Biology subjects

Crook, O.

Publications and source records attributed to Crook, O..

11 recordsLinked to original sources

bioRxiv2026

Robust analysis of comparative subcellular omics with complex designs

Subcellular omics technologies now allow us to obtain insights into steady-state localisation and re-localisation of biomolecules in high-throughput. However, robust analysis of these experiments can be slow and challenging. Here, we show that existing approaches to differential localisation fall into two classes, marker-dependent and marker-free, that fail for fundamentally different statistical reasons, with failure modes that are not completely overlapping. We exploit this observation in SANDLE (statistical analysis of differential localisation experiments), which combines a marker-dependent generative model of subcellular niches with a marker-free regression test for changes in fractionation profiles. This dual strategy provides better control of false positives, up to 100-fold reduction in analysis time, and accommodates more complex experimental designs than existing methods. We demonstrate SANDLEs versatility across a wide range of subcellular omics experiments, including drug-treatment responses, cross-species and life-cycle comparisons, post-translationally modified proteoforms, and RNA re-localisation. Coanalysing transcriptome and proteome dynamics during the unfolded protein response, we further reveal lncRNAs whose steady-state localisation is condition-dependent. By addressing key methodological limitations, SANDLE enables broad applications of subcellular omics from fundamental biology to clinical research.

biochemistry↗

bioRxiv2026

Quantifying evolutionary novelty and design efficiency in generative genome design

Generative genome design models can now produce previously unobserved genome-length sequences, but assessing their capabilities is complicated by limitations in functional prediction. The ability to engineer genomes faster than we can understand them risks creating biosecurity vulnerabilities. To evaluate these potential risks systematically, we propose a framework that distinguishes between (i) evolutionary novelty, quantified through phylogenetic and sequence similarity to natural genomes; and (ii) design efficiency - the efficiency with which a model finds viable sequences compared to simple baseline generators. Applying this framework to bacteriophages designed by the genome language model Evo 2, we find that model likelihood strongly predicts experimental viability, capturing functional constraints beyond simple biological heuristics. However, this efficiency derives largely from staying close to previously observed sequences rather than exploring novel sequence space, reflecting the combined performance of the model and additional filters that were applied to its outputs. Compared to baselines of random mutagenesis and serial passage, the model achieves substantial design efficiency while its outputs remain phylogenetically close to natural genomes. We conclude that the generative capabilities of Evo 2 warrant low to moderate biosecurity concern for de novo hazard creation, although the degree to which these findings generalise to larger or less constrained viral architectures is an open question. Our framework enables an evidence-based capability assessment of generative genome design tools, informing future biosecurity evaluations.

genomics↗

bioRxiv2026

A framework for testing structural hypotheses of protein dynamics against experimental HDX-MS data

Protein dynamics determine biological function, yet extracting structural ensembles from Hydrogen-Deuterium Exchange Mass Spectrometry (HDX-MS) remains a challenging inverse problem. Current ensemble-fitting approaches often achieve good agreement with uptake curves but lack rigorous validation and uncertainty quantification, limiting structural confidence. We propose ValDX, a validation framework for quantitative integration of HDX-MS data with structural ensembles. ValDX combines overlap-aware data splitting, replicate-based uncertainty estimation, and uptake-independent "Work Done" metrics that quantify how much an ensemble must be modified to match experiment. Across 22 ensembles spanning six proteins (58-474 residues), we show that conventional error metrics fail to distinguish structurally representative ensembles from incorrect ones, whereas Work Done metrics robustly discriminate global and local conformational quality. We further demonstrate that clustering yields compact, interpretable ensembles with minimal loss of accuracy, and that staged optimisation enables reliable fitting of both ensemble weights and forward-model parameters without requiring a reference structure. Together, this framework establishes HDX-MS ensemble integration as a quantitative structural hypothesis-testing problem, enabling inference of protein dynamics from HDX-MS data.

biophysics↗

bioRxiv2025

Decoding the physicochemical basis of taxonomy preferences in protein design models

Protein design models have transformed protein engineering by enabling computational exploration of sequence spaces far exceeding experimental capacity. However, their outputs are shaped by both the protein distributions represented in their training corpora and the information available during scoring, so the same model score may reflect backbone-compatible biophysics, taxonomic structure in sequence databases, or other learned regularities rather than protein fitness alone. Here we quantify systematic preferences across 14 protein design models that differ in data modality, training-corpus composition, and scoring context, for comparison grouped as backbone-conditioned, structure plus native-sequence context, or sequence-only. Backbone-conditioned models retain little unexplained taxonomic variance after controlling for protein family and measurable biophysical properties, with residual species variance below 3.3%. In contrast, sequence-only models retain substantial residual taxonomic dependence of 15-20%, indicating that likelihood remains strongly entangled with organism-level sequence statistics. These differences across model classes produce distinct preference landscapes. Backbone-conditioned models organise scores around compactness, packing, and charge, while sequence-only models preserve stronger within-family taxonomic effects. Redesign experiments show that these preferences propagate into generation, shifting templates toward characteristic biophysical profiles rather than uniformly sampling backbone-compatible sequence space. Continued training of ProteinMPNN on ecologically selected extremophile secretomes redirects designed surface chemistry along an acid-base axis while largely preserving structural compatibility and global taxonomic structure. These results show that systematic preferences are not a single failure mode, but separable components arising from scoring context, training-corpus composition, and learned biophysical constraints. Together, they provide a framework for disentangling the sources of model preference and linking them to both scoring behaviour and generated sequence properties.

