Biology subjects

Smyth, P.

Publications and source records attributed to Smyth, P..

5 recordsLinked to original sources

bioRxiv2026

Covalent bond formation caught in a LOV photoreceptor

Light-oxygen-voltage (LOV) domains are blue-light photoreceptors of plants, algae and fungi, and among the most widely used tools in optogenetics. They switch on by forming a covalent thioether bond between a conserved cysteine and their flavin chromophore, in a reaction that needs a proton to cross from the cysteine to the flavin through a pocket containing essentially no water. Its mechanism has been debated for two decades1, and because the chemistry is over within a microsecond its elementary steps have stayed hidden. Here we combine 10 time-resolved serial femtosecond crystallography snapshots and infrared spectroscopy with QM/MM calculations to resolve the entire sequence of events at 1.4 [A] resolution: from excited-state distortion of the flavin ring (10-100 ps), through hydration of a surface channel (10 ns) and a single ordered water reaching the active site as the reactive cysteine shifts between its conformations (100-500 ns), to the thioether bond itself, caught half-formed at 1 {micro}s (half the molecules reacted, half still poised) and complete at 10-100 {micro}s. That water bridges the cysteine and the flavin and shuttles the proton, lowering the barrier from [~]35 to [~]15 kcal/mol and accelerating the reaction by roughly fourteen orders of magnitude (without it, the half-life would be [~]237,000 years), then departs before the bond forms. Proteins can therefore hydrate a dehydrated active site transiently and on demand to overcome otherwise prohibitive reaction barriers, a catalytic strategy that reaches well beyond photoreceptors.

biophysics↗

bioRxiv2026

Pan-cell-type prediction of splicing patterns from sequence and splicing factor expression

AO_SCPLOWBSTRACTC_SCPLOWAlternative splicing is a core determinant of cell-type-specific gene expression in humans, and its dysregulation contributes to many diseases including neurodegeneration, autoimmunity, and cancer. However, current deep learning models for predicting RNA expression from sequence are limited in how they handle cellular context dependence. These models typically achieve cell-type specificity by training separate models or heads for each tissue or cell-type, which assumes discrete, predefined cell types. This design not only prevents learning from pathological or experimentally perturbed transcriptomes, but also prevents generalization to new cellular contexts. Here we introduce PanExonNet, a deep learning framework that integrates cis- and trans-regulation by conditioning sequence-based splicing predictions on an inferred "splicing state" derived from the expression of RNA-binding proteins (RBPs) and spliceosome components. PanExonNet is trained on diploid, individual-specific gene sequences containing indels to predict splicing profiles derived from short-read RNA-seq, and it outputs multiple tracks at single-nucleotide resolution as well as donor-acceptor junction usage. Compared to multi-headed baselines such as Borzoi and Pangolin, PanExonNet exhibits substantially higher context specificity--as quantified by a {Delta}PSI correlation metric designed to isolate context-specific variation--and, crucially, generalizes to unseen cell types. Adding perturbational knockdown followed by RNA sequencing (KD-RNA-seq) data to the training set further improves generalization. This performance is enabled in part by our introduction of contextualizable convolutions, a modular layer that may broadly benefit genomic sequence modeling. This framework provides a scalable foundation for future DNA-to-RNA models that could improve variant effect prediction, the design of oligonucleotide therapeutics, and biomarker discovery across diverse cellular contexts.

genomics↗

bioRxiv2022

T-cell receptor specific protein language model for prediction and interpretation of epitope binding (ProtLM.TCR)

The cellular adaptive immune response relies on epitope recognition by T-cell receptors (TCRs). We used a language model for TCRs (ProtLM.TCR) to predict TCR-epitope binding. This model was pre-trained on a large set of TCR sequences (~62.106) before being fine-tuned to predict TCR-epitope bindings across multiple human leukocyte antigen (HLA) of class-I types. We then tested ProtLM.TCR on a balanced set of binders and non-binders for each epitope, avoiding model shortcuts like HLA categories. We compared pan-HLA versus HLA-specific models, and our results show that while computational prediction of novel TCR-epitope binding probability is feasible, more epitopes and diverse training datasets are required to achieve a better generalized performances in de novo epitope binding prediction tasks. We also show that ProtLM.TCR embeddings outperform BLOSUM scores and hand-crafted embeddings. Finally, we have used the LIME framework to examine the interpretability of these predictions.

bioinformatics↗

bioRxiv2020

Studying the dynamics of the gut microbiota using metabolically stable isotopic labeling and metaproteomics.

BackgroundThe gut microbiome and its metabolic processes are dynamic systems. Surprisingly, our understanding of gut microbiome dynamics is limited. Here we report a metaproteomic workflow that involves protein stable isotope probing (protein-SIP) and identification and quantification of partially labeled peptides. We also developed a package, which we call MetaProfiler, that corrects for false identifications and performs phylogenetic and time series analysis for the study of gut microbiome functional dynamics. ResultsFrom the stool sample of five mice that were fed with 15N hydrolysate from Ralstonia eutropha, we identified 15,297 non-redundant unlabeled peptides of which 10,839 of their heavy counterparts were quantified. These peptides revealed incorporation profiles over time that were different between and within taxa, as well as between and within clusters of orthologous groups (COGs). ConclusionsOur study helps unravel the complex dynamics of protein synthesis and bacterial dynamics in the mouse microbiome. AvailabilityMetaProfiler and the bioinformatic pipeline are available at https://github.com/psmyth94/MetaProfiler.git.

microbiology↗

bioRxiv2018

Bayesian Trees for Automated Cytometry Data Analysis

Cytometry is an important single cell analysis technology in furthering our understanding of cellular biological processes and in supporting clinical diagnoses across a variety hematological and immunological conditions. Current data analysis workflows for cytometry data rely on a manual process called gating to classify cells into canonical types. This dependence on human annotation significantly limits the rate, reproducibility, and scope of cytometrys use in both biological research and clinical practice. We develop a novel Bayesian approach for automated gating that classifies cells into different types by combining cell-level marker measurements with an informative prior. The Bayesian approach allows for the incorporation of biologically-meaningful prior information that captures the domain expertise of human experts. The inference algorithm results in a hierarchically-structured classification of individual cells in a manner that mimics the tree-structured recursive process of manual gating, making the results readily interpretable. The approach can be extended in a natural fashion to handle data from multiple different samples by the incorporation of random effects in the Bayesian model. The proposed approach is evaluated using mass cytometry data, on the problems of unsupervised cell classification and supervised clinical diagnosis, illustrating the benefits of both incorporating prior knowledge and sharing information across multiple samples.

bioinformatics↗