Biology subjects

Feito, A.

Publications and source records attributed to Feito, A..

9 recordsLinked to original sources

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

Determination of nucleotide-nucleotide and nucleotide-amino acid binding interactions from all-atom potential-of-mean-force calculations

Biomolecular condensates emerge from multivalent interactions between proteins and nucleic acids and are frequently modeled using coarse-grained molecular dynamics simulations. The parametrization of these models critically depends on atomistic data describing the underlying molecular interactions. In this work, we employ all-atom molecular dynamics simulations and potential-of-mean-force (PMF) calculations to investigate the interaction landscape between RNA nucleotides and protein amino acids. We begin by characterizing nucleotide-nucleotide binding modes through canonical base-pairing analysis, observing notable agreement in the predictions from both AMBER03ws and CHARMM36 force fields. Further rationalization of different nucleotide-nucleotide interaction modes involves the calculation of PMFs for ribose-ribose, phosphate-phosphate, and RNA tertiary interactions such as G-quadruplex formation. We also examine the effect of salt concentration on these interactions, finding a reduction on electrostatic self-repulsion for phosphate-phosphate binding upon increasing the ionic strength. Extending our analysis to amino acids, we first benchmark the performance of both AMBER03ws and a99SB-disp force fields for describing pairwise amino acid interactions, and then we evaluate different nucleotide-amino acid binding profiles. Our findings reveal a subset of amino acids--Lys and Arg (positively charged), Asp and Glu (negatively charged), and Gln, Ser, and Asn (polar residues)--that consistently engage with the nitrogenous bases of different nucleotides. Such binding is primarily mediated by hydrogen bonding and, in some cases, cation-{pi} interactions. Furthermore, we identify strong{pi} -{pi} stacking interactions with aromatic residues and phosphate-Arg contacts as key contributors to condensate cohesion in RNA-protein condensates. Our comprehensive analysis provides a detailed library of nucleotide-amino acid interactions, offering quantitative insights to inform coarse-grained model parametrization and deepening our understanding of condensate self-assembly, nucleic acid recognition, and phase-separation regulation at submolecular scale.

biophysics↗

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↗

bioRxiv2025

Predicting Saturation Concentrations of Phase-Separating Proteins via Thermodynamic Integration

Phase separation of proteins and nucleic acids into biomolecular condensates contributes to the regulation of cellular compartmentalisation in membrane-less environments. A key parameter controlling the onset of biomolecular condensate formation via liquid--liquid phase separation is the saturation concentration (Csat)-- the threshold concentration above which condensation takes place. While measuring Csat for protein solutions in vitro is experimentally accessible, determining this quantity in simulations remains challenging due to the extremely low equilibrium concentrations at which many proteins phase separate. This occurs because the gold standard in simulations consists on combining a residue-resolution coarse-grained model with the Direct Coexistence simulation method, which yields poor estimates of the equilibrium concentrations of the dilute phase due to lack of statistics. In this work, we present two independent thermodynamic integration (TI) schemes which, when combined with Direct Coexistence simulations, enable accurate calculation of saturation concentrations and phase diagrams--facilitating direct comparison with experimental measurements across a wide range of conditions. Our methods, combined with the Mpipi-Recharged residue-resolution coarse-grained model, accurately estimate Csat for a wide range of intrinsically disordered and multi-domain proteins, including disease-associated RNA- and DNA-binding proteins involved in the formation of stress granules and P granules, as well as engineered mutants of hnRNPA1. Furthermore, we compare our TI methods against a computationally efficient machine-learning predictor trained to estimate saturation concentrations at sub-physiological temperatures. While both approaches yield realistic predictions, explicit molecular dynamics simulations enable the calculation of complete phase diagrams and provide insight into the molecular mechanisms and interactions driving phase-separation. Overall, our approach offers a robust, physically grounded framework for improving and validating coarse-grained models of biomolecular phase behaviour, effectively bridging the gap between simulation and experiment.

biophysics↗

bioRxiv2025

Charged mutations in the FUS low-complexity domain modulate condensate ageing kinetics

The assembly of biomolecular condensates is tightly regulated by the intracellular environment. Disruptions in the balance between condensate formation and dissolution--such as irreversible aggregation of low-complexity aromatic-rich kinked segments (LARKS)--have been implicated in multiple neuropathologies. Here, we employ non-equilibrium, residue-resolution coarse-grained simulations to investigate how specific mutations in FUS, an RNA-binding protein associated with amyotrophic lateral sclerosis and frontotemporal dementia, modulate its phase separation propensity and transition into insoluble aggregates. Our simulations reveal that mutations increasing the content of negatively charged amino acids in the low-complexity domain slow down inter-protein {beta}-sheet accumulation, while preserving the phase diagram and viscoelastic properties of the wild-type sequence. Conversely, mutations increasing the arginine content accelerate disorder-to-order LARKS transitions, driving rapid formation of amorphous kinetically trapped aggregates. Our computational approach thus provides molecular-level insights into how specific amino acid mutations and associated intermolecular interactions control the ageing kinetics of protein condensates, promoting aberrant solid-like phases.

