ArticleNature communications2016
Prediction of allosteric sites and mediating interactions through bond-to-bond propensities.
Article in Nature communications, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
44 citing papers in PubMed.
- Structural instability impairs function of the UDP-xylose synthase 1 Ile181Asn variant associated with short-stature genetic syndrome in humans.FEBS letters · 2026Article
- Deconvolving mutation effects on protein stability and function with disentangled protein language models.bioRxiv : the preprint server for biology · 2026Article
- Single-Molecule DNA Tweezers Enable Programmable Control of Enzyme Activity via Arbitrary Molecular Cues.Angewandte Chemie (International ed. in English) · 2025Article
- AlloBench: A Data Set Pipeline for the Development and Benchmarking of Allosteric Site Prediction Tools.ACS omega · 2025Article
- A new view of missense mutations in α-mannosidosis using molecular dynamics conformational ensembles.Protein science : a publication of the Protein Society · 2025Article
- Comprehensive structural and functional analyses of RAD50 nsSNPs: from prediction to impact assessment.Frontiers in bioinformatics · 2025Article
- The value of protein allostery in rational anticancer drug design: an update.Expert opinion on drug discovery · 2024Review
- A parameterized two-domain thermodynamic model explains diverse mutational effects on protein allostery.eLife · 2024Article
- A parametrized two-domain thermodynamic model explains diverse mutational effects on protein allostery.bioRxiv : the preprint server for biology · 2024Article
- Complementary Pocket and Network-Based Approach to Search for Spike Protein Allosteric Pocket Sites.ACS omega · 2023Article
- Combining structural and coevolution information to unveil allosteric sites.Chemical science · 2023Article
- Predicting allosteric pockets in protein biological assemblages.Bioinformatics (Oxford, England) · 2023Article
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- Probing Mechanisms of Binding and Allostery in the SARS-CoV-2 Spike Omicron Variant Complexes with the Host Receptor: Revealing Functional Roles of the Binding Hotspots in Mediating Epistatic Effects and Communication with Allosteric Pockets.International journal of molecular sciences · 2022Article
- Allosteric Hotspots in the Main Protease of SARS-CoV-2.Journal of molecular biology · 2022Article
- Relative, local and global dimension in complex networks.Nature communications · 2022Article
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- Prediction of allosteric sites and signaling: Insights from benchmarking datasets.Patterns (New York, N.Y.) · 2022Article
- PASSer2.0: Accurate Prediction of Protein Allosteric Sites Through Automated Machine Learning.Frontiers in molecular biosciences · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Allostery is a fundamental mechanism of biological regulation, in which binding of a molecule at a distant location affects the active site of a protein. Allosteric sites provide targets to fine-tune protein activity, yet we lack computational methodologies to predict them. Here we present an efficient graph-theoretical framework to reveal allosteric interactions (atoms and communication pathways strongly coupled to the active site) without a priori information of their location. Using an atomistic graph with energy-weighted covalent and weak bonds, we define a bond-to-bond propensity quantifying the non-local effect of instantaneous bond fluctuations propagating through the protein. Significant interactions are then identified using quantile regression. We exemplify our method with three biologically important proteins: caspase-1, CheY, and h-Ras, correctly predicting key allosteric interactions, whose significance is additionally confirmed against a reference set of 100 proteins. The almost-linear scaling of our method renders it suitable for high-throughput searches for candidate allosteric sites.
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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.