Biology subjects

von Delft, F.

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

36 records · Page 2Linked to original sources

bioRxiv2022

Room-temperature crystallography reveals altered binding of small-molecule fragments to PTP1B

Much of our current understanding of how small-molecule ligands interact with proteins stems from X-ray crystal structures determined at cryogenic (cryo) temperature. For proteins alone, room-temperature (RT) crystallography can reveal previously hidden, biologically relevant alternate conformations. However, less is understood about how RT crystallography may impact the conformational landscapes of protein-ligand complexes. Previously we showed that small-molecule fragments cluster in putative allosteric sites using a cryo crystallographic screen of the therapeutic target PTP1B (Keedy*, Hill*, 2018). Here we have performed two RT crystallographic screens of PTP1B using many of the same fragments, representing the largest RT crystallographic screens of a diverse library of ligands to date, and enabling a direct interrogation of the effect of data collection temperature on protein-ligand interactions. We show that at RT, fewer ligands bind, and often more weakly -- but with a variety of temperature-dependent differences, including unique binding poses, changes in solvation, new binding sites, and distinct protein allosteric conformational responses. Overall, this work suggests that the vast body of existing cryogenic-temperature protein-ligand structures may provide an incomplete picture, and highlights the potential of RT crystallography to help complete this picture by revealing distinct conformational modes of protein-ligand systems. Our results may inspire future use of RT crystallography to interrogate the roles of protein-ligand conformational ensembles in biological function.

biophysics↗

bioRxiv2022

PointVS: A Machine Learning Scoring Function that Identifies Important Binding Interactions

Over the last few years, many machine learning-based scoring functions for predicting the binding of small molecules to proteins have been developed. Their objective is to approximate the distribution which takes two molecules as input and outputs the energy of their interaction. Only a scoring function that accounts for the interatomic interactions involved in binding can accurately predict binding affinity on unseen molecules. However, many scoring functions make predictions based on dataset biases rather than an understanding of the physics of binding. These scoring functions perform well when tested on similar targets to those in the training set, but fail to generalise to dissimilar targets. To test what a machine learning-based scoring function has learnt, input attribution--a technique for learning which features are important to a model when making a prediction on a particular data point--can be applied. If a model successfully learns something beyond dataset biases, attribution should give insight into the important binding interactions that are taking place. We built a machine learning-based scoring function that aimed to avoid the influence of bias via thorough train and test dataset filtering, and show that it achieves comparable performance on the CASF-2016 benchmark to other leading methods. We then use the CASF-2016 test set to perform attribution, and find that the bonds identified as important by PointVS, unlike those extracted from other scoring functions, have a high correlation with those found by a distance-based interaction profiler. We then show that attribution can be used to extract important binding pharmacophores from a given protein target when supplied with a number of bound structures. We use this information to perform fragment elaboration, and see improvements in docking scores compared to using structural information from a traditional, data-based approach. This not only provides definitive proof that the scoring function has learnt to identify some important binding interactions, but also constitutes the first deep learning-based method for extracting structural information from a target for molecule design.

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↗

bioRxiv2022

Gluebodies improve crystal reliability and diversity through transferable nanobody mutations that introduce constitutive crystal contacts

The design of proteins that may assemble in a manner that is transferable and modular remains an enduring challenge. In particular, obtaining well-diffracting protein crystals suitable for characterizing ligands or drug candidates and understanding different protein conformations remains a bottleneck for structural studies. Using nanobodies as crystallization chaperones is one strategy to address the problem, but its reliability is uncharacterized and, in this study, we observed it to have a limited success rate. Here we show that by exploring and testing the nanobody-nanobody interfaces predominant in >200 combinations of surface mutations in multiple iterations we can engineer robust crystallization behaviour into the nanobody scaffold. Strikingly, this survey yielded multiple polymorphs, all mediated by the same interface. The resulting Gluebodies (Gbs) provide far superior resolution and reliability of diffraction and can be routinely generated for chaperone experiments. We furthermore show that Gbs cannot rescue intrinsically non-crystallizing proteins, but instead are a powerful approach to improve the packing and resolution limit of poorly diffracting crystals. The discovery of an engineered, preferred nanobody interface that arises under kinetic control - trapped here by irreversible crystallization - embodies a protein assembly strategy that could prove even more broadly useful for modular assembly trapped by other irreversible methods.

biochemistry↗

bioRxiv2022

Structure-based inhibitor optimization for the Nsp3 Macrodomain of SARS-CoV-2

The nonstructural protein 3 (NSP3) of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) contains a conserved macrodomain enzyme (Mac1) that is critical for pathogenesis and lethality. While small molecule inhibitors of Mac1 have great therapeutic potential, at the outset of the COVID-19 pandemic there were no well-validated inhibitors for this protein nor, indeed, the macrodomain enzyme family, making this target a pharmacological orphan. Here, we report the structure-based discovery and development of several different chemical scaffolds exhibiting low- to sub-micromolar affinity for Mac1 through iterations of computer-aided design, structural characterization by ultra-high resolution protein crystallography, and binding evaluation. Potent scaffolds were designed with in silico fragment linkage and by ultra-large library docking of over 450 million molecules. Both techniques leverage the computational exploration of tangible chemical space and are applicable to other pharmacological orphans. Overall, 160 ligands in 119 different scaffolds were discovered, and 152 Mac1-ligand complex crystal structures were determined, typically to 1 [A] resolution or better. Our analyses discovered selective and cell-permeable molecules, unexpected ligand-mediated protein dynamics within the active site, and key inhibitor motifs that will template future drug development against Mac1. Significance StatementSARS-CoV-2 encodes a viral macrodomain protein (Mac1) that hydrolyzes ribo-adenylate marks on viral proteins, disrupting the innate immune response to the virus. Catalytic mutations in the enzyme make the related SARS-1 virus less pathogenic and non-lethal in animals, suggesting that Mac1 will be a good antiviral target. However, no potent inhibitors of this protein class have been described, and pharmacologically the enzyme remains an orphan. Here, we computationally designed potent inhibitors of Mac1, determining 150 inhibitor-enzyme structures to ultra-high resolution by crystallography. In silico fragment linking and molecular docking of > 450 million virtual compounds led to inhibitors with submicromolar activity. These molecules may template future drug discovery efforts against this crucial but understudied viral target.

