Biology subjects

Frappier, V.

Publications and source records attributed to Frappier, V..

5 recordsLinked to original sources

bioRxiv2025

Functional and structural characterization of a combination of pan-sarbecovirus antibodies with potent antiviral activity

Sarbecoviruses, a subgenus of coronaviruses, include strains with zoonotic spillover risk as exemplified by recent outbreaks (SARS-CoV-1, SARS-CoV-2). Monoclonal antibodies targeting conserved spike protein regions (RBD class 4, S2 fusion machinery) exhibit broad sarbecovirus neutralization, but their utility has been impacted by immune selection leading to escape and suboptimal neutralization. Using structure-conditioned machine-learning protein design we optimized two broadly neutralizing sarbecovirus antibodies to rescue the activity of a class 4 anti-RBD antibody against Omicron variants and improve the neutralization profile of an anti-S2 stem antibody, resulting in the identification of the lead candidates PRO-37587 and GB-0669. Both antibodies displayed remarkable resilience to SARS-CoV-2 evolution. Structural analyses elucidated the mechanistic basis of their broad neutralization profiles. In combination, these antibodies exhibited improved SARS-CoV-2 neutralization in vitro and in vivo and a higher barrier to resistance. These findings support further evaluation of such antibody combination as countermeasure against current and emerging sarbecoviruses.

microbiology↗

bioRxiv2022

Illuminating protein space with a programmable generative model

Three billion years of evolution have produced a tremendous diversity of protein molecules, and yet the full potential of this molecular class is likely far greater. Accessing this potential has been challenging for computation and experiments because the space of possible protein molecules is much larger than the space of those likely to host function. Here we introduce Chroma, a generative model for proteins and protein complexes that can directly sample novel protein structures and sequences and that can be conditioned to steer the generative process towards desired properties and functions. To enable this, we introduce a diffusion process that respects the conformational statistics of polymer ensembles, an efficient neural architecture for molecular systems based on random graph neural networks that enables long-range reasoning with sub-quadratic scaling, equivariant layers for efficiently synthesizing 3D structures of proteins from predicted inter-residue geometries, and a general low-temperature sampling algorithm for diffusion models. We suggest that Chroma can effectively realize protein design as Bayesian inference under external constraints, which can involve symmetries, substructure, shape, semantics, and even natural language prompts. With this unified approach, we hope to accelerate the prospect of programming protein matter for human health, materials science, and synthetic biology.

bioinformatics↗

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↗

bioRxiv2018

Tertiary structural motif sequence statistics enable facile prediction and design of peptides that bind anti-apoptotic Bfl-1 and Mcl-1

Understanding the relationship between protein sequence and structure well enough to rationally design novel proteins or protein complexes is a longstanding goal in protein science. The Protein Data Bank (PDB) is a key resource for defining sequence-structure relationships that has supported the development of critical resources such as rotamer libraries and backbone torsional statistics that quantify the probabilities of protein sequences adopting different structures. Here, we show that well-defined, non-contiguous structural motifs (TERMs) in the PDB can also provide rich information useful for protein-peptide interaction prediction and design. Specifically, we show that it is possible to rapidly predict the binding energies of peptides to Bcl-2 family proteins as accurately as can be done with widely used structure-based tools, without explicit atomistic modeling. One benefit of a TERM-based approach is that prediction performance is less sensitive to the details of the input structure than are methods that evaluate energies using precise atomic coordinates. We show that protein design using TERM energies (dTERMen) can generate highly novel and diverse peptides to target anti-apoptotic proteins Bfl-1 and Mcl-1. 15 of 17 peptides designed using dTERMen bound tightly to their intended targets, and these peptides have just 15 - 38% sequence identity to any known native Bcl-2 family protein ligand. High-resolution structures of four designed peptides bound to their targets provided opportunities to analyze strengths and limitations of this approach. Dramatic success designing peptides using dTERMen, which comprised going from input structure to experimental validation of high-affinity binders in approximately one month, provides strong motivation for further developing TERM-based approaches to design.

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↗