Biology subjects

von Delft, F.

Publications and source records attributed to von Delft, F..

At least 19 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

The first OpenBind release: An open experimental structure-affinity dataset and benchmark for structure-based AI

High-quality experimental datasets that link protein-ligand structures with binding affinity data are essential for developing and evaluating structure-based machine learning methods. To help address this need, we established OpenBind as an open-science initiative to generate large-scale experimental datasets for structure-based AI and molecular discovery. Here, we describe the first public OpenBind release, which, to the best of our knowledge, is the largest public single-target experimental structure-affinity dataset. The dataset focuses on enteroviral 2A protease, comprising 925 crystallographic binding events from 699 compounds and associated affinity measurements for 601 compounds. It combines structures from an initial fragment screen and follow-on molecules, together with affinity data, linking experimentally determined protein-ligand binding modes to biophysical measurements within a coherent antiviral discovery campaign. We used this dataset to evaluate protein-ligand structure prediction, binding-affinity prediction, and virtual screening using representative structure-based methods, including docking and cofolding. This exposed several challenges that are central to practical structure-based modelling: docking performance depends strongly on binding-pocket conformation, poses are difficult to rank, and structure-based affinity prediction remains challenging. Fine-tuning OpenFold3-p2 on the fragment-screen structures substantially improved pose prediction and virtual screening for related follow-on compounds, demonstrating how early-stage experimental structures can support target-specific model adaptation.

bioinformatics↗

bioRxiv2025

Leveraging crystallographic fragment screening, algorithmic merging and digital chemistry to target NCS-1 protein-protein interactions for treatment of neurological disorders

Efficient drug discovery relies on workflows that integrate structural insights with rapid and cost-effective exploration of chemical space. Here, we present a data-driven fragment-based lead discovery approach to target Neuronal Calcium Sensor 1 (NCS-1) protein-protein interactions (PPIs). This study represents a complete implementation of a single high-value design-make-test-analyze cycle that directly yields compounds with micromolar affinity with the potential to modulate NCS-1 interactions with key targets, including the G-protein chaperone Ric-8A and the dopamine D2 and cannabinoid CB1 receptors. X-ray crystallographic fragment screening (CFS) revealed diverse interaction patterns within the NCS-1 hydrophobic crevice. Algorithmically guided fragment evolution and automated synthesis enabled the rapid generation of over 250 derivatives, with biophysical validation using LC-MS and Grating-coupled interferometry. Structural analyses highlighted key pharmacophores, with selected compounds exhibiting favorable drug-like properties and potential blood-brain barrier penetration, making them promising candidates for neurodegenerative and neurodevelopmental disorders. Our results demonstrate the feasibility of accelerated hit-to-lead development at synchrotrons, demonstrating a robust, scalable platform for PPI-targeting drug discovery. The generated chemically diverse scaffolds provide a strong foundation for future therapeutic optimization.

biophysics↗

bioRxiv2025

Discovery of orally bioavailable Zika and West Nile Virus antiviral compounds targeting the NS2B-NS3 protease

Flaviviruses are a class of pathogenic viruses with pandemic potential that are typically transmitted via infected arthropods. In addition, Zika virus is sexually transmissible and causes congenital malformations if infection occurs during pregnancy. Although over 1.5 million people were infected during the 2015-2016 Zika outbreak, to date, there are no clinical-stage vaccines or antivirals. Herein, we report the discovery of potent inhibitors of the Zika virus NS2B-NS3 protease that also show activity against the West Nile virus NS2B-NS3 protease. Starting from a crystallographic fragment screen, we employed a pharmacophore approach coupled with high-throughput library chemistry to elaborate fragments in the active site. Potent, metabolically stable, non-covalent, non-peptidomimetic inhibitors were identified with antiviral activity in vitro. The lead compound was progressed to a three-day Zika virus challenge study in AG129 mice, where it displayed significant viral RNA load reduction in both mouse plasma and spleen.

microbiology↗

bioRxiv2025

Open-science discovery of DNDI-6510, a compound that addresses genotoxic and metabolic liabilities of the COVID Moonshot SARS-CoV-2 Mpro lead inhibitor

