Biology subjects

Andre, I.

Publications and source records attributed to Andre, I..

17 recordsLinked to original sources

bioRxiv2026

Stealth CAR-Tregs evade CD3 antibody therapy and skew immune responses toward regulation

Organ transplantation standard of care relies on lifelong immunosuppression, increasing infection and malignancy risks. An emerging approach using Chimeric Antigen Receptor Regulatory T cells (CAR-Tregs) targeting donor antigens, allows the suppression of alloimmune responses while preserving protective immunity. However, transplant recipients harbor a substantial pool of alloreactive memory T-cells, limiting CAR-Tregs tolerance-promoting effect. To address this challenge, our strategy combines selective depletion of activated T-cells, using non-mitogenic CD3 antibody therapy, with HLA-A2-targeted-CAR-Treg therapy. We established that T-cell receptor alpha chain (TRAC) deletion does not impair CAR-Treg antigen-specific activation, expansion, or their transcriptomic, phenotypic, epigenetic, and functional profiles. TRAC-/-CAR-Tregs exhibit robust protection from CD3 antibody depletion in circulation and peripheral tissues, whereas most TRAC+/+CAR-T cells are eliminated. Importantly, TRAC+/+CAR-T cells that escape CD3 antibody mediated depletion exhibit a transcriptomic signature consistent with T-cell exhaustion. Our findings demonstrate that CD3 antibody therapy provides an in vivo survival advantage to TRAC-/-CAR-Tregs, thereby shifting the immune balance toward regulation. This approach might enhance the translational potential of CAR-Treg therapy by reducing the required cell dose needed for clinical efficacy.

immunology↗

bioRxiv2026

DECODING SYNONYMOUS CODON SELECTION WITH A TRANSFORMER MODEL

AO_SCPLOWBSTRACTC_SCPLOWThe genetic code is highly redundant, with many synonymous codons encoding the same amino acid. Codon usage influences RNA structure, signaling, and translation rates. Differences in tRNA availability modulate elongation, with rare codons slowing translation and affecting co-translational folding and gene expression. Despite their functional importance and non-random distribution, rare codons are underrepresented in natural datasets, restricting the development of predictive models. We developed a transformer-based model that predicts codon sequences from amino acids, substantially improving rare codon prediction. The model learns codon signatures encoding species identity, RNA thermodynamic properties, and elongation constraints without explicit labels. Attention analysis shows that codon choice depends on both short and long-range sequence contexts, recovering dicodon effects and highlighting additional motifs. Finally, predictions correlate with experimental measurements of the impact of synonymous mutations on protein fitness, linking gene sequence to fitness and functional consequences, providing a framework to connect sequence variation, translation, and protein function. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=84 SRC="FIGDIR/small/714798v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@16fc387org.highwire.dtl.DTLVardef@8de223org.highwire.dtl.DTLVardef@f98c05org.highwire.dtl.DTLVardef@157a2ea_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

Ancient amino acid sets enable stable protein folds

Early proteins likely arose from a chemically limited set of amino acids available through prebiotic chemistry, raising a central question in molecular evolution: could such primitive compositions yield stable, functional folds? Using de novo design, we constructed three ancient protein architectures using a reduced, evolution-inspired alphabet of ten amino acids, e.g. lacking all basic and aromatic residues. The resulting structures adopted their intended topologies and showed exceptional resistance to thermal and chemical denaturation. Computational simulations further revealed that proteins built from this restricted alphabet were as mutation-resilient as those using all twenty canonical residues. Besides their evolutionary implications, our results provide a foundation for minimalist protein design and generation of simplified, robust systems in minimal cell engineering.

biochemistry↗

bioRxiv2025

Effects of single synonymous substitutions on folding efficiency demonstrate the influence of rare codons and protein structure

Proteins can misfold during cotranslational folding, but how codon sequences influence this process is not well understood. Here, we develop an in vivo assay to comprehensively study the impact of single synonymous substitutions on protein folding efficiency and apply it to the N-terminal domain of E. Coli protein ddlA. We map the influence of codons along the sequence and demonstrate that codons can substantially influence the folding efficiency and that the impact depends on the structure and topology of the protein. A cluster of codons associated with residues in the center of the domain fold strongly influences the folding efficiency. Moreover, substitutions to rarer codons generally lead to increased folding efficiency. A mRNA sequence exclusively made up of rare codons results in higher expression than one with only common codons. Our results highlight the importance of rare codons in cotranslational folding and the relationship between codon sequence and protein structure.

