Biology subjects

Cocco, S.

Publications and source records attributed to Cocco, S..

At least 19 recordsLinked to original sources

bioRxiv2026

A maximum entropy model for m6A prediction: from human transcripts to influenza A viral segments

N6-methyladenosine (m6A) is the most frequent mRNA modification in eukaryotic cells and is involved in the regulation of gene expression. Since viruses can hijack the host machinery to perform this chemical editing, m6A is also involved in viral replication and innate immune system activation. Nowadays only few m6A viral maps are available, making complementary methods crucial to accelerate the investigation of these mechanisms. Here we propose MEMAR, a Maximum Entropy model, to predict the methylation frequency, depending on the eight-mers nucleotide motif surrounding the central adenosine. MEMAR is a fast, simple and modular model: it can be trained on human methylation maps and refined including the methylation level per transcript and the position of motifs relatively to the coding regions. This modularity allows strong adaptability, leading MEMAR to better perform over influenza A segments than the mammalian state-of-the-art DeepSRAMP method. MEMAR generalization potential is assessed on i) human transcripts from GLORI and miCLIP experiments ii) HEK293T and HeLa cell lines iii) positive and negative influenza A viral segments and iv) altered-methylation experiments. A two-state dynamical model between m6A writers and erasers explains MEMAR predictive power as a function of the global methylation percentage, and allows us to interpret anomalies in altered conditions through variations of methylation or de-methylation binding constants.

biophysics↗

bioRxiv2026

Computational Design of Optimal Sequences for Targeted Hypermutagenesis Using Recombination-Coupled Diversity-Generating Retroelements

Diversity-generating retroelements (DGRs) are natural systems that accelerate evolution via targeted hypermutation at adenines. We previously developed DGRec, a system combining DGRs and recombineering for programmable mutagenesis in Escherichia coli. We here address two important issues with DGRec: the dependence of mutagenesis efficiency on the dgrRNA secondary structure and the variability of the reverse-transcription biases with sequence context and position. First, we introduce and validate a method to recode non-functional templates, i.e. with low mutagenesis efficiency, into highly functional ones through synonymous mutations. Second, we develop a Long Short-Term Memory (LSTM) model to predict DGRec mutational profiles for any given template sequence. By integrating this LSTM model with our recoding method, we establish a comprehensive workflow for customized directed evolution, enabling researchers to precisely fine-tune DGRec in vivo mutagenesis to their engineering needs.

bioinformatics↗

bioRxiv2026

Least Component Analysis reveals ecological constraints in microbial communities

How microbial communities maintain robust and reproducible ecological functions despite their extraordinary taxonomic diversity remains an open question. Here we show that functional organization in microbial communities can be uncovered by repurposing Principal Component Analysis to focus on directions of lowest variance in taxon abundance data, rather than maximal variance. These least-variance components are statistically significant and correspond to ecological constraints on taxon abundances that are consistently fulfilled across samples. Using consumer-resource models, we show that these constraints arise from resource-mediated interactions and express biomass conservation, effectively grouping taxa into producer and consumer guilds. We validate this interpretation in simulated communities and experimental systems under competition and cross-feeding. Finally, we show that low-variance structure is ubiquitous in natural microbial communities and reveals a sparse network of taxa with disproportionate influence on community structure. Together, our results establish low-variance components as indicators of ecological constraints linking taxonomic diversity to functional organization.

ecology↗

bioRxiv2026

Evolutionary Advantage of Diversity-Generating Retroelements in Switching Environments

Diversity-Generating Retroelements (DGRs) create rapid, targeted variation within specific genomic regions in phages and bacteria. They operate through stochastic retro-transcription of a template region (TR) into a variable region (VR), which typically encodes ligand-binding proteins. Despite their prevalence, the conditions under which maintaining such hypermutating system is favorable remain unclear. Here we introduce a two-timescale framework separating fast VR diversification from slow TR evolution, allowing the dynamics of DGR-controlled loci to be analytically understood. Combining data analysis and analytical calculations we quantity the fitness gain provided by the diversification mechanism of DGR with respect to standard mutagenesis. Our framework accounts for observed patterns of DGR activity in human-gut Bacteroides and clarifies when constitutive DGR activation is evolutionarily favored.

evolutionary biology↗

bioRxiv2025

Optimal Maturation Protocols for High-Affinity Antibody Targets: A Path-Integral Approach