The 2020 SARS-CoV-2 coronavirus pandemic highlighted the urgent need for novel small molecule antiviral drugs. (S)-x38 DNDI-6510 is a non-covalent SARS-CoV-2 main protease inhibitor developed by the open science collaboration COVID Moonshot. Here, we report on the metabolic and toxicologic optimization of the lead series previously disclosed by the COVID Moonshot Initiative, leading up to the selection of (S)-x38 DNDI-6510 as the preclinical candidate. We describe the thorough profiling of the series, identifying key risks such as formation of genotoxic metabolites and high clearance, which were successfully addressed during lead optimization. In addition, we disclose the in vitro and in vivo evaluation of (S)-x38 DNDI-6510 in pharmacokinetic and pharmacodynamic models, exploring multiple approaches to ameliorate rodent-specific metabolic clearance, and show that both co-dosing of (S)-x38 DNDI-6510 with an ABT inhibitor and utilizing a metabolically humanized mouse model (8HUM) achieve significant improvements in exposure. Through comparisons of ABT co-dosing and humanized mouse models in efficacy experiments, we demonstrate that continuous exposure over cellular EC90 is required for SARS-CoV-2 antiviral efficacy in vivo in an antiviral model using a mouse-adapted SARS-CoV-2 strain. Finally, (S)-x38 DNDI-6510 was assessed in maximum tolerated dose experiments in two species, demonstrating significant in vivo PXR-linked auto-induction of metabolism, leading to the discontinuation of this compound. In summary, we report the successful effort to overcome series-specific AMES liabilities in a lead development program. Downstream optimization of existing series will require in-depth optimization of rodent-specific liabilities and metabolic induction profile.

pharmacology and toxicology↗

bioRxiv2025

Crystallographic fragment screening of the dengue virus polymerase reveals multiple binding sites for the development of non-nucleoside antiflavivirals

Dengue viruses (DENV) infect approximately 400 million people each year, and there are currently no effective therapeutics available. To explore potential starting points for antiviral drug development, we conducted a large-scale crystallographic fragment screen targeting the RNA-dependent RNA polymerase (RdRp) domain of the non-structural protein 5 (NS5) from DENV serotype 2. Our screening, which involved 1,108 fragments, identified 60 hit compounds across various known binding sites, including the active site, N pocket, and RNA tunnel. Additionally, we discovered a novel binding site and a fragment-binding hotspot in thumb site II. These structural findings open amenable avenues for developing non-nucleoside inhibitors and offer valuable insights for future structure-based drug design aimed at DENV and other flaviviral RdRps.

biophysics↗

bioRxiv2024

Crystal structure of Fatty Acid Thioesterase A bound by 129 fragments provides diverse development opportunities

To alleviate the growing issue of herbicide resistance, diversification of the herbicide portfolio is necessary. A promising yet underutilised mode of action (MoA) are Fatty Acid Thioesterases (Fat), which terminate de novo fatty acid (FA) biosynthesis by releasing fatty acids from acyl carrier protein (ACP) cofactors. These enzymes impact plant growth and sterility by determining the amount and length of FAs present. To find novel chemical matter targeting this MoA we have solved the crystal structure of Arabidopsis thaliana FatA to 1.5[A] and conducted a crystallographic fragment screen which identified 141 hits. Our fragments recapitulated interactions made by current herbicides, but also targeted novel regions, namely the active site and the dimer interface. Ten fragments demonstrated on-scale potency, two of those exploiting different interactions to known herbicides. The screen also revealed large conformational changes exploited by some fragments, including stabilisation of a loop and shifts in the ACP-binding lid domain. These changes alter the accessibility of the substrate binding site by reducing the size of the apparent substrate access channel. Finally, the fragments readily lend themselves to designing compounds that both merge motifs and allow catalogue-based SAR exploration. Elaboration of one of the fragments resulted in an improvement of affinity from [~]20 M to [~]90nM KD. Thus our data may facilitate accelerated early discovery of alternative inhibitors that are novel in both chemical scaffolds and modes of interaction.

