Biology subjects

Llombart, P.

Publications and source records attributed to Llombart, P..

3 recordsLinked to original sources

bioRxiv2026

Compositional control of FUS condensate ageing through aggregation-prone interaction networks

Stress granules are multicomponent biomolecular condensates whose aberrant ageing has been implicated in numerous neurodegenerative diseases. Although their composition is known to influence condensate properties, the molecular principles linking composition to structural maturation remain poorly understood. Here, we perform equilibrium and non-equilibrium residue-resolution molecular dynamics simulations to determine how RNA and heterotypic protein interactions regulate the pathological hardening of multicomponent FUS-containing condensates inspired by stress-granule composition. We show that diverse compositional changes--including RNA concentration, heterotypic protein partitioning, interfacial enrichment of G3BP1, and charged peptide recruitment--reshape condensate organization through distinct molecular mechanisms. Despite these different modes of action, all converge on a common physical principle: modulation of the local clustering and persistence of contacts between low-complexity aromatic-rich kinked segments (LARKS) governs the nucleation and accumulation of long-lived intermolecular cross-{beta}-sheet structures. Intermediate RNA concentrations enhance condensate density and promote LARKS contacts, whereas high RNA levels, heterotypic interactions, and interfacial coating reduce their availability and delay ageing. Our results establish a unified molecular framework linking condensate composition, internal organization and ageing. This framework provides mechanistic insight into the regulation of multicomponent condensate material properties and suggests general design principles for modulating their pathological aggregation.

biophysics↗

bioRxiv2026

Length Scale-Dependent Dynamics in Electrostatic Protein Coacervates

Biomolecular condensates formed by complex coacervation of highly charged proteins provide a powerful framework to understand how microscopic interactions give rise to macroscopic material properties. Atomistic molecular dynamics simulations provide detailed insights but remain limited in accesing the spatio-temporal scales relevant for condensate behavior. Here, we use the residue-level coarse-grained Mpipi-Recharged model to investigate condensates formed by ProT and positively charged partners, including histone H1, protamine, poly-lysine, and poly-arginine. Material properties, in this context, provide a stringent experimental benchamark for coarse-grained models. Our model reproduces salt-dependent phase behavior, protein binding affinities, and sequence-specific stability trends in agreement with in vitro experiments, despite the fact that material properties were not included in the model parametrization. We then establish a direct link between protein dynamics and macroscopic material properties by quantifying monomeric diffusion, conformational reconfiguration, and translational mobility within the dense phase, and relating these to condensate viscosity. By comparing dynamics across dense and dilute phases, we uncover a pronounced length scale-dependent behavior. While residue-level binding and unbinding events remain equally fast in both phases, protein reconfiguration time and self-diffusion are significantly slowed down within the condensates. This decoupling reveals how fast intermolecular interactions coexist with slow mesoscale condensate dynamics depending on the molecular length scale. Together, our results establish a predictive framework that links encoded sequence intermolecular forces and multiscale dynamics to the emergent material properties of complex biomolecular condensates.

biophysics↗

bioRxiv2026

Computational mapping of antibody-receptor energy landscapes to predict membrane internalization

Antibody internalization is critical for the action of antibody-drug conjugates, yet antibody discovery pipelines typically prioritize binding affinity rather than functional internalization. Here we show that molecular dynamics simulations can map the binding energy landscape between antibody clones and the membrane protein JAM-A, enabling computational predictions of antibody internalization. Using the sequences of newly generated anti-JAM-A monoclonal antibodies (mAbs), we perform atomistic potential-of-mean force simulations to evaluate the binding free energy to the JAM-A receptor and their interaction fingerprint at residue-level resolution. We find that internalizing mAb derived from different hybridomas exhibit a unique membrane-oriented contact topology that promotes cooperative receptor-receptor interactions, lowering the energetic barrier for early endocytic events. Reconstruction of the receptor binding energy landscape further reveals that electrostatic interactions between charged residues and multivalent cation-{pi} and polar interactions correlate with successful mAb internalization in ovarian cancer cells. In contrast, strong binding affinity of the fragment antigen-binding domain correlates with poor internalization. Together, our results establish molecular dynamics-guided clonal selection as a predictive framework for optimizing internalizing therapeutic antibodies and provide mechanistic insight into how antibody binding reshapes membrane-proximal receptor energetics to drive endocytosis.

biophysics↗