Space-Related Pharmamotifs for fast search protein binding motifs and environments
| Autor: | Chang, Li-Zen, 張力仁 |
|---|---|
| Rok vydání: | 2010 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 98 It is important to understand the potential target proteins for a chemical compound.During the early drug discovery stage, for example, it could avoid the unnecessary developing cost and time by detecting the potential harmful side effects. On the other hand, it could provide the new usages for old drugs. Recently, multiple target drugs give a new paradigm for diseases with complex mechanism such as cancers and diabetes. Therefore, discovering potential target proteins of a given compound is a valuable issue in bioinformatics and drug development. Previous studies indicate that similar compounds enable to bind the proteins with similar binding environment. Researchers usually search similar proteins by aligning the given protein sequence or global protein structure in sequence or structure databases. However, previous works show that in some cases proteins bound the same ligand may not have significant evolutionary relationship in both sequence and global structure but in their binding environments. In this study, we introduce a concept named Space-Related Pharmamotif (SRP) to discover the proteins with similar binding environment in protein databases. SRP is composed of a set of spatially discontinuous peptide segments, which surround the ligand-binding site. Compared with the previous methods of finding proteins with similar sequence or global structure, SRP focuses on protein-ligand interacting environment. By transforming the 3D structure segments into 1D structural alphabet sequences through 3D-BLAST, we can search the potential target proteins with similar binding environment against Protein Data Bank (PDB) rapidly. Furthermore, we use the structure alignment tool, such as DALI, to precisely locate the possible binding environment in these target protein structures. We collect 530 protein-drug co-crystallized complexes, in which contain 187 different FDA-approved drugs. We build SRPs and screen PDB for each protein-drug complex. For searching the proteins with the same UniProt accession number and the same ligand, the recall achieves 80% and 54%, respectively. Proteins classified into the same homologous superfamily of CATH can be predicted with a precision of 82%. Our results demonstrate that SRP provides a reliable performance in searching the potential target proteins with similarbinding environment. We give an example of Zanamivir to describe how SRP can identify slight structural difference of the binding environments between proteins. In another example, we preliminarily discuss the issue of “new use for old drugs” about Imatinib, which is a marking drug known to against disease chronic myelogenous leukemia and gastrointestinal stromal tumor. Finally, we build a web server to represent the SRP information and the searching results from 530 protein-drug complexes for helping to identify the potential binding protein of 187 known drugs. In this study, we supply evidence to present that SRP is reliable for searching the potential target proteins with similar binding environment. In the future, we will develop SRP to be useful to understand protein-ligand interactions and helpful for drug design. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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