Biology subjects

Rosset, S.

Publications and source records attributed to Rosset, S..

At least 19 recordsLinked to original sources

bioRxiv2026

Widespread 3-Base Periodicity in Complex DNA Mixtures and an Alignment-Free Algorithm to Uncover its Source

Background: Protein-coding DNA exhibits characteristic three-base periodicity. In unaligned DNA libraries, however, this signal should cancel if fragment boundaries are uniformly distributed across codon phases. Yet, we find that in multiple metagenomic libraries, base frequencies depended systematically on distance from fragment boundaries, producing a period-3 pattern. We investigated the origin of this unexpected signal. Results: The signal was observed in modern metagenomic datasets and widely across ancient-DNA datasets. We developed a new alignment-free algorithm to infer latent triplet-phase structure from nucleotide composition. The inferred shifts were strongly nonuniform at fragment boundaries, explaining the read-coordinate periodicity. We initially suspected a protocol artifact. To test an alternative, we derived a probabilistic framework linking nucleotide-dependent boundary selection to codon-phase frequencies. The framework showed that, because nucleotide composition differs among codon positions, preferential breakage at particular nucleotides biases the phases represented at fragment boundaries. Simulations confirmed this mechanism: purine-associated fragmentation produced strong period-3 coherence in a coding-dense bacterial genome, whereas no comparable genome-wide effect appeared in the human reference genome. Conclusions: The observed read-coordinate three-base periodicity does not require an intrinsic phase bias in the source DNA. Instead, sequence-dependent fragment formation or recovery can couple boundaries to codon phase. Purine-associated post-mortem fragmentation plausibly explains the widespread phenomenon in ancient-DNA libraries, although PCR- or library-specific mechanisms may contribute to it in modern datasets. Our alignment-free algorithm provides a practical method for estimating and normalizing phase shifts before searching for positional nucleotide patterns, preventing boundary-associated bias from being mistaken for an intrinsic biological pattern.

bioinformatics↗

bioRxiv2026

Selective conservation of symbiont cell-surface glycans across generations in a vertically transmitting coral

Coral resilience under climate change depends on the stability of coral-Symbiodiniaceae symbioses. While vertically transmitting corals inherit symbionts directly from parental colonies, the extent to which symbiont cellular traits are conserved across life stages remains unclear. Here, we examined cell-surface glycan profiles of Symbiodiniaceae in parental colonies and eggs of the coral Montipora capitata. Glycan signatures were structured by symbiont genus and differed between life stages, with mannose/glucose- and galactose-containing glycoproteins as primary drivers of variation. Despite life-stage differences, parent-offspring comparisons revealed significant conservation of glycan profiles, indicating intergenerational transmission of symbiont cellular traits that differed between Cladocopium and Durusdinium and were driven by distinct glycan classes. These results suggest that vertical transmission preserves key recognition-relevant glycans while allowing flexibility in other symbionts surface traits, providing a mechanistic basis for symbiosis stability.

cell biology↗

bioRxiv2026

Collective Posterior Inference from Highly Variable Empirical Replicates

High-throughput experimental platforms now routinely generate data from dozens or hundreds of independent observations. Simulation-based inference (SBI) offers a powerful framework for estimating model parameters from such complex datasets, but standard methods struggle to scale to the noisy multiple-replicates regime without incurring prohibitive computational costs or careful hyperparameter tuning. Here, we introduce a new method for fast and robust collective posterior inference from multiple independent replicates using a robust product-of-experts aggregation scheme that automatically mitigates the influence of outliers. Evaluating it on synthetic and empirical evolutionary datasets, we find it achieves state-of-the-art estimation accuracy and computational efficiency, including inference from noisy observations. Our method is compatible with any SBI framework, providing a scalable, plug-and-play solution for inference from noisy multiple-replicate datasets.

bioinformatics↗

bioRxiv2025

Computationally designed stem-epitope mimetics elicit broadly reactive antibodies

Broad protection against diverse influenza viruses can be conferred by broadly neutralizing antibodies (bnAbs) targeting a conserved site on the hemagglutinin (HA) stem domain. However, the low immunogenicity of this antigenic region hinders the robust induction of such antibodies. Here, we showcase a structure-based immunogen design strategy focusing on the surface mimicry of antigenic sites. By leveraging the structural definition of a stem epitope, we apply computational protein design to develop epitope mimetics to focus the immune response against this site of viral vulnerability. The structurally complex antigenic site is displayed on heterologous protein scaffolds, retaining excellent binding towards known HA stem-specific bnAbs. Our epitope-mimetic induces stem-specific antibodies against highly divergent group 1 and 2 subtypes. The results provide a general framework for the design of novel immunogens eliciting focused immune responses which may be a valuable tool in the development of effective vaccine candidates against other variable pathogens.

