CavityPlus:
Protein cavity detection and beyond
Start Computing Tutorial

about

CavityPlus is a web server for precise and robust protein cavity detection and functional analysis. With protein 3D structural information as input, CavityPlus applies Cavity to detect the potential binding sites on the surface of a given protein structure and rank them with ligandability and druggability scores. Based on the detected protein cavity information, further functions of a protein cavity are then analyzed by using three submodules, namely CavPharmer, CorrSite and CovCys. CavPharmer uses a receptor-based pharmacophore modeling program Pocket to automatically extract pharmacophore features within cavities. CorrSite identifies potential allosteric ligand binding sites based on motion correlation analysis between allosteric and orthosteric cavities. CovCys automatically detects druggable cysteine residues for covalent ligand design, which is especially useful for identifying novel binding sites for covalent allosteric ligand design. Overall, CavityPlus provides an integrated platform for analyzing comprehensive properties of protein binding cavities, which is useful in many aspects for drug design and discovery such as target selection and identification, virtual screening and de novo drug design, allosteric and covalent-binding drug design.

toolbox

Comprehensive analysis of protein binding cavities.

Cavity

Mapping the druggable protein binding site

CavPharmer

Receptor-based Pharmacophore Modeling

Corrsite

Potential allosteric ligand binding site prediction

CovCys

Detecting druggable cysteine residues for covalent ligand design

reference

1. Xu,Y., Wang,S., Hu,Q., Gao,S., Ma,X., Zhang,W., Shen,Y., Chen,F., Lai,L. and Pei,J. (2018) CavityPlus: a web server for protein cavity detection with pharmacophore modelling, allosteric site identification and covalent ligand binding ability prediction. Nucleic Acids Research, , gky380. Link.

2. Yuan,Y., Pei,J., and Lai,L. (2013) Binding Site Detection and Druggability Prediction of Protein Targets for Structure-Based Drug Design. Curr Pharm Des., 19, 2326-2333. Link.

3. Yuan,Y., Pei,J., and Lai,L. (2011) LigBuilder 2: A Practical de Novo Drug Design Approach. J. Chem. Inf. Model., 51, 1083-1091.Link.

4. Chen,J., and Lai,L. (2006) Pocket v.2: Further Developments on Receptor-Based Pharmacophore Modeling. J. Chem. Inf. Model., 46, 2684-2691.Link.

5. Chen, J., Ma, X., Yuan, Y., Pei, J., and Lai, L. (2014). Protein-protein interface analysis and hot spots identification for chemical ligand design. Curr Pharm Des., 20, 1192-1200. Link.

6. Ma, X., Meng, H., and Lai, L. (2016). Motions of allosteric and orthosteric ligand-binding sites in proteins are highly correlated. J. Chem. Inf. Model., 56, 1725-1733.Link.

7. Zhang, W., Pei, J., and Lai, L. (2017). Statistical Analysis and Prediction of Covalent Ligand Targeted Cysteine Residues. J. Chem. Inf. Model., 51, 1453-1460.Link.

Contact us

Contact

Related resources:

LigBuilder V2.0