Coenzyme A (CoA) is a cofactor that is ubiquitous and essential for the metabolism of carboxylic acids. CoA-protein binding plays an important role in various cellular functions and metabolic pathways. CoABind is a consensus-based algorithm for the prediction of CoA- and CoA derivatives-binding residues, which combines an ab-initio method SVMpred and a template-based method TemPred. In SVMpred, a set of 81 features are designed from two complementary sequence profiles and the predicted secondary structure and solvent accessibility. The engine for classification in SVMpred is selected as the support vector machine. For TemPred, the prediction is transferred from homologous templates in the training set, which are identified by HHsearch. The results returned by CoABind include the predicted CoA- and CoA derivatives-binding residues and the propensity score of each residue to be involved in CoA binding.

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