ArticlePLoS computational biology2017
Are there physicochemical differences between allosteric and competitive ligands?
Article in PLoS computational biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed, 26 citations in OpenAlex.
- Allostery Illuminated: Harnessing AI and Machine Learning for Drug Discovery.ACS medicinal chemistry letters · 2024Review
- G protein-coupled receptors (GPCRs): advances in structures, mechanisms, and drug discovery.Signal transduction and targeted therapy · 2024Review
- Article
- Machine learning and protein allostery.Trends in biochemical sciences · 2023Review
- Specifics of Metabolite-Protein Interactions and Their Computational Analysis and Prediction.Methods in molecular biology (Clifton, N.J.) · 2023Review
- Analysis of the uncharted, druglike property space by self-organizing maps.Molecular diversity · 2022Article
- An Evolutionary Conservation and Druggability Analysis of Enzymes Belonging to the Bacterial Shikimate Pathway.Antibiotics (Basel, Switzerland) · 2022Article
- Article
- Probe Confined Dynamic Mapping for G Protein-Coupled Receptor Allosteric Site Prediction.ACS central science · 2021Article
- Annotation of Allosteric Compounds to Enhance Bioactivity Modeling for Class A GPCRs.Journal of chemical information and modeling · 2020Article
- The Cytomegalovirus Protein Kinase pUL97:Host Interactions, Regulatory Mechanisms and Antiviral Drug Targeting.Microorganisms · 2020Review
- The design and development of covalent protein-protein interaction inhibitors for cancer treatment.Journal of hematology & oncology · 2020Review
- Integrated Computational Approaches and Tools forAllosteric Drug Discovery.International journal of molecular sciences · 2020Review
- AlloFinder: a strategy for allosteric modulator discovery and allosterome analyses.Nucleic acids research · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Previous studies have compared the physicochemical properties of allosteric compounds to non-allosteric compounds. Those studies have found that allosteric compounds tend to be smaller, more rigid, more hydrophobic, and more drug-like than non-allosteric compounds. However, previous studies have not properly corrected for the fact that some protein targets have much more data than other systems. This generates concern regarding the possible skew that can be introduced by the inherent bias in the available data. Hence, this study aims to determine how robust the previous findings are to the addition of newer data. This study utilizes the Allosteric Database (ASD v3.0) and ChEMBL v20 to systematically obtain large datasets of both allosteric and competitive ligands. This dataset contains 70,219 and 9,511 unique ligands for the allosteric and competitive sets, respectively. Physically relevant compound descriptors were computed to examine the differences in their chemical properties. Particular attention was given to removing redundancy in the data and normalizing across ligand diversity and varied protein targets. The resulting distributions only show that allosteric ligands tend to be more aromatic and rigid and do not confirm the increase in hydrophobicity or difference in drug-likeness. These results are robust across different normalization schemes.
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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.