Affinity maturation is a stochastic evolutionary process allowing the adaptive immune system to produce B-cells capable of recognizing antigenic molecules. One of the main factors influencing the quality of the maturation outcome, quantified by the affinity of the produced antibodies to the antigen, is the time-course of antigen availability. In this paper, we introduce a stochastic model for affinity maturation, calibrated against in vivo lineage dynamics and deep mutational scanning data, and address the following question: what is the best antigen-concentration protocol maximizing the probability to reach a target affinity value at the end of the process? We introduce a path-integral formalism to identify the maturation trajectories of the rare clones ending at the desired target affinity, estimate their probabilities, and maximize them over the antigen-concentration temporal profile. The theoretical optimal concentration protocol is approximated by a discrete three-injection schedule; we show that such temporal modulation of selection pressure outperforms constant-dosage regimes.

bioinformatics↗

bioRxiv2025

Design and experimental characterization of specificity-switching mutational paths of WW domains

Specific interactions between proteins and other biomolecules are ubiquitous in cellular processes.How specificity is encoded in the protein sequence and can be modified through a minimal set of concerted mutations is a complex issue. In this work, we focus on the WW protein domain, whose variants specifically bind to different classes of proline-rich peptides. Combining unsupervised learning of homologous WW sequence data with Restricted Boltzmann Machines (RBM) and path-sampling methods, we design mutational paths of putative WW domains interpolating between two natural WW domains with either distinct or similar specificities. Sequences along the designed paths are then experimentally validated with high-throughput in-vitro binding assays against 3 peptides of different classes. The vast majority (93%) of intermediate sequences along the designed paths are responsive to the initial or/and final peptides. On the contrary, domains along scrambled paths, in which the same mutations are introduced in random order are not functional, emphasizing how successful design crucially depends on the ability to model epistatic interactions. Switch in specificity between classes I and IV, whose representative peptides bind to different pockets on the WW domain takes place through intermediates displaying some level of binding cross-reactivity with the tested peptides, contrary to the transition from class I to II, which are associated with the same binding pocket. Lastly, we show that the RBM paths share a high identity with internal nodes obtained from ancestral sequence reconstruction based on the seed WW domains.

bioengineering↗

bioRxiv2025

Constrained Evolutionary Funnels Shape Viral Immune Escape

Understanding how viral proteins adapt under immune pressure while preserving viability is crucial for anticipating antibody-resistant variants. We present a probabilistic framework that predicts viral escape trajectories and shows that immune evasion is channeled into a small set of viable "escape funnels" within the vast mutational space. These escape funnels arise from the combined constraints of protein viability and antibody escape, modeled using a generative model trained on homologs and deep mutational scanning data. We derive a mean-field approximation of evolutionary path ensembles, enabling us to quantify both the fitness and entropy of escape routes. Applied to SARS-CoV-2 receptor binding domain, our framework reveals convergent evolution patterns, predicts mutation sites in variants of concern, and explains differences in antibody-cocktail effectiveness. In particular, cocktails with de-correlated escape profiles slow viral adaptation by forcing longer, higher-cost escape paths. SIGNIFICANCEViruses evolve to evade our immune defenses, but with constraints. Like navigating a minefield, each step toward immune escape comes at the potential cost of structural stability and functionality. We show that despite the vast mutational space, immune escape is funneled into a small set of predictable pathways. Using a statistical-physics model grounded in antibody experiments and SARS-CoV-2 epidemiology data, we identify these escape funnels--enabling therapies designed to block them before they are ever used.

bioinformatics↗

bioRxiv2025

Speed Vascular Patterns in the Spatial Navigation System

The hippocampal formation is central to spatial navigation, hosting neurons that encode position, direction, and speed. Yet, the brain-wide vascular dynamics supporting these processes remain poorly understood, especially during naturalistic behaviors. Here, we adapted functional ultrasound (fUS) imaging to examine how cerebral blood volume (CBV) changes relate to behavioral parameters in freely moving rats. High-resolution imaging of hippocampal-parahippocampal regions during open-field exploration reveals strong correlations between CBV dynamics and animal speed, with distinct regional activation patterns and temporal delays. Lagged general linear modeling uncovers information flow from the thalamus to parahippocampal regions, including the medial entorhinal cortex, and to hippocampal subfields (dentate gyrus, CA1-CA3), consistent with a hierarchical processing framework. The analysis also links CBV with angular head speed and the dorsal thalamus. Decoding analyses show that CBV signals not only encode speed precisely but also capture spatial features like proximity to walls and corners, even when univariate analyses do not. This decoding remains robust across animals, underscoring the universality of speed encoding in vascular dynamics. We also identify slow CBV oscillations in the hippocampus aligned with minute-scale speed fluctuations, suggesting a neurovascular signature of exploratory behavior. These findings reveal a hemodynamic signature of speed representation in the navigation system, arising from energy demands in a continuous attractor network model for path integration, where population activity and synaptic currents increase quadratically with animal speed as both peak firing rates and neuronal recruitment scale linearly with animal speed. Moreover, they highlight functional ultrasound imaging as a powerful approach for probing the hemodynamic basis of navigation.