biochemistry↗

bioRxiv2025

Structure guided significance testing correction for hydrogen deuterium exchange mass spectrometry

Hydrogen deuterium exchange mass spectrometry (HDX-MS) is a powerful technique to probe changes in protein structural dynamics. In differential settings, HDX-MS compares dynamics between protein states, such as conformational changes resulting from antibody-antigen binding or the effects of mutations. As the method becomes more high-throughput, the number of comparisons between peptides and states grows, creating a multiple hypothesis testing challenge where some observed changes may result from statistical randomness rather than biological differences. While this problem can be addressed by applying multiple hypothesis testing correction techniques like false discovery rate (FDR) control, current methods assume statistical independence - an assumption violated by peptide overlap and the influence of protein structure. Here, we develop a structural false discovery rate (sFDR) that accounts for these dependencies by integrating sequence and structural information to estimate the effective number of independent tests. Our approach significantly improves statistical power to detect genuine changes in protein dynamics measured by HDX-MS, as demonstrated through extensive validation using both simulated and experimental datasets. The sFDR method maintains robustness to structural uncertainty, making it applicable even when only predicted structures are available. This advancement enables more sensitive detection of conformational changes in challenging systems with subtle dynamic differences and reduces the number of replicates needed to obtain confidence in results. The method is easily accessible through a Google Collab Notebook and can be completed in minutes.

biochemistry↗

bioRxiv2024

Inferring residue level hydrogen deuterium exchange with ReX

Hydrogen-Deuterium Exchange Mass-Spectrometry (HDX-MS) has emerged as a powerful technique to explore the conformational dynamics of proteins and protein complexes in solution. The bottom-up approach to MS uses peptides to represent an average of residues, leading to reduced resolution of deuterium exchange and complicates the interpretation of the data. Here, we introduce ReX, a method to infer residue-level uptake patterns leveraging the overlap in peptides, the temporal component of the data and the correlation along the sequence dimension. This approach infers statistical significance for individual residues by treating HDX-MS as a multiple change-point problem. By fitting our model in a Bayesian non-parametric framework, we perform parameter number inference, differential HDX confidence assessments, and uncertainty estimation for temporal kinetics. Benchmarking against existing methods using a three-way proteolytic digestion experiment shows our methods superior performance at predicting unseen HDX data. Moreover, it aligns HDX-MS with the reporting standards of other structural methods by providing global and local resolution metrics. Using ReX, we analyze the differential flexibility of BRD4s two Bromodomains in the presence of I-BET151 and quantify the conformational variations induced by a panel of seventeen small molecules on LXR. Our analysis reveals distinct residue-level HDX signatures for ligands with varied functional outcomes, highlighting the potential of this characterisation to inform mode of action analysis.

biochemistry↗

bioRxiv2024

msqrob2TMT: robust linear mixed models for inferring differential abundant proteins in labelled experiments with arbitrarily complex design

Labelling strategies in mass spectrometry (MS)-based proteomics enable increased sample throughput by acquiring multiplexed samples in a single run. However, contemporary designs often require the acquisition of multiple runs, leading to a complex correlation structure. Addressing this correlation is key for correct statistical inference and reliable biomarker discovery. Therefore, we present msqrob2TMT, a set of mixed model-based workflows tailored toward differential abundance analysis for labelled MS-based proteomics data. Thanks to its increased flexibility, msqrob2TMT can model both sample-specific and feature-specific (e.g. peptide or protein) covariates, which unlocks the inference to experiments with arbitrarily complex designs as well as to correct explicitly for feature-specific properties. We benchmark our novel workflows against the state-of-the-art tools MSstatsTMT and DeqMS in a spike-in study. We show that our workflows are modular, more flexible and have improved performance by adopting robust ridge regression. We also found that reference channel normalization and imputation can have a deleterious impact on the statistical outcome. Finally, we demonstrate the significance of msqrob2TMT on a real-life mice study, showcasing the importance of effectively accounting for the hierarchical correlation structure in the data.

