Biology subjects

Rivoire, O.

Publications and source records attributed to Rivoire, O..

8 recordsLinked to original sources

bioRxiv2026

Improved inference of multiscale sequence statistics in generative protein models

High dimensionality and multiscale statistical structure are pervasive features of biological data, posing fundamental challenges for modeling. Because model inference generally proceeds with far fewer data than parameters, statistical patterns across scales are often unevenly represented. Protein sequences provide a paradigmatic example: statistics across homologs are inherently multiscale, displaying collective correlations among conserved residue sectors that encode function, alongside localized correlations corresponding to physical contacts outside these sectors. Standard regularization strategies used to mitigate undersampling during model inference have been shown to capture these patterns unevenly, a bias that compromises generative models of protein sequences by limiting their ability to produce both functional and diverse proteins. This limitation is exemplified by Boltzmann Machine-based generative models, which so far have required post hoc corrections to recover functionality, at the cost of reduced sequence diversity and novelty. Here, we introduce the stochastic Boltzmann Machine (sBM), a new regularization strategy that more accurately captures different correlation scales. Through analyses of theoretical models with known ground-truth parameters and experiments on the chorismate mutase family, we show that sBM effectively mitigates distortions in the estimation of model parameters, enabling the generation of functional sequences with greater diversity and without the need for post hoc corrections. These results advance the inference of generative models that more faithfully reflect the evolutionary constraints shaping protein sequences.

systems biology↗

bioRxiv2023

Inference and design of antibody specificity: from experiments to models and back

Exquisite binding specificity is essential for many protein functions but is difficult to engineer. Many biotechnological or biomedical applications require the discrimination of very similar ligands, which poses the challenge of designing protein sequences with highly specific binding profiles. Current methods for generating specific binders rely on in vitro selection experiments, but these have limitations in terms of library size and control over specificity profiles. We present a multi-stage approach that overcomes these limitations by combining high-throughput sequencing of phage display experiments with machine learning and biophysical modeling. Our models predict the binding profiles of antibodies against multiple ligands and generate antibody sequences with desired specificity profiles. The approach involves the identification of different binding modes, each associated with a particular ligand against which the antibodies are either selected or not. We demonstrate that the model successfully disentangles these modes, even when they are associated with chemically very similar ligands. Additionally, we demonstrate and validate experimentally the computational design of antibodies with customized specificity profiles, either with specific high affinity for a particular target ligand, or with cross-specificity for multiple target ligands. Overall, our results showcase the potential of leveraging a biophysical model learned from selections against multiple ligands to design proteins with tailored specificity, with applications to protein engineering extending beyond the design of antibodies.

biophysics↗

bioRxiv2023

Modulation of kinetic barriers under chemical constraints: a role for allostery in enzyme catalysis

The role played by conformational changes in enzyme catalysis is controversial. In addition to examining specific enzymes, studying formal models can help identify the conditions under which conformational changes promote catalysis. Here, we present a model demonstrating how conformational changes can break a generic trade-off due to the conflicting requirements of successive steps in catalytic cycles, namely high specificity for the transition state to accelerate the chemical transformation and low affinity for the products to favor their release. The mechanism by which the trade-off is broken is a transition between conformations with different affinities for the substrate. The role of the effector that induces the transition is played by a substrate "handle", a part of the substrate that is not chemically transformed but whose interaction with the enzyme is nevertheless essential to rapidly complete the catalytic cycle. A key element of the model is the formalization of the constraints causing the trade-off that the presence of multiple states breaks, which we attribute to the strong chemical similarity between successive reaction states - substrates, transition states and products. For the sake of clarity, we present our model for irreversible one-step unimolecular reactions. In this context, we demonstrate how the different forms that chemical similarities between reaction states can take impose limits on the overall catalytic turnover. We first analyze catalysts without internal degrees of freedom, and then show how two-state catalysts can overcome their limitations. Our results recapitulate previous proposals concerning the role of conformational changes and substrate handles in a formalism that makes explicit the constraints that elicit these features. In addition, our approach establishes links with studies in the field of heterogeneous catalysis, where the same trade-offs are observed and where overcoming them is a well-recognized challenge.

biophysics↗

bioRxiv2022

How sexual dimorphism in phenotypic plasticity may evolve

Phenotypes are partly shaped by the environment, which can impact both short-term adaptation and long-term evolution. In dioecious species, the two sexes may exhibit different degrees of phenotypic plasticity and theoretical models indicate that such differences may confer an adaptive advantage when the population is subject to directional selection, either because of a systematically varying environment or of a load of deleterious mutations. The effect stems from the fundamental asymmetry between the two sexes: female fertility is more limited than male fertility. Whether this asymmetry is sufficient for sexual dimorphism in phenotypic plasticity to evolve is, however, not obvious. Here, we show that even in conditions where it provides an adaptive advantage, dimorphic phenotypic plasticity may be evolutionarily unstable due to sexual selection. This is the case, in particular, for panmictic populations where mating partners are formed at random. However, we show that the effects of sexual selection can be counteracted when mating occurs within groups of related individuals. Under this condition, sexual dimorphism in phenotypic plasticity can not only evolve but offset the twofold cost of males. We demonstrate these points with a simple mathematical model through a combination of analytical and numerical results.

