Biology subjects

Nourmohammad, A.

Publications and source records attributed to Nourmohammad, A..

At least 19 recordsLinked to original sources

bioRxiv2026

T-cell repertoire response in individuals with post-acute sequelae of COVID-19

T-cells are central to SARS-CoV-2 clearance and immunological memory, yet their contribution to the persistence of post-acute sequelae of COVID-19 (PASC) remains poorly understood. The immunological features that distinguish individuals who develop PASC from those who recover fully are unresolved, in part due to the phenotypic heterogeneity of the condition and the likely multiplicity of its underlying mechanisms. Here, we profiled longitudinal bulk TCR{beta} repertoires from 120 individuals in the INCOV cohort-71 with PASC and 49 without-sampled at two to three time points spanning the acute and post-acute phases of infection. Using robust statistical modeling of repertoire composition and clonal dynamics, we found that global statistics such as V, J gene usage and CDR3 length do not differ between groups, but that locally enriched sequence motifs and differentially dynamic clones reveal distinct T-cell signatures associated with PASC status. Clones contracting following the peak of the acute response were significantly enriched for SARS-CoV-2 specificity in both groups. Interestingly, Influenza A-specific TCRs were disproportionately enriched among contracting clones in PASC+ repertoires, implicating viral co-infection as a potential contributor to early disease severity and, possibly, PASC pathogenesis. Rare public TCR clones were markedly enriched for SARS-CoV-2 specificity, with PASC+ individuals harboring a modestly but significantly higher proportion than PASC- individuals. Together, we identified over 1,000 candidate TCR{beta} receptors potentially discriminating PASC+ from PASC- immune responses, opening a path toward the identification of disease-relevant T-cell specificities and the development of T-cell-based immunological biomarkers for long COVID.

immunology↗

bioRxiv2026

Somatic evolution of a cross-reactive germline antibody that expands its breadth to neutralize new SARS-CoV-2 variants

Rapid antigenic drift of the SARS-CoV-2 receptor-binding domain (RBD) underlies immune escape and continues to challenge the durability of antibody-mediated protection. Among the major classes of RBD-directed antibodies, germline-encoded IGHV3-53 responses are highly potent against early SARS-CoV-2 variants but are generally compromised by Omicron-associated mutations. Here, we identify an intrinsically cross-reactive IGHV3-53 germline antibody that recognizes multiple pre-Omicron variants, including SARS-CoV-2 wild-type, Alpha, and Delta. Notably, we demonstrate that targeted somatic evolution can further expand this breadth to overcome the immune escape of different Omicron variants. Guided by integrated structural and sequence analyses, we introduce four somatic mutations (G26E, T28I, S53P, and Y58F) into the germline antibody, resulting in markedly enhanced binding and neutralization of Omicron BA.1, BA.2, and BA.4/5. High-resolution crystal structures reveal that these mutations re-establish critical interactions disrupted by substitutions on Omicron RBD and optimize affinity at a remodeled epitope interface. Collectively, our findings delineate a structural and mechanistic pathway through which an inherently cross-reactive germline antibody lineage can be adaptively refined to counter highly divergent SARS-CoV-2 variants. This work highlights the underappreciated breadth encoded within the naive B-cell repertoire and provides a conceptual framework for engineering and eliciting antibody responses resilient to future antigenic drift.

immunology↗

bioRxiv2025

Genetic and environmental imprints on T cell receptor repertoires as predictors of graft-versus-host disease

