Marc Vidal

The paper investigates how dual-role regulators interact with the Sin3 complex using structural and functional analyses, revealing mechanisms for transcriptional crosstalk.

The paper develops a scalable workflow to reclassify variants of uncertain significance using experimental and predictive evidence, resolving 75% of VUS in 40 genes.

MutPred-PPI predicts interaction-specific effects of missense variants using graph attention networks, showing strong performance on both seen and unseen proteins.

This study systematically profiles 95 viral transcriptional regulators (vTRs) across multiple assays, revealing their diverse mechanisms of rewiring host pathways to promote viral replication and disease links.

The paper identifies two categories of transcription factor isoforms with distinct molecular functions and shows they contribute to gene regulatory complexity.

The paper uses a yeast two-hybrid screen to identify novel effector-effector interactions in L. pneumophila, doubling the known number of such interactions.

The paper presents an AI-guided pipeline for PPI drug discovery, identifying a SARS-CoV-2 inhibitor by combining experimental assays, machine learning, and virtual screening.

The paper introduces paired yeast one-hybrid assays to detect DNA-binding cooperativity and antagonism between transcription factors and demonstrates their application across various TF pairs.

This paper identifies a non-enzymatic HDAC inhibitor with anti-tumor and anti-psychedelic effects, showing reduced gene modulation compared to enzymatic inhibitors.

The paper maps binary SARS-CoV-2 protein interactions with host proteins, identifying key processes like immune signaling and ubiquitination, and suggests therapeutic targets.

This study shows that Tax and HBZ, viral oncoproteins, alter mRNA splicing in T-cells by interacting with spliceosome components, leading to changes in cancer-related genes.

The paper presents a framework combining two high-throughput interaction assays to distinguish direct from indirect protein interactions in complexes.

The paper identifies three causal lipid genes using Mendelian randomization and confirms their effects through mouse knockout studies and RNA-seq analysis.

The paper constructs a high-quality yeast binary interactome map and compares inner- and outer-complexome organization, finding the outer-complexome contains more interactions and exhibits higher functional heterogeneity.

This study identifies disease mutations that perturb protein-protein interactions in the human 3D interactome, showing their association with cancer survival and drug response.

Proximity-dependent biotinylation (BioID) of 28 SARS-CoV-2 proteins in human cells maps 5,415 proximal interactions with 2,128 host targets across basal and poly(I:C) conditions.

ORF Capture-Seq enables targeted full-length isoform sequencing by using cloned ORFs as probes, significantly increasing isoform detection compared to unenriched samples.

The paper creates a proactive map of CBS gene variant function and vitamin B6 response using yeast assays, predicting disease severity and therapy effectiveness.

The paper introduces an assayome concept to optimize binary interactome mapping by combining multiple assay versions, achieving high true positive detection with minimal assays.

The paper presents HuRI, a comprehensive human protein-protein interaction map, and demonstrates its utility in understanding cellular function and disease mechanisms.

The paper challenges the Triadic Closure Principle in protein interaction prediction and introduces the L3 principle, which outperforms existing methods and identifies novel interactions.

The paper identifies NFATC4 as a key regulator of Type 2 Diabetes genes by analyzing a gene regulatory network in pancreatic islets and linking it to eQTLs and differential expression.

This paper develops a deep mutational scanning framework to map functional missense variants in human genes and applies it to four proteins, identifying pathogenic variants and showing scalability to disease genes.