neuroscience↗

bioRxiv2025

Generative model of SARS-CoV-2 variants under functional and immune pressure unveils viral escape potential and antibody resilience

The evolutionary trajectory of SARS-CoV-2 is shaped by competing pressures for ACE2 binding, viability, and escape from neutralizing antibodies targeting its receptor-binding domain (RBD). Here, we present EscapeMap, a modular framework that enables prediction and design of variants escaping antibodies. EscapeMap integrates deep mutational scanning data for ACE2 and 31 monoclonal antibodies with a generative sequence model trained on pre-pandemic Coronaviridae. To experimentally probe escape potential, we designed RBD variants under pressure from four clinically relevant antibodies (SA55, S2E12, S309, VIR-7229). Among these designs, bearing up to 21 mutations from wildtype, 50% expressed as stable proteins. Binding assays confirm that S309 and VIR-7229 retain recognition across diverse mutation combinations. EscapeMap accurately forecasts which antibodies are vulnerable to escape by our designed sequences. Finally, by identifying correlated escape routes, we predict and experimentally verify, antibody combinations less prone to simultaneous escape, offering a quantitative basis for guiding therapeutic strategies.

bioinformatics↗

bioRxiv2024

Linking Brain and Behavior States in Zebrafish Larvae Locomotion using Hidden Markov Models

Understanding how collective neuronal activity in the brain orchestrates behavior is a central question in integrative neuroscience. Addressing this question requires models that can offer a unified interpretation of multimodal data. In this study, we jointly examine video-recordings of zebrafish larvae freely exploring their environment and calcium imaging of the Anterior Rhombencephalic Turning Region (ARTR) circuit, which is known to control swimming orientation, recorded in vivo under tethered conditions. We show that both behavioral and neural data can be accurately modeled using a Hidden Markov Model (HMM) with three hidden states. In the context of behavior, the hidden states correspond to leftward, rightward, and forward swimming. The HMM robustly captures the key statistical features of the swimming motion, including bout-type persistence and its dependence on bath temperature, while also revealing inter-individual phenotypic variability. For neural data, the three states correspond to left- and right-lateral activation of the ARTR circuit, known to govern the selection of left vs. right reorientation, and a balanced state, which likely corresponds to the behavioral forward state. To further unify the two analysis, we exploit the generative nature of the HMM, using the neural sequences to generate synthetic trajectories whose statistical properties are similar to the behavioral data. Overall, this work demonstrates how state-space models can be used to link neuronal and behavioral data, providing insights into the mechanisms of self-generated action.

animal behavior and cognition↗

bioRxiv2024

Restoring data balance via generative models of T-cell receptors for antigen-binding prediction

Unveiling specificity in T cell recognition of antigens represents a major step to understand the immune system response. Many supervised machine learning approaches have been designed to build sequence-based predictive models of such specificity using binding and non-binding receptor-antigen data. Due to the scarcity of known specific T cell receptors for each antigen compared to the abundance of non-specific ones, available datasets are heavily imbalanced and make the goal of achieving solid predictive performances very challenging. Here, we propose to restore data balance through data augmentation using generative unsupervised models. We then use these augmented data to train supervised models for prediction of peptide-specific T cell receptors, or binding pairs of peptide and T cell receptor sequences. We show that our pipeline yields increased performance in prediction tasks of T cell receptors specificity. More broadly, our pipeline provides a general framework that could be used to restore balance in other computational problems involving biological sequence data.