molecular biology↗

bioRxiv2024

Encoding of T=1 virus capsid structures through the interfaces of oligomer subcomponents

Virus capsid formation is one the most complex self-assembly processes in nature, involving the association of a large number of protein subunits into well-defined structural states. The ability of protein subunits to efficiently self-assembly is encoded in the protein sequence, and ultimately in the protein-protein interfaces within the growing capsid. The relative strengths of interfaces can be important in directing the self-assembly process, and determine which intermediates are formed early in the self-assembly process. In this study we characterize the interfaces in homomeric T=1 virus capsids to investigate to what extent interfaces within the assemblies are different from other protein-protein interfaces, and which interfaces are most critical for self-assembly. Interfaces were divided into dimers, trimers, and pentamers and compared to interfaces of non-viral homomeric dimers, trimers, and pentamers. The analysis suggests that viral interfaces are larger than non-viral counterparts, and differ in amino acid content, but are energetically similar in terms of the quality of intermolecular interactions. Trimers are predicted to be the most stable oligomers, which may imply that they form early in the self-assembly process. However, dimeric and pentameric interfaces are typically similar in terms of predicted stability suggesting that assembly formation in T=1 capsids may progress through many different routes, rather than progressing through a single dominant intermediate species. With symmetric docking calculations, the energy landscape of the assembled capsid was characterized, and the results highlight that the assemblies exhibit deeply funneled energy landscapes encoded by protein-protein interfaces that have a high degree of specificity.

bioinformatics↗

bioRxiv2024

Accurate prediction of site- and amino-acid substitution rates with a mutation-selection model

The pattern of substitutions at sites in proteins provides invaluable information about their biophysical and functional importance and what selection pressures are acting at individual sites. Amino acid site rates are typically estimated using phenomenological models in which the sequence variability is described by rate factors that scale the overall substitution rate in a protein to sites. In this study, we demonstrate that site rates can be calculated accurately from amino acid sequences using a mutation-selection model in combination with a simple nucleotide substitution model. The method performs better than the standard phylogenetic approach on sequences generated by structure-based evolutionary dynamics simulations, robustly estimates rates for shallow multiple sequence alignments, and can be rapidly calculated also on larger sequence alignments. On natural sequences, site rates from the mutation-selection model are strongly correlated to rates calculated with the empirical Bayes methods. The model provides a link between amino acid substitution rates and equilibrium frequency distributions at sites in proteins. We show how an ensemble of equilibrium frequency vectors can be used to represent the rate variation encoded in empirical amino acid substitution matrices. This study demonstrates that a rapid and simple method can be developed from the mutation-selection model to predict substitution rates from amino acid data, complementing the standard phylogenetic approach.

evolutionary biology↗

bioRxiv2024

Improved prediction of site-rates from structure with averaging across homologs

Variation in mutation rates at sites in proteins can largely be understood by the constraint that proteins must fold into stable structures. Models that calculate site-specific rates based on protein structure and a thermodynamic stability model have shown a significant but modest ability to predict empirical site-specific rates calculated from sequence. Models that use detailed atomistic models of protein energetics do not outperform simpler approaches using packing density. We demonstrate that a fundamental reason for this is that empirical site-specific rates are the result of the average effect of many different microenvironments in a phylogeny. By analyzing the results of evolutionary dynamics simulations, we show how averaging site-specific rates across many extant protein structures can lead to correct recovery of site-rate prediction. This result is also demonstrated in natural protein sequences and experimental structures. Using predicted structures, we demonstrate that atomistic models can improve upon contact density metrics in predicting site-specific rates from a structure. The results give fundamental insights into the factors governing the distribution of site-specific rates in protein families.

