Biology subjects

Crehuet, R.

Publications and source records attributed to Crehuet, R..

11 recordsLinked to original sources

bioRxiv2026

Shedding light on Gaussia Luciferase conformational space using multi-conformation enhanced sampling simulation

Gaussia luciferase (GLuc) has broad biotechnological applications owing to its bioluminescence, yet its catalytic mechanism remains poorly understood and rational engineering efforts are hindered by the absence of a structural model. This gap stems from GLuc's highly dynamic nature, which allows it to populate a wide range of conformations rather than adopt a single, well-defined fold. Aiming to bridge this gap, in this work we employ structure predictors and conformational sampling techniques to prod GLuc's structure. Our results indicate that structure predictors such as AlphaFold, as well as ensemble predictors such as BioEmu, fail to adequately sample this conformational space, and even extended molecular dynamics simulations do not yield converged conformational ensembles. However, enhanced sampling via PT-WTE provides a more satisfactory description of GLu's conformational landscape, allowing us to cluster distinct conformational basins, identify loosely defined substrate-binding pockets, and characterize the conformational changes induced by substrate binding. These insights advance our understanding of GLuc's catalytic mechanism and inform future efforts to engineer improved variants.

biophysics↗

bioRxiv2025

Assessing Structural Prediction Accuracy for Nanobody-Small Molecule Complexes

Generative models have advanced profusely in recent years, generating great impact in computational modelling and structural bioinformatics. A part of computational protein design focuses on antibody and nanobody engineering, targeting proteic epitopes. By contrast, the development of nanobodies for small-molecule sensing remains a largely experimental field. In this work, we evaluate the performance of several state-of-the-art prediction softwares (AlfaFold3, Chai-1, Boltz-1, RosettaFold-AllAtom, FlowDock and OmegaFold), on nanobody-small molecule complexes, in an effort to pave the way of computational studies in this area. We tested the general nanobody and CDR accuracy, as well as ligand placement and orientation. We also explored the correlation of the results with intrinsic metrics of the models, such as pLDDT, and with the number of samples and recycles. Results show that most predictors perform well at predicting nanobody structures, but some struggle at ligand placement. AlphaFold3 outperformed the other softwares in all these tasks. Co-folding increased the accuracy of the predictions, modelling better CDR1. Contact analysis revealed that CDR1 was mostly involved in ligand binding instead of CDR3. This could point to a memorization tendency in the models, as most nanobody-antigen complexes target other proteins. Results also pointed to pLDDT as a good score to indicate CDR accuracy. Accuracy did slightly improve when increasing the number of samples but did not with the number of recycles. These findings highlight both the advantages and limitations of structure prediction methods for nanobody-small molecule complexes.

biophysics↗

bioRxiv2025

NEMAT: An Automated Non-Equilibrium Free-Energy Framework for Predicting Ligand Affinity in Membrane Proteins

Quantifying the strength of small-molecule binding to proteins is essential for understanding biological function and for advancing drug discovery. Computational free-energy methods aim to predict these interactions accurately, yet membrane proteins remain particularly difficult due to the added thermo-dynamic effects of the lipid bilayer. Alchemical free-energy calculations provide state-of-the-art accuracy by transforming one ligand into another, and recent non-equilibrium approaches (NEQ-FEP) offer improved efficiency and parallelization. However, no existing workflow automates NEQ-FEP for membrane proteins and enables decomposition of the observed binding free energy into membrane-partitioning and protein-specific components. NEMAT is an open-source pipeline that performs automated non-equilibrium alchemical transformations in water, membranes, and membrane-embedded protein environments. Here we show that NEMAT reproduces experimental binding-energy trends for P2Y1 ligands with accuracy comparable to established equilibrium methods. These findings demonstrate that NEQ-FEP can be applied reliably to membrane systems when supported by a consistent workflow. In contrast to previous approaches, NEMAT provides systematic control over transition length, sampling, and replica averaging, enabling stable work-distribution overlap and reproducible free-energy estimates. In a broader context, NEMAT offers a practical route to mechanistically interpretable affinity predictions for membrane-embedded targets. Its ability to dissect membrane and protein contributions advances our understanding of lig- and selectivity at lipid-facing pockets and supports the routine application of non-equilibrium free-energy methods in drug discovery.