biochemistry↗

bioRxiv2024

Accurate single domain scaffolding of three non-overlapping protein epitopes using deep learning

De novo protein design has seen major success in scaffolding single functional motifs, however, in nature most proteins present multiple functional sites. Here we describe an approach to simultaneously scaffold multiple functional sites in a single domain protein using deep learning. We designed small single domain immunogens, under 130 residues, that simultaneously present three distinct and irregular motifs from respiratory syncytial virus. These motifs together comprise nearly half of the designed proteins, and hence the overall folds are quite unusual with little global similarity to proteins in the PDB. Despite this, X-ray crystal structures confirm the accuracy of presentation of each of the motifs, and the multi-epitope design yields improved cross-reactive titers and neutralizing response compared to a single-epitope immunogen. The successful presentation of three distinct binding surfaces in a small single domain protein highlights the power of generative deep learning methods to solve complex protein design problems.

bioengineering↗

bioRxiv2023

A scalable and robust variance components method reveals insights into the architecture of gene-environment interactions underlying complex traits

Understanding the contribution of gene-environment interactions (GxE) to complex trait variation can provide insights into mechanisms underlying disease risk, explain sources of heritability, and improve the accuracy of genetic risk prediction. While biobanks that collect genetic and deep phenotypic data over large numbers of individuals offer the promise of obtaining novel insights into GxE, our understanding of the architecture of GxE in complex traits remains limited. We introduce a method that can estimate the proportion of trait variance explained by GxE (GxE heritability) and additive genetic effects (additive heritability) across the genome and within specific genomic annotations. We show that our method is accurate in simulations and computationally efficient for biobank-scale datasets. We applied our method to{approx} 500, 000 common array SNPs (MAF[≥] 1%), fifty quantitative traits, and four environmental variables (smoking, sex, age, and statin usage) measured across{approx} 300, 000 unrelated white British individuals in the UK Biobank. We found 69 trait-environmental variable pairs with significant genome-wide GxE heritability (p < 0.05/200 correcting for the number of trait-E pairs tested) with an average ratio of GxE to additive heritability{approx} 6.8% that include BMI with smoking (ratio of GxE to additive heritability = 6.3 {+/-} 1.1%), WHR (waist-to-hip ratio adjusted for BMI) with sex (ratio = 19.6 {+/-} 2%), LDL cholesterol with age (ratio = 9.8 {+/-} 3.9%), and HbA1c with statin usage (ratio = 11 {+/-} 2%). Analyzing nearly 8 million common and low-frequency imputed SNPs (MAF[≥] 0.1%), we document an increase in genome-wide GxE heritability of about 28% on average over array SNPs. We partitioned GxE heritability across minor allele frequency (MAF) and local linkage disequilibrium values (LD score) of each SNP to observe that analogous to the relationship that has been observed for additive allelic effects, the magnitude of GxE allelic effects tends to increase with decreasing MAF and LD. Testing whether GxE heritability is enriched around genes that are highly expressed in specific tissues, we find significant tissue-specific enrichments that include brain-specific enrichment for BMI and Basal Metabolic Rate in the context of smoking, adipose-specific enrichment for WHR in the context of sex, and cardiovascular tissue-specific enrichment for total cholesterol in the context of age. Our analyses provide detailed insights into the architecture of GxE underlying complex traits.

genetics↗

bioRxiv2023

Exploring "dark matter" protein folds using deep learning

De novo protein design aims to explore uncharted sequence-and structure areas to generate novel proteins that have not been sampled by evolution. One of the main challenges in de novo design involves crafting "designable" structural templates that can guide the sequence search towards adopting the target structures. Here, we present an approach to learn patterns of protein structure based on a convolutional variational autoencoder, dubbed Genesis. We coupled Genesis with trRosetta to design sequences for a set of protein folds and found that Genesis is capable of reconstructing native-like distance-and angle distributions for five native folds and three novel, so-called "dark-matter" folds as a demonstration of generalizability. We used a high-throughput assay to characterize protease resistance of the designs, obtaining encouraging success rates for folded proteins and further biochemically characterized folded designs. The Genesis framework enables the exploration of the protein sequence and fold space within minutes and is not bound to specific protein topologies. Our approach addresses the backbone designability problem, showing that structural patterns in proteins can be efficiently learned by small neural networks and could ultimately contribute to the de novo design of proteins with new functions.