evolutionary biology↗

bioRxiv2024

Engineering multivalent Fc display for FcγR blockade

Autoimmune diseases, driven by Fc{gamma} receptor (Fc{gamma}R) activation through autoantibody immune complexes (IC), present a complex therapeutic challenge of achieving pharmacological blockade of Fc{gamma}R without triggering receptor activation. The assembly of ICs into polydisperse, higher-order structures is required for Fc{gamma}R activation. However, engineered multimeric, monodisperse Fc assemblies have been reported to prevent Fc{gamma}R activation, suggesting that Fc spatial organization determines Fc{gamma}R activation. In this study, we engineered a functional single-chain Fc domain protein (scFc) for unidirectional, multivalent presentation by virus-like particles (VLPs), used as a display platform. We found that the multivalent display of scFc on the VLPs elicited distinct cellular responses compared with monovalent scFc, highlighting the importance of the structural context of scFc on its function. scFc-VLPs had minimal impact on the nanoscale spatial organization of Fc{gamma}R at the cell membrane and caused limited receptor activation and internalization. In contrast, the monovalent scFc acted as an Fc{gamma}R agonist, inducing receptor clustering, activation, and internalization. Increasing scFc valency in scFc-VLPs was associated with increased binding to monocytes, reaching a plateau at high valencies. Notably, the ability of scFc-VLPs to block IC-mediated phagocytosis in vitro increased with scFc valency. In a murine model of passive immune thrombocytopenia (ITP), a high valency scFc-VLP variant with a desirable immunogenicity profile induced attenuation of thrombocytopenia. Here we show that multivalent presentation of an engineered scFc on a display platform can be tailored to promote suppression of IC-mediated phagocytosis while preventing Fc{gamma}R activation. This work introduces a new paradigm that can contribute to the development of therapies for autoimmune diseases.

bioengineering↗

bioRxiv2023

Accurate prediction of protein assembly structure by combining AlphaFold and symmetrical docking

AlphaFold can predict the structures of monomeric and multimeric proteins with high accuracy but has a limit on the number of chains and residues it can fold. Here we show that a combination of AlphaFold and all-atom symmetric docking simulations enables highly accurate prediction of the structure of complex symmetrical assemblies. We present a method to predict the structure of complexes with cubic - tetrahedral, octahedral and icosahedral - symmetry from sequence. Focusing on proteins where AlphaFold can make confident predictions on the subunit structure, 21 cubic systems were assembled with a median TM-score of 0.99 and a DockQ score of 0.71. 15 had TM-scores of above 0.8 and were categorized as high-quality according to DockQ. The resulting models are energetically optimized and can be used for detailed studies of intermolecular interactions in higher-order symmetrical assemblies. The results demonstrate how explicit treatment of structural symmetry can significantly expand the size and complexity of AlphaFold predictions.

bioinformatics↗

bioRxiv2022

Atomistic simulation of protein evolution reveals sequence covariation and timedependent fluctuations of site-specific substitution rates

Thermodynamic stability is a crucial fitness constraint in protein evolution and is a central factor in shaping the sequence landscapes of proteins. The correlation between stability and molecular fitness depends on the mechanism that relates the biophysical property with biological function. In the simplest case, stability and fitness are related by the amount of folded protein. However, when proteins are toxic in the unfolded state, the fitness function shifts, resulting in higher stability under mutation-selection balance. Likewise, a higher population size results in a similar change in protein stability, as it magnifies the effect of the selection pressure in evolutionary dynamics. This study investigates how such factors affect the evolution of protein stability, site-specific mutation rates, and residue-residue covariation. To simulate evolutionary trajectories with realistic modeling of protein energetics, we develop an all-atom simulator of protein evolution, RosettaEvolve. By evolving proteins under different fitness functions, we can study how the fitness function affects the distribution of proposed and accepted mutations, site-specific rates, and the prevalence of correlated amino acid substitutions. We demonstrate that fitness pressure affects the proposal distribution of mutational effects, that changes in stability can largely explain variations in site-specific substitution rates in evolutionary trajectories, and that increased fitness pressure results in a stronger covariation signal. Our results give mechanistic insight into the evolutionary consequences of variation in protein stability and provide a basis to rationalize the strong covariation signal observed in natural sequence alignments.

bioinformatics↗

bioRxiv2021

DnaK response to expression of protein mutants is dependent on translation rate and stability

