Composite structural motifs of binding sites for delineating biological functions of proteins

TL;DR

This study identifies recurring atomic-level motifs in protein binding sites and demonstrates that composite motifs better predict protein functions and biological processes than traditional sequence-based methods.

Contribution

The paper introduces a comprehensive method for classifying protein binding sites into elementary and composite motifs, linking atomic structures to biological functions.

Findings

Composite motifs outperform sequence similarity in function prediction.

Meta-composite motifs provide detailed annotations of biological processes.

All-against-all structural comparisons reveal fundamental elementary motifs.

Abstract

Most biological processes are described as a series of interactions between proteins and other molecules, and interactions are in turn described in terms of atomic structures. To annotate protein functions as sets of interaction states at atomic resolution, and thereby to better understand the relation between protein interactions and biological functions, we conducted exhaustive all-against-all atomic structure comparisons of all known binding sites for ligands including small molecules, proteins and nucleic acids, and identified recurring elementary motifs. By integrating the elementary motifs associated with each subunit, we defined composite motifs which represent context-dependent combinations of elementary motifs. It is demonstrated that function similarity can be better inferred from composite motif similarity compared to the similarity of protein sequences or of individual binding sites. By integrating the composite motifs associated with each protein function, we define meta-composite motifs each of which is regarded as a time-independent diagrammatic representation of a biological process. It is shown that meta-composite motifs provide richer annotations of biological processes than sequence clusters. The present results serve as a basis for bridging atomic structures to higher-order biological phenomena by classification and integration of binding site structures.