bioinformatics↗

bioRxiv2023

Computational design of soluble analogues of integral membrane protein structures

De novo design of complex protein folds using solely computational means remains a significant challenge. Here, we use a robust deep learning pipeline to design complex folds and soluble analogues of integral membrane proteins. Unique membrane topologies, such as those from GPCRs, are not found in the soluble proteome and we demonstrate that their structural features can be recapitulated in solution. Biophysical analyses reveal high thermal stability of the designs and experimental structures show remarkable design accuracy. The soluble analogues were functionalized with native structural motifs, standing as a proof-of-concept for bringing membrane protein functions to the soluble proteome, potentially enabling new approaches in drug discovery. In summary, we designed complex protein topologies and enriched them with functionalities from membrane proteins, with high experimental success rates, leading to a de facto expansion of the functional soluble fold space.

bioinformatics↗

bioRxiv2023

Dating ancient humans splits by estimating Poisson rates from mitochondrial DNA parity samples

We tackle the problem of estimating species divergence times, given a genome sequence from each species and a large known phylogenetic tree with a known structure (typically from one of the species). The number of transitions at each site from the first sequence to the other is assumed to be Poisson distributed, and only the parity of the number of transitions is observed. The detailed phylogenetic tree contains information about the transition rates in each site. We use this formulation to develop and analyze multiple estimators of the divergence between the species. To test our methods, we use mtDNA substitution statistics from the well-established Phylotree as a baseline for data simulation such that the substitution rate per site mimics the real-world observed rates. We evaluate our methods using simulated data and compare them to the Bayesian optimizing software BEAST2, showing that our proposed estimators are accurate for a wide range of divergence times and significantly outperform BEAST2. We then apply the proposed estimators on Neanderthal, Denisovan, and Chimpanzee mtDNA genomes to better estimate their TMRCA (Time to Most Recent Common Ancestor) with modern humans and find that their TMRCA is sub-stantially later, compared to values cited recently in the literature.

evolutionary biology↗

bioRxiv2022

Rational design of chemically controlled antibodies and protein therapeutics

Protein-based therapeutics such as monoclonal antibodies and cytokines are important therapies in various pathophysiological conditions such as oncology, auto-immune disorders, and viral infections. However, the wide application of such protein therapeutics is often hindered by dose-limiting toxicities and adverse effects, namely cytokine storm syndrome, organ failure and others. Therefore, spatiotemporal control of the activities of these proteins is crucial to further expand their application. Here, we report the design and application of small molecule-controlled switchable protein therapeutics by taking advantage of a previously engineered OFF-switch system. We used Rosetta modeling suite to computationally optimize the affinity between B-cell lymphoma 2 (Bcl-2) protein and a previously developed computationally designed protein partner (LD3) to obtain a fast and efficient heterodimer disruption upon addition of a competing drug (Venetoclax). The incorporation of the engineered OFF-switch system into CTLA4, anti-HER2 antibodies or an Fc-fused IL-15 cytokine demonstrated an efficient disruption in vitro, as well as fast clearance in vivo upon addition of the competing drug Venetoclax. These results provide a proof-of-concept for the rational design of controllable biologics by introducing a drug-induced OFF-switch into existing protein-based therapeutics.

bioengineering↗

bioRxiv2022

De novo protein design by inversion of the AlphaFold structure prediction network

De novo protein design enhances our understanding of the principles that govern protein folding and interactions, and has the potential to revolutionize biotechnology through the engineering of novel protein functionalities. Despite recent progress in computational design strategies, de novo design of protein structures remains challenging, given the vast size of the sequence-structure space. AlphaFold2 (AF2), a state-of-the-art neural network architecture, achieved remarkable accuracy in predicting protein structures from amino acid sequences. This raises the question whether AF2 has learned the principles of protein folding sufficiently for de novo design. Here, we sought to answer this question by inverting the AF2 network, using the prediction weight set and a loss function to bias the generated sequences to adopt a target fold. Initial design trials resulted in de novo designs with an overrepresentation of hydrophobic residues on the protein surface compared to their natural protein family, requiring additional surface optimization. In silico validation of the designs showed protein structures with the correct fold, a hydrophilic surface and a densely packed hydrophobic core. In vitro validation showed that several designs were folded and stable in solution with high melting temperatures. In summary, our design workflow solely based on AF2 does not seem to fully capture basic principles of de novo protein design, as observed in the protein surfaces hydrophobic vs. hydrophilic patterning. However, with minimal post-design intervention, these pipelines generated viable sequences as assessed experimental characterization. Thus such pipelines show the potential to contribute to solving outstanding challenges in de novo protein design.

