (40% similarity) dataset using five-fold cross-validation. Firstly, we have developed neural network based models using single sequence and PSSM profile and achieved maximum Matthews Correlation Coefficient (MCC) 0.24 (Acc 61.30%) and 0.39 (Acc 68.88%) respectively. Secondly, we have developed a support vector machine (SVM) based models using single sequence and PSSM profile and achieved maximum MCC 0.37 (Prec 0.73, Rc 0.57, Acc 67.98%) and 0.55 (Prec 0.80, Rc 0.73, Acc 77.17%) respectively. In this work, we have introduced a new concept of predicting GTP interacting dipeptide (two consecutive GTP interacting residues) and tripeptide (three consecutive GTP interacting residues) for the first time. We have developed SVM based model for predicting GTP interacting dipeptides using PSSM profile and achieved MCC 0.64 with precision 0.87, recall 0.74 and accuracy 81.37%. Similarly, SVM based model have been developed for predicting GTP interacting tripeptides using PSSM profile and achieved MCC 0.70 with precision 0.93, recall 0.73 and accuracy 83.98%. Conclusion These results show that PSSM based method performs better than single sequence based method. The prediction models based on dipeptides or tripeptides are more accurate than the traditional model based on single residue. A web server "GTPBinder" http://www.imtech.res.in/raghava/gtpbinder/ based on above models has been developed for predicting GTP interacting residues in a protein.
Bibliographie:ObjectType-Article-1
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ISSN:1471-2105
1471-2105
DOI:10.1186/1471-2105-11-301