Clustering protein environments for function prediction: finding PROSITE motifs in 3D.

Clustering protein environments for function prediction: finding PROSITE motifs in 3D. Research Abstract Details 

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Clustering protein environments for function prediction: finding PROSITE motifs in 3D. Abstract Text:

Sungroh Yoon,Jessica C Ebert,Eui-Young Chung,Giovanni De Micheli,Russ B Altman,

BACKGROUND: Structural genomics initiatives are producing increasing numbers of three-dimensional (3D) structures for which there is little functional information. Structure-based annotation of molecular function is therefore becoming critical. We previously presented FEATURE, a method for describing microenvironments around functional sites in proteins. However, FEATURE uses supervised machine learning and so is limited to building models for sites of known importance and location. We hypothesized that there are a large number of sites in proteins that are associated with function that have not yet been recognized. Toward that end, we have developed a method for clustering protein microenvironments in order to evaluate the potential for discovering novel sites that have not been previously identified. RESULTS: We have prototyped a computational method for rapid clustering of millions of microenvironments in order to discover residues whose surrounding environments are similar and which may therefore share a functional or structural role. We clustered nearly 2,000,000 environments from 9,600 protein chains and defined 4,550 clusters. As a preliminary validation, we asked whether known 3D environments associated with PROSITE motifs were "rediscovered". We found examples of clusters highly enriched for residues that share PROSITE sequence motifs. CONCLUSION: Our results demonstrate that we can cluster protein environments successfully using a simplified representation and K-means clustering algorithm. The rediscovery of known 3D motifs allows us to calibrate the size and intercluster distances that characterize useful clusters. This information will then allow us to find new clusters with similar characteristics that represent novel structural or functional sites.

Clustering protein environments for function prediction: finding PROSITE motifs in 3D. Publishing Authors By Initials

S Yoon,JC Ebert,EY Chung,G De Micheli,RB Altman,

For similar investigative techniques: genetic techniques: sequence analysis: sequence analysis, protein research abstracts see: investigative techniques: genetic techniques: sequence analysis: sequence analysis, protein research

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Clustering protein environments for function prediction: finding PROSITE motifs in 3D. Journal Published:

PUBLICATION TYPE: Research Support, N.I.H., Extr

Journal: BMC bioinformatics

VOLUME: 8 Suppl 4

Page Numbers: S10

Journal Abbreviation: BMC Bioinformatics

ISSN: 1471-2105

DAY: 22

MONTH: 05

YEAR: 2007

Clustering protein environments for function prediction: finding PROSITE motifs in 3D. Information

Number of References:

LANGUAGE: eng

NlmUniqueID: 100965194

Clustering protein environments for function prediction: finding PROSITE motifs in 3D. Keywords Mesh Terms:

KEYWORDS: Sequence Analysis, Protein

MESH TERMS: methods

Chemical & Substance for Abstract: Clustering protein environments for function prediction: finding PROSITE motifs in 3D. Information

Substance Name: Proteins

Registry Number: 0

Grant and Affiliation Information for Clustering protein environments for function prediction: finding PROSITE motifs in 3D.

AFFILIATION: Computer Systems Laboratory, Stanford University, Stanford, CA 94305, USA. sungroh.yoon@intel.com

Country: England

AGENCY: United States NLM

GRANT: LM05652

ACRONYM: LM

MEDLINETA: BMC Bioinformatics

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