AbstractHematopoietic cell transplantation (HCT) as potentially curative treatment for patients with hematologic malignancies relies on T cells to mediate the potentially curative graft-versus-tumor (GVT) effect, which may be associated with graft-versus-host disease (GVHD), a potentially life-threatening complication. T cells recognize peptides presented by Human Leukocyte Antigen (HLA) molecules. It is commonly believed that matching HLA alleles between donors and recipients ensures similarity in their T cell receptor (TCR) repertoires, thereby reducing the risk of GVHD and graft rejection. However, TCR repertoires are shaped by multiple factors beyond HLA genetics, including sex, age, and immune history. The extent to which genetic variation and past infections influence TCR repertoire composition and GVHD risk remains unclear. Here, we show that while HLA haplotypes contribute to broad TCR repertoire differences, recent viral infections significantly impact TCR composition and influence GVHD risk. Analyzing 401 patients who were uniformly transplanted from healthy HLA-identical sibling HCT donors, we introduced HLA-TCR coherence, a metric that quantifies the extent to which an individuals TCR repertoire reflects their HLA haplotype. We find that higher TCR-HLA coherence is associated with greater HLA heterozygosity and an increased incidence of severe acute GVHD (grade 3-4) in transplant recipients. Furthermore, in silico identification of virus-associated TCRs (vaTCRs) using TCR sequencing and viral serology reveal specific vaTCRs predictive of either increased or decreased GVHD risk. These findings suggest that beyond HLA allele matching, donor-specific immune history and repertoire characteristics are critical determinants of GVHD risk. Thus, a more systematic integration of donor immune history may offer a complementary avenue for refining donor selection and potentially improving transplant outcomes. One Sentence SummaryWhile genetics shape the potential of a donors T cell repertoire, factors like infections and HLA diversity influence its composition and impact on transplant outcomes, including GVHD risk.

immunology↗

bioRxiv2025

Design principles of the cytotoxic CD8+ T-cell response

Cytotoxic T lymphocytes eliminate infected or malignant cells, safeguarding surrounding tissues. Although experimental and systems-immunology studies have cataloged many molecular and cellular actors involved in an immune response, the design principles governing how the speed and magnitude of T-cell responses emerge from cellular decision-making remain elusive. Here, we recast the T-cell response as a feedback-controlled program, wherein the rates of activation, proliferation, differentiation and death are regulated through antigenic, pro- and anti-inflammatory cues. By exploring a broad class of feedback-controller designs as potential immune programs, we demonstrate how the speed and magnitude of T-cell responses emerge from optimizing signal-feedback to protect against diverse infection settings. We recover an inherent trade-off: infection clearance at the cost of immunopathology. We show how this trade-off is encoded into the logic of T-cell responses by hierarchical sensitivity to different immune signals. Notably, we find that designs that balance harm from acute infections and autoimmunity produce immune responses consistent with experimentally observed patterns of T-cell effector expansion in mice. Extending our model to immune-based T-cell therapies for cancer tumors, we identify a trade-off between the affinity for tumor antigens ("quality") and the abundance ("quantity") of infused T-cells necessary for effective treatment. Finally, we show how therapeutic efficacy can be improved by targeted genetic perturbations to T-cells. Our findings offer a unified control-logic for cytotoxic T-cell responses and point to specific regulatory programs that can be engineered for more robust T-cell therapies.

immunology↗

bioRxiv2025

Replaying germinal center evolution on a quantified affinity landscape

Darwinian evolution of immunoglobulin genes within germinal centers (GCs) underlies the progressive increase in antibody affinity following antigen exposure. Whereas the cellular mechanics of how competition between B cells produces increases in affinity are well established, the evolutionary dynamics of this process are less clear. We developed an experimental evolution model where we "replay" over one hundred monoclonal GC reactions, assigning affinities to each cell using deep mutational scanning. Our data reveal how GCs achieve predictable outcomes by means of noisy but persistent selection on an affinity landscape whose exploration is heavily constrained by somatic hypermutation biases. We infer a fitness landscape that quantitatively recapitulates the affinity maturation trajectory of our clone and find that apparent features of GC selection such as permissiveness to low-affinity lineages and rapid plateauing of affinity are likely artifacts of survivorship biases that distort our view of how B cell affinity progresses over time.

immunology↗

bioRxiv2025

T-cell receptor specificity landscape revealed through de novo peptide design

T-cells play a key role in adaptive immunity by mounting specific responses against diverse pathogens. An effective binding between T-cell receptors (TCRs) and pathogen-derived peptides presented on Major Histocompatibility Complexes (MHCs) mediate an immune response. However, predicting these interactions remains challenging due to limited functional data on T-cell reactivities. Here, we introduce a computational approach to predict TCR interactions with peptides presented on MHC class I alleles, and to design novel immunogenic peptides for specified TCR-MHC complexes. Our method leverages HERMES, a structure-based, physics-guided machine learning model trained on the protein universe to predict amino acid preferences based on local structural environments. Despite no direct training on TCR-pMHC data, the implicit physical reasoning in HERMES enables us to make accurate predictions of both TCR-pMHC binding affinities and T-cell activities across diverse viral epitopes and cancer neoantigens, achieving up to 0.72 correlation with experimental data. Leveraging our TCR recognition model, we develop a computational protocol for de novo design of immunogenic peptides. Through experimental validation in three TCR-MHC systems targeting viral and cancer peptides, we demonstrate that our designs--with up to five substitutions from the native sequence--activate T-cells at success rates of up to 50%. Lastly, we use our generative framework to quantify the diversity of the peptide recognition landscape for various TCR-MHC complexes, offering key insights into T-cell specificity in both humans and mice. Our approach provides a platform for immunogenic peptide and neoantigen design, as well as for evaluating TCR specificity, offering a computational framework to inform design of engineered T-cell therapies and vaccines.

