Biology subjects

Hutchinson, M.

Publications and source records attributed to Hutchinson, M..

4 recordsLinked to original sources

bioRxiv2026

Predicting Antibody Self-Association with Sequence Structure Fusion Models: The Central Role of CSI-BLI in Early Developability Screening

Antibody-based biologics are expanding rapidly, yet challenges in development from self-association, high viscosity, aggregation, and unfavorable clearance underscore the need for accurate in silico screening. Clone self-interaction biolayer interferometry (CSI-BLI) is a plate-based, low-material assay of weak, reversible self-association that serves as an early proxy for high-concentration viscosity and a complementary predictor of in vivo clearance. In a 246-mAb panel, CSI-BLI moderately correlates with viscosity; further, in hFcRn Tg32 mice (41 antibodies), CSI-BLI strongly associates with clearance. Here, we present an end-to-end framework that distinguishes high versus low self-interacting clones (CSI-BLI class) by coupling a fine-tuned protein language model (ESM-2) with residue-aligned 3D context from AlphaFold-predicted structures encoded as residue graphs. Disentangled multi-stream attention fuses sequence content, chain-aware positional information, and structural signals to capture spatially proximate interactions that are distant in sequence. Edit-distance-controlled splits across 1499 IgGs and 988 VHHs assess generalization. The structure-aware model achieves the highest hold-out performance (VHH-Fc F1 = 0.76; IgG F1 = 0.57), while a sequence-only disentangled variant outperforms a standard PLM baseline without structural inputs. Complementary biophysical feature-based models, built from AlphaFold structures and sequence/structure-derived physicochemical descriptors with cluster-aware selection, deliver robust, interpretable performance (VHH F1 = 0.72; IgG F1 = 0.57), with SHAP analyses highlighting charge/dipole, hydrophobicity, and aggregation-propensity drivers across CDRs and frameworks. This interaction-aware sequence-structure framework, supported by interpretable feature models, is extensible to other developability endpoints and broader protein classification tasks where joint modeling of language-derived representations and residue-level geometry is advantageous.

bioinformatics↗

bioRxiv2026

A stable subgenomic reporter coronavirus enables transcriptional profiling of bystander cells.

Insertion of fluorescent reporter genes into viral genomes is a powerful tool for monitoring infection. In coronaviruses, this is commonly achieved by replacing accessory open reading frames, thereby deleting endogenous gene functions. An alternative strategy is to manipulate viral RNA synthesis by inserting copies of the viral transcription regulatory sequence (TRS) which drive the transcription of viral subgenomic RNAs. However, coronavirus transcription is tightly regulated, and these modifications frequently disrupt native subgenomic RNA synthesis and attenuate viral growth. Here, we describe a reporter coronavirus that overcomes these limitations. Using human coronavirus (HCoV)-OC43 as a model system, we inserted an mNeonGreen reporter between the Spike and ORF5 coding regions, engineering the TRS and surrounding sequence to minimise off-target effects to transcription. This virus is genetically stable, with wildtype growth kinetics and unaltered subgenomic RNA transcriptional ratios. We developed a flexible reverse genetics system, which allows rapid cloning and virus recovery, supported by optimised HCoV-OC43 culture conditions, for high-titre stock generation, and validated analytical reagents. Our reporter virus enabled sensitive detection and isolation of infected cells, facilitating transcriptomic analyses that distinguish host responses in infected and bystander populations. We found that transcriptional responses to infection of cells in culture were predominantly inflammatory, rather than interferon-mediated, and that bystander cells upregulated pathways associated with cytokine response signalling and cell-cell contact sensing. Together, these tools expand the experimental utility of HCoV-OC43, an important seasonal respiratory pathogen and low containment model for betacoronavirus biology.

microbiology↗

bioRxiv2023

Enhancement of antibody thermostability and affinity by computational design in the absence of antigen

Over the last two decades, therapeutic antibodies have emerged as a rapidly expanding domain within the field biologics. In silico tools that can streamline the process of antibody discovery and optimization are critical to support a pipeline that is growing more numerous and complex every year. In this study, DeepAb, a deep learning model for predicting antibody Fv structure directly from sequence, was used to design 200 potentially stabilized variants of an anti-hen egg lysozyme (HEL) antibody. We sought to determine whether DeepAb can enhance the stability of these antibody variants without relying on or predicting the antibody-antigen interface, and whether this stabilization could increase antibody affinity without impacting their developability profile. The 200 variants were produced through a robust highthroughput method and tested for thermal and colloidal stability (Tonset, Tm, Tagg), affinity (KD) relative to the parental antibody, and for developability parameters (non-specific binding, aggregation propensity, self-association). In the designed clones, 91% and 94% exhibited increased thermal and colloidal stability and affinity, respectively. Of these, 10% showed a significantly increased affinity for HEL (5-to 21-fold increase), with most clones retaining the favorable developability profile of the parental antibody. These data open the possibility of in silico antibody stabilization and affinity maturation without the need to predict the antibody-antigen interface, which is notoriously difficult in the absence of crystal structures.

molecular biology↗

bioRxiv2023

Establishment and comparative genomics of a high-quality collection of mosquito-associated bacterial isolates -- MosAIC (Mosquito-Associated Isolate Collection)

Mosquitoes transmit medically important human pathogens, including viruses like dengue virus and parasites such as Plasmodium spp., the causative agent of malaria. Mosquito microbiomes are critically important for the ability of mosquitoes to transmit disease-causing agents. However, while large collections of bacterial isolates and genomic data exist for vertebrate microbiomes, the vast majority of work in mosquitoes to date is based on 16S rRNA gene amplicon data that provides limited taxonomic resolution and no functional information. To address this gap and facilitate future studies using experimental microbiome manipulations, we generated a bacterial Mosquito-Associated Isolate Collection (MosAIC) consisting of 392 bacterial isolates with extensive metadata and high-quality draft genome assemblies that are publicly available for use by the scientific community. MosAIC encompasses 142 species spanning 29 bacterial families, with members of the Enterobacteriaceae comprising 40% of the collection. Phylogenomic analysis of three genera, Enterobacter, Serratia, and Elizabethkingia, reveal lineages of mosquito-associated bacteria isolated from different mosquito species in multiple laboratories. Investigation into species pangenomes further reveals clusters of genes specific to these lineages, which are of interest for future work to identify functions underlying mosquito host association. Altogether, we describe the generation of a physical collection of mosquito-associated bacterial isolates, their genomic data, and analyses of selected groups in context of genome data from closely related isolates, providing a unique, highly valuable resource to investigate factors for bacterial colonisation and adaptation within mosquito hosts. Future efforts will expand the collection to include broader geographic and host species representation, especially from individuals collected from field populations, as well as other mosquito-associated microbes, including fungi, archaea, and protozoa.

microbiology↗