The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
A novel computational machine learning pipeline to quantify similarities in 3D protein structures
Open accessPublished version of a preprint
Abstract
Animal models are widely used during drug development. The selection of suitable animal model relies on various factors such as target biology, animal resource availability, and legacy species. It is imperative that the selected animal species exhibit the highest resemblance to humans, in terms of target biology as well as the similarity in the target protein. The current practice to address cross-species protein similarity relies on pairwise sequence comparison using protein sequences, instead of the biologically relevant 3D structure of proteins. We developed a novel quantitative machine learning pipeline using 3D structure-based feature data from the Protein Data Bank, nominal data from UNIPROT, and bioactivity data from ChEMBL, all of which were matched for human and animal data. Using the XGBoost regression model, similarity scores between targets were calculated and based on these scores, the best animal species for a target was identified. For real-world application, targets from an alternative source, i.e. AlphaFold, were tested using the model, and the animal species that had the most similar protein to the human counterparts were predicted. These targets were then grouped based on their associated phenotype such that the pipeline could predict an optimal animal species.
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Cites
Highly accurate protein structure prediction with AlphaFold
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Research works printed but not linked: 2
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- Rose, 2016, The RCSB Protein Data Bank: integrative view of protein, gene and 3D structural information, Nucleic Acids Res, 45, D271
Metadata
- Authors
- Hirway, S. U., Xu, X., & Fan, F.
- Affiliations
- Janssen Research & Development (United States) (US)
- Corresponding author
- Fan, Fan
- Venue
- Toxicological Sciences
- Publisher
- Oxford University Press (OUP)
- Published
- 2025-01-17
- Volume
- 207
- Issue
- 1
- Pages
- 48-56
- ISSN
- 1096-0929 (ISSN-L)
- 1096-6080
- OpenAlex
- W4406528802
- Cited by
- 0
- References
- 40
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Toxicological Sciences (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Health Sciences › Veterinary › Small Animals › Animal testing and alternatives
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Keywords
- protein 3D structure, animal model selection, XGBoost regression, AlphaFold, UniProt, drug development
- MeSH terms
- Machine Learning, Animals, Humans, Protein Conformation, Proteins, Species Specificity, Computational Biology, Databases, Protein
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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