bioinformatics↗

bioRxiv2022

De novo design of site-specific protein interactions with learned surface fingerprints

Physical interactions between proteins are essential for most biological processes governing life. However, the molecular determinants of such interactions have been challenging to understand, even as genomic, proteomic, and structural data grows. This knowledge gap has been a major obstacle for the comprehensive understanding of cellular protein-protein interaction (PPI) networks and for the de novo design of protein binders that are crucial for synthetic biology and translational applications. We exploit a geometric deep learning framework operating on protein surfaces that generates fingerprints to describe geometric and chemical features critical to drive PPIs. We hypothesized these fingerprints capture the key aspects of molecular recognition that represent a new paradigm in the computational design of novel protein interactions. As a proof-of-principle, we computationally designed several de novo protein binders to engage four protein targets: SARS-CoV-2 spike, PD-1, PD-L1, and CTLA-4. Several designs were experimentally optimized while others were purely generated in silico, reaching nanomolar affinity with structural and mutational characterization showing highly accurate predictions. Overall, our surface-centric approach captures the physical and chemical determinants of molecular recognition, enabling a novel approach for the de novo design of protein interactions and, more broadly, of artificial proteins with function.

bioengineering↗

bioRxiv2022

A generic framework for hierarchical de novo protein design

De novo protein design enables the exploration of novel sequences and structures absent from the natural protein universe. De novo design also stands as a stringent test to our understanding of the underlying physical principles of protein folding and may lead to the development of proteins with unmatched functional characteristics. The first fundamental challenge of de novo design is to devise "designable" structural templates leading to sequences that will adopt the predicted fold. Here, we built on the TopoBuilder de novo design method to automatically assemble structural templates with native-like features starting from string descriptors that capture the overall topology of proteins. Our framework eliminates the dependency of hand-crafted and fold-specific rules through an iterative, data-driven approach that extracts geometrical parameters from structural tertiary motifs. We evaluated the TopoBuilder framework by designing sequences for a set of five protein folds and experimental characterization revealed that several sequences were folded and stable in solution. The TopoBuilder de novo design framework will be broadly useful to guide the generation of artificial proteins with customized geometries, enabling the exploration of the protein universe.

bioinformatics↗

bioRxiv2021

A deep mutational scanning platform to characterize the fitness landscape of anti-CRISPR proteins

Deep mutational scanning is a powerful method to explore the mutational fitness landscape of proteins. Its adaptation to anti-CRISPR proteins, which are natural CRISPR-Cas inhibitors and key players in the co-evolution of microbes and phages, would facilitate their in-depth characterization and optimization. Here, we developed a robust anti-CRISPR deep mutational scanning pipeline in Escherichia coli combining synthetic gene circuits based on CRISPR interference with flow cytometry-coupled sequencing and mathematical modeling. Using this pipeline, we created and characterized comprehensive single point mutation libraries for AcrIIA4 and AcrIIA5, two potent inhibitors of Streptococcus pyogenes Cas9. The resulting mutational fitness landscapes revealed that both Acrs possess a considerable mutational tolerance as well as an intrinsic redundancy with respect to Cas9 inhibitory features, suggesting evolutionary pressure towards high plasticity and robustness. Finally, to demonstrate that our pipeline can inform the optimization and fine-tuning of Acrs for genome editing applications, we cross-validated a subset of AcrIIA4 mutants via gene editing assays in mammalian cells and in vitro affinity measurements. Together, our work establishes deep mutational scanning as powerful method for anti-CRISPR protein characterization and optimization.

