Biology subjects

Lake, M.

Publications and source records attributed to Lake, M..

4 recordsLinked to original sources

bioRxiv2026

Leukocyte Immunoglobulin-Like Receptor B1 and its Interactions with Human Leukocyte Antigens

Interactions between Human Leukocyte Antigen (HLA) molecules and their cognate immunoreceptors are essential for regulating innate and adaptive immune cell functions. Leukocyte Immunoglobulin-like Receptors (LILRs) are key regulators of HLA-mediated immune responses, owing to their broad expression across immune cell populations and their ability to modulate both immune activation and tolerance. Among these, LILRB1-HLA interactions are increasingly recognised as important in transplantation, chronic infection and cancer therapies. Unlike other HLA-binding receptors, which recognise epitopes specific to HLA subsets, LILRB1 primarily engages the relatively conserved 3 and {beta}2-microglobulin components of HLA molecules, supporting its role as a broad regulator of pan-HLA class I-mediated functions. Nonetheless, there have been conflicting findings regarding the breadth of LILRB1-HLA-I interactions. While direct affinity studies on a limited subset of HLA-I molecules have revealed no significant differences in LILRB1 binding, broader analyses using single-antigen bead arrays suggest underlying variability. Here, we show through a broad binding assay that, while LILRB1 is a broad HLA-I-binding receptor, it exhibits differential preferences across HLA-I allotypes. Molecular dynamics analyses of the HLA-I-LILRB1 interface suggest that HLA-3 domain dynamism underlies these binding differences. We further determined the crystal structure of LILRB1 and used it to highlight intrinsic structural flexibility within its domains. Finally, these structural insights were leveraged to refine our understanding of the binding modalities of therapeutic monoclonal antibodies currently described. Together, our findings establish structural and mechanistic bases for differential HLA-I recognition by LILRB1 and provide insights into immunotherapeutic targeting of LILRB1.

immunology↗

bioRxiv2026

Correlative SHG-AFM imaging workflow for label-free quantitative analysis of collagen structure-function relationships

We present a user-friendly correlative second harmonic generation (SHG) and atomic force microscopy (AFM) imaging workflow for quantifying the nanomechanical properties of collagen in unfixed, unlabeled tissue sections. SHG Aligned Profiling for Elasticity and Segmentation or SHAPES utilizes SHG imaging to guide AFM force mapping, enabling label-free, anatomically specific selection of regions of interest, facilitating spatially resolved characterization of fibrous collagen morphology and local stiffness. Compared to brightfield or confocal contrast-guided AFM mapping, SHG improves anatomical specificity without fixation or staining, enabling downstream analysis on the same tissue section while preserving spatial correspondence between structural (SHG) and mechanical (AFM) data. Rapid identification of regions of interest also reduces the risk of costly AFM tip breakage, improving throughput and reducing operator burden. Utilizing only standard turn-key commercial systems common in many user facilities and clinical laboratories, multimodal image coregistration and automated identification of anatomical regions of interest are employed to integrate SHG and AFM datasets across complex sample topographies. Coregistration of SHG and AFM images substantially increases the number of usable datasets per measurement session, facilitating translation of these complementary modalities and bridging nanomechanical imaging with clinical research practice.

biophysics↗

bioRxiv2025

Scalable prediction of symmetric protein complex structures

All life relies on proteins to function, yet accurately modeling protein structures that exceed {approx} 10, 000 amino acids or have higher-order geometries remains difficult. Existing solutions are limited to specific scenarios, require considerable computational resources, or are otherwise unscalable. Consequently, many large, disease-relevant protein complexes in the human proteome, as well as nearly all viruses and numerous other classes, are impractical to model with high fidelity for drug development. To modulate these protein complexes and viruses, structural information is eminently valuable, and often essential. In the last two years, machine learning based-tools that can generate binders to a given target structure with high hit rates have emerged. Combined with high-throughput screening, these technologies can far outpace traditional drug discovery. However, they cannot function well without accurate models of their target structures. Thus, to unlock the full power of AI-driven drug discovery, a scalable method must be developed to predict large protein complex structures. To overcome this bottleneck, we introduce Plica-1, a physics-based method to rapidly and accurately predict the structure of arbitrarily large, symmetric protein complexes. Validated across 4 major symmetry classes (icosahedral, tetrahedral, octahedral, and cyclic), the method consistently achieves near-experimental levels of accuracy, i.e., RMSD < 5[A]. In test cases, the method runs in < 5 minutes on consumer hardware, 103-105 times faster than the closest comparable software. The largest structure currently built, at {approx}40,000 amino acids, is > 8 times the limit of existing machine learning methods. The results demonstrate that protein complexes can be modeled at significantly improved speeds and scales, making Plica-1 a promising tool for protein engineering and drug development.

bioengineering↗

bioRxiv2021

Growth rate as a modulator of tooth patterning during adaptive radiations

Tooth classes are a mammalian innovation that has contributed to the evolutionary success of mammals. However, our understanding of how tooth classes have evolved and diversified remains limited. Here, we use the evolutionary radiation of noctilionoid bats, the most diverse clade of mammals in terms of diet type, as a model system to show how the tooth developmental program evolved during the adaptation to new diet types. We combined morphological, developmental, cellular, and modeling approaches, to investigate the developmental differences between two tooth classes, molars and premolars and the mechanisms driving their diversification. We demonstrate that tooth classes develop through independent developmental cascades that deviate from classical models. Then we showed that the dramatic diversification of tooth number and size is driven by the modulation of the growth rate of the jaw, explaining the rapid gain/loss of teeth during the evolution of this clade. Finally, we propose a mathematical model that recapitulates the successive appearance of tooth buds and supports the hypothesis that growth acts as a key driver of the evolution of tooth number and size by tinkering with reaction/diffusion processes. Our results demonstrate developmental independence between mammalian tooth classes and provide a mechanism to explain their rapid diversification. More broadly, these results reveal how simple modifications of one developmental mechanism by another can drive the evolution of repeated structures during adaptive radiations.

evolutionary biology↗