Biology subjects

Mailhot, O.

Publications and source records attributed to Mailhot, O..

9 recordsLinked to original sources

bioRxiv2026

Toward a Random Background for Ligand Optimization

Ligand optimization is central to drug discovery as hundreds of analogs might be designed and synthesized between an initial hit and a therapeutic candidate. The efficiency of this process is unclear, at least partly because there is no random background for optimization against which to compare. Such a random background might emerge from synthetically accessible but otherwise systematic random small substitutions across starting ligands, measuring likelihood of achieving a substantial improvement in affinity/potency or other property by any single perturbation. Recent literature and ligand-affinity/potency databases suggest that perhaps 10% of analogs with minor modifications improve upon a parents potency substantially (by [≥]10-fold), but this number is clouded by reporting bias, intentional improvement, and inter-group reproducibility. To begin to establish a background expectation for ligand optimization, we comprehensively and systematically modified 18 lead molecules across six targets with single atom changes; 257 compounds were synthesized. Unexpectedly, 11.2% of these random small perturbation analogs improved potency by [≥]10-fold over their parents. Conversely, these more potent analogs typically had worse in vitro pharmacokinetics (e.g. reduced metabolic stability, lower plasma free fraction). While it was possible to find analogs where the potency increase compensated for inferior exposure and half-life, resulting in more potent compounds in vivo, overall a frustrated landscape for ligand optimization is revealed. This study begins to establish a background expectation for ligand potency optimization and offers a simple strategy to do so. It also begins to quantify the challenges confronting the field in moving beyond in vitro potency.

pharmacology and toxicology↗

bioRxiv2025

Large scale prospective evaluation of co-folding across 557 Mac1-ligand complexes and three virtual screens

Accurate prediction of ligand-bound protein complexes and ranking them by affinity are central problems in drug discovery. While deep learning co-folding methods can help address these challenges, their evaluation has been hampered by the difficulties in assessing independence from training data and insufficiently large test sets. Here we test the ability of co-folding methods to predict the structures of 551 ligands, 489 of which are of sufficient quality for evaluation, bound to the SARS-CoV-2 NSP3 macrodomain (Mac1) that were determined after the training cut-off dates. AlphaFold3 (AF3), Boltz-2, and Chai-1 each reproduced >50% of the Mac1 ligand poses to better than 2 [A] RMSD of experiment. Despite the potential for co-folding to describe protein conformational changes that stabilize ligand binding, we did not find that common conformational rearrangements, including peptide flip and a large loop opening, were recapitulated by the co-folding prediction. For AF3 and Chai-1, ligand pose prediction confidence weakly, but significantly, tracked experimental potency, while DOCK3.7 energies were only weakly correlated. Boltz-2 affinity predictions showed the strongest correlation with measured potency and, after calibration, achieved lower mean absolute error than a baseline predictor. We next assessed whether co-folding scores could rescore docking hit-lists to distinguish true ligands from non-binders among hundreds of molecules prospectively experimentally tested against AmpC {beta}-lactamase, the dopamine D4 and the {sigma}2 receptors. AF3 ligand pose confidence values did not separate true ligands from high-scoring false-positives as effectively as docking scores or Boltz-2 affinity predictions did. Taken together, the modest, but independent correlations of docking score and co-folding confidence or affinity suggests that integrating physics-based and deep-learning approaches may help with hit prioritization and subsequent optimization in structure-based ligand discovery.

biophysics↗

bioRxiv2024

The impact of Library Size and Scale of Testing on Virtual Screening

Virtual libraries for ligand discovery have recently increased 10,000-fold, and this is thought to have improved hit rates and potencies from library docking. This idea has not, however, been experimentally tested in direct comparisons of larger-vs-smaller libraries. Meanwhile, though libraries have exploded, the scale of experimental testing has little changed, with often only dozens of high-ranked molecules investigated, making interpretation of hit rates and affinities uncertain. Accordingly, we docked a 1.7 billion molecule virtual library against the model enzyme AmpC {beta}-lactamase, testing 1,521 new molecules and comparing the results to the same screen with a library of 99 million molecules, where only 44 molecules were tested. Encouragingly, the larger screen outperformed the smaller one: hit rates improved by two-fold, more new scaffolds were discovered, and potency improved. Overall, 50-fold more inhibitors were found, supporting the idea that there are many more compounds to be discovered than are being tested. With so many compounds evaluated, we could ask how the results vary with number tested, sampling smaller sets at random from the 1521. Hit rates and affinities were highly variable when we only sampled dozens of molecules, and it was only when we included several hundred molecules that results converged. As docking scores improved, so too did the likelihood of a molecule binding; hit rates improved steadily with docking score, as did affinities. This also appeared true on re-analysis of large-scale results against the {sigma}2 and dopamine D4 receptors. It may be that as the scale of both the virtual libraries and their testing grows, not only are better ligands found but so too does our ability to rank them.

