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A new protein-ligand binding sites prediction method based on the integration of protein sequence conservation information.
- Source :
-
BMC bioinformatics [BMC Bioinformatics] 2011 Dec 14; Vol. 12 Suppl 14, pp. S9. Date of Electronic Publication: 2011 Dec 14. - Publication Year :
- 2011
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Abstract
- Background: Prediction of protein-ligand binding sites is an important issue for protein function annotation and structure-based drug design. Nowadays, although many computational methods for ligand-binding prediction have been developed, there is still a demanding to improve the prediction accuracy and efficiency. In addition, most of these methods are purely geometry-based, if the prediction methods improvement could be succeeded by integrating physicochemical or sequence properties of protein-ligand binding, it may also be more helpful to address the biological question in such studies.<br />Results: In our study, in order to investigate the contribution of sequence conservation in binding sites prediction and to make up the insufficiencies in purely geometry based methods, a simple yet efficient protein-binding sites prediction algorithm is presented, based on the geometry-based cavity identification integrated with sequence conservation information. Our method was compared with the other three classical tools: PocketPicker, SURFNET, and PASS, and evaluated on an existing comprehensive dataset of 210 non-redundant protein-ligand complexes. The results demonstrate that our approach correctly predicted the binding sites in 59% and 75% of cases among the TOP1 candidates and TOP3 candidates in the ranking list, respectively, which performs better than those of SURFNET and PASS, and achieves generally a slight better performance with PocketPicker.<br />Conclusions: Our work has successfully indicated the importance of the sequence conservation information in binding sites prediction as well as provided a more accurate way for binding sites identification.
Details
- Language :
- English
- ISSN :
- 1471-2105
- Volume :
- 12 Suppl 14
- Database :
- MEDLINE
- Journal :
- BMC bioinformatics
- Publication Type :
- Academic Journal
- Accession number :
- 22373099
- Full Text :
- https://doi.org/10.1186/1471-2105-12-S14-S9