Identification of family-specific residue packing motifs and their use for structure-based protein function prediction: I. Method development

Bandyopadhyay, Deepak; Huan, Jun; Prins, Jan F.; Snoeyink, Jack; Wang, Wei; Tropsha, Alexander

doi:10.1007/s10822-009-9273-4

Cited by 14 publications

(24 citation statements)

References 93 publications

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“…[Kohler et al 2002; Chisholm and Motherwell 2004;Willett 2008;Bandyopadhyay et al 2009;Hassan 2009]. For binary constraints, bit-vector domain reduction gains speed by using bit-parallel operations that are available in conventional processors.…”

Section: Resultsmentioning

confidence: 99%

Bit-vector algorithms for binary constraint satisfaction and subgraph isomorphism

Ullmann

2010

ACM J. Exp. Algorithmics

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A solution to a binary constraint satisfaction problem is a set of discrete values, one in each of a given set of domains, subject to constraints that allow only prescribed pairs of values in specified pairs of domains. Solutions are sought by backtrack search interleaved with a process that removes from domains those values that are currently inconsistent with provisional choices already made in the course of search. For each value in a given domain, a bit-vector shows which values in another domain are or are not permitted in a solution. Bit-vector representation of constraints allows bit-parallel, therefore fast, operations for editing domains during search. This article revises and updates bit-vector algorithms published in the 1970's, and introduces focus search, which is a new bit-vector algorithm relying more on search and less on domain-editing than previous algorithms. Focus search is competitive within a limited family of constraint satisfaction problems.Determination of subgraph isomorphism is a specialized binary constraint satisfaction problem for which bit-vector algorithms have been widely used since the 1980's, particularly for matching molecular structures. This article very substantially updates the author's 1976 subgraph isomorphism algorithm, and reports experimental results with random and real-life data.

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Section: Resultsmentioning

confidence: 99%

Bit-vector algorithms for binary constraint satisfaction and subgraph isomorphism

Ullmann

2010

ACM J. Exp. Algorithmics

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“…Please refer to the companion paper, Part I [1] for a detailed description of the methods used in this paper. For the characterization of the method, we refer frequently to sections (Family Classification Based on Motifs, Motif Library) and tables in Paper I [1], where we list and categorize the protein families under study.…”

Section: Methodsmentioning

confidence: 99%

“…For the characterization of the method, we refer frequently to sections (Family Classification Based on Motifs, Motif Library) and tables in Paper I [1], where we list and categorize the protein families under study.…”

Section: Methodsmentioning

confidence: 99%

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Identification of family-specific residue packing motifs and their use for structure-based protein function prediction: II. Case studies and applications

Bandyopadhyay

Huan

Prins

et al. 2009

J Comput Aided Mol Des

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This paper describes several case studies concerning protein function inference from its structure using our novel approach described in the accompanying paper. This approach employs family-specific motifs, i.e. three-dimensional amino acid packing patterns that are statistically prevalent within a protein family. For our case studies we have selected families from the SCOP and EC classifications and analyzed the discriminating power of the motifs in depth. We have devised several benchmarks to compare motifs mined from unweighted topological graph representations of protein structures with those from distance-labeled (weighted) representations, demonstrating the superiority of the latter for function inference in most families. We have tested the robustness of our motif library by inferring the function of new members added to SCOP families, and discriminating between several families that are structurally similar but functionally divergent. Furthermore we have applied our method to predict function for several proteins characterized in structural genomics projects, including orphan structures, and we discuss several selected predictions in depth. Some of our predictions have been corroborated by other computational methods, and some have been validated by independent experimental studies, validating our approach for protein function inference from structure.

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“…A similar strategy could be used with our approach, e.g., precomputing the points inside B p (r + e) for each point p in each target structure, for different values of r and e.) Graph-based methods. Even before tackling the NP-hard subgraph isomorphism, Bandyopadhyay et al (2009) compute an almostDelaunay triangulation. The proposed algorithm (Bandyopadhyay and Snoeyink, 2004) requires time O (n 5 log n) in the worst case, but runs in O (n 2 log n) expected time (no result whp.…”

Section: =3mentioning

confidence: 99%

Ballast: A Ball-Based Algorithm for Structural Motifs

Vandin

Pandurangan

et al. 2012

Lecture Notes in Computer Science

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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.

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Identification of family-specific residue packing motifs and their use for structure-based protein function prediction: I. Method development

Cited by 14 publications

References 93 publications

Bit-vector algorithms for binary constraint satisfaction and subgraph isomorphism

Bit-vector algorithms for binary constraint satisfaction and subgraph isomorphism

Identification of family-specific residue packing motifs and their use for structure-based protein function prediction: II. Case studies and applications

Ballast: A Ball-Based Algorithm for Structural Motifs

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