evolutionary biology↗

bioRxiv2022

Assessing in vivo the impact of gene context on transcription through DNA supercoiling

Gene context can have significant impact on gene expression but is currently not integrated in quantitative models of gene regulation despite known biophysical principles and quantitative in vitro measurements. Conceptually, the simplest gene context consists of a single gene framed by two topological barriers, known as the twin transcriptional-loop model, which illustrates the interplay between transcription and DNA supercoiling. In vivo, DNA supercoiling is additionally modulated by topoisomerases, whose modus operandi remains to be quantified. Here, we bridge the gap between theory and in vivo properties by realizing in Escherichia coli the twin transcriptional-loop model and by measuring how gene expression varies with promoters and distances to the topological barriers. We find that gene expression depends on the distance to the upstream barrier but not to the downstream barrier, with a promoter-dependent intensity. We rationalize these findings with a first-principle biophysical model of DNA transcription. Our results are explained if TopoI and gyrase both act specifically, respectively upstream and downstream of the gene, with antagonistic effects of TopoI, which can repress initiation while facilitating elongation. Altogether, our work sets the foundations for a systematic and quantitative description of the impact of gene context on gene regulation.

biophysics↗

bioRxiv2021

Undersampling and the inference of coevolution in proteins

Protein structure, function, and evolution depend on local and collective epistatic interactions between amino acids. A powerful approach to defining these interactions is to construct models of couplings between amino acids that reproduce the empirical statistics (frequencies and correlations) observed in sequences comprising a protein family. The top couplings are then interpreted. Here, we show that as currently implemented, this inference is always biased, a problem that fundamentally arises from the distinct scales at which epistasis occurs in proteins in the context of limited sampling. We show that these issues explain the ability of current approaches to predict tertiary contacts between amino acids and the inability to obviously expose larger networks of functionally-relevant, collectively evolving residues called sectors. This work provides a necessary foundation for more deeply understanding and improving evolution-based models of proteins.

biophysics↗

bioRxiv2019

Parameters and determinants of responses to selection in antibody libraries

Antibody repertoires contain binders to nearly any target antigen. The sequences of these antibodies differ mostly at few sites located on the surface of a scaffold that itself consists of much less varied amino acids. What is the impact of this scaffold on the response to selection of a repertoire? To gauge this impact, we carried out quantitative phage display experiments with three antibody libraries based on distinct scaffolds harboring the same diversity at randomized sites, which we selected for binding to four arbitrary targets. We first show that the response to selection of an antibody library is captured by a simple and measurable parameter with direct physical and information-theoretic interpretations. Second, we identify a major determinant of this parameter which is encoded in the scaffold, its degree of evolutionary maturation. Antibodies undergo an accelerated evolutionary process, called affinity maturation, to improve their affinity to a given target antigen as part of the adaptive immune response. We find that libraries of antibodies built around such maturated scaffolds have a lower response to selection to other arbitrary targets than libraries built around naive scaffolds of germline origin. Our results are a first step towards quantifying and controlling the evolutionary potential of biomolecules.

biophysics↗

bioRxiv2017

An evolutionary module in central metabolism

Metabolic enzyme function and evolution is influenced by the larger context of a biochemical pathway - deleterious mutations or perturbations in one enzyme can often be compensated by mutations to others. To explore strategies for mapping adaptive dependencies between enzymes, we used a combination of comparative genomics and experiments to examine interactions with the model metabolic enzyme Dihydrofolate Reductase (DHFR). Biochemically, DHFR shares a metabolic intermediate with numerous folate metabolic enzymes. In contrast, comparative genomics analyses of synteny and gene co-occurrence indicate a sparse pattern of evolutionary couplings in which DHFR is coupled to the enzyme thymidylate synthase (TYMS), but is relatively independent from the rest of folate metabolism. To test this apparent modularity, we used quantitative growth rate measurements and forward evolution in E. coli to demonstrate that the two enzymes are coupled to one another, and can adapt independently from the remainder of the genome. Mechanistically, the coupling between DHFR and TYMS is driven by a constraint wherein TYMS activity must not greatly exceed that of DHFR - both to avoid depletion of reduced folates and prevent accumulation of the metabolic intermediate dihydrofolate. Extending our comparative genomics analyses genome-wide reveals over 200 gene pairs with statistical signatures similar to DHFR/TYMS, suggesting the possibility that cellular pathways might be decomposed into small adaptive units.

systems biology↗