Chaperones play a central part in the quality control system in cells by clearing misfolded and aggregated proteins. The chaperone DnaK acts as a sensor for molecular stress by recognising short hydrophobic stretches of misfolded proteins. As the level of unfolded protein is a function of protein stability, we hypothesised that the level of DnaK response upon overexpression of recombinant proteins would be correlated to stability. Using a set of mutants of the{lambda} -repressor with varying thermal stabilities and a fluorescent reporter system, the effect of stability on DnaK response and protein abundance was investigated. Our results demonstrate that the initial DnaK response is largely dependent on protein synthesis rate but as the recombinantly expressed protein accumulates and homeostasis is approached the response correlates strongly with stability. Furthermore, we observe a large degree of cell-cell variation in protein abundance and DnaK response in more stable proteins.

biochemistry↗

bioRxiv2021

A memetic algorithm enables global all-atom protein-protein docking with sidechain flexibility

Protein complex formation is encoded by specific interactions at the atomic scale, but the computational cost of modeling proteins at this level often requires the use of simplified energy models and limited conformational flexibility. In particular, the use of all-atom energy functions, backbone and sidechain flexibility results in rugged energy landscapes that are difficult to explore. In this study we develop a protein-protein docking algorithm, EvoDOCK, that combine the strength of a differential evolution algorithm for efficient exploration of the global search space with the benefits of a local optimization method to refine detailed atomic interactions. EvoDOCK enabled accurate and fast local and global protein-protein docking using an all-atom energy function with side-chain flexibility. Comparison with a standard method built on Monte Carlo optimization demonstrated improved accuracy and with increases in computational speed of up to 35 times. The evolutionary algorithm also enabled efficient atomistic docking with backbone flexibility.

bioinformatics↗

bioRxiv2021

Linearised loop kinematics to study pathways between conformations

AO_SCPLOWBSTRACTC_SCPLOWConformational changes are central to the function of many proteins. Characterization of these changes using molecular simulation requires methods to effectively sample pathways between protein conformational states. In this paper we present an iterative algorithm that samples conformational transitions in protein loops, referred to as the Jacobian-based Loop Transition (JaLT) algorithm. The method uses internal coordinates to minimise the sampling space, while Cartesian coordinates are used to maintain loop closure. Information from the two representations is combined to push sampling towards a desired target conformation. The innovation that enables the simultaneous use of Cartesian coordinates and internal coordinate is the linearisation of the inverse kinematics of a protein backbone. The algorithm uses the Rosetta all-atom energy function to steer sampling through low-energy regions and uses Rosettas side-chain energy minimiser to update side-chain conformations along the way. Because the JaLT algorithm combines a detailed energy function with a low-dimensional conformational space, it is positioned in between molecular dynamics (MD) and elastic network model (ENM) methods. As a proof of principle, we apply the JaLT algorithm to study the conformational transition between the open and occluded state in the MET20 loop of the Escherichia coli dihydrofolate reductase enzyme. Our results show that the algorithm generates semi-continuous pathways between the two states with realistic energy profiles. These pathways can be used to identify energy barriers along the transition. The effect of a single point mutation of the MET20 loop was also investigated and the predicted increase in energy barrier is consistent with the experimentally observed reduction in catalytic rate of the enzyme. Additionally, it is demonstrated how the JaLT algorithm can be used to identify dominant degrees of freedom during a transition. This can be valuable input for a more extensive characterization of the free energy pathway along a transition using molecular dynamics, which is often performed with a reduced set of degrees of freedom. This study has thereby provided the first examples of how linearisation of inverse kinematics can be applied to the analysis of proteins.

bioinformatics↗

bioRxiv2020

A thermodynamic model of protein structure evolution explains empirical amino acid rate matrices

Proteins evolve under a myriad of biophysical selection pressures that collectively control the patterns of amino acid substitutions. Averaged over time and across proteins, these evolutionary pressures are sufficiently consistent to produce global substitution patterns that can be used to successfully find homologues, infer phylogenies, and reconstruct ancestral sequences. Although the factors which govern the variation of protein substitution rates has received much attention, the influence of thermodynamic stability constraints remains unresolved. Here we develop a simple model to calculate amino acid rate matrices from evolutionary dynamics controlled by a fitness function that reports on the thermodynamic effects of amino acid mutations in protein structures. This hybrid biophysical and evolutionary model accounts for nucleotide transition/transversion rate bias, multi-nucleotide codon changes, the number of codons per amino acid, and thermodynamic protein stability. We find that our theoretical model accurately recapitulates the complex pattern of empirical rates observed in common global amino acid substitution matrices used in phylogenetics. These results suggest that selection for thermodynamically stable proteins, coupled with nucleotide mutation bias filtered by the structure of the genetic code, is the primary global driver behind the amino acid substitution patterns observed in proteins throughout the tree of life.

