Biology subjects

Howard, B.

Publications and source records attributed to Howard, B..

6 recordsLinked to original sources

bioRxiv2026

Empirical Geometry-Guided Modeling Enables Robust, High-Throughput Collagen Structure Prediction

Protein structure prediction is increasingly dominated by large learned generative models, yet for proteins governed by strong structural constraints, much of the relevant conformational space may be captured by substantially more compact representations. Collagen provides a compelling test case: its repeating Gly-X-Y sequence, restricted backbone conformations, and conserved triple-helical topology define a highly constrained structural manifold. Here, we develop a Collagen-specific Deterministic Structure Modeler (CDSM), extending the empirical geometric parameterization of THeBuScr into a robust, all-atom structure-prediction pipeline, and benchmark it against AlphaFold 3, Boltz-2, Chai-1, and Protenix-v1 on 80 experimentally resolved collagen triple-helical structures. CDSM increases coverage of this benchmark from 8.8% for native THeBuScr to 93.8%. Across the 75 structures successfully predicted by all methods, CDSM closely reproduces experimental backbone and global geometry, with fewer large-error predictions, while the learned models achieve modestly higher local and side-chain accuracy. When evaluated only on structures deposited after each learned model's training-data cutoff, CDSM becomes more competitive, with aggregate win rates increasing from 29% to 55% for TM-score and from 40% to 65% for backbone RMSD, while CDSM performance remains comparatively stable across the same subsets. CDSM generates structures in 2.4 s on a single CPU core at approximately $3 x 10^-5 per structure, making it 400 to 790x cheaper than the learned methods even when each is run on its lowest-cost compatible GPU. These results demonstrate how identifying and explicitly encoding relevant geometric constraints can dramatically compress a structure-prediction problem, retaining much of the accuracy at orders-of-magnitude lower computational cost. More broadly, CDSM illustrates the potential for compact, executable scientific representations that complement large learned models, and motivates future AI-driven searches over algorithms, representations, and empirical parameterizations to discover and continually refine such models for other structural protein domains.

bioinformatics↗

bioRxiv2026

Leveraging Targeted Gene Sets and Neural Networks for Zebrafish Transcriptome Extrapolation in High-Throughput Toxicogenomics

BackgroundZebrafish (Danio rerio) are a powerful vertebrate model for developmental toxicology and chemical safety assessment, yet large-scale transcriptomics in zebrafish remains limited by cost and data heterogeneity. Targeted transcriptomics offers a cost-effective alternative, but gene extrapolation methods tailored to zebrafish have not been systematically developed or evaluated. ObjectivesWhile the S1500+ platform is widely used for toxicogenomics research with rat, mouse, and human cell lines as model systems, its use in zebrafish has been limited due to data scarcity and lack of suitable bioinformatics approaches for analysis of such data. To that end, we sought to (i) curate a large zebrafish transcriptomic training data resource, and (ii) evaluate multiple machine learning strategies for reconstructing unmeasured transcriptome-wide expression profiles for data originating from the zebrafish-specific reduced representation gene set ("Zf S1500+"). MethodsWe assembled 14,924 zebrafish RNA-Seq samples covering 21,930 genes across 1,246 studies. Using the Zf S1500+ gene subset (3,062 genes), we trained and tested three extrapolation approaches: principal components regression (PCR), a locally weighted extension of PCR (PCR+), and a neural network mixture-of-experts model (NN-MoE). Model performance was assessed using mean absolute error (MAE), mean squared regression error (MSRE), and weighted variants of these metrics. ResultsExtrapolation performance using the baseline approach was strongly influenced by tissue and developmental context, with within-tissue models outperforming cross-tissue models. Errors were lowest when training and testing were conducted within the same tissue or between developmentally related tissues. Both PCR+ and NN-MoE improved upon the baseline PCR approach, with NN-MoE reducing average MAE by [~]20% and MSRE by [~]17%. Importantly, extrapolation remained reliable for the majority of genes, even when limiting output to high-confidence predictions using an empirical MAE threshold. ConclusionsWe demonstrate that targeted transcriptomics can be effectively extended to zebrafish, enabling robust transcriptome-wide extrapolation at reduced cost. The NN-MoE method provided the most substantial gains, highlighting the value of non-linear and ensemble modeling in heterogeneous datasets. These results establish a scalable framework for zebrafish toxicogenomics and suggest that accuracy will continue to improve with larger, better-annotated datasets, paving the way for broader application in chemical safety assessments.

bioinformatics↗

bioRxiv2025

Rewiring the fusion oncoprotein EWS/FLI1 in Ewing sarcoma with bivalent small molecules