biophysics↗

bioRxiv2024

Understanding and predicting ligand efficacy in the mu-opioid receptor through quantitative dynamical analysis of complex structures

The {micro}-opioid receptor (MOR) is a G-protein coupled receptor involved in nociception and is the primary target of opioid drugs. Understanding the relationships between ligand structure, receptor dynamics, and efficacy in activating MOR is crucial for drug discovery and development. Here, we use coarse-grained normal mode analysis to predict ligand-induced changes in receptor dynamics with the Quantitative Dynamics Activity Relationships (QDAR) DynaSig-ML methodology, training a LASSO regression model on the entropic signatures (ES) computed from ligand-receptor complexes. We train and validate the methodology using a dataset of 179 MOR ligands with experimentally measured efficacies split into strickly chemically different cross-validation sets. By analyzing the coefficients of the ES LASSO model, we identified key residues involved in MOR activation, several of which have mutational data supporting their role in MOR activation. Additionally, we explored a contacts-only LASSO model based on ligand-protein interactions. While the model showed predictive power, it failed at predicting efficacy for ligands with low structural similarity to the training set, emphasizing the importance of receptor dynamics for predicting ligand-induced receptor activation. Moreover, the low computational cost of our approach, at 3 CPU seconds per ligand-receptor complex, opens the door to its application in large-scale virtual screening contexts. Our work contributes to a better understanding of dynamics-function relationships in the {micro}-opioid receptor and provides a framework for predicting ligand efficacy based on ligand-induced changes in receptor dynamics. Contactrafael.najmanovich@umontreal.ca

biophysics↗

bioRxiv2023

Ebola Virus VP35 Interacts Non-Covalently with Ubiquitin Chains to Promote Viral Replication Creating New Therapeutic Opportunities

Ebolavirus (EBOV) belongs to a family of highly pathogenic viruses that cause severe hemorrhagic fever in humans. EBOV replication requires the activity of the viral polymerase complex, which includes the co-factor and Interferon antagonist VP35. We previously showed that the covalent ubiquitination of VP35 promotes virus replication by regulating interactions with the polymerase complex. In addition, VP35 can also interact non-covalently with ubiquitin (Ub); however, the function of this interaction is unknown. Here, we report that VP35 interacts with free (unanchored) K63-linked polyUb chains. Ectopic expression of Isopeptidase T (USP5), which is known to degrade unanchored polyUb chains, reduced VP35 association with Ub and correlated with diminished polymerase activity in a minigenome assay. Using computational methods, we modeled the VP35-Ub non-covalent interacting complex, identified the VP35-Ub interacting surface and tested mutations to validate the interface. Docking simulations identified chemical compounds that can block VP35-Ub interactions leading to reduced viral polymerase activity that correlated with reduced replication of infectious EBOV. In conclusion, we identified a novel role of unanchored polyUb in regulating Ebola virus polymerase function and discovered compounds that have promising anti-Ebola virus activity. Significance StatementEbola virus infection can result in high mortality rates with extreme risk of person-to-person transmission. Sporadic outbreaks in Africa have resulted in thousands of fatal cases, highlighting that there is still insufficient knowledge to develop effective antiviral therapies. Like other viruses, Ebola utilizes the host machinery to replicate. Understanding how viral and host proteins interact can help identifying targets for the rational design of antiviral drugs. Here, we identified interactions between the cellular ubiquitin machinery and the Ebola virus polymerase cofactor protein VP35, which are important for efficient virus replication. We developed an approach to identify and block these virus-host interactions using small chemical compounds, which provides a useful tool to study functional molecular mechanisms and at the same time a potential approach to antiviral therapies.

immunology↗

bioRxiv2022

The DynaSig-ML Python package: automated learning of biomolecular dynamics-function relationships