biochemistry↗

bioRxiv2024

Identifying novel chemical matter against the Chikungunya virus nsP3 macrodomain through crystallographic fragment screening

Chikungunya virus (CHIKV) causes severe fever, rash and debilitating joint pain that can last for months 1,2or even years. Millions of people have been infected with CHIKV, mostly in low and middle-income countries, and the virus continues to spread into new areas due to the geographical expansion of its mosquito hosts. Its genome encodes a macrodomain, which functions as an ADP-ribosyl hydrolase, removing ADPr from viral and host-cell proteins interfering with the innate immune response. Mutational studies have shown that the CHIKV nsP3 macrodomain is necessary for viral replication, making it a potential target for the development of antiviral therapeutics. We, therefore, performed a high-throughput crystallographic fragment screen against the CHIKV nsP3 macrodomain, yielding 109 fragment hits covering the ADPr-binding site and two adjacent subsites that are absent in the homologous macrodomain of SARS-CoV-2 but may be present in other alphaviruses, such as Venezuelan equine encephalitis virus (VEEV) and eastern equine encephalitis virus (EEEV). Finally, a subset of overlapping fragments was used to manually design three fragment merges covering the adenine and oxyanion subsites. The rich dataset of chemical matter and structural information discovered from this fragment screen is publicly available and can be used as a starting point for developing a CHIKV nsP3 macrodomain inhibitor.

biochemistry↗

bioRxiv2024

Discovery of potent SARS-CoV-2 nsp3 macrodomain inhibitors uncovers lack of translation to cellular antiviral response

A strategy for pandemic preparedness is the development of antivirals against a wide set of viral targets with complementary mechanisms of action. SARS-CoV-2 nsp3-mac1 is a viral macrodomain with ADP-ribosylhydrolase activity, which counteracts host immune response. Targeting the virus immunomodulatory functionality offers a differentiated strategy to inhibit SARS-CoV-2 compared to approved therapeutics, which target viral replication directly. Here we report a fragment-based lead generation campaign guided by computational approaches. We discover tool compounds which inhibit nsp3-mac1 activity at low nanomolar concentrations, and with responsive structure-activity relationships, high selectivity, and drug-like properties. Using our inhibitors, we show that inhibition of nsp3-mac1 increases ADP-ribosylation, but surprisingly does not translate to demonstrable antiviral activity in cell culture and iPSC-derived pneumocyte models. Further, no synergistic activity is observed in combination with interferon gamma, a main protease inhibitor, nor a papain-like protease inhibitor. Our results question the extent to which targeting modulation of innate immunity-driven ADP-ribosylation can influence SARS-CoV-2 replication. Moreover, these findings suggest that nsp3-mac1 might not be a suitable target for antiviral therapeutics development.

microbiology↗

bioRxiv2024

Expanding the scope of a catalogue search to bioisosteric fragment merges using a graph database approach

The efficiency of fragment-to-lead optimization could be improved by automated workflows for the design of follow-up compounds. Pipelines that are able to fully exploit the interaction opportunities identified from the crystal structures of bound fragments would greatly aid this goal. To do so, these pipelines need to require minimal intervention from the user and be computationally efficient. In this work, we describe an updated version of our fragment merging methodology, which provides several feature enhancements, primarily by expanding the chemical space searched, allowing the identification of more diverse follow-up compounds, thus maximizing the chances of finding successful hits. While the original method focused on finding perfect merges, meaning compounds that directly incorporate substructures from the original fragments, here we expand the search to what we term bioisosteric merges, involving the incorporation of substructures that replicate the pharmacophoric features of the original fragments but may not be exactly identical. Unlike existing pharmacophore and shape-based descriptors used for virtual screening, this approach combines the search for these properties with the incorporation of novelty, which is necessary when searching for ways to link together distinct substructures. Compared with perfect merging, our new approach is able to find compounds that are directly informed by structures within the original fragments but are more chemically diverse. We contrast our approach with the use of a pharmacophore-constrained docking pipeline, run in parallel for select fragment pairs, and show that our method requires between 1.1-45.9-fold less computational time for conformer generation per merging hit identified, referring to compounds that show a favourable degree of shape and colour overlap and recapitulation of original fragment interactions. Overall, our results show that our method has potential to be used to generate designs inspired by all fragments within a given pocket.

