ReviewProtein science : a publication of the Protein Society2013
Toward a "structural BLAST": using structural relationships to infer function.
Review in Protein science : a publication of the Protein Society, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Genomic mechanism of aroma terpenoids biosynthesis in plants.Journal of advanced research · 2026Review
- Dockground resource for protein recognition studies.Protein science : a publication of the Protein Society · 2022Article
- In Silico Methods for Identification of Potential Active Sites of Therapeutic Targets.Molecules (Basel, Switzerland) · 2022Review
- Artificial intelligence in the prediction of protein-ligand interactions: recent advances and future directions.Briefings in bioinformatics · 2022Review
- Applications of machine learning in computer-aided drug discovery.QRB discovery · 2022Review
- Computational methods and tools for binding site recognition between proteins and small molecules: from classical geometrical approaches to modern machine learning strategies.Journal of computer-aided molecular design · 2019Article
- Dockground: A comprehensive data resource for modeling of protein complexes.Protein science : a publication of the Protein Society · 2018Article
- Structure-based prediction of ligand-protein interactions on a genome-wide scale.Proceedings of the National Academy of Sciences of the United States of America · 2017Article
- Article
- Structural templates for comparative protein docking.Proteins · 2015Article
- Template-based prediction of protein function.Current opinion in structural biology · 2015Review
- Protein models docking benchmark 2.Proteins · 2015Article
- Exploring the "dark matter" of a mammalian proteome by protein structure and function modeling.Proteome science · 2013Article
- Integrating 3D structural information into systems biology.The Journal of biological chemistryReview
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
We outline a set of strategies to infer protein function from structure. The overall approach depends on extensive use of homology modeling, the exploitation of a wide range of global and local geometric relationships between protein structures and the use of machine learning techniques. The combination of modeling with broad searches of protein structure space defines a "structural BLAST" approach to infer function with high genomic coverage. Applications are described to the prediction of protein-protein and protein-ligand interactions. In the context of protein-protein interactions, our structure-based prediction algorithm, PrePPI, has comparable accuracy to high-throughput experiments. An essential feature of PrePPI involves the use of Bayesian methods to combine structure-derived information with non-structural evidence (e.g. co-expression) to assign a likelihood for each predicted interaction. This, combined with a structural BLAST approach significantly expands the range of applications of protein structure in the annotation of protein function, including systems level biological applications where it has previously played little role.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.