SummaryThe DynaSig-ML ("Dynamical Signatures - Machine Learning") Python package allows the efficient, user-friendly exploration of 3D dynamics-function relationships in biomolecules, using datasets of experimental measures from large numbers of sequence variants. The DynaSig-ML package is built around the Elastic Network Contact Model (ENCoM), the first and only sequence-sensitive coarse-grained NMA model, which is used to generate the input Dynamical Signatures. Starting from in silico mutated structures, the whole pipeline can be run with just a few lines of Python and modest computational resources. The compute-intensive steps can also easily be parallelized in the case of either large biomolecules or vast amounts of sequence variants. As an example application, we use the DynaSig-ML package to predict the evolutionary fitness of the bacterial enzyme VIM-2 lactamase from deep mutational scan data. Availability and implementationDynaSig-ML is open source software available at https://github.com/gregorpatof/dynasigml_package Contactrafael.najmanovich@umontreal.ca

biophysics↗

bioRxiv2022

Sequence-sensitive elastic network captures dynamical features necessary for miR-125a maturation

The Elastic Network Contact Model (ENCoM) is a coarse-grained normal mode analysis (NMA) model unique in its all-atom sensitivity to the sequence of the studied macromolecule and thus to the effect of mutations. We adapted ENCoM to simulate the dynamics of ribonucleic acid (RNA) molecules, benchmarked its performance against other popular NMA models and used it to study the 3D structural dynamics of human microRNA miR-125a, leveraging high-throughput data on the maturation efficiency of over 26 000 sequence variants. We also introduce a novel way of using dynamical information from NMA to train multivariate linear regression models, with the purpose of highlighting the most salient contributions of dynamics to function. ENCoM has a similar performance profile on RNA than on proteins when compared to the Anisotropic Network Model (ANM), the most widely used coarse-grained NMA model; it has the advantage on predicting large-scale motions while ANM performs better on B-factors prediction. A stringent benchmark from the miR-125a maturation dataset, in which the training set contains no sequence information in common with the testing set, reveals that ENCoM is the only tested model able to capture signal beyond the sequence. This ability translates to better predictive power on a second benchmark in which sequence features are shared between the train and test sets. When training the linear regression model using all available data, the dynamical features identified as necessary for miR-125a maturation point to known patterns but also offer new insights into the biogenesis of microRNAs. Our novel approach combining NMA with multivariate linear regression is generalizable to any macromolecule for which relatively high-throughput mutational data is available. AvailabilityENCoM is open source software part of the NRGTEN package available at https://github.com/gregorpatof/nrgten_package Contactrafael.najmanovich@umontreal.ca, francois.major@umontreal.ca

biophysics↗

bioRxiv2020

Modelling conformational state dynamics and its role on infection for SARS-CoV-2 Spike protein variants

The SARS-CoV-2 Spike protein needs to be in an open-state conformation to interact with ACE2 as part of the viral entry mechanism. We utilise coarse-grained normal-mode analyses to model the dynamics of Spike and calculate transition probabilities between states for 17081 Spike variants. Our results correctly model an increase in open-state occupancy for the more infectious D614G via an increase in flexibility of the closed-state and decrease of flexibility of the open-state. We predict the same effect for several mutations on Glycine residues (404, 416, 504, 252) as well as residues K417, D467 and N501, including the N501Y mutation, explaining the higher infectivity of the B.1.1.7 and 501.V2 strains. This is, to our knowledge, the first use of normal-mode analysis to model conformational state transitions and the effect of mutations thereon. The specific mutations of Spike identified here may guide future studies to increase our understanding of SARS-CoV-2 infection mechanisms and guide public health in their surveillance efforts.

biophysics↗

bioRxiv2017

The Elastic Network Contact Model applied to RNA: enhanced accuracy for conformational space prediction

MotivationThe use of Normal Mode Analysis (NMA) methods to study both protein and nucleic acid dynamics is well established. However, the most widely used coarse-grained methods are based on backbone geometry alone and do not take into account the chemical nature of the residues. Elastic Network Contact Model (ENCoM) is a coarse-grained NMA method that includes a pairwise atom-type non-bonded interaction term, which makes it sensitive to the sequence of the studied molecule. We adapted ENCoM to simulate the dynamics of ribonucleic acid (RNA) molecules.\n\nResultsENCoM outperforms the most commonly used coarse-grained model on RNA, Anisotropic Network Model (ANM), in the prediction of b-factors, in the prediction of conformational change as measured by overlap (a measure of effective prediction of structural transitions) and in the prediction of structural variance from NMR ensembles. These benchmarks were derived from the set of all RNA structures available from the Protein Data Bank (PDB) and contain more total cases than previous studies applying NMA to RNA. We thus established ENCoM as an attractive tool for fast and accurate exploration of the conformational space of RNA molecules.\n\nAvailabilityENCoM is open source software available at https://github.com/NRGlab/ENCoM

bioinformatics↗