bioinformatics↗

bioRxiv2024

Di-Gluebodies as covalently-rigidified, modular protein assemblies enable simultaneous determination of high-resolution, low-size, cryo-EM structures

Cryo-EM has become a routine structural biology method, yet elucidation of small proteins (<100 kDa) and increasing throughput remain challenging. Here, we describe covalently-dimerized engineered nanobodies, Di-Gluebodies, as novel and modular imaging scaffolds to address the challenges. They can simultaneously display two copies of the same protein or two distinct proteins, providing sufficient rigidity required by cryo-EM. We validate this method with multiple soluble and membrane targets, demonstrating that this provides a flexible and scalable platform for expanded protein structure determination.

synthetic biology↗

bioRxiv2024

Crystallographic fragment screen of Enterovirus D68 3C protease and iterative design of lead-like compounds using structure-guided expansions

The development of effective broad-spectrum antivirals forms an important part of preparing for future pandemics. One cause for concern is the currently emerging pathogen Enterovirus D68 (EV-D68) which primarily spreads through respiratory routes causing mostly mild to severe respiratory illness but, in severe cases, acute flaccid myelitis. The 3C protease of EV-D68 (3Cpro) is a potential target for the development of antiviral drugs due to its essential role in the viral life cycle and high sequence conservation amongst family members. In this study, we describe the identification of fragments which bind to3Cpro using crystallographic screening and the expansion of these into more lead-like compounds. The hits revealed interesting directions for hit-to-lead progression, specifically the importance of the pocket occupied by the conserved glutamine sidechain of the substrates and the interactions formed. Additionally, two pockets could be joined by not following the backbone of the native substrates, thus circumventing the screening issues arising from the flexibility of the catalytic triad. These observations of the novel binding modes of the chemical matter found by this screen can help shape future drug design campaigns against 3C proteases.

biochemistry↗

bioRxiv2024

High-throughput crystallographic fragment screening of Zika virus NS3 Helicase

The Zika virus (ZIKV), discovered in Africa in 1947, swiftly spread across continents, causing significant concern due to its recent association with microcephaly in newborns and Guillain-Barre syndrome in adults. Despite a decrease in prevalence, the potential for a resurgence remains, necessitating urgent therapeutic interventions. Like other flaviviruses, ZIKV presents promising drug targets within its replication machinery, notably the NS3 helicase (NS3Hel) protein, which plays critical roles in viral replication. However, a lack of structural information impedes the development of specific inhibitors targeting NS3Hel. Here we applied high-throughput crystallographic fragment screening on ZIKV NS3Hel, which yielded structures that reveal 3D binding poses of 46 fragments at multiple sites of the protein, including 11 unique fragments in the RNA-cleft site. These fragment structures provide templates for direct design of hit compounds and should thus assist the development of novel direct-acting antivirals against ZIKV and related flaviviruses, thus opening a promising avenue for combating future outbreaks.

biophysics↗

bioRxiv2024

Crystallographic fragment screening delivers diverse chemical scaffolds for Zika virus NS2B-NS3 protease inhibitor development

The Zika viral protease NS2B-NS3 is essential for the cleavage of viral polyprotein precursor into individual structural and non-structural (NS) proteins and is therefore an attractive drug target. Generation of a robust crystal system of co-expressed NS2B-NS3 protease has enabled us to perform a crystallographic fragment screening campaign with 1076 fragments. 47 fragments with diverse scaffolds were identified to bind in the active site of the protease, with another 6 fragments observed in a potential allosteric site. To identify binding sites that are intolerant to mutation and thus suppress the outgrowth of viruses resistant to inhibitors developed from bound fragments, we performed deep mutational scanning of NS2B-NS3 protease. Merging fragment hits yields an extensive set of mergers, defined as synthetically accessible compounds that recapitulate constellations of observed fragment-protein interactions. In addition, the highly sociable fragment hits enable rapid exploration of chemical space via algorithmic calculation and thus yield diverse possible starting points that maximally explore the binding opportunities to NS2B-NS3 protease, facilitating its resistance-resilient antiviral development.