bioinformatics↗

bioRxiv2023

Deciphering the code of viral-host adaptation through maximum entropy models

Understanding how the genome of a virus evolves depending on the host it infects is an important question that challenges our knowledge about several mechanisms of host-pathogen interactions, including mutational signatures, innate immunity, and codon optimization. A key facet of this general topic is the study of viral genome evolution after a host-jumping event, a topic which has experienced a surge in interest due to the fight against emerging pathogens such as SARS-CoV-2. In this work, we tackle this question by introducing a new method to learn Maximum Entropy Nucleotide Bias models (MENB) reflecting single, di- and tri-nucleotide usage, which can be trained from viral sequences that infect a given host. We show that both the viral family and the host leave a fingerprint in nucleotide usages which MENB models decode. When the task is to classify both the host and the viral family for a sequence of unknown viral origin MENB models outperform state of the art methods based on deep neural networks. We further demonstrate the generative properties of the proposed framework, presenting an example where we change the nucleotide composition of the 1918 H1N1 Influenza A sequence without changing its protein sequence, while manipulating the nucleotide usage, by diminishing its CpG content. Finally we consider two well-known cases of zoonotic jumps, for the H1N1 Influenza A and for the SARS-CoV-2 viruses, and show that our method can be used to track the adaptation to the new host and to shed light on the more relevant selective pressures which have acted on motif usage during this process. Our work has wide-ranging applications, including integration into metagenomic studies to identify hosts for diverse viruses, surveillance of emerging pathogens, prediction of synonymous mutations that effect immunogenicity during viral evolution in a new host, and the estimation of putative evolutionary ages for viral sequences in similar scenarios. Additionally, the computational frame-work introduced here can be used to assist vaccine design by tuning motif usage with fine-grained control. Author summaryIn our research, we delved into the fascinating world of viruses and their genetic changes when they jump from one host to another, a critical topic in the study of emerging pathogens. We developed a novel computational method to capture how viruses change the nucleotide usage of their genes when they infect different hosts. We found that viruses from various families have unique strategies for tuning their nucleotide usage when they infect the same host. Our model could accurately pinpoint which host a viral sequence came from, even when the sequence was vastly different from the ones we trained on. We demonstrated the power of our method by altering the nucleotide usage of an RNA sequence without affecting the protein it encodes, providing a proof-of-concept of a method that can be used to design better RNA vaccines or to fine-tune other nucleic acid-based therapies. Moreover the framework we introduce can help tracking emerging pathogens, predicting synonymous mutations in the adaptation to a new host and estimating how long viral sequences have been evolving in it. Overall, our work sheds light on the intricate interactions between viruses and their hosts.

evolutionary biology↗

bioRxiv2023

Minimal epistatic networks from integrated sequence and mutational protein data.

Predicting the functional effects of mutations to a wild-type protein sequence is a major computational challenge. We introduce here a computationally efficient procedure to identify the few, most informative epistatic links between residues in a protein, integrating sequence data and functional measurements with mutational scans. Our approach shows performances comparable to state-of-the-art deep networks, while requiring much less parameters and being hence much more interpretable. The selected network links mostly focus on the protein functional sites, adapt to the in vitro or in vivo function experimentally tested, and are not necessary related to structural contacts.

bioinformatics↗

bioRxiv2023

Designing molecular RNA switches with Restricted Boltzmann machines

Riboswitches are structured allosteric RNA molecules that change conformation upon metabolite binding, triggering a regulatory response. Here we focus on the de novo design of riboswitch-like aptamers, the core part of the riboswitch undergoing structural changes. We use Restricted Boltzmann machines (RBM) to learn generative models from homologous sequence data. We first verify, on four different riboswitch families, that RBM-generated sequences correctly capture the conservation, covariation and diversity of natural aptamers. The RBM model is then used to design new SAM-I riboswitch aptamers. To experimentally validate the properties of the structural switch in designed molecules, we resort to chemical probing (SHAPE and DMS), and develop a tailored analysis pipeline adequate for high-throughput tests of diverse sequences. We probe a total of 476 RBM-designed and 201 natural sequences. Designed molecules with high RBM scores, with 20% to 40% divergence from any natural sequence, display{approx} 30% success rate of responding to SAM with a structural switch similar to their natural counterparts. We show how the capability of the designed molecules to switch conformation is connected to fine energetic features of their structural components.

bioinformatics↗

bioRxiv2023

Computational design of novel Cas9 PAM-interacting domains using evolution-based modelling and structural quality assessment