biophysics↗

bioRxiv2025

Compositional control of ageing kineticsin TDP-43 condensates

Biomolecular compartments orchestrate the spatiotemporal organisation of cells. The spontaneous assembly of proteins and nucleic acids through liquid-liquid phase separation into biomolecular condensates has been shown to ubiquitously contribute to the functional compartmentalisation of the cytoplasm and nucleoplasm. However, some condensates can undergo an additional phase transition from functional liquid states to pathological solid-like assemblies (i.e., ageing). This liquid-to-solid transition, driven by the accumulation of protein cross-{beta}-sheet structures, represents a hallmark of multiple neurodegenerative disorders. In this study, we employ Molecular Dynamics simulations to explore the role of various biomolecules in regulating the ageing kinetics of condensates scaffolded by TDP-43, a key RNA-binding protein linked to amyotrophic lateral sclerosis and frontotemporal dementia. We find that the recruitment of arginine-rich peptides, such as those produced by the C9orf72 gene, accelerates the nucleation of cross-{beta}-sheet structures. In contrast, the inclusion of poly-Uridine RNA and the HSP70 chaperone significantly slows the emergence of these structures. Remarkably, we observe a correlation between the compactness of the low-complexity domain of TDP-43-- which drives the transition to cross-{beta}-sheet structures--and the condensate ageing kinetics as we vary the composition of the condensates. Moreover, we find that near-interfacial regions of TDP-43 condensates exhibit faster {beta}-sheet transitions than the bulk core of the condensate. Together, our findings underscore the critical role of client biomolecules in modulating protein conformational ensembles and intermolecular interactions, thereby controlling the propensity of condensates to transition into harmful solid-like states.

biophysics↗

bioRxiv2024

Charged peptides enriched in aromatic residues decelerate condensate ageing driven by cross-beta-sheet formation

Biomolecular condensates, formed through liquid-liquid phase separation, play wide-ranging roles in cellular compartmentalization and biological processes. However, their transition from a functional liquid-like phase into a solid-like state--usually termed as condensate ageing--represents a hallmark associated with the onset of multiple neurodegenerative diseases. In this study, we design a computational pipeline to explore potential candidates, in the form of small peptides, to regulate ageing kinetics in biomolecular condensates. By combining equilibrium and non-equilibrium simulations of a sequence-dependent residue-resolution force field, we investigate the impact of peptide insertion--with different composition, patterning, and net charge--in the condensate phase diagram and ageing kinetics of archetypal proteins driving condensate ageing: TDP-43 and FUS. We reveal that small peptides composed of a specific balance of aromatic and charged residues can substantially decelerate ageing over an order of magnitude. The mechanism is controlled through condensate density reduction induced by peptide self-repulsive electrostatic interactions that specifically target protein regions prone to form cross-{beta}-sheet fibrils. Our work proposes an efficient computational framework to rapidly scan the impact of small molecule insertion in condensate ageing and develop novel pathways for controlling phase transitions relevant to disease prevention.

biophysics↗

bioRxiv2024

Capturing single-molecule properties does not ensure accurate prediction of biomolecular phase diagrams

Intracellular liquid-liquid phase separation of proteins and nucleic acids represents a fundamental mechanism by which cells organise their components into biomolecular condensates that perform multiple biological tasks. Computer simulations provide powerful tools to investigate biomolecular phase separation, offering microscopic insights into the physicochemical principles that regulate these systems. In this study, we investigate the phase behaviour of the low-complexity domain (LCD) of hnRNPA1 and several mutants via Molecular Dynamics simulations. We systematically compare the performance of five state-of-the-art residue-resolution coarse-grained protein models: HPS, HPS-cation-{pi}, CALVADOS2, Mpipi, and Mpipi-Recharged. Our evaluation focuses on how well these models reproduce experimental coexistence densities and single-protein radii of gyration for the LCD-hnRNPA1 set of mutants. While most models yield similar intramolecular contact maps and reasonable estimates of the single-protein radius of gyration compared to in vitro measurements, only Mpipi-Recharged, Mpipi, and CALVADOS2 accurately predict phase diagrams that align with experimental data. This suggests that force field parameterizations optimized solely to reproduce single-protein properties may not always capture the phase behaviour of protein solutions. Additionally, our findings reveal that some residue-resolution coarse-grained models can lead to significant discrepancies in predicting the roles of individual intermolecular interactions, even for relatively simple intrinsically disordered proteins like the low-complexity domain of hnRNPA1. Our work highlights the importance of balancing both single-molecule and collective properties of proteins to accurately predict condensate formation and material properties.

biophysics↗

bioRxiv2024

Phase behaviour of hnRNPA1 low-complexity domain mutants described by different sequence-dependent models

Intracellular liquid-liquid phase separation (LLPS) of proteins and nucleic acids is a fundamental mechanism by which cells compartmentalize their components and perform essential biological functions. Molecular simulations play a crucial role in providing microscopic insights into the physicochemical processes driving this phenomenon. In this study, we systematically compare six state-of-the-art sequence-dependent, residue-resolution models to evaluate their performance in reproducing the phase behaviour and material properties of condensates formed by seven variants of the low-complexity domain (LCD) of the hnRNPA1 protein (A1-LCD)--a protein implicated in the pathological liquid-to-solid transition of stress granules. Specifically, we assess the HPS, HPS-cation-{pi}, HPS-Urry, CALVADOS2, Mpipi, and Mpipi-Recharged models in their predictions of the condensate saturation concentration, critical solution temperature, and condensate viscosity for the A1-LCD variants. Our analyses demonstrate that, among the tested models, Mpipi, Mpipi-Recharged, and CALVADOS2 provide accurate descriptions of the critical solution temperatures and saturation concentrations for the various A1-LCD variants tested. Regarding the prediction of material properties for condensates of A1-LCD and its variants, Mpipi-Recharged stands out as the most reliable model. Overall, this study benchmarks a range of residue-resolution coarse-grained models for the study of the thermodynamic stability and material properties of condensates and establishes a direct link between their performance and the ranking of intermolecular interactions these models consider.

biophysics↗