Andrés R. Tejedor

This study shows RNA synthesis and degradation dynamically regulate transcriptional condensates through non-equilibrium feedback, affecting their composition and stability.

The paper identifies LGR5-high colorectal cancer as a Wnt-active stem-like state with therapeutic vulnerabilities across multiple modalities.

This study reveals a dimer-seeded nucleation mechanism for ProTα-H1 biomolecular condensates using simulations and shows how electrostatic screening and protein supersaturation control nucleation and condensate properties.

PepSpace designs specific peptides using physics-driven directed evolution, achieving stronger predicted interactions and better binding specificity than machine learning models.

This study identifies how RNA-regulated fusion dynamics of hnRNPK-based nucleolar condensates prevent coalescence and maintain size control.

This study shows how RNA and protein interactions influence the aging of FUS condensates through molecular mechanisms involving LARKS and cross-β-sheet structures.

This study uses atomistic simulations to show how charge changes in diruthenium complexes affect their interaction with Aβ42 fibrils, enhancing fibril disruption through electrostatic tuning.

This study shows that cross-β-sheet nucleation, linked to ageing, preferentially occurs at biomolecular condensate interfaces due to a general physical mechanism, not specific protein sequences.

The paper uses atomistic simulations to identify molecular interactions governing PARP1 inhibitor selectivity and binding affinity, revealing key residues and cooperative networks that influence inhibitor specificity.

RNA2PS predicts RNA structure and phase separation behavior from sequence, showing sequence-dependent duplex stability and condensation differences in trinucleotide repeat RNAs.

The paper identifies condensate-driven transcriptional programs in esophageal and gastric cancers, showing their role in tumor viability and suggesting therapeutic targets.

This study uses all-atom simulations to analyze nucleotide-nucleotide and nucleotide-amino acid interactions, revealing key binding modes and their role in biomolecular condensate formation.

This study benchmarks computational methods to predict PARP1 vs PARP2 inhibitor selectivity, finding FEP and PMF outperform MM/PBSA in accuracy.

This study uses molecular dynamics simulations to reveal how misfolded α1-antitrypsin forms liquid-like droplets that harden into solid clusters, explaining the pathogenesis of α1-antitrypsin deficiency.

The paper shows that DNAJB6's conserved intrinsically disordered region enables its surveillance of FG-Nup condensates by forming stable assemblies that outcompete FG-Nup interactions.

This paper introduces two thermodynamic integration schemes to accurately calculate saturation concentrations of phase-separating proteins in simulations, enabling comparison with experimental data.

The paper designs a computational pipeline to identify small peptides that slow condensate ageing in TDP-43 and FUS by reducing condensate density through electrostatic interactions.

This study uses simulations to show how charged mutations in FUS affect condensate ageing kinetics by altering β-sheet accumulation and aggregate formation.

This study uses Molecular Dynamics simulations to show how different biomolecules affect the ageing kinetics of TDP-43 condensates, with arginine-rich peptides accelerating and poly-Uridine RNA slowing the formation of harmful cross-β-sheet structures.

The paper introduces Mpipi-Recharged, a coarse-grained model that improves electrostatic force description in charge-rich biomolecular condensates by incorporating asymmetric potentials from atomistic simulations.

This study benchmarks six residue-resolution coarse-grained models to evaluate their accuracy in simulating the phase behavior and material properties of hnRNPA1 LCD condensates.

This study evaluates five coarse-grained protein models for predicting phase diagrams of hnRNPA1 LCD mutants, finding that only three accurately reproduce experimental data, highlighting the need to balance single-molecule and collective properties.