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Federico Barone

As seen in: PLOS, BioRxiv
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Articles

Unsupervised domain classification of AlphaFold2-predicted protein structures

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By Federico Barone, Alessandro Laio, Marco Punta, Stefano Cozzini
| BioRxiv
Abstract The release of the AlphaFold database, which contains 214 million predicted protein structures, represents a major leap forward for proteomics and its applications. However, lack of comprehensive protein annotation limits its accessibility and usability. Here, we present DPCstruct, an unsupervised clustering algorithm designed to provide domain-level classification of protein structures.
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Protein family annotation for the Unified Human Gastrointestinal Proteome by DPCfam clustering

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By Federico Barone, Elena Russo, Marco Punta, Stefano Cozzini
| BioRxiv
Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website.
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DPCfam: Unsupervised protein family classification by Density Peak Clustering of large sequence datasets

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By Elena Russo, Alex Bateman, Federico Barone, Stefano Cozzini
| PLOS
Loading metrics Open Access Peer-reviewed Research Article ? This is an uncorrected proof. Abstract Proteins that are known only at a sequence level outnumber those with an experimental characterization by orders of magnitude. Classifying protein regions (domains) into homologous families can generate testable functional hypotheses for yet unannotated sequences.
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