biochemistry↗

bioRxiv2024

Crystallographic Fragment Screen of Coxsackievirus A16 2A Protease identifies new opportunities for the development of broad-spectrum anti-enterovirals

Enteroviruses are the causative agents of paediatric hand-foot-and-mouth disease, and a target for pandemic preparedness due to the risk of higher order complications in a large-scale outbreak. The 2A protease of these viruses is responsible for the self-cleavage of the poly protein, allowing for correct folding and assembly of capsid proteins in the final stages of viral replication. These 2A proteases are highly conserved between Enterovirus species, such as Enterovirus A71 and Coxsackievirus A16. Inhibition of the 2A protease deranges capsid folding and assembly, preventing formation of mature virions in host cells and making the protease a valuable target for antiviral activity. Herein, we describe a crystallographic fragment screening campaign that identified 75 fragments which bind to the 2A protease including 38 unique compounds shown to bind within the active site. These fragments reveal a path for the development of non-peptidomimetic inhibitors of the 2A protease with broad-spectrum anti-enteroviral activity.

biochemistry↗

bioRxiv2024

A selection of Golden Gate vectors to simplify recombinant protein production in Escherichia coli

In this work multiple plasmids have been created to allow the simple Golden Gate cloning of a target gene for recombinant protein production in Escherichia coli. To simplify as much as possible the generation of different target gene vector combinations, the 22 plasmids contain the same Golden Gate cloning sites (BsaI), antibiotic resistance (kanamycin) and promoter (T7) for expression in the standard protein production strain of E. coli BL21[DE3]. The plasmid set includes commonly used tags for purification and assays (his, twin-strep and avi tag) as well as fusion protein partners that may aid target protein solubility and yield, SUMO, MBP, GST and sfGFP. Also included are plasmids with secretion peptide signals for transport of the target protein to the E. coli periplasm via various pathways (SEC, SRP, TatA). We have evaluated the 4 of the vectors using a test super folder GFP insert and found that using the Golden Gate process allows cloning efficiencies of greater than 90% to be routinely obtained. Vectors were further evaluated by expressing and purifying the target insert. The plasmid vector set described herein should prove useful to any investigator who has to routinely evaluate numerous protein expression constructs and are freely available through Addgene.

molecular biology↗

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

The use of a graph database is a complementary approach to a classical similarity search for identifying commercially available fragment merges

AO_SCPLOWBSTRACTC_SCPLOWFragment screening using X-ray crystallography can yield rich structural data to help guide the optimization of low-molecular-weight compounds into more potent binders. Fragment merging, whereby substructural motifs from partially overlapping fragments are incorporated into a single larger compound, represents a potentially powerful and efficient approach for increasing potency. Searching commercial catalogues provides one useful way to quickly and cheaply identify follow-up compounds for purchase and further screening, and circumvents the challenge of synthetic accessibility. The Fragment Network is a graph database that provides a novel way to explore the chemical space surrounding fragment hits. We use an iteration of the database containing >120 million catalogue compounds to find fragment merges for four XChem fragment screening campaigns. Retrieved molecules were filtered using a pipeline of 2D and 3D filters and contrasted against a traditional fingerprint-based similarity search. The two search techniques were found to have complementary results, identifying merges in different regions of chemical space. Both techniques were able to identify merges that are predicted to replicate the interactions made by the parent fragments. This work demonstrates the use of the Fragment Network to increase the yield of fragment merges beyond that of a classical catalogue search, thus increasing the likelihood of finding promising follow-up compounds. We present a pipeline that is able to systematically exploit all known fragment hits by performing large-scale enumeration of all possible fragment pairs for merging.

bioinformatics↗