ArticleACS omega2025
AlloBench: A Data Set Pipeline for the Development and Benchmarking of Allosteric Site Prediction Tools.
Article in ACS omega, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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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
3 citing papers in PubMed.
- From physics-based intelligence to molecular design: linking mechanistic learning across scales.MedScience · 2026Article
- Structural motif search across the protein universe with Folddisco.Nature biotechnology · 2026Article
- Targeting frizzled receptors (FZDs) for anti-tumor therapy: From orthosteric to allosteric inhibition.Acta pharmaceutica Sinica. B · 2026Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Allostery refers to the activity regulation of biological macromolecules originating from the binding of an effector molecule at the allosteric site that is distant from the active site. The few existing allosteric data sets have not been updated with recent discoveries of allosteric proteins and are challenging to use for data-intensive tasks. Instead of providing another data set bound to become outdated, we present the AlloBench pipeline to create high-quality data sets of biomolecules with allosteric and active site information suitable for computational and data-driven studies of protein allostery. The pipeline produces a data set of 2141 allosteric sites from 2034 protein structures with 418 unique protein chains by integrating information from AlloSteric Database, UniProt, Mechanism and Catalytic Site Atlas, and Protein Data Bank. Furthermore, we use a subset of 100 proteins from the AlloBench data set to quantitatively compare the performance of currently available allosteric site prediction tools: APOP, PASSer, Ohm, ALLO, Allosite, STRESS, and AlloPred. Such a large-scale benchmarking of these programs has not been undertaken on a common test set. The results show a significant need for improvement, as the accuracy for all programs is well below 60%, with PASSer (Ensemble) outperforming the rest. The AlloBench pipeline will not only promote the development of improved allosteric site prediction tools but also serve as a reference for studying allostery in general.
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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.