biophysics↗

bioRxiv2023

Checkpoint activation by Spd1: a competition-based system relying on tandem disordered PCNA binding motifs

DNA regulation, replication and repair are processes fundamental to all known organisms and the sliding clamp proliferating cell nuclear antigen (PCNA) is central to all these processes. S-phase delaying protein 1 (Spd1) from S. pombe, an intrinsically disordered protein that causes checkpoint activation by inhibiting the enzyme ribonucleotide reductase, has one of the most divergent PCNA binding motifs known. Using NMR spectroscopy, in vivo assays, X-ray crystallography, calorimetry, and Monte Carlo simulations, an additional PCNA binding motif in Spd1, a PIP-box, is revealed. The two tandemly positioned, low affinity sites exchange rapidly on PCNA exploiting the same binding sites. Increasing or decreasing the binding affinity between Spd1 and PCNA through mutations of either motif compromised the ability of Spd1 to cause checkpoint activation in yeast. These results pinpoint a role for PCNA in Spd1-mediated checkpoint activation and suggest that its tandemly positioned short linear motifs create a neatly balanced competition-based system, involving PCNA, Spd1 and the small ribonucleotide reductase subunit, Suc22R2. Similar mechanisms may be relevant in other PCNA binding ligands where divergent binding motifs so far have gone under the PIP-box radar.

biophysics↗

bioRxiv2022

A glutamine-based single α-helix scaffold to target globular proteins

The binding of intrinsically disordered proteins to globular ones often requires the folding of motifs into [a]-helices. These interactions offer opportunities for therapeutic intervention but their modulation with small molecules is challenging because they bury large surfaces. Linear peptides that display the residues that are key for binding can be targeted to globular proteins when they form stable helices, which in most cases requires their chemical modification. Here we present rules to design peptides that fold into single [a]-helices by instead concatenating glutamine side chain to main chain hydrogen bonds recently discovered in polyglutamine helices. The resulting peptides are uncharged, contain only natural amino acids, and their sequences can be optimized to interact with specific targets. Our results provide design rules to obtain single [a]-helices for a wide range of applications in protein engineering and drug design.

biophysics↗

bioRxiv2021

Mg2+-dependent conformational equilibria in CorA: an integrated view on transport regulation

The CorA family of proteins regulates the homeostasis of divalent metal ions in many bacteria, archaea, and eukaryotic mitochondria, making it an important target in the investigation of the mechanisms of transport and its functional regulation. Although numerous structures of open and closed channels are now available for the CorA family, the mechanism of the transport regulation remains elusive. Here, we investigated the conformational distribution and associated dynamic behaviour of the pentameric Mg2+ channel CorA at room temperature using small-angle neutron scattering (SANS) in combination with molecular dynamics (MD) simulations and solid-state nuclear magnetic resonance spectroscopy (NMR). We find that neither the Mg2+-bound closed structure nor the Mg2+-free open forms are sufficient to explain the average conformation of CorA. Our data support the presence of conformational equilibria between multiple states, and we further find a variation in the behaviour of the backbone dynamics with and without Mg2+. We propose that CorA must be in a dynamic equilibrium between different non-conducting states, both symmetric and asymmetric, regardless of bound Mg2+ but that conducting states become more populated in Mg2+-free conditions. These properties are regulated by backbone dynamics and are key to understanding the functional regulation of CorA.

biophysics↗

bioRxiv2021

Accurate model of liquid-liquid phase behaviour of intrinsically-disordered proteins from data-driven optimization of single-chain properties

Many intrinsically disordered proteins (IDPs) may undergo liquidliquid phase separation (LLPS) and participate in the formation of membraneless organelles in the cell, thereby contributing to the regulation and compartmentalisation of intracellular biochemical reactions. The phase behaviour of IDPs is sequence-dependent, and its investigation through molecular simulations requires protein models that combine computational efficiency with an accurate description of intra- and intermolecular interactions. We developed a general coarse-grained model of IDPs, with residue-level detail, based on an extensive set of experimental data on single-chain properties. Ensemble-averaged experimental observables are predicted from molecular simulations, and a data-driven parameter-learning procedure is used to identify the residue-specific model parameters that minimize the discrepancy between predictions and experiments. The model accurately reproduces the experimentally observed conformational propensities of a set of IDPs. Through two-body as well as large-scale molecular simulations, we show that the optimization of the intramolecular interactions results in improved predictions of protein self-association and LLPS.

