SiteHound-web is currenlty maintained by Yingjie Lin.

The original version of SiteHound-web was developed by Marylens Hernandez.

The EasyMIFs and SiteHound applications were developed by Dario Ghersi.

© Sanchez Lab, Department of Structural and Chemical Biology, Mount Sinai School of Medicine, New York, NY 10029.


This material is based upon work supported by the National Science Foundation under Grant No. MCB 0517352 and by the National Institutes of Health under Grant No. GM081713.

"Any opinions, findings and conclusions or recomendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation (NSF) or the National Institutes of Health (NIH)."

 EasyMIFs and SiteHound Publications

SITEHOUND-web: a server for ligand binding site identification in protein structures.

M. Hernandez, D. Ghersi and R. Sanchez, Nuc. Acids. Res., 17, W413-W416, 2009.
( Users of SiteHound-web are requested to cite this article in their publications.)

EasyMIFS and SiteHound: a toolkit for the identification of ligand-binding sites in protein structures.

D. Ghersi and R. Sanchez, Bioinformatics, 25, 3185-3186, 2009.

Improving accuracy and efficiency of blind protein-ligand docking by focusing on predicted binding sites.

D. Ghersi and R. Sanchez, Proteins, 74, 417-424, 2009.

Beyond structural genomics: computational approaches for the identification of ligand binding sites in protein structures. D. Ghersi and R. Sanchez, J Struct Funct Genomics, 12, 109-117, 2011.

 Articles citing EasyMIFs and SiteHound

Phosphate binding sites identification in protein structures. L. Parca et al., 2011.

Toward prediction of functional protein pockets using blind docking and pocket search algorithms. C. Hetényi et al., 2011.

FunFOLD: an improved automated method for the prediction of ligand binding residues using 3D models of proteins. D.B. Roche et al., 2011.

Phosfinder: a web server for the identification of phosphate-binding sites on protein structures. L. Parca, 2011.

Insights from molecular modeling and dynamics simulation of pathogen resistance (R) protein from brinjal. D. Shrivastava, 2011.

AADS - An Automated Active Site Identification, Docking, and Scoring Protocol for Protein Targets Based on Physicochemical Descriptors. T. Singh et al., 2011.

Identification of cavities on protein surface using multiple computational approaches for drug binding site prediction. Z. Zhang et al., 2011.

Drug-like density: a method of quantifying the "bindability" of a protein target based on a very large set of pockets and drug-like ligands from the Protein Data Bank. R.P. Sheridan et al., 2010.

Inferred Biomolecular Interaction Server--a web server to analyze and predict protein interacting partners and binding sites. B.A. Shoemaker et al., 2010.

3DLigandSite: predicting ligand-binding sites using similar structures. M.N. Wass et al., 2010.

Knowledge-based annotation of small molecule binding sites in proteins. RR. Thangudu et al., 2010.

3V: cavity, channel and cleft volume calculator and extractor. NR. Voss et al., 2010.

Predicting protein ligand binding sites by combining evolutionary sequence conservation and 3D structure. J.A. Capra et al., 2009.