Biology subjects

De Sancho, D.

Publications and source records attributed to De Sancho, D..

12 recordsLinked to original sources

bioRxiv2026

Molecular basis of AMPA receptor labeling by ligand-directed acyl imidazole chemistry in living neurons

Rational design of covalent protein-labeling reagents in complex biological environments requires a molecular-level understanding of how the protein microenvironment governs chemical reactivity; yet, such mechanistic details remain inaccessible to experimental methods alone. In living neurons, Ligand-Directed Acyl Imidazole (LDAI) chemistry has been used to label AMPA receptors as a traceless, affinity-based protein labeling method. Although LDAI labeling reagents have been optimized in the lab, the atomic details of their interactions with the protein and the underlying mechanism remain elusive. In this work, we combined Quantum Mechanical (QM) calculations and molecular dynamics (MD) simulations to propose a detailed reaction mechanism for AMPAR labeling by LDAI reagents and to clarify how the protein microenvironment governs reactivity. Although Lys residues are usually protonated at physiological pH and therefore less nucleophilic in water, our QM results show that Lys labeling is energetically more favorable than competing reactions with Ser or water. MD simulations reveal that PFQX ---the LDAI reagent precursor--- binds dynamically to the GluA2 AMPAR as an antagonist, inducing conformational changes that reshape the local environment of the acyl imidazole (AI) warhead, underscoring that ligand identity strongly affects labeling outcomes. We also identified intra and intermolecular hydrogen bond networks that may contribute to further immobilize and pre-organize the LDAI reagent. Moreover, the probe's chemical nature shapes its interactions with the Ligand Binding Domain (LBD), offering a plausible rationale for the previously experimentally observed ligand-dependent fluorescent response. Taken together, our results establish design principles for exploiting the reagent geometry and binding pocket hydrogen-bonding networks for the rational design of LDAI reagents.

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

MartiniSurf: Automated Simulations of Surface-Immobilized Biomolecular Systems with Martini

The rational design of biomolecule immobilization strategies requires molecular-level understanding of how surface properties, tethering geometry, and structural dynamics jointly influence stability and function. Recently, coarse-grained molecular dynamics simulations based on the Martini force field have emerged as an efficient framework for studying enzyme-surface interactions. However, the reproducible construction of immobilized systems with controlled orientations remains technically challenging, usually involving multiple computational tools. Here we present MartiniSurf, an open-source command-line framework for the preparation of protein and DNA systems immobilized on solid supports within the Martini paradigm. MartiniSurf integrates automated structure retrieval and cleaning, coarse graining via tools from the Martini force field software ecosystem, customizable surface generation, and biomolecule orientation based on user-defined anchoring residues, producing complete GROMACS-ready simulation systems. The framework supports both implicit restraint-based anchoring and explicit linker-mediated immobilization, including surfaces functionalized with user-defined ligands or linker-like moieties, enabling representation of mono- and multivalent attachment geometries at different modeling resolutions. Structure-based G[o]Martini potentials can be incorporated for proteins, while DNA systems are modeled using Martini 2. Optional substrate insertion, pre-coarse-grained complex handling, and automated solvation and ionization further extend system flexibility. By integrating these components into a unified workflow, MartiniSurf enables systematic and high-throughput in silico exploration of surface-tethered biomolecules and provides a robust computational platform for rational immobilization studies. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=146 SRC="FIGDIR/small/714767v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@bc1ac4org.highwire.dtl.DTLVardef@1813b43org.highwire.dtl.DTLVardef@159b19borg.highwire.dtl.DTLVardef@19b60d6_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2025

Arginine versus Lysine: Molecular Determinants of Cation-π Interactions in Biomolecular Condensates

Biomolecular condensates formed by intrinsically disordered regions of proteins are primarily stabilised by specific types of amino acids, usually labelled "stickers" for their ability to crosslink distinct polypeptide chains into droplet-spanning networks. Both aromatic and positively charged residues can play this role, but they differ in their interaction types and relative strengths. An outstanding problem is the quantification of these differences, since they determine the fate of multicomponent mixtures. Here we focus on the interactions formed by aromatic residues with the two main positively charged stickers, Arg and Lys, whose experimental behaviour in condensates shows a clear hierarchy: Arg is consistently observed to promote phase separation more effectively than Lys. We use molecular dynamics simulations and alchemical transformations together with quantum chemical calculations to resolve the differences in their cation-{pi} interaction strengths in different media. We find that, unlike the aromatic residues Phe and Tyr, Arg and Lys consistently maintain a hierarchy of interaction strengths across molecular environments, with Arg being favoured over Lys. While cation-{pi} interactions are important, the primary factor underlying this difference is the higher dehydration penalty of Lys. By contrast, the identity of the aromatic partner that forms the strongest interaction with a cation depends on the dielectric environment. These results provide a clear molecular-level explanation for the distinct contributions of interactions between cationic and aromatic residues to condensate stability.

biophysics↗

bioRxiv2024

Crossover in Aromatic Amino Acid Interaction Strength: Tyrosine vs. Phenylalanine in Biomolecular Condensates

Biomolecular condensates often form through the self-assembly of disordered proteins with low-complexity sequences. In these polypeptides, the aromatic amino acids phenylalanine and tyrosine act as key "sticker" residues, driving the cohesion of dense phases. Recent studies on condensates suggest a hierarchy in sticker strength, with tyrosine being more adhesive than phenylalanine. This hierarchy aligns with experimental data on amino acid solubilities and potentials of mean force derived from atomistic simulations. However, it contradicts conventional chemical intuition based on hydrophobicity scales and pairwise contact statistics from experimental structures of proteins, which suggest phenylalanine should be the stronger sticker. In this work, we use molecular dynamics simulations and quantum chemistry calculations to resolve this apparent discrepancy. Using simple model peptides and side-chain analogues, we demonstrate that the experimentally observed hierarchy arises from the lower free energy of transfer of tyrosine into the condensate, mediated by both stronger protein-protein interactions and solvation effects in the condensate environment. Notably, as the dielectric constant of the media surrounding the stickers approaches that of an apolar solvent, the trend reverses, and phenylalanine becomes the stronger sticker. These findings highlight the role of the chemical environment in modulating protein-protein interactions, providing a clear explanation for the crossover in sticker strength between tyrosine and phenylalanine in different media.

biophysics↗

bioRxiv2024

The Strongest Protein Binder is Surprisingly Labile

Bacterial adhesins are cell-surface proteins that anchor to the cell wall of the host, thus initiating infection. The initial step in infection is precisely the binding to fibrinogen (Fg) from human tissue, after which bacteria can colonize the heart valves by the formation of biofilms. The study of this family of proteins is hence essential to develop new strategies to fight bacterial infections. In the case of Staphylococcus aureus, there exists a type of adhesins known as Microbial Surface Components Recognizing Adhesive Matrix Molecules (MSCRAMMs). Here, we focus on one of them, the Clumping Factor A (ClfA), which has been found to bind Fg through the dock-lock-latch (DLL) mechanism. Interestingly, it has recently been discovered that MSCRAMMs proteins employ a catch-bond to withstand forces exceeding 2 nN, making this type of interaction as mechanically strong as a covalent bond. However, whether this strength is an evolved feature characteristic of the bacterial protein or is typical only of the interaction with its partner is not known. Here we combine single-molecule force spectroscopy (smFS), biophysical binding assays and molecular simulations to study the intrinsic mechanical strength of ClfA. We find that despite the extremely high forces required to break its interactions with Fg, ClfA is not by itself particularly strong, in the absence of its human target. Integrating the results from both theory and experiments we dissect contributions to the mechanical stability of this protein.

biophysics↗

bioRxiv2023

Amino acid substitutions hydrophilizing the core of titin domains cause dilated cardiomyopathy

The underlying genetic defect in most cases of dilated cardiomyopathy (DCM), a common inherited heart disease, remains unknown. Intriguingly, many patients carry single missense variants of uncertain pathogenicity targeting the giant protein titin, a fundamental sarcomere component. To explore the deleterious potential of these variants, we first solved the wild-type and mutant crystal structures of I21, the titin domain targeted by pathogenic variant p.C3575S. Although both structures are remarkably similar, the increase in hydrophilicity of deeply buried position 3575 strongly destabilizes the mutant domain, a scenario supported by molecular dynamics simulations and by biochemical assays that show no disulfide involving C3575. Prompted by these observations, we have found that thousands of similar hydrophilizing variants associate specifically with DCM. Hence, our results imply that titin domain destabilization causes DCM, a conceptual framework that not only informs pathogenicity assessment of gene variants but also points to therapeutic strategies counterbalancing protein destabilization.

biochemistry↗

bioRxiv2023

Atomistic molecular simulations of Aβ-Zn conformational ensembles

The amyloid-forming A{beta} peptide is able to interact with metal cations to form very stable complexes that influence fibril formation and contribute to the onset of Alzheimers disease. Multiple structures of peptides derived from A{beta} in complex with different metals have been resolved experimentally to provide an atomic-level description of the metal-protein interactions. However, A{beta} is intrinsically disordered, and hence more amenable to an ensemble description. Molecular dynamics simulations can now reach the timescales needed to generate ensembles for this type of complexes. However, this requires accurate force fields both for the protein and the protein-metal interactions. Here we use state-of-the-art methods to generate force field parameters for the Zn(II) cations in a set of A{beta} complexes and combine them with the Amber99SB[*]-ILDN optimized force field. Upon comparison of NMR experiments with the simulation results, further optimized with a Bayesian/Maximum entropy approach, we provide an accurate description of the molecular ensembles for most A{beta}-metal complexes. We find that the resulting conformational ensembles are more heterogeneous than the NMR models deposited in the Protein Data Bank.

biophysics↗

bioRxiv2022

Liquid-liquid phase separation in amino acid mixtures is governed by composition

Macromolecular phase separation has recently come to immense prominence as it is central to the formation of membraneless organelles, leading to a new paradigm of cellular organization. This type of phase transition, often termed liquid-liquid phase separation (LLPS), is mediated by molecular interactions between biomolecules, including nucleic acids and both ordered and disordered proteins. In the latter case, the separation between protein-dense and dilute phases is often interpreted using models adapted from polymer theory. Specifically, the "stickers and spacers" model proposes that the formation of condensate-spanning networks in protein solutions originates from the interplay between two classes of residues and that the main determinants for phase separation are multivalency and sequence patterning. The duality of roles of stickers (aromatics like Phe and Tyr) and spacers (Gly and polar residues) may apply more broadly in protein-like mixtures, and the presence of these two types of components alone may suffice for LLPS to take place. In order to explore this hypothesis, we use atomistic molecular dynamics simulations of capped amino-acid residues as a minimal model system. We study the behaviour of pure amino acids in water for three types of residues corresponding to the spacer and sticker categories, and their multicomponent mixtures. In agreement with previous observations, we find that the spacer-type amino acids fail to phase-separate on their own, while the sticker is prone to aggregation. However, ternary amino acid mixtures involving both types of amino acids phase-separate into two phases that retain intermediate degrees of compaction and greater fluidity than sticker-only condensates. Our results suggest that LLPS is an emergent property of amino acid mixtures determined by composition.

biophysics↗

bioRxiv2021

Markov state models from hierarchical density-based assignment

Markov state models (MSMs) have become one of the preferred methods for the analysis and interpretation of molecular dynamics (MD) simulations of conformational transitions in biopolymers. While there is great variation in terms of implementation, a well-defined workflow involving multiple steps is often adopted. Typically, molecular coordinates are first subjected to dimensionality reduction and then clustered into small "microstates", which are subsequently lumped into "macrostates" using the information from the slowest eigenmodes. However, the microstate dynamics is often non-Markovian and long lag times are required to converge the MSM. Here we propose a variation on this typical workflow, taking advantage of hierarchical density-based clustering. When applied to simulation data, this type of clustering separates high population regions of conformational space from others that are rarely visited. In this way, density-based clustering naturally implements assignment of the data based on transitions between metastable states. As a result, the state definition becomes more consistent with the assumption of Markovianity and the timescales of the slow dynamics of the system are recovered more effectively. We present results of this simplified workflow for a model potential and MD simulations of the alanine dipeptide and the FiP35 WW domain.

biophysics↗

bioRxiv2020

Coarse-grained molecular simulations of the binding of the SARS-CoV 2 spike protein RBD to the ACE2 cell receptor

Since it was first observed, the COVID-19 pandemic has created a global emergency for national health systems due to millions of confirmed cases and hundreds of thousands of deaths. At a molecular level, the bottleneck for the infection is the binding of the receptor binding domain (RBD) of the viral spike protein to ACE2, an enzyme exposed on human cell membranes. Several experimental structures of the ACE2:RBD complex have been made available, however they offer only a static description of the arrangements of the molecules in either the free or bound states. In order to gain a dynamic description of the binding process that is key to infection, we use molecular simulations with a coarse grained model of the RBD and ACE2. We find that binding occurs in an all-or-none way, without intermediates, and that even in the bound state, the RBD exhibits a considerably dynamic behaviour. From short equilibrium simulations started in the unbound state we provide snapshots that result in a tentative mechanism of binding. Our findings may be important for the development of drug discovery strategies that target the RBD.

biophysics↗

bioRxiv2018

Reversible two-state folding of the ultrafast protein gpW under mechanical force

Ultrafast folding proteins have limited cooperativity and thus are excellent models to resolve, via single-molecule experiments, the fleeting molecular events that proteins undergo during folding. Here we report single-molecule atomic force microscopy (AFM) experiments on gpW, a protein that, in bulk, folds in a few microseconds over a marginal folding barrier (~1 kBT). Applying pulling forces of only 5 pN we maintain gpW in quasi-equilibrium near its mechanical unfolding midpoint, and detect how it interconverts stochastically between the folded and an extended state. This binary pattern indicates that, under an external force, gpW (un)folds over a significant free energy barrier. Using molecular simulations and a theoretical model we rationalize how force induces such barrier in an otherwise downhill free energy surface. Force-induced folding barriers are likely a general occurrence for ultrafast folding biomolecules studied with single molecule force spectroscopy.

biophysics↗