SuperSite: dictionary of metabolite and drug binding sites in proteins

Bauer, Raphael André; Günther, Stefan; Jansen, Dominic; Heeger, Carolin; Thaben, P.F.; Preißner, Robert

doi:10.1093/nar/gkn618

Cited by 32 publications

(20 citation statements)

References 29 publications

(28 reference statements)

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“…The structural information of protein-vitamin complexes extracted from SuperSite [52]. We found total 1061 protein-vitamin complexes, in which 181 and 843 complexes proteins are bind with vitamin A and B respectively.…”

Section: Discussionmentioning

confidence: 99%

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

2013

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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/).

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Section: Discussionmentioning

confidence: 99%

“…In this study, we collected data from SuperSite documentation [52] and extracted 1061 PDB IDs of protein having contact with vitamins in PDB. We downloaded the sequence of all chains of these PDB Ids from Protein Data Bank [53].…”

Section: Methodsmentioning

confidence: 99%

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

2013

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“…Two benchmark datasets were used to demonstrate the effectiveness of the proposed TargetATPsite. The first dataset was selected from SuperSite encyclopedia [37] by Chauhan et al [6] and consists of 168 non-redundant protein sequences, denoted as ATP168. The sequence identity between any two sequences in ATP168 is below 40%.…”

Section: Benchmark Datasetsmentioning

confidence: 99%

TargetATPsite: A template‐free method for ATP‐binding sites prediction with residue evolution image sparse representation and classifier ensemble

Huang

et al. 2013

J Comput Chem

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Understanding the interactions between proteins and ligands is critical for protein function annotations and drug discovery. We report a new sequence-based template-free predictor (TargetATPsite) to identify the Adenosine-5'-triphosphate (ATP) binding sites with machine-learning approaches. Two steps are implemented in TargetATPsite: binding residues and pockets predictions, respectively. To predict the binding residues, a novel image sparse representation technique is proposed to encode residue evolution information treated as the input features. An ensemble classifier constructed based on support vector machines (SVM) from multiple random under-samplings is used as the prediction model, which is effective for dealing with imbalance phenomenon between the positive and negative training samples. Compared with the existing ATP-specific sequence-based predictors, TargetATPsite is featured by the second step of possessing the capability of further identifying the binding pockets from the predicted binding residues through a spatial clustering algorithm. Experimental results on three benchmark datasets demonstrate the efficacy of TargetATPsite.

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“…They are Nicotinamide adenine dinucleotide (NAD), Flavin adenine dinucleotide (FAD), and Adenosine-5'-triphosphate (ATP). Data sets were collected respectively as follows: Firstly, PDB IDs that have contact with NAD, FAD, and ATP were extracted from Supersite [20]. Next, Ligand Protein Contact (LPC) [21] was used to identify side chains containing the NAD, FAD, and ATP binding sites.…”

Section: Data Setsmentioning

confidence: 99%

SCPSSMpred: A General Sequence-based Method for Ligand-binding Site Prediction

Fang

Noguchi

Yamana

2013

IPSJ Transactions on Bioinformatics

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In this paper, we propose a novel method, named SCPSSMpred (Smoothed and Condensed PSSM based prediction), which uses a simplified position-specific scoring matrix (PSSM) for predicting ligand-binding sites. Although the simplified PSSM has only ten dimensions, it combines abundant features, such as amino acid arrangement, information of neighboring residues, physicochemical properties, and evolutionary information. Our method employs no predicted results from other classifiers as input, i.e., all features used in this method are extracted from the sequences only. Three ligands (FAD, NAD and ATP) were used to verify the versatility of our method, and three alternative traditional methods were also analyzed for comparison. All the methods were tested at both the residue level and the protein sequence level. Experimental results showed that the SCPSSMpred method achieved the best performance besides reducing 50% of redundant features in PSSM. In addition, it showed a remarkable adaptability in dealing with unbalanced data compared to other methods when tested on the protein sequence level. This study not only demonstrates the importance of reducing redundant features in PSSM, but also identifies sequence-derived hallmarks of ligand-binding sites, such that both the arrangements and physicochemical properties of neighboring residues significantly impact ligand-binding behavior * 1 .

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SuperSite: dictionary of metabolite and drug binding sites in proteins

Cited by 32 publications

References 29 publications

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

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

TargetATPsite: A template‐free method for ATP‐binding sites prediction with residue evolution image sparse representation and classifier ensemble

SCPSSMpred: A General Sequence-based Method for Ligand-binding Site Prediction

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