synthetic biology↗

bioRxiv2021

RosettaSurf - a surface-centric computational design approach

Proteins are typically represented by discrete atomic coordinates providing an accessible framework to describe different conformations. However, in some fields proteins are more accurately represented as near-continuous surfaces, as these are imprinted with geometric (shape) and chemical (electrostatics) features of the underlying protein structure. Protein surfaces are dependent on their chemical composition and, ultimately determine protein function, acting as the interface that engages in interactions with other molecules. In the past, such representations were utilized to compare protein structures on global and local scales and have shed light on functional properties of proteins. Here we describe RosettaSurf, a surface-centric computational design protocol, that focuses on the molecular surface shape and electrostatic properties as means for protein engineering, offering a unique approach for the design of proteins and their functions. The RosettaSurf protocol combines the explicit optimization of molecular surface features with a global scoring function during the sequence design process, diverging from the typical design approaches that rely solely on an energy scoring function. With this computational approach, we attempt to address a fundamental problem in protein design related to the design of functional sites in proteins, even when structurally similar templates are absent in the characterized structural repertoire. Surface-centric design exploits the premise that molecular surfaces are, to a certain extent, independent of the underlying sequence and backbone configuration, meaning that different sequences in different proteins may present similar surfaces. We benchmarked RosettaSurf on various sequence recovery datasets and showcased its design capabilities by generating epitope mimics that were biochemically validated. Overall, our results indicate that the explicit optimization of surface features may lead to new routes for the design of functional proteins. Author SummaryFinely orchestrated protein-protein interactions are at the heart of virtually all fundamental cellular processes. Altering these processes or encoding new functions in proteins has been a long-standing goal in computational protein design. Protein design methods commonly rely on scoring functions that seek to identify amino acid sequences that optimize structural configurations of atoms while minimizing a variety of physics-based and statistical terms. The objectives of the large majority of computational design protocols have been focused on obtaining a predefined structural conformation. However, routinely introducing a functional aspect on designer proteins has been more challenging. Our results suggest that the molecular surface features can be a useful optimization parameter to guide the design process towards functional surfaces that mimic known protein binding sites and interact with their intended targets. Specifically, we demonstrate that our design method can optimize experimental libraries through computational screening, creating a basis for highly specific protein binders, as well as design a potent immunogen that engages with site-specific antibodies. The ability to create proteins with novel functions will be transformative for biomedical applications, providing many opportunities for the design of novel immunogens, protein components for synthetic biology, and other protein-based biotechnologies.

bioinformatics↗

bioRxiv2021

Calling differential DNA methylation at cell-type resolution: addressing misconceptions and best practices

We benchmarked two approaches for the detection of cell-type-specific differential DNA methylation: Tensor Composition Analysis (TCA) and a regression model with interaction terms (CellDMC). Our experiments alongside rigorous mathematical explanations show that TCA is superior over CellDMC, thus resolving recent criticisms suggested by Jing et al. Following misconceptions by Jing and colleagues with modelling cell-type-specificity and the application of TCA, we further discuss best practices for performing association studies at cell-type resolution. The scripts for reproducing all of our results and figures are publicly available at github.com/cozygene/CellTypeSpecificMethylationAnalysis.

genomics↗

bioRxiv2021

A rational blueprint for the design of chemically-controlled protein switches

Small-molecule responsive protein switches are crucial components to control synthetic cellular activities. However, the repertoire of small-molecule protein switches is insufficient for many applications, including those in the translational spaces, where properties such as safety, immunogenicity, drug half-life, and drug side-effects are critical. Here, we present a computational protein design strategy to repurpose drug-inhibited protein-protein interactions as OFF- and ON-switches. The designed binders and drug-receptors form chemically-disruptable heterodimers (CDH) which dissociate in the presence of small molecules. To design ON-switches, we converted the CDHs into a multi-domain architecture which we refer to as activation by inhibitor release switches (AIR) that incorporate a rationally designed drug-insensitive receptor protein. CDHs and AIRs showed excellent performance as drug responsive switches to control combinations of synthetic circuits in mammalian cells. This approach effectively expands the chemical space and logic responses in living cells and provides a blueprint to develop new ON- and OFF-switches for basic and translational applications.

biochemistry↗

bioRxiv2020

A bottom-up approach for the de novo design of functional proteins

De novo protein design has enabled the creation of novel protein structures. To design novel functional proteins, state-of-the-art approaches use natural proteins or first design protein scaffolds that subsequently serve as templates for the transplantation of functional motifs. In these approaches, the templates are function-agnostic and motifs have been limited to those with regular secondary structure. Here, we present a bottom-up approach to build de novo proteins tailored to structurally complex functional motifs. We applied a bottom-up strategy to design scaffolds for four different binding motifs, including one bi-functionalized protein with two motifs. The de novo proteins were functional as biosensors to quantify epitope-specific antibody responses and as orthogonal ligands to activate a signaling pathway in engineered mammalian cells. Altogether, we present a versatile strategy for the bottom-up design of functional proteins, applicable to a wide range of functional protein design challenges.

bioengineering↗