ArticleBMC structural biology2010
Identification of recurring protein structure microenvironments and discovery of novel functional sites around CYS residues.
Article in BMC structural biology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Exploring the structural lexicon of the Proteome via Metric Geometry.PLoS computational biology · 2026Article
- Exploring the Structural Lexicon of the Proteome via Metric Geometry.bioRxiv : the preprint server for biology · 2025Article
- Unsupervised learning reveals landscape of local structural motifs across protein classes.Bioinformatics (Oxford, England) · 2025Article
- Article
- A deep learning framework to predict binding preference of RNA constituents on protein surface.Nature communications · 2019Article
- Functional and Structural Diversity of Acyl-coA Binding Proteins in Oil Crops.Frontiers in genetics · 2018Review
- Article
- Prediction of functionally important residues in globular proteins from unusual central distances of amino acids.BMC structural biology · 2011Article
- Mining the TRAF6/p62 interactome for a selective ubiquitination motif.BMC proceedings · 2011Article
- Amino Acid Features of P1B-ATPase Heavy Metal Transporters Enabling Small Numbers of Organisms to Cope with Heavy Metal Pollution.Bioinformatics and biology insights · 2011Article
- Remote thioredoxin recognition using evolutionary conservation and structural dynamics.Structure (London, England : 1993) · 2011Article
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Authors and funding
3 authors.
Funding
Abstract
backgroundThe emergence of structural genomics presents significant challenges in the annotation of biologically uncharacterized proteins. Unfortunately, our ability to analyze these proteins is restricted by the limited catalog of known molecular functions and their associated 3D motifs.
resultsIn order to identify novel 3D motifs that may be associated with molecular functions, we employ an unsupervised, two-phase clustering approach that combines k-means and hierarchical clustering with knowledge-informed cluster selection and annotation methods. We applied the approach to approximately 20,000 cysteine-based protein microenvironments (3D regions 7.5 A in radius) and identified 70 interesting clusters, some of which represent known motifs (e.g. metal binding and phosphatase activity), and some of which are novel, including several zinc binding sites. Detailed annotation results are available online for all 70 clusters at http://feature.stanford.edu/clustering/cys.
conclusionsThe use of microenvironments instead of backbone geometric criteria enables flexible exploration of protein function space, and detection of recurring motifs that are discontinuous in sequence and diverse in structure. Clustering microenvironments may thus help to functionally characterize novel proteins and better understand the protein structure-function relationship.
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