biophysics↗

bioRxiv2024

Design of high specificity binders for peptide-MHC-I complexes

Class I MHC molecules present peptides derived from intracellular antigens on the cell surface for immune surveillance, and specific targeting of these peptide-MHC (pMHC) complexes could have considerable utility for treating diseases. Such targeting is challenging as it requires readout of the few outward facing peptide antigen residues and the avoidance of extensive contacts with the MHC carrier which is present on almost all cells. Here we describe the use of deep learning-based protein design tools to de novo design small proteins that arc above the peptide binding groove of pMHC complexes and make extensive contacts with the peptide. We identify specific binders for ten target pMHCs which when displayed on yeast bind the on-target pMHC tetramer but not closely related peptides. For five targets, incorporation of designs into chimeric antigen receptors leads to T-cell activation by the cognate pMHC complexes well above the background from complexes with peptides derived from proteome. Our approach can generate high specificity binders starting from either experimental or predicted structures of the target pMHC complexes, and should be widely useful for both protein and cell based pMHC targeting.

immunology↗

bioRxiv2024

Adaptive efficiency of information processing in immune-pathogen co-evolution

Organisms have evolved immune systems that can counter pathogenic threats. The adaptive immune system in vertebrates consists of a diverse repertoire of immune receptors that can dynamically reorganize to specifically target the ever-changing pathogenic landscape. Pathogens in return evolve to escape the immune challenge, forming an co-evolutionary arms race. We introduce a formalism to characterize out-of-equilibrium interactions in co-evolutionary processes. We show that the rates of information exchange and entropy production can distinguish the leader from the follower in an evolutionary arms races. Lastly, we introduce co-evolutionary efficiency as a metric to quantify each populations ability to exploit information in response to the other. Our formalism provides insights into the conditions necessary for stable co-evolution and establishes bounds on the limits of information exchange and adaptation in co-evolving systems.

evolutionary biology↗

bioRxiv2024

HERMES: Holographic Equivariant neuRal network model for Mutational Effect and Stability prediction

Accurately predicting how amino acid substitutions alter protein function is a central challenge in biology, with applications from interpreting disease variants to designing vaccines and therapeutics. We introduce HERMES, a family of fast, structure-based models that predict mutational effects from the local atomic environment around each residue. Pre-trained on masked amino acid prediction, HERMES shows strong zero-shot performance for predicting changes in thermodynamic stability and protein-protein binding affinity. Analyzing its predictions, we uncover a pre-training bias toward size-conserving substitutions, which we reduce through an amortized fine-tuning strategy that incorporates packing flexibility. When fine-tuned on experimental data, HERMES matches state-of-the-art stability predictors without costly data augmentation. HERMES also identifies antigen-stabilizing mutations across multiple viral envelope proteins, enabling an efficient pipeline for designing mutation libraries for vaccine development. Together, this work establishes HERMES as a fast and practical structure-based framework for mutation screening and offers insight into the mechanisms underlying its predictions.

bioinformatics↗

bioRxiv2023

Variability in the local and global composition of human T-cell receptor repertoires during thymic development across cell types and individuals

The adaptive immune response relies on T cells that combine phenotypic specialization with diversity of T cell receptors (TCRs) to recognize a wide range of pathogens. TCRs are acquired and selected during T cell maturation in the thymus. Characterizing TCR repertoires across individuals and T cell maturation stages is important for better understanding adaptive immune responses and for developing new diagnostics and therapies. Analyzing a dataset of human TCR repertoires from thymocyte subsets, we find that the variability between individuals generated during the TCR V(D)J recombination is maintained through all stages of T cell maturation and differentiation. The inter-individual variability of repertoires of the same cell type is of comparable magnitude to the variability across cell types within the same individual. To zoom in on smaller scales than whole repertoires, we defined a distance measuring the relative overlap of locally similar sequences in repertoires. We find that the whole repertoire models correctly predict local similarity networks, suggesting a lack of forbidden T cell receptor sequences. The local measure correlates well with distances calculated using whole repertoire traits and carries information about cell types.

