Biology subjects

Macdonald, C.

Publications and source records attributed to Macdonald, C..

12 recordsLinked to original sources

bioRxiv2026

Distinct activation mechanisms underlie ligand efficacy at a GPCR

Ligands that bind to the same G protein-coupled receptor (GPCR) can produce widely different levels of signaling. These differences in efficacy are central to pharmacology, yet how a receptor sequence determines signaling level remains incompletely understood. Here we develop a deep mutational pharmacology approach to measure how each residue in the -opioid receptor (OR) shapes responses to 15 opioids, yielding ~400,000 measurements of variant effects on signaling and expression, including full concentration-response series for three agonists. Mutational effects on potency are strongest near the orthosteric pocket, whereas effects on efficacy extend through the transmembrane core and intracellular face. Ligands share many functional determinants, but sensitivity to mutation differs with efficacy. As efficacy decreases, the residues required for signaling shift from the binding pocket towards the intracellular face; partial agonists therefore depend on residues that are dispensable for full agonists. Mutations can broadly increase receptor responses, allowing antagonists such as naloxone to produce signaling comparable to a full agonist and increasing the efficacy of partial agonist ligands. Combined mutations can also reorder the relative efficacies of ligands, showing that sequence changes reshape pharmacological differences rather than simply increasing or decreasing all responses together. These findings support a model in which ligands of different efficacy activate the receptor through distinct allosteric mechanisms which are determined by residues distributed across the receptor. Mapping these contributions across diverse ligands with deep mutational pharmacology provides a way to understand how one receptor converts molecular recognition into a range of signaling outputs.

biophysics↗

bioRxiv2026

Modelling Plant Cortical Mictrotubules: Curvature Sensing from Bending Energy

Within plant cells, the self-organization of cortical microtubules (MTs) into ordered arrays is an important process for directional growth. There is growing evidence that cortical MTs respond to cell shape and/or mechanical stresses in the cell wall, requiring in silico models on complicated surfaces to provide a complete understanding. Most models assume that MTs are directionally persistent, following geodesics of the surface. This ignores the expected tendency of these elastic filaments to minimize curvature. Our recent model incorporated minimization of MT curvature in cylindrical cells and found curvature to be significant in biasing the array organization. Here, we generalize to a larger class of surfaces, studying individual microtubule shapes to provide insights into the role of geometric cues and highlight differences with previous models that use the geodesic assumption. We first show that geodesic models, including current models with finite persistence lengths, exhibit an invariance across certain geometries, leading to biophysically counterintuitive results. Incorporating curvature minimization, we show the difficulties imposed by high-curvature cell edges, elucidating potential new roles of proteins in helping microtubules traverse edges. Lastly, we show that geometries with competing curvature cues result in diverse curves previously not considered. These results provide geometric intuition for how various cell geometries affect individual cortical microtubules, helping us to better understand the processes required for the establishment of microtubule arrays in broad contexts such as: bundles spanning adjacent cell faces in prism-like root and leaf epidermis cells, protruding geometries of trichome cells, and rounded surfaces such as confined protoplasts.

Developmental Biology↗

bioRxiv2025

Leaf microbiome assembly is linked to plant phylogeny

Background and AimsThe plant microbiome is considered as an extended part of the plant genome, and it provides key functions in regulating plant fitness, and stress tolerance. Plants and associated microbiomes have co-evolved over millennia, yet evidence for a strong influence of plant phylogeny in influencing their microbiomes is largely lacking. Our main aims was to identify key drivers of plant microbiome assembly. MethodsHere, we conducted a full factorial experiment that included three levels of soil microbial diversity, five plant species from three functional groups (C3, C4, and C3 nitrogen-fixing), and two moisture availability levels. ResultsOur results showed that host identity and plant functional group exerted the strongest effect on leaf microbial assembly, while root and soil microbiomes showed less sensitivity to host selection. The initial soil microbial diversity and community structure significantly impacted soil and root microbial composition, but not leaf microbiomes. Importantly, we observed significant positive linkage between host phylogeny distance and Bray-Curtis dissimilarity index in leaf microbiomes. This finding was further validated through analysis of microbiome data from seven plant species grown across different field and environmental conditions. Interestingly, there was no significant impact of short-term water stress on plant microbial communities. ConclusionsBy providing empirical evidence for important role of host selection in shaping plant microbiomes, this study advances our fundamental knowledge of plant-microbe interactions and their co-evolutionary relationships, and enhances our ability to develop future tools harnessing plant microbiome to improve plant health and productivity.

microbiology↗

bioRxiv2025

Accurate variant effect estimation in FACS-based deep mutational scanning data with Lilace

Deep mutational scanning (DMS) experiments interrogate the effect of genetic variants on protein function, often using fluorescence-activated cell sorting (FACS) to quantitatively measure molecular phenotypes, such as abundance or activity. Analysis of DMS experiments with a FACS readout is challenging due to measurement variance and the unique multidimensional nature of the phenotype. However, no statistical method has yet been developed to address the challenges of FACS-based DMS. Here we present Lilace, a Bayesian statistical model to estimate variant effects with uncertainty quantification from FACS-based DMS experiments. We validate Lilaces performance and robustness using simulated data and apply it to OCT1 and Kir2.1 DMS experiments, demonstrating an improved false discovery rate (FDR) while largely maintaining sensitivity.

