Ballast: A Ball-Based Algorithm for Structural Motifs

Abstract

Structural motifs encapsulate local sequence-structure-function relationships characteristic of related proteins, enabling the prediction of functional characteristics of new proteins, providing molecular-level insights into how those functions are performed, and supporting the development of variants specifically maintaining or perturbing function in concert with other properties. Numerous computational methods have been developed to search through databases of structures for instances of specified motifs. However, it remains an open problem how best to leverage the local geometric and chemical constraints underlying structural motifs in order to develop motif-finding algorithms that are both theoretically and practically efficient. We present a simple, general, efficient approach, called Ballast (ball-based algorithm for structural motifs), to match given structural motifs to given structures. Ballast combines the best properties of previously developed methods, exploiting the composition and local geometry of a structural motif and its possible instances in order to effectively filter candidate matches. We show that on a wide range of motif-matching problems, Ballast efficiently and effectively finds good matches, and we provide theoretical insights into why it works well. By supporting generic measures of compositional and geometric similarity, Ballast provides a powerful substrate for the development of motif-matching algorithms.

Coupling Graphs, Efficient Algorithms and B-Cell Epitope Prediction

“…Compared to generic bipartite graphs, the integrative notion of coupling graphs has advantages for deciphering biological associations, identifying structural motifs in protein complexes, predicting context-awareness binding sites of proteins, and constructing binding partners for an input protein [7], [8]. Taking a paratope-epitope interacting complex as example, the coupling graph representation of this complex has several advantages.…”