Biology subjects

Weber, J. M.

Publications and source records attributed to Weber, J. M..

6 recordsLinked to original sources

bioRxiv2026

The Quagmire of Immunogenicity-Guided Antibody Design: Labels Limit Progress

Generative sequence-based antibody design is increasingly steered by learned property predictors, so a design objective is only as well specified as the labels behind its oracle. Immunogenicity is among the most clinically consequential properties to condition on, and among the least well specified: the supervisory signal is a single anti-drug-antibody percentage (ADA%) per molecule, aggregated from published trials. To analyze that signal, we first extended and corrected the widely used 217-entry dataset to 231 therapeutics with paired sequences, spanning formats from mouse to fully human. Then, we manually reconstructed the clinical evidence for 110 of them from primary sources. We find that an antibody format lookup outperforms every sequence-based learned predictor on the 231-antibody dataset. Apparent accuracy tracks the format composition of the evaluation split rather than sequence signal. Furthermore, the labels understate their own evidence: molecules labeled at [≤]6 ADA% span up to 86% across trials. We recomputed labels from the trial-level measurements and found that the advantage of the format-based approach disappears, while the sequence-based predictors degrade less under the cohort-weighted mean. We conclude that progress is limited by the label itself, and recommend reporting ADA% at the trial level, treating the target as a distribution rather than a point value, and evaluating against a format-based baseline.

bioinformatics↗

bioRxiv2026

Clonal dynamics deviate from neutral drift in zebrafish spermatogenesis

Spermatogonial stem cells (SSCs) maintain male fertility, but how their clonal dynamics change with age remains poorly understood. Here, we use in vivo CRISPR barcoding in zebrafish to label SSCs and track their contributions to sperm production through monthly sampling across the fertile lifespan. We find that only a subset of embryonic germ cells contributes to adult sperm production and that the relative contributions of individual clones shift dramatically over time. To interpret these dynamics, we developed a mathematical model that quantifies clonal drift rates and formally tests neutral versus non-neutral hypotheses. Most SSC clones show significant evidence of drift, yet their dynamics are inconsistent with a neutral model where clones have equal fitness and their dynamics are driven solely by random chance. Instead, we observe heterogeneous clonal dynamics, including a positive relationship between clone size and drift rate. Together, these findings demonstrate that SSC clonal dynamics are non-neutral across the zebrafish reproductive lifespan with important implications for allele transmission.

developmental biology↗

bioRxiv2025

All-Atom Protein Sequence Design using Discrete Diffusion Models

Advancing protein design is crucial for breakthroughs in medicine and biotechnology. Traditional approaches for protein sequence representation often rely solely on the 20 canonical amino acids, limiting the representation of non-canonical amino acids and residues that undergo post-translational modifications. This work explores discrete diffusion models for generating novel protein sequences using the all-atom chemical representation SELFIES. By encoding the atomic composition of each amino acid in the protein, this approach expands the design possibilities beyond standard sequence representations. Using a modified ByteNet architecture within the discrete diffusion D3PM framework, we evaluate the impact of this all-atom representation on protein quality, diversity, and novelty, compared to conventional amino acid-based models. To this end, we develop a comprehensive assessment pipeline to determine whether generated SELFIES sequences translate into valid proteins containing both canonical and non-canonical amino acids. Additionally, we examine the influence of two noise schedules within the diffusion process--uniform (random replacement of tokens) and absorbing (progressive masking)--on generation performance. While models trained on the all-atom representation struggle to consistently generate fully valid proteins, the successfully generated proteins show improved novelty and diversity compared to their amino acid-based model counterparts. Furthermore, the all-atom representation achieves structural foldability results comparable to those of amino acid-based models. Lastly, our results highlight the absorbing noise schedule as the most effective for both representations. Data and code are available at https://github.com/Intelligent-molecular-systems/All-Atom-Protein-Sequence-Generation. Scientific ContributionThis work introduces a discrete diffusion-based framework for protein sequence generation using an all-atom representation, enabling the incorporation of non-canonical amino acids and post-translational modifications. Additionally, it provides a comprehensive evaluation pipeline to assess the validity of generated proteins, demonstrating how noise schedules within the diffusion process impact sequence novelty, diversity, and structural foldability.

bioinformatics↗

bioRxiv2023

Guiding diffusion models for antibody sequence and structure co-design with developability properties

Recent advances in deep generative methods have allowed antibody sequence and structure co-design. This study addresses the challenge of tailoring the highly variable complementarity-determining regions (CDRs) in antibodies to fulfill developability requirements. We introduce a novel approach that integrates property guidance into the antibody design process using diffusion probabilistic models. This approach allows us to simultaneously design CDRs conditioned on antigen structures while considering critical properties like solubility and folding stability. Our property-conditioned diffusion model offers versatility by accommodating diverse property constraints, presenting a promising avenue for computational antibody design in therapeutic applications. Code is available at https://github.com/amelvim/antibody-diffusion-properties.

bioinformatics↗

bioRxiv2023

Germ cells do not progress through spermatogenesis in the infertile zebrafish testis

Vertebrate spermatogonial stem cells maintain sperm production over the lifetime of an animal but fertility declines with age. While morphological studies have greatly informed our understanding of typical spermatogenesis, the molecular and cellular mechanisms underlying spermatogenesis are not yet understood, particularly with respect to the onset of fertility. We used single-cell RNA sequencing to generate a developmental atlas of the zebrafish testis. Using 5 timepoints across the adult life of a zebrafish, we described cellular profiles in the testis during and after fertility. While all germ cell stages of spermatogenesis are detected in testes from fertile adult zebrafish, testes from older infertile males only contained spermatogonia and a reduced population of spermatocytes. These remaining germ cells are transcriptionally distinct from fertile spermatogonia. Immune cells including macrophages and lymphocytes drastically increase in abundance in infertile testes. Our developmental atlas reveals the cellular changes as the testis ages and defines a molecular roadmap for the regulation of male fertility.

developmental biology↗

bioRxiv2022

ERnet: a tool for the semantic segmentation and quantitative analysis of endoplasmic reticulum topology for video-rate super-resolution imaging

The topology of endoplasmic reticulum (ER) network is highly regulated by various cellular and environmental stimuli and affects major functions such as protein quality control and the cells response to metabolic changes. The ability to quantify the dynamical changes of the ER structures in response to cellular perturbations is crucial for the development of novel therapeutic approaches against ER associated diseases, such as hereditary spastic paraplegias and Niemann Pick Disease type C. However, the rapid movement and small spatial dimension of ER networks make this task challenging. Here, we combine video-rate super-resolution imaging with a state-of-the-art semantic segmentation method capable of automatically classifying sheet and tubular ER domains inside individual cells. Data are skeletonised and represented by connectivity graphs to enable the precise and efficient quantification and comparison of the network connectivity from different complex ER phenotypes. The method, called ERnet, is powered by a Vision Transformer architecture, and integrates multi-head self-attention and channel attention into the model for adaptive weighting of frames in the time domain. We validated the performance of ERnet by measuring different ER morphology changes in response to genetic or metabolic manipulations. Finally, as a means to test the applicability and versatility of ERnet, we showed that ERnet can be applied to images from different cell types and also taken from different imaging setups. Our method can be deployed in an automatic, high-throughput, and unbiased fashion to identify subtle changes in cellular phenotypes that can be used as potential diagnostics for propensity to ER mediated disease, for disease progression, and for response to therapy.

bioengineering↗