bioinformatics↗

bioRxiv2025

Rosace-AA: Enhancing Interpretation of Deep Mutational Scanning Data with Amino Acid Substitution and Position-Specific Insights

Proteins are dynamic systems whose function and behavior are sensitive to environmental conditions and often involve multiple cellular roles. Deep mutational scanning (DMS) experiments generate extensive datasets to capture the functional consequences of mutations. However, the sheer volume of data presents challenges in visualization and interpretation. Current approaches often rely on heatmaps, but these methods fail to capture the nuanced effects of amino acid (AA) substitutions, which are essential for understanding mutational impact. To address this, we extend the Rosace framework with Rosace-AA, a model that incorporates both position-specific information and AA substitution trends. Using substitution matrices like BLOSUM90, Rosace-AA offers a flexible and interpretable approach to summarize DMS data oil both protein-level and position-level. We demonstrate its utility across datasets, including OCTI and MET kinase, showing that Rosace-AA highlights key positions where mutations deviate from expected substitution patterns and captures functionally relevant variation in protein behavior across multiple DMS screens. These results suggest that Rosace-AA enables more robust and interpretable analysis of complex DMS datasets.

bioinformatics↗

bioRxiv2024

PLM-interact: extending protein language models to predict protein-protein interactions

Computational prediction of protein structure from amino acid sequences alone has been achieved with unprecedented accuracy, yet the prediction of protein-protein interactions (PPIs) remains an outstanding challenge. Here we assess the ability of protein language models (PLMs), routinely applied to protein folding, to be retrained for PPI prediction. Existing PPI prediction models that exploit PLMs use a pre-trained PLM feature set, ignoring that the proteins are physically interacting. Our novel method, PLM-interact, goes beyond a single protein, jointly encoding protein pairs to learn their relationships, analogous to the next-sentence prediction task from natural language processing. This approach provides a significant improvement in performance: Trained on human-human PPIs, PLM-interact predicts mouse, fly, worm, E. coli and yeast PPIs, with 16-28% improvements in AUPR compared with state-of-the-art PPI models. Additionally, it can detect changes that disrupt or cause PPIs and be applied to virus-host PPI prediction. Our work demonstrates that large language models can be extended to learn the intricate relationships among biomolecules from their sequences alone.

bioinformatics↗

bioRxiv2024

A Suite of Foundation Models Captures the Contextual Interplay Between Codons

In the canonical genetic code, many amino acids are assigned more than one codon. Work by us and others has shown that the choice of these synonymous codon is not random, and carries regulatory and functional consequences. Existing protein foundation models ignore this context-dependent role of coding sequence in shaping the protein landscape of the cell. To address this gap, we introduce cdsFM, a suite of codon-resolution large language models, including both EnCodon and DeCodon models, with up to 1B parameters. Pre-trained on 60 million protein-coding sequences from more than 5,000 species, our models effectively learn the relationship between codons and amino acids, recapitualing the overall structure of the genetic code. In addition to outperforming state-of-the-art genomic foundation models in a variety of zero-shot and few-shot learning tasks, the larger pre-trained models were superior in predicting the choice of synonymous codons. To systematically assess the impact of synonymous codon choices on protein expression and our models ability to capture these effects, we generated a large dataset measuring overall and surface expression levels of three proteins as a function of changes in their synonymous codons. We showed that our EnCodon models could be readily fine-tuned to predict the contextual consequences of synonymous codon choices. Armed with this knowledge, we applied EnCodon to existing clinical datasets of synonymous variants, and we identified a large number of synonymous codons that are likely pathogenic, several of which we experimentally confirmed in a cellbased model. Together, our findings establish the cdsFM suite as a powerful tool for decoding the complex functional grammar underlying the choice of synonymous codons.

genomics↗

bioRxiv2023

Rosace: a robust deep mutational scanning analysis framework employing position and mean-variance shrinkage

Deep mutational scanning (DMS) enables functional insight into protein mutations with multiplexed measurements of thousands of genetic variants in a protein simultaneously. The small sample size of DMS renders classical statistical methods ineffective, for example, p-values cannot be correctly calibrated when treating variants independently. We propose Rosace, a Bayesian framework for analyzing growth-based deep mutational scanning data. Rosace leverages amino acid position information to increase power and control the false discovery rate by sharing information across parameters via shrinkage. To benchmark Rosace against existing methods, we developed Rosette, a simulation framework that simulates the distributional properties of DMS. Further, we show that Rosace is robust to the violation of model assumptions and is more powerful than existing tools under Rosette simulation and real data.

bioinformatics↗

bioRxiv2023

The full spectrum of OCT1 (SLC22A1) mutations bridges transporter biophysics to drug pharmacogenomics

