Prediction of zinc binding sites in proteins using sequence derived information Abhishikha Srivastava Manish Kumar 10.6084/m9.figshare.5787591.v1 https://tandf.figshare.com/articles/journal_contribution/Prediction_of_zinc_binding_sites_in_proteins_using_sequence_derived_information/5787591 <p>Zinc is one the most abundant catalytic cofactor and also an important structural component of a large number of metallo-proteins. Hence prediction of zinc metal binding sites in proteins can be a significant step in annotation of molecular function of a large number of proteins. Majority of existing methods for zinc-binding site predictions are based on a data-set of proteins, which has been compiled nearly a decade ago. Hence there is a need to develop zinc-binding site prediction system using the current updated data to include recently added proteins. Herein, we propose a support vector machine-based method, named as ZincBinder, for prediction of zinc metal-binding site in a protein using sequence profile information. The predictor was trained using fivefold cross validation approach and achieved 85.37% sensitivity with 86.20% specificity during training. Benchmarking on an independent non-redundant data-set, which was not used during training, showed better performance of ZincBinder vis-à-vis existing methods. Executable versions, source code, sample datasets, and usage instructions are available at <a href="http://proteininformatics.org/mkumar/znbinder/" target="_blank">http://proteininformatics.org/mkumar/znbinder/</a></p> 2018-01-15 13:47:34 zinc metal binding site machine learning support vector machine PSSM fivefold cross-validation