Deregulated transcription is a defining hallmark of cancer, especially pediatric malignancies, which are frequently driven by fusion transcription factors. Targeting transcription factors directly has been challenging as they lack druggable pockets. Recently, chemically induced proximity has enabled the rewiring of transcriptional activators to drive expression of pro-apoptotic genes using bivalent small molecules. Targeting fusion transcription factors, such as EWS/FLI1 in Ewing sarcoma, with these compounds, may open new therapeutic avenues. Here, we develop a small molecule, EB-TCIP, that recruits FKBP12F36V-tagged EWS/FLI1 to DNA sites bound by the transcriptional regulator BCL6, leading to rapid expression of BCL6 target genes. EB-TCIP activity is dependent on ternary complex formation and specific to cells that express FKBP-EWS/FLI1. This proof-of-concept study demonstrates that EWS/FLI1 can be relocalized on chromatin to induce genes that are ordinarily regulated by a transcriptional repressor. Insights herein will guide the development of bivalent molecules that rewire fusion transcription factors.

cancer biology↗

bioRxiv2024

Identifying the Fusarium species involved in foot rot disease of faba beans in the UK using a combined molecular and microbiological approach

Foot rot is a devastating disease of faba bean crops globally, including in the United Kingdom, the worlds third largest producer. To identify the causal agents, we have sampled foot rot-affected plants and soils from faba bean crops across England. We isolated organisms associated with foot rot disease in culture and assessed pathogenicity in vivo to evaluate the infectivity of the isolates on faba bean. We identified the pathogenic isolates using DNA barcoding of the Internal Transcribed Spacer (ITS) and Translation Elongation Factor one (TEF1 ) molecular markers. A total of 113 clonal isolates were obtained from infected plants and soil samples across England. Of these, 60 were pathogenic, inducing mild to severe symptoms on faba bean. Sequencing of the ITS and TEF1 loci and comparison against sequence databases (Genbank and Fusarium_ID) enabled the identification of pathogenic isolates, in decreasing order of frequency, as Fusarium oxysporum (26.6 %), F. vanettenii (25%), F. redolens (15 %), F. solani (11.6%), F. culmorum (8.3 %), F. avenaceum (6.7 %), F. equiseti (1.7 %), F. clavum (1.7 %), Clonostachys rosea (1.7%) and Alternaria alternata (1.7%). F. oxysporum, F. redolens and F. avenaceum induced the most severe symptoms, whilst F. solani induced the least severe symptoms. Determining the most prevalent causal agents of foot rot in UK faba beans will facilitate targeted disease monitoring and intervention for enhanced productivity.

microbiology↗

bioRxiv2020

Optogenetic activation of CA1 pyramidal neurons in vivo induces hypersynchronous and low voltage fast seizures

Increasing evidence supports the idea that the CA1 of the hippocampus plays an important role in the pathogenesis of temporal lobe epilepsy (TLE). There is however a lack of proof that the over-excitation of CA1 alone is sufficient in inducing seizures in vivo. Furthermore, the relevance of the seizures induced from the over-excitation of CA1 to the pathophysiology of TLE is undetermined. Here, we employed optogenetics to activate pyramidal neurons (PNs) in CA1, which reliably induced generalized seizures in freely moving non-epileptic mice. We showed that repeated photostimulations had a kindling effect. In addition, seizures induced by over-active CA1 PNs were dominated by two distinctive onset patterns, i.e. hypersynchronous (HYP) and low voltage fast (LVF) activities, which are widely recorded in patients with and animal models of TLE. In our study, HYP seizures were predominantly associated with the first photostimulation and were entirely replaced by the LVF type afterwards. This phenomenon suggests that the activation of CA1 PNs, when occurring after the first seizure, could lead to the recruitment of GABAergic interneurons to participate in the seizure generation. These findings suggest that seizures induced from the over-excitation of CA1 PNs likely involved the same hippocampal networks and cellular mechanisms underlying TLE.

neuroscience↗

bioRxiv2017

Transparency in Authors’ Contributions and Responsibilities to Promote Integrity in Scientific Publication

In keeping with the growing movement in scientific publishing toward transparency in data and methods, we argue that the names of authors accompanying journal articles should provide insight into who is responsible for which contributions, a process should exist to confirm that the list is complete, clearly articulated standards should establish whether and when the contributions of an individual justify authorship credit, and those involved in the generation of scientific knowledge should follow these best practices.\n\nTo accomplish these goals, we recommend that journals adopt common and transparent standards for authorship, outline responsibilities for corresponding authors, adopt the CRediT (Contributor Roles Taxonomy)1 methodology for attributing contributions, include this information in article metadata, and encourage authors to use the digital persistent identifier ORCID.2 Furthermore, we suggest that research institutions have regular open conversations on authorship criteria and ethics and that funding agencies adopt ORCID and accept CRediT. Scientific societies should further authorship transparency by promoting these recommendations through their meetings and publications programs.

scientific communication and education↗