Membrane transporters play a fundamental role in the tissue distribution of endogenous compounds and xenobiotics and are major determinants of efficacy and side effects profiles. Polymorphisms within these drug transporters result in inter-individual variation in drug response, with some patients not responding to the recommended dosage of drug whereas others experience catastrophic side effects. For example, variants within the major hepatic Human organic cation transporter OCT1 (SLC22A1) can change endogenous organic cations and many prescription drug levels. To understand how variants mechanistically impact drug uptake, we systematically study how all known and possible single missense and single amino acid deletion variants impact expression and substrate uptake of OCT1. We find that human variants primarily disrupt function via folding rather than substrate uptake. Our study revealed that the major determinants of folding reside in the first 300 amino acids, including the first 6 transmembrane domains and the extracellular domain (ECD) with a stabilizing and highly conserved stabilizing helical motif making key interactions between the ECD and transmembrane domains. Using the functional data combined with computational approaches, we determine and validate a structure-function model of OCT1s conformational ensemble without experimental structures. Using this model and molecular dynamic simulations of key mutants, we determine biophysical mechanisms for how specific human variants alter transport phenotypes. We identify differences in frequencies of reduced function alleles across populations with East Asians vs European populations having the lowest and highest frequency of reduced function variants, respectively. Mining human population databases reveals that reduced function alleles of OCT1 identified in this study associate significantly with high LDL cholesterol levels. Our general approach broadly applied could transform the landscape of precision medicine by producing a mechanistic basis for understanding the effects of human mutations on disease and drug response.

biophysics↗

bioRxiv2020

Shark conservation risks associated with the use of shark liver oil in SARS-CoV-2 vaccine development

The COVID-19 pandemic may create new demand for wildlife-generated products for human health, including a shark-derived ingredient used in some vaccines. Adjuvants are a vaccine component that increases efficacy, and some adjuvants contain squalene, a natural compound derived from shark liver oil which is found most abundantly in deep-sea sharks. In recent decades, there has been growing conservation concern associated with the sustainability of many shark fisheries. The need for a potentially massive number of adjuvant-containing SARS-CoV-2 vaccines may increase global demand for shark-derived squalene, with possible consequences for shark conservation, especially of vulnerable and understudied deep-sea species. A shift to non-animal-derived sources of squalene, which are similar in cost and identical in effectiveness, or an emphasis on increasing traceability and sustainability of shark-derived squalene from existing well-managed fisheries, could better support conservation and public health goals.

ecology↗

bioRxiv2018

The chemistry and pharmacology of putative synthetic cannabinoid receptor agonist (SCRA) new psychoactive substances (NPS) 5F-PY-PICA, 5F-PY-PINACA, and their analogues

The structural diversity of synthetic cannabinoid receptor agonist (SCRA) new psychoactive substances (NPS) has increased since the first examples were reported a decade ago. 5F-PY-PICA and 5F-PY-PINACA were identified in 2015 as putative SCRA NPS, although nothing is known of their pharmacology. 5F-PY-PICA, 5F-PY-PINACA, and analogues intended to explore structure-activity relationships within this class of SCRAs were synthesized and characterized by nuclear magnetic resonance spectroscopy and liquid chromatography-quadrupole time-of-flight-mass spectrometry. Using competitive binding experiments and fluorescence-based plate reader membrane potential assays, the affinities and activities of all analogues at cannabinoid type 1 and type 2 receptors (CB1 and CB2) were evaluated. All ligands showed minimal affinity for CB1 (pKi < 5), although several demonstrated moderate CB2 binding (pKi = 5.45-6.99). At 10 M none of the compounds produced an effect > 50% of CP55,950 at CB1, while several compounds showed a slightly higher relative efficacy at CB2. Unlike other SCRA NPS, 5F-PYPICA and 5F-PY-PINACA did not produce cannabimimetic effects in mice at doses up to 10 mg/kg.

pharmacology and toxicology↗

bioRxiv2017

COPI Mediates Recycling Of An Exocytic SNARE From Endosomes By Recognition Of A Ubiquitin Sorting Signal

ABSTRACTThe COPI coat forms transport vesicles from the Golgi complex and plays a poorly defined role in endocytic trafficking. Here we show that COPI mediates delivery of a budding yeast SNARE (Snc1) from early endosomes to the Golgi complex through recognition of a polyubiquitin sorting signal. Snc1 is a v-SNARE that drives fusion of exocytic vesicles with the plasma membrane, and then recycles through early endosomes back to the Golgi for reuse. Removal of ubiquitin from Snc1, or deletion of a {beta}-COP subunit propeller domain that binds K63-linked polyubiquitin, causes aberrant accumulation of Snc1 in early endosomes. Moreover, replacement of the {beta}-COP propeller domain with unrelated ubiquitin-binding domains restores Snc1 recycling. These results indicate that ubiquitination, a modification well known to target membrane proteins to the lysosome or vacuole for degradation, can also function as recycling signal to sort a SNARE into COPI vesicles at early endosomes for Golgi delivery.

cell biology↗