Prediction of vitamin interacting residues in a vitamin binding protein using evolutionary information

Abstract

BackgroundThe 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.ResultsIn 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.ConclusionsThis 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/).

Computing wide range of protein/peptide features from their sequence and structure

“…In past, binary profiles have been widely used for residue level annotation that includes prediction of protein's secondary structure as well as nucleotide or ligand binding sites in a protein (H. Harpreet Kaur andRaghava, 2003, 2004;Kumar et al, 2007;Ansari and Raghava, 2010;Panwar et al, 2013). We have integrated binary profile in Pfeature and the details about binary profile is already described in previous studies (Singh et al, 2015;Chauhan et al, 2013Chauhan et al, , 2012, following is brief procedure for creating binary profile.…”

Protein ligand-specific binding residue predictions by an ensemble classifier

“…Recently, researchers have paid attention to the differences in ligands, and many ligand-specific methods have been developed to obtain more accurate predictions. For example, Bharat et al developed VitaPred [29] to predict vitamin-interacting residues, Moreover, nucleotide-binding residues were predicted using SITEpred [30] and ATP binding residue predictions were extensively investigated using many methods [31, 32]. Other ligands have also been explored, such as HEME [33], FAD [34], calcium [35], GTP [36], NAD [37], and zinc [38].…”

Enhancing protein-vitamin binding residues prediction by multiple heterogeneous subspace SVMs ensemble

“…However, to the best of our knowledge, minimal work has been performed to design a specific predictor for predicting protein-vitamin binding residues. Recently, Panwar et al [ 30 ] published their pioneering work on protein-vitamin binding prediction and a predictor, called VitaPred, was implemented. VitaPred [ 30 ] is a sequence-based ligand-specific predictor specifically designed for predicting protein-vitamin binding residues, and it consists of four independent prediction modules for predicting vitamin, vitamin-A, vitamin-B, and pyridoxal-5-phosphate (vitamin-B6) binding residues.…”

“…Recently, Panwar et al [ 30 ] published their pioneering work on protein-vitamin binding prediction and a predictor, called VitaPred, was implemented. VitaPred [ 30 ] is a sequence-based ligand-specific predictor specifically designed for predicting protein-vitamin binding residues, and it consists of four independent prediction modules for predicting vitamin, vitamin-A, vitamin-B, and pyridoxal-5-phosphate (vitamin-B6) binding residues. VitaPred encodes each residue into a 340-D feature vector by applying a sliding window to the position-specific scoring matrix (PSSM) of a protein sequence; then, a support vector machine (SVM) is trained on the set of feature vectors of all the training residues.…”

“…This paper follows the pioneering work of Panwar et al [ 30 ] and aims to further improve the performance of protein-vitamin binding residue prediction. A new predictor, called TargetVita, which utilises multiple sequence-derived features and heterogeneous SVMs ensemble based on feature selection, is developed.…”