ArticleJournal of computer-aided molecular design2009
Identification of family-specific residue packing motifs and their use for structure-based protein function prediction: I. Method development.
Article in Journal of computer-aided molecular design, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Modulating Glycoside Hydrolase Activity between Hydrolysis and Transfer Reactions Using an Evolutionary Approach.Molecules (Basel, Switzerland) · 2021Article
- An amino acid code for irregular and mixed protein packing.Proteins · 2015Article
- Ballast: a ball-based algorithm for structural motifs.Journal of computational biology : a journal of computational molecular cell biology · 2013Article
- PASS2 version 4: an update to the database of structure-based sequence alignments of structural domain superfamilies.Nucleic acids research · 2012Article
- Characterizing the regularity of tetrahedral packing motifs in protein tertiary structure.Bioinformatics (Oxford, England) · 2010Article
- Identification of family-specific residue packing motifs and their use for structure-based protein function prediction: II. Case studies and applications.Journal of computer-aided molecular design · 2009Article
- Identification of family-specific residue packing motifs and their use for structure-based protein function prediction: I. Method development.Journal of computer-aided molecular design · 2009Article
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Authors and funding
6 authors.
Funding
Abstract
Protein function prediction is one of the central problems in computational biology. We present a novel automated protein structure-based function prediction method using libraries of local residue packing patterns that are common to most proteins in a known functional family. Critical to this approach is the representation of a protein structure as a graph where residue vertices (residue name used as a vertex label) are connected by geometrical proximity edges. The approach employs two steps. First, it uses a fast subgraph mining algorithm to find all occurrences of family-specific labeled subgraphs for all well characterized protein structural and functional families. Second, it queries a new structure for occurrences of a set of motifs characteristic of a known family, using a graph index to speed up Ullman's subgraph isomorphism algorithm. The confidence of function inference from structure depends on the number of family-specific motifs found in the query structure compared with their distribution in a large non-redundant database of proteins. This method can assign a new structure to a specific functional family in cases where sequence alignments, sequence patterns, structural superposition and active site templates fail to provide accurate annotation.
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