Biology subjects

Privat, C.

Publications and source records attributed to Privat, C..

3 recordsLinked to original sources

bioRxiv2026

Condensate-Driven Transcriptional Reprogramming Defines Core Vulnerabilities in Esophageal and Gastric Cancers

Biomolecular condensates organize key nuclear functions by compartmentalizing biomolecules, yet their contribution to gastrointestinal tumorigenesis remains poorly defined. Integrating multi-omics profiling, functional genomics, and molecular dynamics simulations, we reveal that esophageal and gastric cancers share a condensate-enriched transcriptional program driven by intrinsically disordered proteins involved in transcription, RNA processing, and replication stress. Transcriptomic analyses identify a hyperactive transcriptional state with upregulation of condensate-associated genes, including TOPBP1 and CHERP. Dependency mapping demonstrates that these proteins are essential for tumor cell viability, defining a conserved condensate core across different tumor types. Machine-learned predictions and residue-resolution coarse-grained simulations confirm that TOPBP1 and CHERP undergo phase separation through homotypic interactions mediated by intrinsically disordered regions, with saturation concentrations below 2 {micro}M, consistent with spontaneous condensate formation observed in vitro. Together, these findings establish condensate organization as a fundamental mesoscale principle in upper gastrointestinal cancers and nominate condensate scaffolds as tractable therapeutic vulnerabilities.

bioinformatics↗

bioRxiv2025

Benchmarking Free Energy Computational Methods for Revealing the Interactions Driving PARP1 Selective Inhibition

Accurate prediction of inhibitor selectivity across protein paralogues remains a central challenge in computational drug discovery. Here, we systematically benchmark three computational methods--Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA), free energy perturbation (FEP) and potential of mean force (PMF) calculations--in their ability to recapitulate PARP1 versus PARP2 selectivity for eight clinically relevant PARP enzyme inhibitors used in ovarian, breast and prostate tumors among others. We demonstrate how MM/PBSA calculations offer rapid and qualitative insights, but show pronounced sensitivity to the chosen static conformational pose, being particularly challenging for ligands with subtle energetic differences between distinct protein paralogues. In contrast, both FEP and PMF calculations using atomistic models with explicit solvent result in substantially improved agreement with experimental binding affinities. The FEP method exhibits the strongest quantitative correlation with experimental binding free energy differences, remarkably reproducing selectivity trends even among nearly isoenergetic complexes. Notably, our structural contact analysis reveals how contact connectivity controls ligand selectivity, providing valuable mechanistic and molecular insight into the key residues that stabilize each inhibitor in both protein enzymes. Together, our multi-method computational study contributes to elucidate potential chemical modifications across the ligand chemical space to enhance potency and specificity, informing the future design and evaluation of selective inhibitors for precision oncology, including therapies targeting homologous recombination-deficient cancers.

bioinformatics↗

bioRxiv2025

Decoding PARP1 Selectivity: Atomistic Insights for Next-Generation Cancer Inhibitors

Selective inhibition of PARP1 represents a promising strategy to improve the therapeutic index of PARP inhibitors, a class of anticancer agents that exploit defects in DNA repair pathways. While PARP inhibitors have shown remarkable clinical benefit, particularly in BRCA-mutated tumors, the lack of discrimination between PARP1 and its close homolog PARP2, often leads to hematological toxicity and limits treatment efficacy. Thus, achieving molecular selectivity for PARP1 remains a central challenge in the rational design of safer and more potent inhibitors. To explore the molecular determinants of ligand selectivity, we focus on four clinically relevant PARP inhibitors--two PARP1-selective (saruparib and NMS-P118) and two non-selective (veliparib and olaparib) inhibitors--and perform atomistic potential-of-mean-force calculations of the PARP1 catalytic binding domain in the presence of these molecules. Our simulations near-quantitatively capture the experimental relative binding preferences, demonstrating that our approach reliably reflects selectivity patterns. Based on these findings, we analyze protein-ligand contact frequencies to identify the stabilizing interaction network and contact connectivity inducing protein selectivity. The most frequent protein-inhibitor contacts are primarily mediated by tyrosine triads and electrostatic interactions, showing a cooperative complex network of intermolecular contacts which strongly relies on protein multivalency. To dissect the decisive role of individual residues across the binding site, we also perform targeted mutagenesis of the PARP1 catalytic pocket in complex with saruparib, replacing several active-site amino acids by glycines. Progressively increasing the number of mutations markedly reduces binding stability, with distinct residue combinations exerting two primary effects: destabilization of the final bound state and the emergence of energetic barriers along the ligand association pathway. Together, our results provide a coherent mechanistic framework for understanding PARP1 selectivity and informs the rational design of next-generation inhibitors with improved efficacy and safety.

molecular biology↗