immunology↗

bioRxiv2022

Flexible and scalable control of T cell memory by a reversible epigenetic switch

The immune system encodes information about the severity of a pathogenic threat in the quantity and type of memory cell populations formed in response. This encoding emerges from the decisions of lymphocytes to maintain or lose self-renewal and memory potential during a challenge. By tracking responding CD8 T cells at the single-cell and clonal lineage level using time-resolved transcriptomics and quantitative live imaging, we identify a remarkably flexible decision-making strategy, whereby T cells initially choose whether to maintain or lose memory potential early after antigen recognition, but following pathogen clearance may regain memory potential if initially lost. Mechanistically, this flexibility is implemented by a cis-epigenetic switch that silences the memory regulator TCF1 in a stochastic and reversible manner in response to stimulatory inputs. Mathematical modeling shows how this strategy allows memory T cell numbers to scale robustly with pathogen virulence and immune response magnitudes. We propose that flexibility and stochasticity in cellular decision making ensures optimal immune responses against diverse threats.

immunology↗

bioRxiv2022

Learning the shape of protein micro-environments with a holographic convolutional neural network

Proteins play a central role in biology from immune recognition to brain activity. While major advances in machine learning have improved our ability to predict protein structure from sequence, determining protein function from structure remains a major challenge. Here, we introduce Holographic Convolutional Neural Network (H-CNN) for proteins, which is a physically motivated machine learning approach to model amino acid preferences in protein structures. H-CNN reflects physical interactions in a protein structure and recapitulates the functional information stored in evolutionary data. H-CNN accurately predicts the impact of mutations on protein function, including stability and binding of protein complexes. Our interpretable computational model for protein structure-function maps could guide design of novel proteins with desired function.

biophysics↗

bioRxiv2022

Holographic-(V)AE: an end-to-end SO(3)-Equivariant (Variational) Autoencoder in Fourier Space

Group-equivariant neural networks have emerged as a data-efficient approach to solve classification and regression tasks, while respecting the relevant symmetries of the data. However, little work has been done to extend this paradigm to the unsupervised and generative domains. Here, we present Holographic-(Variational) Auto Encoder (H-(V)AE), a fully end-to-end SO(3)-equivariant (variational) autoencoder in Fourier space, suitable for unsupervised learning and generation of data distributed around a specified origin in 3D. H-(V)AE is trained to reconstruct the spherical Fourier encoding of data, learning in the process a low-dimensional representation of the data (i.e., a latent space) with a maximally informative rotationally invariant embedding alongside an equivariant frame describing the orientation of the data. We extensively test the performance of H-(V)AE on diverse datasets. We show that the learned latent space efficiently encodes the categorical features of spherical images. Moreover, H-(V)AEs latent space can be used to extract compact embeddings for protein structure microenvironments, and when paired with a Random Forest Regressor, it enables state-of-the-art predictions of protein-ligand binding affinity.

bioinformatics↗

bioRxiv2022

Determining probabilities of HIV-1 bNAb development in healthy and chronically infected individuals

HIV-1 broadly neutralizing antibodies (bNAbs) are able to suppress viremia and prevent infection. Their induction by vaccination is therefore a major goal. However, in contrast to antibodies that neutralize other pathogens, HIV-1-specific bNAbs frequently carry uncommon molecular characteristics that might prevent their induction. Here, we performed unbiased sequence analyses of B cell receptor repertoires from 57 healthy and 46 chronically HIV-1- or HCV-infected individuals and learned probabilistic models to predict the likelihood of bNAb development. We formally show that lower probabilities for bNAbs are predictive of higher HIV-1 neutralization activity. Moreover, ranking of bNAbs by their probabilities allowed to identify highly potent antibodies with superior generation probabilities as preferential targets for vaccination approaches. Importantly, we found equal bNAb probabilities across infected and healthy donors. This implies that chronic infection is not a prerequisite for the generation of bNAbs, fostering the hope that HIV-1 vaccines can induce bNAb development in healthy individuals. Significance StatementWhile HIV-1 broadly neutralizing antibodies (bNAbs) can develop in chronically HIV-1-infected individuals, they could not yet be elicited by active vaccination. Here, we computationally demonstrate that HIV-1 bNAbs carry distinct sequence features making them unlikely outcomes of the antibody evolution. However, our approach allowed us to identify bNAbs with higher probabilities of being generated. These candidates can now serve as the most promising targets to be induced by vaccination. Moreover, we show that chronic infection has no influence on the probabilities of finding typical bNAb sequence features in the memory B cell compartment. Both findings are critical to design effective vaccination strategies.

