ArticlePatterns (New York, N.Y.)2022
Prediction of allosteric sites and signaling: Insights from benchmarking datasets.
Article in Patterns (New York, N.Y.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- A systematic evaluation of protein allosteric site prediction tools with independent datasets.Journal of computer-aided molecular design · 2026Article
- AI accelerate the identification of druggable targets by 3D structures of proteins and compounds.NPJ precision oncology · 2026Review
- Computational approaches to druggable site identification: Current status and future perspective.Acta pharmaceutica Sinica. B · 2026Review
- 'Intelligent' proteins.Cellular and molecular life sciences : CMLS · 2025Review
- Comprehensive structural and functional analyses of RAD50 nsSNPs: from prediction to impact assessment.Frontiers in bioinformatics · 2025Article
- Allosteric activation of AMPK ADaM's site by structural analogs of Epigallocatechin and Galegine: computational molecular modeling investigation.In silico pharmacology · 2025Article
- The value of protein allostery in rational anticancer drug design: an update.Expert opinion on drug discovery · 2024Review
- PASSerRank: Prediction of allosteric sites with learning to rank.Journal of computational chemistry · 2023Article
- PASSer: fast and accurate prediction of protein allosteric sites.Nucleic acids research · 2023Article
- Article
- Mapping allosteric pathway in NIa-Pro using computational approach.Quantitative biology (Beijing, China) · 2023Article
- Allosteric Hotspots in the Main Protease of SARS-CoV-2.Journal of molecular biology · 2022Article
- Simulation and Machine Learning Methods for Ion-Channel Structure Determination, Mechanistic Studies and Drug Design.Frontiers in pharmacology · 2022Review
Corrections and comments
- Commented on by
Authors and funding
3 authors.
Funding
Abstract
Allostery is a pervasive mechanism that regulates protein activity through ligand binding at a site different from the orthosteric site. The universality of allosteric regulation complemented by the benefits of highly specific and potentially non-toxic allosteric drugs makes uncovering allosteric sites invaluable. However, there are few computational methods to effectively predict them. Bond-to-bond propensity analysis has successfully predicted allosteric sites in 19 of 20 cases using an energy-weighted atomistic graph. We here extended the analysis onto 432 structures of 146 proteins from two benchmarking datasets for allosteric proteins: ASBench and CASBench. We further introduced two statistical measures to account for the cumulative effect of high-propensity residues and the crucial residues in a given site. The allosteric site is recovered for 127 of 146 proteins (407 of 432 structures) knowing only the orthosteric sites or ligands. The quantitative analysis using a range of statistical measures enables better characterization of potential allosteric sites and mechanisms involved.
Indexed as
Identifiers
What OpenQuestion holds
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.