Francesco Raimondi

Atlantis: An integrative database for human proteome structural and functional sites

Atlantis integrates structural and functional residue-level data for the human proteome, enabling complex queries and analysis of protein interactions and mutations.

GABAergic Circuit Activation Induces a Therapeutically Responsive State for anti-PD-L1 Immunotherapy in Glioblastoma

This study shows that activating GABAergic circuits with baclofen enhances the effectiveness of anti-PD-L1 immunotherapy in glioblastoma, leading to tumor regression and prolonged survival in mice.

The mutational landscape of innervation axes in cancer

This study analyzes mutations in neural signaling pathways across cancers, finding 65% of patients have alterations linked to poor survival and complex pathway interactions.

Evolutionary divergence of LRRK2 interaction domains contributes to human- and mouse-specific protein interaction networks

This study shows that evolutionary divergence in LRRK2 interaction domains leads to species-specific protein interaction networks in humans and mice.

Proximity proteomics reveals a co-evolved LRRK2-regulatory network linked to centrosomes

This study identifies a LRRK2-regulatory network through proximity proteomics and structural analysis, showing its link to centrosomes and how its conformation and activity state influence interactions.

Autofluorescence intensity patterns encode α/β cell identity in human islets

This paper shows that endogenous autofluorescence patterns can distinguish α and β cells in human islets without labeling, using LTP descriptors and morphological features.

Dissection of Gαs and Hedgehog signaling crosstalk reveals therapeutic opportunities to target adenosine receptor 2b in Hedgehog-dependent tumors

The paper identifies Gs pathway inactivation as an SMO-independent Hedgehog signaling driver in BCC and shows adenosine 2B receptor activation can counteract SMO-driven oncogenesis.

Communication breakdown and evolution of the cancer cell

The paper analyzes cell-cell interactions (CCIs) in transcriptomic data, showing they are more co-expressed in cancer and associated with patient survival and immunotherapy response.

Learning and actioning general principles of cancer cell drug sensitivity

The paper develops interpretable models to predict cancer drug sensitivity using transcriptomics data and enhances predictions with large language models, validated on clinical samples.

Prediction and discovery of protein-protein direct interactions and stable complexes based on gene co-expression and co-evolution

This study develops a method to predict protein-protein direct interactions and stable complexes using co-expression and co-evolution data, achieving high AUROC scores and better performance than existing tools.

An improved TEAD dominant-negative protein inhibitor to study Hippo YAP1/TAZ-dependent transcription.

This study improves a dominant-negative TEAD inhibitor, TEADi, by modifying its binding domains to enhance its ability to block YAP1 and TAZ interactions with TEAD, revealing differences in their displacement mechanisms.

The landscape of cancer rewired GPCR signaling axes

The paper analyzes GPCR signaling dysregulation in cancer transcriptomics data to identify therapeutic targets and survival-associated signaling axes.

Brain histone beta-hydroxy-butyrylation couples metabolism with gene expression

The paper investigates how fasting-induced beta-hydroxybutyrate (BHB) affects histone modifications and gene expression in mouse brain tissue, revealing its role in modulating circadian rhythms and epigenetic landscapes.

GPCRome-wide structural analysis of G-protein-coupling selectivity

The paper analyzes GPCR-G-protein complexes to identify structural determinants of G-protein coupling selectivity, revealing differences in contact patterns and stability between Gs and Gi/o complexes.

ProMetheusDB: an in-depth analysis of the high-quality human methyl-proteome

The paper presents a high-quality human methyl-proteome dataset generated using hmSILAC and a machine learning tool, showing improved identification of methyl-peptides and novel histone methylation marks.

Employing a Systematic Approach to Biobanking and Analyzing Genetic and Clinical Data for Advancing COVID-19 Research

The paper describes a biobanking initiative collecting genetic and clinical data from over 1,000 SARS-CoV-2-positive individuals to study multi-organ involvement and genetic factors in COVID-19 severity.