Jacopo Sgrignani

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.

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

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.

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.

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

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

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

This paper identifies two resistance mechanisms to the CD37-targeting ADC naratuximab emtansine in lymphoma cell lines and suggests combination therapies to overcome resistance.

This study identifies HERV-K as a therapeutic target in DLBCL, develops a humanized nanobody with anti-lymphoma activity, and shows its potential through antibody-dependent cytotoxicity.

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.