Biology subjects

Skyner, R.

Publications and source records attributed to Skyner, R..

6 recordsLinked to original sources

bioRxiv2026

Discovery of a molecular glue inhibitor that stabilises a non-productive Kalirin-Rac1 complex

Rho guanosine triphosphatases (GTPases) are molecular-switches implicated in neurodegenerative diseases, yet targeting them through competitive inhibition remains a challenge due to their high affinity for guanine nucleotides. Guanine nucleotide exchange factors (GEFs) catalyse GDP-to-GTP nucleotide exchange to activate GTPases, providing an alternative opportunity for GTPase modulation. Herein, we describe a complex-targeted strategy to inhibit nucleotide exchange with covalent molecular glues that engage the Kalirin-Rac1 GEF-GTPase complex at the nucleotide binding site are sequester the GEF Kalirin. Fragment hits were identified through XChem and in silico screening, and a fragment merging approach resulted in the generation of covalent inhibitors RS-009 and MC-278. Multiple analyses demonstrate our compounds inhibit nucleotide exchange both through competition with the nucleotides and by stabilising a ternary inhibitor-Rac1-Kalirin complex, thereby trapping Kalirin in a non-productive state and reducing GEF turnover. Biochemical selectivity screening and cellular activity-based protein profiling (ABPP) show that selectivity can be achieved across distinct GEF-GTPase complexes, which may result in improved spatiotemporal control over targeting the GTPase alone. This work provides evidence for targeting GTPase signalling via stabilization of the GEF-GTPase complex in a unique covalent molecular glue mechanism and provides the basis of a chemical probe or therapeutic.

biochemistry↗

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↗

bioRxiv2023

Learnt representations of proteins can be used for accurate prediction of small molecule binding sites on experimentally determined and predicted protein structures

Protein-ligand binding site prediction is a useful tool for understanding the functional behaviour and potential drug-target interactions of a novel protein of interest. However, most binding site prediction methods are tested by providing crystallised ligand-bound (holo) structures as input. This testing regime is insufficient to understand the performance on novel protein targets where experimental structures are not available. An alternative option is to provide computationally predicted protein structures, but this is not commonly tested. However, due to the training data used, computationally-predicted protein structures tend to be extremely accurate, and are often biased toward a holo conformation. In this study we describe and benchmark IF-SitePred, a protein-ligand binding site prediction method which is based on the labelling of ESM-IF1 protein language model embeddings combined with point cloud annotation and clustering. We show that not only is IF-SitePred competitive with state-of-the-art methods when predicting binding sites on experimental structures, but it performs better on proxies for novel proteins where low accuracy has been simulated by molecular dynamics. Finally, IF-SitePred outperforms other methods if ensembles of predicted protein structures are generated.

bioinformatics↗

bioRxiv2022

Allosteric regulation and crystallographic fragment screening of SARS-CoV-2 NSP15 endoribonuclease

SARS-CoV-2 is the causative agent of COVID-19. The highly conserved viral NSP15 endoribonuclease, NendoU, is a key enzyme involved in viral immune evasion, and a promising target for the development of new classes of antivirals. Yet, the broad variety of recognition sequences, complex assembly and kinetics, and lack of structural complexes hampers the development of new competitive or allosteric inhibitors for this target. Here, we performed enzymatic characterization of NendoU in its monomeric and hexameric form, showing that hexamers are allosteric enzymes with a positive cooperative index of 2. By using cryo-EM at distinct pHs combined with X-ray crystallography and structural analysis, we demonstrate the potential for NendoU to shift between open and closed states, and assembly in larger supramolecular entities, which might serve as a mechanism of self-regulation. Further, we report results from a large fragment screening campaign against NendoU, revealing multiple new allosteric sites for the development of inhibitors.

biochemistry↗

bioRxiv2021

Structure, Mechanism and Crystallographic fragment screening of the SARS-CoV-2 NSP13 helicase

The global COVID-19 pandemic is caused by the SARS-CoV-2 virus and has infected over 100 million and caused over 2 million fatalities worldwide at the point of writing. There is currently a lack of effective drugs to treat people infected with SARS-CoV-2. The SARS-CoV-2 Non-structural protein 13 (NSP13) is a superfamily1B helicase that has been identified as a possible target for anti-viral drugs due to its high sequence conservation and essential role in viral replication. In this study we present crystal structures of SARS-CoV-2 NSP13 solved in the APO form and in the presence of both phosphate and the non-hydrolysable ATP analogue (AMP-PNP). Comparisons of these structures reveal details of global and local conformational changes that are induced by nucleotide binding and hydrolysis and provide insights into the helicase mechanism and possible modes of inhibition. Structural analysis reveals two pockets on NSP13 that are classified as "druggable" and include one of the most conserved sites in the entire SARS-CoV-2 proteome. To identify possible starting points for anti-viral drug development we have performed a crystallographic fragment screen against SARS-CoV-2 NSP13 helicase. The fragment screen reveals 65 fragment hits across 52 datasets, with hot spots in pockets predicted to be of functional importance, including the druggable nucleotide and nucleic acid binding sites, opening the way to structure guided development of novel antiviral agents.

biochemistry↗

bioRxiv2020

Crystallographic and electrophilic fragment screening of the SARS-CoV-2 main protease

COVID-19, caused by SARS-CoV-2, lacks effective therapeutics. Additionally, no antiviral drugs or vaccines were developed against the closely related coronavirus, SARS-CoV-1 or MERS-CoV, despite previous zoonotic outbreaks. To identify starting points for such therapeutics, we performed a large-scale screen of electrophile and non-covalent fragments through a combined mass spectrometry and X-ray approach against the SARS-CoV-2 main protease, one of two cysteine viral proteases essential for viral replication. Our crystallographic screen identified 71 hits that span the entire active site, as well as 3 hits at the dimer interface. These structures reveal routes to rapidly develop more potent inhibitors through merging of covalent and non-covalent fragment hits; one series of low-reactivity, tractable covalent fragments was progressed to discover improved binders. These combined hits offer unprecedented structural and reactivity information for on-going structure-based drug design against SARS-CoV-2 main protease.

biophysics↗