Biology subjects

Daouda, T.

Publications and source records attributed to Daouda, T..

4 recordsLinked to original sources

bioRxiv2026

scDisentangle: sequential disentanglement for single-cell perturbation response prediction

Predicting the transcriptional response of single cells to perturbation is a central goal of single-cell modeling, with broad application to analysis and experimental design. Existing methods often fail to beat simple baselines and offer little insight into how biological variation is organized. We present scDisentangle, a model that decomposes single-cell expression into observed attributes and a sequence of residual components, generating counterfactuals that preserve source cell identity. scDisentangle outperformed current models and naive baselines on cytokine stimulation, liver infection, and unseen combinatorial genetic perturbations, on which accuracy was largely independent of how closely a response resembled the average perturbation effect. In an immune checkpoint inhibitor-associated myocarditis cohort, cell-resolved counterfactuals predicted donor-specific intracardiac CD8+ T cell states from peripheral blood. In a castration-regeneration prostate atlas, predictions recapitulated luminal dynamics and progenitor-marker kinetics upon androgen add-back. scDisentangle's coarse-to-fine latent structure aligned with hematopoietic lineage organization, with early levels capturing a less differentiated core progressively refined into cell-type-specific programs.

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bioRxiv2026

Neurotox: Deep learning decodes conserved hallmarks of neurotoxicity across venomous species

Neurotoxic proteins drive the most pathophysiological effects of animal envenomation, yet it remains unclear whether neurotoxicity is encoded directly within the protein sequence or emerges from higher-order structure binding and interactions with their target receptor. To address this, we developed Neurotox, a sequence-based deep learning framework trained on 200,000 curated protein sequences, with balanced representation of neurotoxic and non-neurotoxic proteins across taxa, achieving high classification accuracy (96%) with strong performance on unseen toxin families. We further introduced a controlled sequence-representation warping strategy that selectively perturbs neurotoxicity-relevant features, inducing a systematic loss of predicted neurotoxicity while preserving primary sequence identity. Structural modeling using AlphaFold 3 showed that, for most top-ranked toxins, warping disrupted {beta}-sheet architectures and reduced interface precision, with all top candidates showing highly significant effects (p < 0.0001). These structural changes were accompanied by recurrent cysteine-centered substitutions, implicating disruption of conserved disulfide frameworks. A single exception retained its global fold (C RMSD = 2.8 [A]), maintained low PAE, high pLDDT, and high pDockQ scores, and preserved a close arginine-glutamate contact (Arg53-Glu75), yet still exhibited marked attenuation of predicted neurotoxicity. These results suggest that neurotoxicity arises from distributed sequence features that shape secondary-structure organization and receptor interaction, rather than from isolated contact residues alone. TeaserDeep learning suggests the identification of neurotoxicity hallmarks directly from amino acid sequences across diverse species and toxin families.

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bioRxiv2021

Codon arrangement modulates MHC-I peptides presentation: implications for a SARS-CoV-2 peptide-based vaccine

Among various vaccination strategies, peptide-based vaccines appear as excellent candidates because they are cheap to produce, are highly stable and harbor low toxicity. However, predicting which MHC-I Associated Peptide (MAP) will ultimately reach cell surface remains challenging, due to high false discovery rates. Previously, we demonstrated that synonymous codon arrangement (usage and placement) is predictive of, and modulates MAP presentation. Here, we apply CAMAP (Codon Arrangement MAP Predictor), the artificial neural network we used to unveil the role of codon arrangement in MAP presentation, to predict SARS-CoV MAPs. We report that experimentally identified SARS-CoV-1 and SARS-CoV-2 MAPs are associated with significantly higher CAMAP scores. Based on CAMAP scores and binding affinity, we identified 48 non-overlapping MAP candidates for a peptide-based vaccine, ensuring coverage for a high proportion of HLA haplotypes in the US population (>78%) and SARS-CoV-2 strains (detected in >98% of SARS-CoV-2 strains present in the GISAID database). Finally, we built an interactive web portal (https://www.epitopes.world) where researchers can freely explore CAMAP predictions for SARS-CoV-1/2 viruses. Collectively, we present an analysis framework that can be generalizable to empower the rapid identification of virus-specific MAPs, including in the context of an emergent virus, to help accelerate target identification for peptide-based vaccine designs that could be critical in safely attaining group immunity in the context of a global pandemic.

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bioRxiv2020

Codon arrangement modulates MHC-I peptides presentation

MHC-I associated peptides (MAPs) play a central role in the elimination of virus-infected and neoplastic cells by CD8 T cells. However, accurately predicting the MAP repertoire remains difficult, because only a fraction of the transcriptome generates MAPs. In this study, we investigated whether codon arrangement (usage and placement) regulates MAP biogenesis. We developed an artificial neural network called Codon Arrangement MAP Predictor (CAMAP), predicting MAP presentation solely from mRNA sequences flanking the MAP-coding codons (MCCs), while excluding the MCC per se. CAMAP predictions were significantly more accurate when using original codon sequences than shuffled codon sequences which reflect amino acid usage. Furthermore, predictions were independent of mRNA expression and MAP binding affinity to MHC-I molecules and applied to several cell types and species. Combining MAP ligand scores, transcript expression level and CAMAP scores was particularly useful to increaser MAP prediction accuracy. Using an in vitro assay, we showed that varying the synonymous codons in the regions flanking the MCCs (without changing the amino acid sequence) resulted in significant modulation of MAP presentation at the cell surface. Taken together, our results demonstrate the role of codon arrangement in the regulation of MAP presentation and support integration of both translational and post-translational events in predictive algorithms to ameliorate modeling of the immunopeptidome. Author summaryMHC-I associated peptides (MAPs) are small fragments of intracellular proteins presented at the surface of cells and used by the immune system to detect and eliminate cancerous or virus-infected cells. While it is theoretically possible to predict which portions of the intracellular proteins will be naturally processed by the cells to ultimately reach the surface, current methodologies have prohibitively high false discovery rates. Here we introduce an artificial neural network called Codon Arrangement MAP Predictor (CAMAP) which integrates information from mRNA-to-protein translation to other factors regulating MAP biogenesis (e.g. MAP ligand score and transcript expression levels) to improve MAP prediction accuracy. While most MAP predictive approaches focus on MAP sequences per se, CAMAPs novelty is to analyze the MAP-flanking mRNA sequences, thereby providing completely independent information for MAP prediction. We show on several datasets that the integration of CAMAP scores with other known factors involved in MAP presentation (i.e. MAP ligand score and mRNA expression) significantly improves MAP prediction accuracy, and further validate CAMAP learned features using an in-vitro assay. These findings may have major implications for the design of vaccines against cancers and viruses, and in times of pandemics could accelerate the identification of relevant MAPs of viral origins.

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