Biology subjects

Louet, A. A. B.

Publications and source records attributed to Louet, A. A. B..

4 recordsLinked to original sources

bioRxiv2026

Effects of PTMs on Tau Protein Aggregation: Insights from HCG and Atomistic MD Simulations

Post-translational modifications (PTMs) of the tau protein are increasingly recognized as pivotal regulators in the onset and progression of tauopathies, such as Alzheimers disease (AD). To systematically evaluate the structural and functional consequences of specific PTMs, we generated and analyzed seven distinctly modified variants of the tau-K32 construct. These included phosphorylation at Ser202/Thr205, phosphorylation at Ser258/Ser262/Ser356, full phosphorylation at all reported Ser/Thr sites, acetylation at Lys274/Lys281, acetylation at Lys280, full acetylation at all sites, and an unmodified control. Selection of PTM sites was guided by prior experimental literature. By incorporating fully modified tau models, we assessed the global impact of widespread modifications on structural properties and aggregation behavior. Our findings establish a comparative framework for understanding how discrete and cumulative PTMs modulate tau aggregation and provide mechanistic insight into PTM-induced tau dysfunction relevant to neurodegenerative diseases.

biochemistry↗

bioRxiv2026

Does the sequence of a disordered protein encode small molecule binding paths?

Ligand binding to intrinsically disordered proteins resists description in terms of conventional binding pockets, yet it can be analysed as a dynamic process in which ligands move across transient surface interaction sites. Here we characterise a pathway-based representation in which ligand binding is described as a sequence of transitions between residue-defined microstates, enabling ligand-specific effects to be distinguished from intrinsic properties of the peptide conformational ensemble. Using all-atom molecular dynamics simulations of A{beta}42 and the C-terminal region of -synuclein in complex with chemically diverse small molecules, we construct transition matrices that encode ligand movement across the peptide surface and use Markov state models to identify dominant binding pathways and relative binding propensities. Pairwise enrichment-factor and AUC analyses reveal strong conservation of the highest-ranked pathways across chemically diverse ligands, with enrichment factors of 15-45 for the top-ranked states and AUC values typically [≥]0.75, well above random expectation. These dominant pathways are also preserved across changes in pH and temperature, whereas a urea control, included as a non-specific binder, shows reduced enrichment, indicating that ligands primarily modulate pathway weights rather than define the underlying network topology. Ensemble docking across chemically diverse libraries further supports the presence of recurrent ligand-accessible hotspots within the peptide conformational ensemble. Building on this framework, we apply a prospective screening pipeline to A{beta}42, combining MSM-derived hotspots with sequence-based Ligand-Transformer scoring and Gnina docking across 1.66 million compounds, to nominate 19 candidates for prospective experimental evaluation. Together, these results indicate that disordered protein sequences give rise to conformational ensembles that exhibit characteristic binding pathways for small molecules.

biophysics↗

bioRxiv2026

OmniBind: Proteome-Wide Promiscuity Predictions for Early-Stage Drug Screening

Off-target binding remains a leading cause of drug attrition, yet no method exists for rapidly quantifying small-molecule promiscuity across the human proteome. Here, we define promiscuity as the mean predicted binding affinity over 15,405 human proteins and derive a specificity score combining target affinity with this proteome-wide distribution. To make this assessment tractable at scale, we introduce OmniBind, a message-passing neural network that predicts promiscuity directly from a SMILES string at about a thousand compounds per second, several orders of magnitude faster than proteome-wide profiling. OmniBind promiscuity scores correlate with experimental binding data near assay reproducibility limits. Ranking candidates by specificity rather than affinity alone improves enrichment of approved drugs across all thresholds tested, an advantage robust to the choice of affinity predictor. OmniBind fills an unoccupied niche in the early-stage screening landscape as a fast, proteome-scale complement to traditional safety panels, with accuracy that will scale as the underlying affinity predictors continue to improve.

molecular biology↗

bioRxiv2025

Binding Paths: Describing Small Molecule Interactionswith Disordered Proteins via Markov State Models

Disordered proteins are challenging targets for drug discovery because they lack well-defined binding pockets. Although small molecules can form relatively stable complexes with disordered proteins, the highly dynamic nature of these proteins complicates the understanding of their binding mechanism. To address this problem, we analyze the binding of the small molecule 10074-G5 to A{beta}42, which results in the formation of a disordered complex. We describe the binding mechanism in terms of binding paths, which are stochastic trajectories along which small molecule diffuse across disordered protein surfaces, forming transient contacts with overlapping groups of residues. To identify these binding paths, we define them as realizations of a stochastic process defined by a Markov State Model (MSM). The MSM is built from distinct states of the disordered complex and their corresponding transition probabilities, enabling both the dynamic mapping of binding hotspots and targetable regions where static pockets cannot be defined-extending the notion of binding pockets to disordered systems - and the quantitative calculation of binding affinities. The visualization of the MSM via knowledge graphs provides an intuitive representation of the binding paths. We further validated this approach across four additional systems, comprising C-terminal -synuclein with three distinct small-molecule binders, and the full-length peptide (140 residues) with binder fasudil. By generalizing the concept of static binding pockets to dynamic binding paths, our approach rationalizes small-molecule recognition by disordered proteins and establishes a framework for identifying druggable regions on systems that were previously considered intractable, providing insights for future drug design programs.

biophysics↗