Author

A. Pagnani

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Biophysics· 4

Signature Assays, Organisms & Methods

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Assay:proteomics· 3 Approach:deep learning· 2 Approach:machine learning (classical)· 2 Organism:human· 1 Organism:mouse· 1 Research Focus:protein design· 1 Research Focus:protein folding & dynamics· 1 Approach:statistical model· 1

Frequent Co-Authors

G. Uguzzoni· 4 shared J. Fernandez-de-Cossio-Diaz· 3 shared L. Sesta· 2 shared C. Nizak· 1 shared F. Anselmi· 1 shared J. Ferndadez-de-Cossio-Diaz· 1 shared K. Ricard· 1 shared M. De Leonardis· 1 shared

Activity by Year

2021-2023 · peak 3/yr

Co-Author Collaboration Network

9 top collaborators · 21 mutual co-author links

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4 shared papersBiophysics

G. Uguzzoni

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Author profile →

4 papers

Recent·Most cited

Unsupervised modeling of mutational landscapes of adeno-associated viruses viability

M. De Leonardis, J. Fernandez-de-Cossio-Diaz, G. Uguzzoni, A. Pagnani

This paper introduces a biophysically-inspired model to predict the viability of AAV2 capsid variants using DMS data, evaluating its performance on log-selectivity and binary classification.

bioRxiv · Oct 26, 2023Biophysics

Inference and design of antibody specificity: from experiments to models and back

J. Fernandez-de-Cossio-Diaz, G. Uguzzoni, K. Ricard, F. Anselmi, A. Pagnani, C. Nizak, O. Rivoire

This paper develops a multi-stage approach combining phage display experiments, machine learning, and biophysical modeling to design antibodies with customized specificity profiles.

bioRxiv · Oct 23, 2023Biophysics

Inference of annealed protein fitness landscapes with AnnealDCA

L. Sesta, A. Pagnani, J. Fernandez-de-Cossio-Diaz, G. Uguzzoni

This paper introduces AnnealDCA, a machine-learning framework to infer protein fitness landscapes from sequencing data, demonstrating its effectiveness on antibody Rep-Seq and screening experiments.

bioRxiv · May 19, 2023Biophysics

AMaLa: analysis of Directed Evolution experiments via Annealed Mutational approximated Landscape

L. Sesta, G. Uguzzoni, J. Ferndadez-de-Cossio-Diaz, A. Pagnani

AMaLa infers fitness landscapes from Directed Evolution experiments by modeling mutation and selection dynamics across sequenced rounds, outperforming existing methods.

bioRxiv · Jul 26, 2021Biophysics