Densest subgraph-based methods for protein-protein interaction hot spot prediction.

ABSTRACT:

Background

Hot spots play an important role in protein binding analysis. The residue interaction network is a key point in hot spot prediction, and several graph theory-based methods have been proposed to detect hot spots. Although the existing methods can yield some interesting residues by network analysis, low recall has limited their abilities in finding more potential hot spots.

Result

In this study, we develop three graph theory-based methods to predict hot spots from only a single residue interaction network. We detect the important residues by finding subgraphs with high densities, i.e., high average degrees. Generally, a high degree implies a high binding possibility between protein chains, and thus a subgraph with high density usually relates to binding sites that h

SUBMITTER: Li R 

PROVIDER: S-EPMC9623998 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature