Biology subjects

Lopez, X.

Publications and source records attributed to Lopez, X..

7 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

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

Long-term efficacy of adoptive cell therapy is determined by host CD8+ T cells and undermined by lymphodepleting preconditioning

Adoptive T cell therapy (ACT) has demonstrated remarkable efficacy in treating hematological cancers. However, its efficacy against solid tumors remains limited and the emergence of cancer cells that lose expression of targeted antigens often promotes resistance to ACT. Importantly, the mechanisms underlying effective and durable ACT-mediated tumor control are incompletely understood. Here, we show that adoptive transfer of TCR-transgenic CD8+ T cells eliminates established murine melanoma tumors, with concomitant accumulation of tumor-infiltrating CD8+ T cells exhibiting both progenitor-exhausted and terminally-differentiated phenotypes. Interestingly, host CD8+ T cells contributed to ACT-mediated elimination of primary tumors and rejected ACT-resistant melanoma cells lacking the targeted antigen. Mechanistically, ACT induced TNF-- and cross-presenting dendritic cell-dependent tumor accumulation of endogenous CD8+ T cells and effective tumor elimination. Importantly, although lymphodepleting preconditioning enhanced ACT-mediated tumor elimination, it abrogated host antitumor immunity and protection against ACT-resistant melanoma cells. Enrichment of transcriptional signatures associated with TNF- signaling, cross-presenting dendritic cells and tumor-specific CD8+ T cells in human melanoma tumors correlated with favorable responses to ACT and increased survival. Our findings reveal that long-term efficacy of ACT is determined by the interplay between transferred and endogenous CD8+ T cells and is undermined by lymphodepleting preconditioning, which ultimately favors ACT resistance.

immunology↗

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↗

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↗