Jacopo Sgrignani

An integrated, transcript-resolution atlas of prostate cancer progression unmasks associations with oncogenic pathways

A transcript-resolution prostate cancer atlas, built by uniformly reprocessing public RNA-seq cohorts, links isoform switches in cancer genes to oncogenic pathway activity and some to CRPC survival.

Dissociation kinetics and avidity gate SARS-CoV-2 neutralization by HR2 stem helix antibodies

This study identifies hr2.016, a potent and broad SARS-CoV-2 neutralizing antibody, and shows its neutralization depends on kinetic stability and avidity.

AI-Driven Computational Design of Peptide-Based WWP1 Inhibitors as Promising Therapeutic Agents Against Breast Cancer, Including Triple-Negative Subtype

This study uses AI to design a peptide inhibitor of WWP1, showing potent activity against breast cancer, especially TNBC, with promising cytotoxic effects and synergistic potential with PI3K inhibitors.

A CYFIP1-Inspired Peptidomimetic Modulates eIF4E-Dependent Translational Control in Cancer and Neurodevelopmental Disorders

This paper develops a peptidomimetic inhibitor of eIF4E that suppresses cap-dependent translation in cancer and neurodevelopmental disorders, showing efficacy in cell and animal models.

Targeting FOXA1 and FOXA2 Disrupts the Lineage-Specific Oncogenic Output Program in Prostate Cancer

This study shows that disrupting FOXA1 and FOXA2 disrupts oncogenic programs in both AR-positive and AR-negative prostate cancers.

AI assisted design of ligands for Lipocalin-2

The paper designs AI-generated miniproteins that bind LCN2 with nanomolar affinity, demonstrating a computational workflow's effectiveness in generating functional binders.

A Potent Nonapeptide Inhibitor for the CXCL12/HMGB1 heterocomplex: A Computational and Experimental Approach

The paper computationally optimizes a peptide inhibitor of the CXCL12/HMGB1 heterocomplex, resulting in a variant with significantly improved affinity and activity.

Machine learning predicts immunoglobulin light chain toxicity through somatic mutations

This paper introduces LICTOR, a machine learning model that predicts immunoglobulin light chain toxicity in AL using somatic mutations, achieving 83% accuracy in predicting clinical phenotypes.