Biology subjects

Webb, G.

Publications and source records attributed to Webb, G..

4 recordsLinked to original sources

bioRxiv2026

ACENet: a graph neural network for predicting enzyme pHmin using structure and sequence insights

The acid activity of enzymes, characterized by the minimum pH at which enzymes remain active (pHmin), is crucial for industrial applications in acidic environments. However, the rational design of acid-active enzymes remains challenging due to limited understanding of sequence-structure- activity relationships under acidic conditions. Here, we propose ACENet, a graph neural network that predicts enzyme pHmin by integrating surface features of protein structures with evolutionary representations derived from the large-scale protein language model ESM-2. ACENet achieved a Pearson correlation coefficient of 0.85 on the test dataset, significantly outperforming other deep learning baseline models and maintains stable pHmin predictions under various conditions. Even on a subset of the dataset with less than 20% homology, the PCC remains above 0.5, with an RMSE (Root mean square error) less than 1.4. ACENet also present excellent performance in the annotation of pHmin for homologous proteins and the predictive screening of minimal active pH in protein mutants. Remarkably, ACENet could identify the catalytic region as key determinants of acid activity through residue-level interpretability analysis. Overall, ACENet accelerates the development of highly efficient biocatalysts for diverse applications where acidic conditions predominate.

bioinformatics↗

bioRxiv2025

Predicting Disease-Specific Histone Modifications and Functional Effects of Non-coding Variants by Leveraging DNA Language Models

BackgroundEpigenetic modifications play a vital role in the pathogenesis of human diseases, particularly neurodegenerative disorders such as Alzheimers disease (AD), where dysregulated histone modifications are strongly implicated in disease mechanisms. While recent advances underscore the importance of accurately identifying these modifications to elucidate their contribution to AD pathology, existing computational methods remain limited by their generic approaches that overlook disease-specific epigenetic signatures. ResultsTo bridge this gap, we developed a novel large language model (LLM)-based deep learning framework tailored for disease-contextual prediction of histone modifications and variant effects. Focusing on AD as a case study, we integrated epigenomic data from multiple patient samples to construct a comprehensive, disease-specific histone modification dataset, enabling our model to learn AD-associated molecular signatures. A key innovation of our approach is the incorporation of a Mixture of Experts (MoE) architecture, which effectively distinguishes between disease and healthy epigenetic states, allowing for precise identification of AD-relevant epigenetic modification patterns. Our model demonstrates robust performance in disease-specific histone modification prediction, achieving mean area under receiver-operating curves (AUROCs) ranging from 0.7863 to 0.9142, significantly outperforming existing state-of-the-art methods that lack disease context. Beyond accurate modification site prediction, our framework provides important biological insights by successfully prioritizing AD-associated genetic variants, which show significant enrichment in disease-relevant pathways, supporting their potential functional role in AD pathogenesis. These findings suggest that differential modification loci identified by our model may represent key regulatory elements in AD. ConclusionsOur framework establishes a powerful new paradigm for epigenetic research that can be extended to other complex diseases, offering both a valuable tool for variant effect interpretation and a promising strategy for uncovering novel disease mechanisms through epigenetic profiling.

bioinformatics↗

bioRxiv2022

Anatomy of the heart of the leatherback turtle

Non-crocodylian reptiles have hearts with a single ventricle, which is partially separated by a muscular ridge that provide some separation of blood flows. An exceptional situation exists in monitor lizards and pythons, where the ventricular left side generates a much higher systolic blood pressure than the right side, thus resembling mammals and birds. This functional division of the ventricle depends on a large muscular ridge and may relate to high metabolic demand. The large leatherback turtle (<1000 kg), with its active ocean-going lifestyle and elevated body temperatures, may have similar adaptations. Here, we report on the anatomy the hearts of two leatherback turtles. One stranded in Ballum, Denmark in 2020, and was examined in detail, supplemented by observations and photos of an additional stranding specimen from Canada. The external morphology of the leatherback heart resembles that of other turtles, but it is large. We made morphometric measurements of the Ballum heart and created an interactive 3D model using high resolution MRI. The volume of the ventricle was 950 ml, from a turtle of 300 kg, which is almost twice as large as in other reptiles. The Ballum heart was compared to MRI scans of the hearts of a tortoise, a python, and a monitor lizard. Internally, the leatherback heart is typical of non-crocodylian reptiles, and did not contain the well-developed septation found in pythons and monitor lizards. We conclude that if leatherback turtles have exceptional circulation needs, they are sustained with a relatively large but otherwise typical non-crocodylian reptile heart.

evolutionary biology↗

bioRxiv2018

Structural Capacitance in Protein Evolution and Human Diseases

Canonical mechanisms of protein evolution include the duplication and diversification of pre-existing folds through genetic alterations that include point mutations, insertions, deletions, and copy number amplifications, as well as post-translational modifications that modify processes such as folding efficiency and cellular localization. Following a survey of the human mutation database, we have identified an additional mechanism, that we term structural capacitance, which results in the de novo generation of microstructure in previously disordered regions. We suggest that the potential for structural capacitance confers select proteins with the capacity to evolve over rapid timescales, facilitating saltatory evolution as opoposed to exclusively canonical Darwinian mechanisms. Our results implicate the elements of protein microstructure generated by this distinct mechanism in the pathogenesis of a wide variety of human diseases. The benefits of rapidly furnishing the potential for evolutionary change conferred by structural capacitance are consequently counterbalanced by this accompanying risk, with the extent of this determined by the host immune system. The phenomenon of structural capacitance has implications ranging from the ancestral diversification of protein folds to the engineering of synthetic proteins with enhanced evolvability.

biochemistry↗