bioinformatics↗

bioRxiv2022

A functional Bayesian model for hydrogen-deuterium exchange mass-spectrometry

Proteins often undergo structural perturbations upon binding to other proteins or ligands or when they are subject to environmental changes. Hydrogen deuterium exchange mass-spectrometry (HDX-MS) can be used to explore conformational changes in proteins by examining differences in the rate of deuterium incorporation in different contexts. To determine deuterium incorporation rates, HDX-MS measurements are typically made over a time course. Recently introduced methods show that incorporating the temporal dimension into the statistical analysis improves power and interpretation. However, these approaches have technical assumptions which hinder their flexibility. Here, we propose a more flexible methodology by reframing these methods in a Bayesian framework. Our proposed framework has improved algorithmic stability, allows us to perform uncertainty quantification, and can calculate statistical quantities that are inaccessible to other approaches. We demonstrate the general applicability of the method by showing it can perform rigorous model selection on a spike-in HDX-MS experiment and improved interpretation in an epitope mapping experiment. Bayesian analysis of an HDX experiment with an antibody dimer bound to an E3 ubiquitin ligase identifies at least two interaction interfaces where previous methods obtained confounding results due to the complexities of conformation change on binding. Our findings are consistent with the co-crystal structure of these proteins, demonstrating a bayesian approach can identify important binding epitopes from HDX data.

biophysics↗

bioRxiv2021

Inferring differential subcellular localisation in comparative spatial proteomics using BANDLE

The steady-state localisation of proteins provides vital insight into their function. These localisations are context specific with proteins translocating between different sub-cellular niches upon perturbation of the subcellular environment. Differential localisation, that is a change in the steady-state subcellular location of a protein, provides a step towards mechanistic insight of subcellular protein dynamics. Aberrant localisation has been implicated in a number of pathologies, thus differential localisation may help characterise disease states and facilitate rational drug discovery by suggesting novel targets. High-accuracy high-throughput mass spectrometry-based methods now exist to map the steady-state localisation and re-localisation of proteins. Here, we propose a principled Bayesian approach, BANDLE, that uses these data to compute the probability that a protein differentially localises upon cellular perturbation, as well quantifying the uncertainty in these estimates. Furthermore, BANDLE allows information to be shared across spatial proteomics datasets to improve statistical power. Extensive simulation studies demonstrate that BANDLE reduces the number of both type I and type II errors compared to existing approaches. Application of BANDLE to datasets studying EGF stimulation and AP-4 dependent localisation recovers well studied translocations, using only two-thirds of the provided data. Moreover, we potentially implicate TMEM199 with AP-4 dependent localisation. In an application to cytomegalovirus infection, we obtain novel insights into the rewiring of the host proteome. Integration of high-throughput transcriptomic and proteomic data, along with degradation assays, acetylation experiments and a cytomegalovirus intcractome allows us to provide the functional context of these data.

systems biology↗

bioRxiv2020

A Bayesian semi-parametric model for thermal proteome profiling

The thermal stability of proteins can be altered when they interact with small molecules, other biomolecules or are subject to post-translation modifications. Thus monitoring the thermal stability of proteins under various cellular perturbations can provide insights into protein function, as well as potentially determine drug targets and off-targets. Thermal proteome profiling is a highly multiplexed mass-spectrommetry method for monitoring the melting behaviour of thousands of proteins in a single experiment. In essence, thermal proteome profiling assumes that proteins denature upon heating and hence become insoluble. Thus, by tracking the relative solubility of proteins at sequentially increasing temperatures, one can report on the thermal stability of a protein. Standard thermodynamics predicts a sigmoidal relationship between temperature and relative solubility and this is the basis of current robust statistical procedures. However, current methods do not model deviations from this behaviour and they do not quantify uncertainty in the melting profiles. To overcome these challenges, we propose the application of Bayesian functional data analysis tools which allow complex temperature-solubility behaviours. Our methods have improved sensitivity over the state-of-the art, identify new drug-protein associations and have less restrictive assumptions than current approaches. Our methods allows for comprehensive analysis of proteins that deviate from the predicted sigmoid behaviour and we uncover potentially biphasic phenomena with a series of published datasets.

systems biology↗

bioRxiv2020

A semi-supervised Bayesian approach for simultaneous protein sub-cellular localisation assignment and novelty detection

The cell is compartmentalised into complex micro-environments allowing an array of specialised biological processes to be carried out in synchrony. Determining a proteins sub-cellular localisation to one or more of these compartments can therefore be a first step in determining its function. High-throughput and high-accuracy mass spectrometry-based sub-cellular proteomic methods can now shed light on the localisation of thousands of proteins at once. Machine learning algorithms are then typically employed to make protein-organelle assignments. However, these algorithms are limited by insufficient and incomplete annotation. We propose a semi-supervised Bayesian approach to novelty detection, allowing the discovery of additional, previously unannotated sub-cellular niches. Inference in our model is performed in a Bayesian framework, allowing us to quantify uncertainty in the allocation of proteins to new sub-cellular niches, as well as in the number of newly discovered compartments. We apply our approach across 10 mass spectrometry based spatial proteomic datasets, representing a diverse range of experimental protocols. Application of our approach to hyper LOPIT datasets validates its utility by recovering enrichment with chromatin-associated proteins without annotation and uncovers sub-nuclear compartmentalisation which was not identified in the original analysis. Moreover, using sub-cellular proteomics data from Saccharomyces cerevisiae, we uncover a novel group of proteins trafficking from the ER to the early Golgi apparatus. Overall, we demonstrate the potential for novelty detection to yield biologically relevant niches that are missed by current approaches.

systems biology↗