immunology↗

bioRxiv2021

Risk-utility tradeoff shapes memory strategies for evolving patterns

Keeping a memory of evolving stimuli is ubiquitous in biology, an example of which is immune memory for evolving pathogens. However, learning and memory storage for dynamic patterns still pose challenges in machine learning. Here, we introduce an analytical energy-based framework to address this problem. By accounting for the tradeoff between utility in keeping a high-affinity memory and the risk in forgetting some of the diverse stimuli, we show that a moderate tolerance for risk enables a repertoire to robustly classify evolving patterns, without much fine-tuning. Our approach offers a general guideline for learning and memory storage in systems interacting with diverse and evolving stimuli.

biophysics↗

bioRxiv2021

Antigenic evolution of human influenza H3N2 neuraminidase is constrained by charge balancing

As one of the main influenza antigens, neuraminidase (NA) in H3N2 virus has evolved extensively for more than 50 years due to continuous immune pressure. While NA has emerged as an effective vaccine target recently, biophysical constraints on the antigenic evolution of NA remain largely elusive. Here, we apply deep mutational scanning to characterize the local fitness landscape in an antigenic region of NA in six different human H3N2 strains that were isolated around 10 years apart. The local fitness landscape correlates well among strains and the pairwise epistasis is highly conserved. Our analysis further demonstrates that local net charge governs the pairwise epistasis in this antigenic region. In addition, we show that residue coevolution in this antigenic region can be predicted by charge states and pairwise epistasis. Overall, this study demonstrates the importance of quantifying epistasis and the underlying biophysical constraint for building a predictive model of influenza evolution.

evolutionary biology↗

bioRxiv2021

Learning and organization of memory for evolving patterns

Storing memory for molecular recognition is an efficient strategy for responding to external stimuli. Biological processes use different strategies to store memory. In the olfactory cortex, synaptic connections form when stimulated by an odor, and establish distributed memory that can be retrieved upon re-exposure. In contrast, the immune system encodes specialized memory by diverse receptors that recognize a multitude of evolving pathogens. Despite the mechanistic differences between the olfactory and the immune memory, these systems can still be viewed as different information encoding strategies. Here, we present a theoretical framework with artificial neural networks to characterize optimal memory strategies for both static and dynamic (evolving) patterns. Our approach is a generalization of the energy-based Hopfield model in which memory is stored as a networks energy minima. We find that while classical Hopfield networks with distributed memory can efficiently encode a memory of static patterns, they are inadequate against evolving patterns. To follow an evolving pattern, we show that a distributed network should use a higher learning rate, which in turn, can distort the energy landscape associated with the stored memory attractors. Specifically, narrow connecting paths emerge between memory attractors, leading to misclassification of evolving patterns. We demonstrate that compartmentalized networks with specialized subnetworks are the optimal solutions to memory storage for evolving patterns. We postulate that evolution of pathogens may be the reason for the immune system to encoded a focused memory, in contrast to the distributed memory used in the olfactory cortex that interacts with mixtures of static odors.

evolutionary biology↗

bioRxiv2020

Deep generative selection models of T and B cell receptor repertoires with soNNia

Subclasses of lymphocytes carry different functional roles to work together and produce an immune response and lasting immunity. Additionally to these functional roles, T and B-cell lymphocytes rely on the diversity of their receptor chains to recognize different pathogens. The lymphocyte subclasses emerge from common ancestors generated with the same diversity of receptors during selection processes. Here we leverage biophysical models of receptor generation with machine learning models of selection to identify specific sequence features characteristic of functional lymphocyte repertoires and subrepertoires. Specifically, using only repertoire level sequence information, we classify CD4+ and CD8+ T-cells, find correlations between receptor chains arising during selection, and identify T-cell subsets that are targets of pathogenic epitopes. We also show examples of when simple linear classifiers do as well as more complex machine learning methods.

immunology↗