biophysics↗

bioRxiv2020

DEER-PREdict: Software for Efficient Calculation of Spin-Labeling EPR and NMR Data from Conformational Ensembles

Owing to their plasticity, intrinsically disordered and multidomain proteins require descriptions based on multiple conformations, thus calling for techniques and analysis tools that are capable of dealing with conformational ensembles rather than a single protein structure. Here, we introduce DEER-PREdict, a software to predict Double Electron-Electron Resonance distance distributions as well as Paramagnetic Relaxation Enhancement rates from ensembles of protein conformations. DEER-PREdict uses an established rotamer library approach to describe the paramagnetic probes which are bound covalently to the protein. DEER-PREdict has been designed to operate efficiently on large conformational ensembles, such as those generated by molecular dynamics simulation, to facilitate the validation or refinement of molecular models as well as the interpretation of experimental data. The performance and accuracy of the software is demonstrated with experimentally characterized protein systems: HIV-1 protease, T4 Lysozyme and Acyl-CoA-binding protein. DEER-PREdict is open source (GPLv3) and available at github.com/KULL-Centre/DEERpredict and as a Python PyPI package pypi.org/project/DEERPREdict.

biophysics↗

bioRxiv2019

Bayesian-Maximum-Entropy reweighting of IDPs ensembles based on NMR chemical shifts

Bayesian and Maximum Entropy approaches allow for a statistically sound and systematic fitting of experimental and computational data. Unfortunately, assessing the relative confidence in these two types of data remains difficult as several steps add unknown error. Here we propose the use of a validation-set method to determine the balance, and thus the amount of fitting. We apply the method to synthetic NMR chemical shift data of an intrinsically disordered protein. We show that the method gives consistent results even when other methods to assess the amount of fitting cannot be applied. Finally, we also describe how the errors in the chemical shift predictor can lead to an incorrect fitting and how using secondary chemical shifts could alleviate this problem.

biophysics↗

bioRxiv2019

Analyzing and comparing the radius of gyration and hydrodynamic radius in conformational ensembles of intrinsically disordered proteins

The level of compaction of an intrinsically disordered protein may affect both its physical and biological properties, and can be probed via different types of biophysical experiments. Small-angle X-ray scattering (SAXS) probe the radius of gyration (Rg) whereas pulsed-field-gradient nuclear magnetic resonance (NMR) diffusion, fluorescence correlation spectroscopy and dynamic light scattering experiments can be used to determine the hydrodynamic radius (Rh). Here we show how to calculate Rg and Rh from a computationally-generated conformational ensemble of an intrinsically disordered protein. We further describe how to use a Bayesian/Maximum Entropy procedure to integrate data from SAXS and NMR diffusion experiments, so as to derive conformational ensembles in agreement with those experiments.

biophysics↗

bioRxiv2018

Side chain to main chain hydrogen bonds stabilize polyglutamine helices in transcription factors

Polyglutamine (polyQ) tracts are regions of low sequence complexity of variable length found in more than one hundred human proteins. These tracts are frequent in activation domains of transcription factors and their length often correlates with transcriptional activity. In addition, in nine proteins, tract elongation beyond specific thresholds causes polyQ disorders. To study the structural basis of the association between tract length, transcriptional activity and disease, here we addressed how the conformation of the polyQ tract of the androgen receptor (AR), a transcription factor associated with the polyQ disease spinobulbar muscular atrophy (SBMA), depends on its length. We found that the tract folds into a helical structure stabilized by unconventional hydrogen bonds between glutamine side chains and main chain carbonyl groups. These bonds are bifurcate with the conventional main chain to main chain hydrogen bonds stabilizing -helices. In addition, since tract elongation provides additional interactions, the helicity of the polyQ tract directly correlates with its length. These findings suggest a plausible rationale for the association between polyQ tract length and AR transcriptional activity and have implications for establishing the mechanistic basis of SBMA.

biophysics↗