2026-06-14T13:00:29Zhttps://repisalud.isciii.es/rest/oai/requestoai:repisalud.isciii.es:20.500.12105/160342025-06-17T07:18:56Zcom_20.500.12105_2052com_20.500.12105_2051col_20.500.12105_19609
Repisalud
author
Sicilia, Carlos
author
Corral-Lugo, Andres
author
Smialowski, Pawel
author
McConnell, Michael J
author
Martin-Galiano, Antonio Javier
funder
Instituto de Salud Carlos III
funder
Comunidad de Madrid (España)
2023-05-09T12:27:26Z
2023-05-09T12:27:26Z
2022-12
ACS Omega. 2022 Dec 6;7(50):46131-46145.
10.1021/acsomega.2c04076
2470-1343
ACS omega
36570227
http://hdl.handle.net/20.500.12105/16034
Uncharacterized proteins have been underutilized as targets for the development of novel therapeutics for difficult-to-treat bacterial infections. To facilitate the exploration of these proteins, 2819 predicted, uncharacterized proteins (19.1% of the total) from reference strains of multidrug Acinetobacter baumannii, Klebsiella pneumoniae, and Pseudomonas aeruginosa species were organized using an unsupervised k-means machine learning algorithm. Classification using normalized values for protein length, pI, hydrophobicity, degree of conservation, structural disorder, and %AT of the coding gene rendered six natural clusters. Cluster proteins showed different trends regarding operon membership, expression, presence of unknown function domains, and interactomic relevance. Clusters 2, 4, and 5 were enriched with highly disordered proteins, nonworkable membrane proteins, and likely spurious proteins, respectively. Clusters 1, 3, and 6 showed closer distances to known antigens, antibiotic targets, and virulence factors. Up to 21.8% of proteins in these clusters were structurally covered by modeling, which allowed assessment of druggability and discontinuous B-cell epitopes. Five proteins (4 in Cluster 1) were potential druggable targets for antibiotherapy. Eighteen proteins (11 in Cluster 6) were strong B-cell and T-cell immunogen candidates for vaccine development. Conclusively, we provide a feature-based schema to fractionate the functional dark proteome of critical pathogens for fundamental and biomedical purposes.
eng
Unsupervised Machine Learning Organization of the Functional Dark Proteome of Gram-Negative "Superbugs": Six Protein Clusters Amenable for Distinct Scientific Applications
research article
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URL
https://repisalud.isciii.es/bitstreams/c879eafc-3d9f-44e8-90ea-1cfc87eddf79/download
File
MD5
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application/pdf
UnsupervisedMachineLearningOrganization_2022.pdf
URL
https://repisalud.isciii.es/bitstreams/49afe473-dc9f-4fa1-bf31-d07de53e3c75/download
File
MD5
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text/plain
UnsupervisedMachineLearningOrganization_2022.pdf.txt