Biology subjects

Najmanovich, R.

Publications and source records attributed to Najmanovich, R..

10 recordsLinked to original sources

bioRxiv2025

NRGRank: Coarse-grained structurally-informed ultra-massive virtual screening

NRGRank is a coarse-grained structurally-informed virtual screening Python package with accuracy comparable to docking-based methodologies but up to 100-fold speed increase. NRGRank is based on a coarse-grained evaluation of pairwise atom-type pseudo-energy interactions that implicitly accounts for compound and side-chain flexibility as well as limited backbone movements. We compare NRGRank to docking-based virtual screening software Glide, Autodock Vina and DOCK 3.7 on the DUD-E virtual screening benchmark using enrichment factors at 1% (EF1). We observe broad variations of EF1 values across targets, structural models and methods. For apo form or AlphaFold2 models, out of a subset of 37 targets from DUD-E, NRGRank has better EF1 values than Glide for 12 and 13 targets respectively. Even in holo form, where the accuracy of classical docking software increases, NRGRank has better EF1 values than 13, 10 and 5 targets out of 37 compared to AutoDock Vina, DOCK 3.7 and Glide respectively. Comparing the rank of true binders in Glide and NRGRank shows that true binders ranked in the top 1% are complementary between methods irrespective of the target form (AlphaFold, apo or holo). That is, utilizing NRGRank detects binders that are missed by Glide (and presumably other methods), whereas those found by Glide are missed by NRGRank. Furthermore, we observe that most hits found by NRGRank within the top 50 predictions (4.38 {+/-} 5.49 hits on average for AF2 targets) remain once the top 1% of predictions are re-scored with Glide, but the hit rate within the top 50 predictions increases. NRGRank can evaluate one molecule in 0.3 s on average, enabling a modern laptop with 8 cores to screen 1,000,000 molecules in 24 hours - up to two orders of magnitude faster than the reported speed of DOCK 3.7, AutoDock Vina running on GPUs and Glide. NRGRank occupies a unique niche among tools for virtual screening being insensitive to structural inaccuracies but with comparable accuracy as state-of-the-art docking methods and fast as AI-based methods but without the dangers of overfitting as it is based on 780 pseudo-energy parameters. Combined with the fact that NRGRank does not require extensive or expensive computational resources or expert pre-processing of targets, it is unique in making high-performance ultra-massive virtual screening accessible to all.

biophysics↗

bioRxiv2025

NRGSuite-Qt: A PyMOL plugin for high-throughput virtual screening, molecular docking, normal-mode analysis, the study of molecular interactions and the detection of binding-site similarities

SummaryWe introduce NRGSuite-Qt, a PyMOL plugin that provides a comprehensive toolkit for protein modeling, virtual screening, normal mode analysis, and binding-site similarity calculations. Building on the original NRGSuite plugin for FlexAID, this updated version integrates five new functionalities: protein-protein and protein-ligand interaction analysis using Surfaces, ultra-massive virtual screening with NRGRank, binding-site similarity detection with IsoMIF, normal mode analysis using NRGTEN, and mutational studies through integration with the Modeller Suite. By merging these advanced tools into a cohesive platform, NRGSuite-Qt streamlines complex workflows and facilitates high-throughput computational studies within a single interface. Additionally, we benchmark a newer version of the Elastic Network Contact Model for normal mode analysis method ENCoM, utilizing the same 40 atom-type pairwise interaction matrix that is used in all other software. This version outperforms the default model in multiple benchmarking tests. AvailabilityThe Installation guide and tutorial is available at https://nrg-qt.readthedocs.io/ Contactrafael.najmanovich@umontreal.ca

biophysics↗

bioRxiv2024

Comprehensive Analysis of SARS-CoV-2 Spike Evolution: Epitope Classification and Immune Escape Prediction