We present here an approach to protein design that enables to leverage (i) scarce functional information such as experimental data (ii) evolutionary information learned from a natural sequence variants and (iii) physics-grounded modeling. Using a Restricted Boltzmann Machine (RBM), we learned a sequence model of a protein family. We use semi-supervision to leverage available functional information during the RBM training. We then propose a strategy to explore the protein representation space that can be informed by external models such as an empirical force field method (FoldX). This method was applied to a domain of the Cas9 protein responsible for recognition of a short DNA motif. We experimentally assessed the functionality of 71 variants that were generated to explore a range of RBM and FoldX energies. We show how a combination of functional, structural and evolutionary information can identify functional variants with high accuracy. Sequences with as many as 50 differences (20% of the protein domain) to the wild-type retained functionality. Overall, 21/71 sequences designed with our method were functional. Interestingly, 6/71 sequences showed an improved activity in comparison with the original wild-type protein sequence. These results demonstrate the interest in further exploring the synergies between machine-learning of protein sequence representations and physics grounded modeling strategies informed by structural information.

bioinformatics↗

bioRxiv2022

Infer global, predict local: quantity-quality trade-off in protein fitness predictions from sequence data

Predicting the effects of mutations on protein function is an important issue in evolutionary biology and biomedical applications. Computational approaches, ranging from graphical models to deep-learning architectures, can capture the statistical properties of sequence data and predict the outcome of high-throughput mutagenesis experiments probing the fitness landscape around some wild-type protein. However, how the complexity of the models and the characteristics of the data combine to determine the predictive performance remains unclear. Here, based on a theoretical analysis of the prediction error, we propose descriptors of the sequence data, characterizing their quantity and quality relative to the model. Our theoretical framework identifies a trade-off between these two quantities, and determines the optimal subset of data for the prediction task, showing that simple models can outperform complex ones when inferred from adequately-selected sequences. We also show how repeated subsampling of the sequence data allows for assessing how much epistasis in the fitness landscape is not captured by the computational model. Our approach is illustrated on several protein families, as well as on in silico solvable protein models. Significance StatementIs more data always better? Or should one prefer fewer data, but of higher quality? Here, we investigate this question in the context of the prediction of fitness effects resulting from mutations to a wild-type protein. We show, based on theory and data analysis, that simple models trained on a small subset of carefully chosen sequence data can perform better than complex ones trained on all available data. Furthermore, we explain how comparing the simple local models obtained with different subsets of training data reveals how much of the epistatic interactions shaping the fitness landscape are left unmodeled.

biophysics↗

bioRxiv2022

Learning the differences: a transfer-learning approach to predict antigen immunogenicity and T-cell receptor specificity

Antigen immunogenicity and the specificity of binding of T-cell receptors to antigens are key properties underlying effective immune responses. Here we propose diffRBM, an approach based on transfer learning and Restricted Boltzmann Machines, to build sequence-based predictive models of these properties. DiffRBM is designed to learn the distinctive patterns in amino acid composition that, one the one hand, underlie the antigens probability of triggering a response, and on the other hand the T-cell receptors ability to bind to a given antigen. We show that the patterns learnt by diffRBM allow us to predict putative contact sites of the antigen-receptor complex. We also discriminate immunogenic and non-immunogenic antigens, antigen-specific and generic receptors, reaching performances that compare favorably to existing sequence-based predictors of antigen immunogenicity and T-cell receptor specificity. More broadly, diffRBM provides a general framework to detect, interpret and leverage selected features in biological data.

bioinformatics↗

bioRxiv2022

Displacement and dissociation of oligonucleotidesduring DNA hairpin closure under strain

The simple hybridization kinetic of an oligonucleotide to its template is a fundamental step in many biological processes such as replication arrest, CRISPR recognition, DNA sequencing, DNA origami, etc. Although approaches exist that address special cases of this problem, there are no simple general prediction schemes. In this work, we have measured experimentally, with no fluorescent labelling, the displacement of an oligonucleotide from its substrate in two situations: one corresponding to oligonucleotide binding/unbinding on ssDNA and one where the oligonucleotide is displaced by the refolding of a dsDNA fork. In this second situation, the fork is expelling the oligonucleotide thus reducing significantly its residence time. To account for our data in these two situations, we have constructed a mathematical model, based on the known nearest neighbor dinucleotide free energies, and provided a good estimate of the residence times of different oligonucleotides (DNA, RNA, LNA) with various length in different experimental conditions (force, temperature, buffer conditions, presence of mismatches, etc.). This study provides a foundation for the dynamics of oligonucleotide displacement, a process of importance in a number of biological and bioengineering contexts.

biophysics↗