A unified statistical model to support local sequence order independent similarity searching for ligand-binding sites and its application to genome-based drug discovery.
Functional relationships between proteins that do not share global structure similarity can be established by detecting their ligand-binding-site similarity. For a large-scale comparison, it is critical to accurately and efficiently assess the statistical significance of this similarity. Here, we report an efficient statistical model that supports local sequence order independent ligand-binding-site similarity searching. Most existing statistical models only take into account the matching vertices between two sites that are defined by a fixed number of points. In reality, the boundary of the binding site is not known or is dependent on the bound ligand making these approaches limited. To address these shortcomings and to perform binding-site mapping on a genome-wide scale, we developed a sequence-order independent profile-profile alignment (SOIPPA) algorithm that is able to detect local similarity between unknown binding sites a priori. The SOIPPA scoring integrates geometric, evolutionary and physical information into a unified framework. However, this imposes a significant challenge in assessing the statistical significance of the similarity because the conventional probability model that is based on fixed-point matching cannot be applied. Here we find that scores for binding-site matching by SOIPPA follow an extreme value distribution (EVD). Benchmark studies show that the EVD model performs at least two-orders faster and is more accurate than the non-parametric statistical method in the previous SOIPPA version. Efficient statistical analysis makes it possible to apply SOIPPA to genome-based drug discovery. Consequently, we have applied the approach to the structural genome of Mycobacterium tuberculosis to construct a protein-ligand interaction network. The network reveals highly connected proteins, which represent suitable targets for promiscuous drugs.
Xie
Lei
L
San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093, USA. lxie@sdsc.edu
Xie
Li
L
Bourne
Philip E
PE
D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D055808:Drug Discovery / Q000379:methods*; D016678:Genome; D008024:Ligands; D015233:Models, Statistical; D009169:Mycobacterium tuberculosis / Q000235:genetics / Q000378:metabolism; D011506:Proteins / Q000737:chemistry*
D008024:Ligands; D011506:Proteins