Biology subjects

Major, F.

Publications and source records attributed to Major, F..

7 recordsLinked to original sources

bioRxiv2023

Building RNA Backbone in Constant Time by Numerical Approximation

We present a numerical approximation method designed to swiftly create RNA ribose conformations in constant time. The methods parameterization relies on the atomic coordinates of a given base along with its two neighboring phosphate groups. Such a parameterization suits three dimensional modeling engines that determine the RNA conformational search space using base operations instead of backbone sampling. These engines consider phosphate groups as part of the base rigid bodies. Reconstructing ribose conformations result in less than 1 [A] of RMSD (root-mean-square deviation) compared to original conformations derived from high-resolution X-ray crystallographic structures. By incorporating this ribose construction method into MC-Sym, a well-established RNA three dimensional modeling software, we streamline the modeling process into two phases. This enhances the search algorithms speed and improves model consistency and precision. Additionally, we employed the method to pinpoint 27 irregular ribose stereoisomers in high-resolution RNA X-ray crystal structures.

bioinformatics↗

bioRxiv2023

Long-term severe hypoxia adaptation induces non-canonical EMT and a novel Wilms Tumor 1 (WT1) isoform.

The majority of cancer deaths are caused by solid tumors, where the four most prevalent cancers (breast, lung, colorectal and prostate) account for more than 60% of all cases (1). Tumor cell heterogeneity driven by variable cancer microenvironments, such as hypoxia, is a key determinant of therapeutic outcome. We developed a novel culture protocol, termed the Long-Term Hypoxia (LTHY) time course, to recapitulate the gradual development of severe hypoxia seen in vivo, to mimic conditions observed in primary tumors. Cells subjected to LTHY underwent a non-canonical epithelial to mesenchymal transition (EMT) based on miRNA and mRNA signatures as well as displayed EMT-like morphological changes. Concomitant to this, we report production of a novel truncated isoform of WT1 transcription factor (tWt1), a non-canonical EMT driver, with expression driven by a yet undescribed intronic promoter through hypoxia-responsive elements (HREs). We further demonstrated that tWt1 initiates translation from an intron-derived start codon, retains proper subcellular localization, DNA binding, and its human ortholog negatively predicts long-term patient survival. Our study demonstrates the importance of culture conditions that better mimic those observed in cancers, especially with regards to hypoxia, and identifies a novel isoform of WT1 which correlates with poor long-term survival in ovarian cancer.

molecular biology↗

bioRxiv2023

Automatic recognition of complementary strands: Lessons regarding machine learning abilities in RNA folding

Prediction of RNA secondary structure from single sequences still needs substantial improvements. The application of machine learning (ML) to this problem has become increasingly popular. However, ML algorithms are prone to overfitting, limiting the ability to learn more about the inherent mechanisms governing RNA folding. It is natural to use high-capacity models when solving such a difficult task, but poor generalization is expected when too few examples are available. Here, we report the relation between capacity and performance on a fundamental related problem: determining whether two sequences are fully complementary. Our analysis focused on the impact of model architecture and capacity as well as dataset size and nature on classification accuracy. We observed that low-capacity models are better suited for learning with mislabelled training examples, while large capacities improve the ability to generalize to structurally dissimilar data. It turns out that neural networks struggle to grasp the fundamental concept of base complementarity, especially in lengthwise extrapolation context. Given a more complex task like RNA folding, it comes as no surprise that the scarcity of usable examples hurdles the applicability of machine learning techniques to this field.

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↗

bioRxiv2020

Codon arrangement modulates MHC-I peptides presentation

MHC-I associated peptides (MAPs) play a central role in the elimination of virus-infected and neoplastic cells by CD8 T cells. However, accurately predicting the MAP repertoire remains difficult, because only a fraction of the transcriptome generates MAPs. In this study, we investigated whether codon arrangement (usage and placement) regulates MAP biogenesis. We developed an artificial neural network called Codon Arrangement MAP Predictor (CAMAP), predicting MAP presentation solely from mRNA sequences flanking the MAP-coding codons (MCCs), while excluding the MCC per se. CAMAP predictions were significantly more accurate when using original codon sequences than shuffled codon sequences which reflect amino acid usage. Furthermore, predictions were independent of mRNA expression and MAP binding affinity to MHC-I molecules and applied to several cell types and species. Combining MAP ligand scores, transcript expression level and CAMAP scores was particularly useful to increaser MAP prediction accuracy. Using an in vitro assay, we showed that varying the synonymous codons in the regions flanking the MCCs (without changing the amino acid sequence) resulted in significant modulation of MAP presentation at the cell surface. Taken together, our results demonstrate the role of codon arrangement in the regulation of MAP presentation and support integration of both translational and post-translational events in predictive algorithms to ameliorate modeling of the immunopeptidome. Author summaryMHC-I associated peptides (MAPs) are small fragments of intracellular proteins presented at the surface of cells and used by the immune system to detect and eliminate cancerous or virus-infected cells. While it is theoretically possible to predict which portions of the intracellular proteins will be naturally processed by the cells to ultimately reach the surface, current methodologies have prohibitively high false discovery rates. Here we introduce an artificial neural network called Codon Arrangement MAP Predictor (CAMAP) which integrates information from mRNA-to-protein translation to other factors regulating MAP biogenesis (e.g. MAP ligand score and transcript expression levels) to improve MAP prediction accuracy. While most MAP predictive approaches focus on MAP sequences per se, CAMAPs novelty is to analyze the MAP-flanking mRNA sequences, thereby providing completely independent information for MAP prediction. We show on several datasets that the integration of CAMAP scores with other known factors involved in MAP presentation (i.e. MAP ligand score and mRNA expression) significantly improves MAP prediction accuracy, and further validate CAMAP learned features using an in-vitro assay. These findings may have major implications for the design of vaccines against cancers and viruses, and in times of pandemics could accelerate the identification of relevant MAPs of viral origins.

bioinformatics↗

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↗