ArticleACS omega2020
Computational Analysis of Crystallization Additives for the Identification of New Allosteric Sites.
Article in ACS omega, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Frags2Drugs: A Novel In Silico Fragment-Based Approach to the Discovery of Kinase Inhibitors.Pharmaceuticals (Basel, Switzerland) · 2026Article
- LigExtract: Large-scale Automated Identification of Ligands from Protein Structures in the Protein Data Bank.Genomics, proteomics & bioinformatics · 2025Article
- Autonomous Nucleic Acid and Protein Nanocomputing Agents Engineered to Operate in Living Cells.ACS nano · 2025Review
- Machine learning and protein allostery.Trends in biochemical sciences · 2023Review
- Estimating the Similarity between Protein Pockets.International journal of molecular sciences · 2022Review
- Search for Novel Potent Inhibitors of the SARS-CoV-2 Papain-like Enzyme: A Computational Biochemistry Approach.Pharmaceuticals (Basel, Switzerland) · 2022Article
- Prediction of allosteric sites and signaling: Insights from benchmarking datasets.Patterns (New York, N.Y.) · 2022Article
- Engineering an Allosteric Control of Protein Function.The journal of physical chemistry. B · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Allosteric effect can modulate the biological activity of a protein. Thus, the discovery of new allosteric sites is very attractive for designing new modulators or inhibitors. Here, we propose an innovative way to identify allosteric sites, based on crystallization additives (CA), used to stabilize proteins during the crystallization process. Density and clustering analyses of CA, applied on protein kinase and nuclear receptor families, revealed that CA are not randomly distributed around protein structures, but they tend to aggregate near common sites. All orthosteric and allosteric cavities described in the literature are retrieved from the analysis of CA distribution. In addition, new sites were identified, which could be associated to putative allosteric sites. We proposed an efficient and easy way to use the structural information of CA to identify allosteric sites. This method could assist medicinal chemists for the design of new allosteric compounds targeting cavities of new drug targets.
Identifiers
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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.