title: Prediction of vitamin interacting residues in a vitamin binding protein using evolutionary information.
abstract: BACKGROUND
  The vitamins are important cofactors in various enzymatic-reactions. In past, many inhibitors have been designed against vitamin binding pockets in order to inhibit vitamin-protein interactions. Thus, it is important to identify vitamin interacting residues in a protein. It is possible to detect vitamin-binding pockets on a protein, if its tertiary structure is known. Unfortunately tertiary structures of limited proteins are available. Therefore, it is important to develop in-silico models for predicting vitamin interacting residues in protein from its primary structure.
  
  
  RESULTS
  In this study, first we compared protein-interacting residues of vitamins with other ligands using Two Sample Logo (TSL). It was observed that ATP, GTP, NAD, FAD and mannose preferred {G,R,K,S,H}, {G,K,T,S,D,N}, {T,G,Y}, {G,Y,W} and {Y,D,W,N,E} residues respectively, whereas vitamins preferred {Y,F,S,W,T,G,H} residues for the interaction with proteins. Furthermore, compositional information of preferred and non-preferred residues along with patterns-specificity was also observed within different vitamin-classes. Vitamins A, B and B6 preferred {F,I,W,Y,L,V}, {S,Y,G,T,H,W,N,E} and {S,T,G,H,Y,N} interacting residues respectively. It suggested that protein-binding patterns of vitamins are different from other ligands, and motivated us to develop separate predictor for vitamins and their sub-classes. The four different prediction modules, (i) vitamin interacting residues (VIRs), (ii) vitamin-A interacting residues (VAIRs), (iii) vitamin-B interacting residues (VBIRs) and (iv) pyridoxal-5-phosphate (vitamin B6) interacting residues (PLPIRs) have been developed. We applied various classifiers of SVM, BayesNet, NaiveBayes, ComplementNaiveBayes, NaiveBayesMultinomial, RandomForest and IBk etc., as machine learning techniques, using binary and Position-Specific Scoring Matrix (PSSM) features of protein sequences. Finally, we selected best performing SVM modules and obtained highest MCC of 0.53, 0.48, 0.61, 0.81 for VIRs, VAIRs, VBIRs, PLPIRs respectively, using PSSM-based evolutionary information. All the modules developed in this study have been trained and tested on non-redundant datasets and evaluated using five-fold cross-validation technique. The performances were also evaluated on the balanced and different independent datasets.
  
  
  CONCLUSIONS
  This study demonstrates that it is possible to predict VIRs, VAIRs, VBIRs and PLPIRs from evolutionary information of protein sequence. In order to provide service to the scientific community, we have developed web-server and standalone software VitaPred (http://crdd.osdd.net/raghava/vitapred/).
authors: Bioinformatics Centre, Institute of Microbial Technology (CSIR), Chandigarh, India.
authors: Bharat
authors: B
authors: Panwar
authors: Sudheer
authors: S
authors: Gupta
authors: Gajendra P S
authors: GP
authors: Raghava
mesh_terms: D000596:Amino Acids / Q000737:chemistry; D001665:Binding Sites; D002352:Carrier Proteins / Q000737:chemistry* / Q000378:metabolism; D019143:Evolution, Molecular; D008024:Ligands; D056510:Position-Specific Scoring Matrices; D011732:Pyridoxal Phosphate / Q000378:metabolism; D020539:Sequence Analysis, Protein / Q000379:methods*; D060388:Support Vector Machine; D014801:Vitamin A / Q000378:metabolism; D014803:Vitamin B Complex / Q000378:metabolism; D014815:Vitamins / Q000378:metabolism*
chemical_list: D000596:Amino Acids; D002352:Carrier Proteins; D008024:Ligands; D014815:Vitamins; D014801:Vitamin A; D014803:Vitamin B Complex; D011732:Pyridoxal Phosphate
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