Biology subjects

Lax, B. M.

Publications and source records attributed to Lax, B. M..

3 recordsLinked to original sources

bioRxiv2026

Data-efficient distal engineering of fluorinase using zero-shot models

Fluorinases have high potential for industrial biofluorination but any applications have been precluded by low catalytic efficiency and resistance to active site engineering. In this work, we employed PRIZM, a computational workflow utilizing an existing low-N dataset and zero-shot models for in silico prediction of activity-enhancing mutations at distal sites. The combination of these predictions with expert opinion led to the identification of 21 fluorinase mutants with enhanced relative activities, while 3 variants showed increased melting temperatures. A mutation in the hexameric interface, K237R, resulted in the largest stability gain, a more than 3.2-fold improvement in catalytic efficiency at 57{degrees}C, and an 8-fold increase in relative activity at 62{degrees}C. These results highlight the potential of distal fluorinase engineering for improving properties required to realize its industrial applications.

bioengineering↗

bioRxiv2026

PRIZM: Combining Low-N Data and Zero-shot Models to Design Enhanced Protein Variants

Machine learning has repeatedly shown the ability to accelerate protein engineering, but many approaches demand large amounts of robust, high-quality training data as well as substantial computational expertise. While large pre-trained models can function as zero-shot proxies for predicting variant effects, selecting the best model for a given protein property is often non-trivial. Here, we introduce Protein Ranking using Informed Zero-shot Modelling (PRIZM), a two-phase workflow that first uses a high-quality low-N dataset to identify the most suitable pre-trained zero-shot model for a target protein property and then applies that model to rank and prioritize an in silico variant library for experimental testing. Across diverse benchmark datasets spanning multiple protein properties, PRIZM reliably separated low- from high-performing models using datasets of [~]20 labelled variants. We further demonstrate PRIZM in enzyme engineering case studies targeting sucrose synthase thermostability and glycosyltransferase activity, where PRIZM-guided selection identified improved variants, including gains of [~]3{degrees}C in apparent melting temperature and [~]20% higher relative activity. PRIZM provides an accessible, data-efficient route to leverage foundation models for protein design while requiring minimal experimental data.

bioinformatics↗

bioRxiv2023

Tregs constrain CD8+ T cell priming required for curative intratumorally anchored anti-4-1BB immunotherapy

Although co-stimulation of T cells with agonist antibodies targeting 4-1BB (CD137) improves antitumor immune responses in preclinical studies, clinical development has been hampered by on-target, off-tumor toxicity. Here, we report the development of a tumor-anchored 4-1BB agonist (4-1BB-LAIR), which consists of an 4-1BB antibody fused to the collagen binding protein LAIR. While combination treatment with an antitumor antibody (TA99) displayed only modest efficacy, simultaneous depletion of CD4+ T cells boosted cure rates to over 90% of mice. We elucidated two mechanisms of action for this synergy: CD4 eliminated tumor draining lymph node Tregs, enhancing priming and activation of CD8+ T cells, and TA99 + 4-1BB-LAIR supported the cytotoxic program of these newly primed CD8+ T cells within the tumor microenvironment. Replacement of CD4 with CTLA-4, a clinically approved antibody that enhances T cell priming, produced equivalent cure rates while additionally generating robust immunological memory against secondary tumor rechallenge. One Sentence SummaryInhibition of nodal Tregs enhances CD8+ T cell priming, improving antitumor responses to collagen-anchored 4-1BB combination therapy.

immunology↗