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Cavity Server: a structure-based protein binding site detection Server.

The technological advances in NMR spectroscopy and X-ray crystallography, as well as the success of structural genomics and high-throughput structural biology, are leading to a significant increase in protein three dimensional structural data, which is the basis and of vital importance for structure-based rational drug design. Typically, protein receptors bind small molecule ligands applying a lock-and-key rule which means that proteins should provide suitable binding sites, typically shaped as concave clefts, for ligand binding. Given a protein structure, it is important to assess its possibility to be a good target or not before doing structure-based drug design on it. Identifying the location of ligand binding sites on a protein is of fundamental importance for a range of applications including molecular docking, de novo drug design, structural identification and comparison of functional sites.

Cavity Server is a freely accessed web-server designed to identify potential protein binding site. It uses the purely geometrical method to find potential ligand binding site, then uses geometrical structure and physical chemistry property information to locate ligand binding sites. CAVITY will provide a maximal ligand binding affinity prediction for the binding site. The most important, CAVITY could define accurate and clear binding site for drug design. The cavities detected by CAVITY can provide clear, accurate information about the shapes and boundaries of ligand binding sites. As a result, ligands are designed to fit within the binding cavities and overgrowth is eliminated. At present, CAVITY is also integrated by LigBuilder V2 which is a multiple-purposed program developed for structure-based de novo drug design and optimization.

Here, we describe the CAVITY algorithm briefly. First, the program imports the coordinates of protein atoms from a PDB file, and then a validity-checking process is carried out. CAVITY skips all hydrogen atoms while water radius are added to the van der Waals radius of each atom. Then, CAVITY creates 3D grid points to contain the whole protein.The Tripos force field is used to compute the properties of each grid points. In the next step, CAVITY creates a probe sphere with a radius value.These steps mark all the putative cavity space of the protein as vacant grid. Then, we need to separate conjoint pockets on the surface into separate sub-cavities where a “shrink-and-expand algorithm” is implemented and remove improper cavities. At last we define a potential binding cavity as “a cavity composed of one to several concave clefts which could be bound by one ligand”.

To use Cavity Server, please upload your receptor and ligand (optimal) files in the Computing page. The job information as well as the structures of submitted molecules is invisible between different users. Here is a CAVITY V1.1 software. You can download and install it in your Linux system.


  • Yaxia Yuan, Jianfeng Pei, Luhua Lai. Binding Site Detection and Druggability Prediction of Protein Targets for Structure-Based Drug Design. Current Pharmaceutical Design, 2013,19 (12), 2326-2333(8). Link.

  • Yaxia Yuan, Jianfeng Pei, Luhua Lai. LigBuilder 2: A Practical de Novo Drug Design Approach. J. Chem. Inf. Model., 2011, 51 (5), 1083-1091. Link.

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