Author

Gianmattia Sgueglia

University of Naples Federico IIORCID 0000-0002-8729-179XUnclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 1 Research Focus:protein design· 1 Assay:protein sequence· 1 Approach:statistical model· 1

Frequent Co-Authors

Giulia PeteaniiD· 1 shared Marco ChinoiD· 1 shared Thomas LemminiD· 1 shared

Activity by Year

2026-2026 · peak 1/yr

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3 top collaborators · 3 mutual co-author links

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1 shared paperBioinformatics

Giulia PeteaniORCID

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1 paper

Recent·Most cited

BioinformaticsbioRxivPreprintMay 5, 2026

Structure-derived synthetic sequences guide a protein language model toward metalloproteins

Giulia Peteani, Gianmattia Sgueglia, Thomas Lemmin, Marco Chino

The paper fine-tunes a protein language model using synthetic metalloprotein sequences to improve recovery of metal-binding motifs and identifies novel metal-binding sites.

DOI 10.64898/2026.04.30.722007

protein sequencedeep learning

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