evolutionary biology↗

bioRxiv2020

CD28 costimulatory domain protects against tonic signaling-induced functional impairment in CAR-Tregs

The use of chimeric antigen receptor (CAR)-engineered regulatory T cells (Tregs) has emerged as a promising strategy to promote immune tolerance in transplantation. However, in conventional T cells (Tconvs), CAR expression is often associated with tonic signaling resulting from ligand-independent baseline activation. Tonic signaling may cause CAR-T cell dysfunction, especially when the CAR structure incorporates the CD28 costimulatory domain (CSD) rather than the 4-1BB CSD. Here, we explored the impact of tonic signaling on human CAR-Tregs according to the type of CSD. Compared to CD28-CAR-Tregs, 4-1BB-CAR-Tregs showed enhanced proliferation and greater activation of MAP kinase and mTOR pathways but exhibited decreased lineage stability and reduced abilities to produce IL-10 and be restimulated through the CAR. Although both CAR-Treg populations were suppressive in vivo, cell tracking with bioluminescence imaging found longer persistence for CD28-CAR-Tregs than for 4-1BB-CAR-Tregs. This study demonstrates that CD28-CAR best preserves Treg function and survival in the context of tonic signaling, in contrast with previous findings for Tconvs. SUMMARYLamarthee et al investigated the impact of chimeric antigen receptor (CAR) tonic signaling on CAR-engineered Tregs according to the incorporated costimulatory domain (either CD28 or 4-1BB). CD28 ameliorated Treg stability, long-term survival and function compared to 4-1BB.

immunology↗

bioRxiv2020

Single cell analysis of FOXP3 deficiencies in humans and mice unmasks intrinsic and extrinsic CD4+ T cell perturbations

ABSTRACTFOXP3 deficiency in humans with IPEX syndrome and mice results in fatal systemic autoimmunity by altering regulatory T cell (Treg) physiology, but actual cellular and molecular mechanisms of disease are unclear, part because Treg surface markers may be unreliable in disease states. We used deep profiling by flow cytometry, population and single-cell RNAseq to analyze Tregs and conventional (Tconv) CD4+ T lymphocytes in cohorts of IPEX patients with a range of genetic lesions, and in Foxp3-deficient mice. In all patients and mice, heterogeneous Treg-like cells with an active FOXP3 locus were observed, some differing very little from normal Tregs, others more distant. Tconv showed no widespread activation or Th bias. The dominant mark was a monomorphic signature equally affecting all CD4+ T cells, unexpectedly dampening tumor-Treg and cytokine-signaling modules. In mixed bone marrow chimeras, WT Tregs exerted dominant suppression, normalizing the states of mutant Treg and Tconv, extinguishing the disease signature, and revealing a small gene cluster truly regulated, cell-intrinsically, by FOXP3. These results suggest a two-step pathogenesis model, with therapeutic implications: limited downregulation of a few core Treg genes de-represses a systemic mediator(s), which imprints the disease signature on all T cells, and further dampens Treg function.Competing Interest StatementThe authors have declared no competing interest.View Full Text

immunology↗

bioRxiv2018

DNA-mediated self-assembly of gold nanoparticles on protein superhelix

Recent advances in protein engineering have enabled methods to control the self-assembly of protein on various length-scales. One attractive application for designed proteins is to direct the spatial arrangement of nanomaterials of interest. Until now, however, a reliable conjugation method is missing to facilitate site-specific positioning. In particular, bare inorganic nanoparticles tend to aggregate in the presence of buffer conditions that are often required for the formation of stable proteins. Here, we demonstrated a DNA mediated conjugation method to link gold nanoparticles with protein structures. To achieve this, we constructed de novo designed protein fibers based on previously published uniform alpha-helical units. DNA modification rendered gold nanoparticles with increased stability against ionic solutions and the use of complementary strands hybridization guaranteed the site-specific binding to the protein. The combination of high resolution placement of anchor points in designed protein assemblies with the increased control of covalent attachment through DNA binding can enable investigations of multilevel physical coupling events of nanocomponents on protein templates and expand the application of protein structures to material sciences.

biophysics↗