The evolution of SARS-CoV-2, the virus responsible for the COVID-19 pandemic, has produced unprece-dented numbers of structures of the Spike protein. This study presents a comprehensive analysis of 1,560 published Spike protein structures, capturing most variants that emerged throughout the pandemic and covering diverse heteromerization and interacting complexes. We employ an interaction-energy informed geometric clustering to identify 14 epitopes characterized by their conformational specificity, shared interface with ACE2 binding, and glycosylation patterns. Our per-residue interaction evaluations accurately predict each residues role in antibody recognition and as well as experimental measurements of immune escape, showing strong correlations with DMS data, thus making it possible to predict the behaviour of future variants. We integrate the structural analysis with a longitudinal analysis of nearly 3 million viral sequences. This broad-ranging structural and longitudinal analysis provides insight into the effect of specific mutations on the energetics of interactions and dynamics of the SARS-CoV-2 Spike protein during the course of the pandemic. Specifically, with the emergence of widespread immunity, we observe an enthalpic trade-off in which mutations in the receptor binding motif (RBM) that promote immune escape also weaken the interaction with ACE2. Additionally, we also observe a second mechanism, that we call entropic trade-off, in which mutations outside of the RBM contribute to decrease the occupancy of the open state of SARS-CoV-2 Spike, thus also contributing to immune escape at the expense of ACE2 binding but without changes on the ACE2 binding interface. This work not only highlights the role of mutations across SARS-CoV-2 Spike variants but also reveals the complex interplay of evolutionary forces shaping the evolution of the SARS-CoV-2 Spike protein over the course of the pandemic.

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

Surfaces: A software to quantify and visualize interactions within and between proteins and ligands

Computational methods for the quantification and visualization of the relative contribution of molecular interactions to the stability of biomolecular structures and complexes are fundamental to understand, modulate and engineer biological processes. Here we present Surfaces, an easy to use, fast and customizable software for quantification and visualization of molecular interactions based on the calculation of surface areas in contact. Surfaces calculations shows equivalent levels of correlations with experimental data as computationally expensive methods based on molecular dynamics. All scripts are available at https://github.com/nataliateruel/Surfaces Documentation is available at https://surfaces-tutorial.readthedocs.io/en/latest/index.html

bioinformatics↗

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↗

bioRxiv2021

Computational analysis of the effect of SARS-CoV-2 variant Omicron Spike protein mutations on dynamics, ACE2 binding and propensity for immune escape

The recently reported Omicron (B.1.1.529) SARS-CoV-2 variant has a large number of mutations in the Spike (S) protein compared to previous variants. Here we evaluate the potential effect of Omicron S mutations on S protein dynamics and ACE2 binding as contributing factors to infectivity as well as propensity for immune escape. We define a consensus set of mutations from 77 sequences assigned as Omicron in GISAID as of November 25. We create structural models of the Omicron S protein in the open and closed states, as part of a complex with ACE2 and for each of 77 complexes of S bound to different antibodies with known structures. We have previously utilized Dynamical Signatures (DS) and the Vibrational Entropy Score (VDS) to evaluate the propensity of S variants to favour the open state. Here, we introduce the Binding Influence Score (BIS) to evaluate the influence of mutations on binding affinity based on the net gain or loss of interactions within the protein-protein interface. BIS shows excellent correlation with experimental data (Pearson correlation coefficient of 0.87) on individual mutations in the ACE2 interface for the Alpha, Beta, Gamma, Delta and Omicron variants combined. On the one hand, the DS of Omicron highly favours a more rigid open state and a more flexible closed state with the largest VDS of all variants to date, suggesting a large increase in the chances to interact with ACE2. On the other hand, the BIS shows that apart from N501Y, all other mutations in the interface reduce ACE2 binding affinity. VDS and BIS show opposing effects on the overall effectiveness of Omicron mutations to promote binding to ACE2 and therefore initiate infection. To evaluate the propensity for immune escape we calculated the net change of favourable and unfavourable interactions within each S-antibody interface. The net change of interactions shows a positive score (a net increase of favourable interactions and decrease of unfavourable ones) for 41 out of 77 antibodies, a nil score for 15 and a negative score for 21 antibodies. Therefore, in only 28% of S-antibody complexes (21/77) we predict some level of immune escape due to a weakening of the interactions with Omicron S. Considering that most antibody epitopes and the mutations are within the S-ACE2 interface our results suggest that mutations within the RBD of Omicron may give rise to only partial immune escape, which comes at the expense of reduced ACE2 binding affinity. However, this reduced ACE2 affinity appears to have been offset by increasing the occupancy of the open state of the Spike protein.

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↗