biophysics↗

bioRxiv2022

Fragment libraries designed to be functionally diverse recover protein binding information more efficiently than standard structurally diverse libraries

Current fragment-based drug design relies on the efficient exploration of chemical space though the use of structurally diverse libraries of small fragments. However, structurally dissimilar compounds can exploit the same interactions on a target, and thus be functionally similar. Using 3D structures of many fragments bound to multiple targets, we examined if there exists a better strategy for selecting fragments for screening libraries. We show that structurally diverse fragments can be described as functionally redundant, often making the same interactions. Ranking fragments by the number of novel interactions they made, we show that functionally diverse selections of fragments substantially increase the amount of information recovered for unseen targets compared to other methods of selection. Using these results, we design small functionally efficient libraries that are able to give significantly more information about new protein targets than similarly sized structurally diverse libraries. By covering more functional space (rather than chemical space), more diverse sets of drug leads can be generated, increasing the chances of fragment screens resulting in viable drug candidates.

bioinformatics↗

bioRxiv2021

Turning high-throughput structural biology into predictive inhibitor design

A common challenge in drug design pertains to finding chemical modifications to a ligand that increases its affinity to the target protein. An underutilised advance is the increase in structural biology throughput, which has progressed from an artisanal endeavour to a monthly throughput of up to 100 different ligands against a protein in modern synchrotrons. However, the missing piece is a framework that turns high throughput crystallography data into predictive models for ligand design. Here we designed a simple machine learning approach that predicts protein-ligand affinity from experimental structures of diverse ligands against a single protein paired with biochemical measurements. Our key insight is using physics-based energy descriptors to represent protein-ligand complexes, and a learning-to-rank approach that infers the relevant differences between binding modes. We ran a high throughput crystallography campaign against the SARS-CoV-2 Main Protease (MPro), obtaining parallel measurements of over 200 protein-ligand complexes and the binding activity. This allows us to design a one-step library syntheses which improved the potency of two distinct micromolar hits by over 10-fold, arriving at a non-covalent and non-peptidomimetic inhibitor with 120 nM antiviral efficacy. Crucially, our approach successfully extends ligands to unexplored regions of the binding pocket, executing large and fruitful moves in chemical space with simple chemistry.

biophysics↗

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

Novel quaternary structures of the human prion protein globular domain

Prion disease is caused by the misfolding of the cellular prion protein, PrPC, into a self-templating conformer, PrPSc. Nuclear magnetic resonance (NMR) and X-ray crystallography revealed the 3D structure of the globular domain of PrPC and the possibility of its dimerization via an interchain disulfide bridge that forms due to domain swap or by non-covalent association of two monomers. On the contrary, PrPSc is composed by a complex and heterogeneous ensemble of poorly defined conformations and quaternary arrangements that are related to different patterns of neurotoxicity. Targeting PrPC with molecules that stabilize the native conformation of its globular domain emerged as a promising approach to develop anti-prion therapies. One of the advantages of this approach is employing structure-based drug discovery methods to PrPC. Thus, it is essential to expand our structural knowledge about PrPC as much as possible to aid such drug discovery efforts. In this work, we report a crystallographic structure of the globular domain of human PrPC that shows a novel dimeric form and a novel oligomeric arrangement. We use molecular dynamics simulations to explore its structural dynamics and stability and discuss potential implications of these new quaternary structures to the conversion process.

biochemistry↗

bioRxiv2020

An automatic pipeline for the design of irreversible derivatives identifies a potent SARS-CoV-2 Mpro inhibitor.

Designing covalent inhibitors is a task of increasing importance in drug discovery. Efficiently designing irreversible inhibitors, though, remains challenging. Here, we present covalentizer, a computational pipeline for creating irreversible inhibitors based on complex structures of targets with known reversible binders. For each ligand, we create a custom-made focused library of covalent analogs. We use covalent docking, to dock these tailored covalent libraries and to find those that can bind covalently to a nearby cysteine while keeping some of the main interactions of the original molecule. We found ~11,000 cysteines in close proximity to a ligand across 8,386 protein-ligand complexes in the PDB. Of these, the protocol identified 1,553 structures with covalent predictions. In prospective evaluation against a panel of kinases, five out of nine predicted covalent inhibitors showed IC50 between 155 nM - 4.2 M. Application of the protocol to an existing SARS-CoV-1 Mpro reversible inhibitor led to a new acrylamide inhibitor series with low micromolar IC50 against SARS-CoV-2 Mpro. The docking prediction was validated by 11 co-crystal structures. This is a promising lead series for COVID-19 antivirals. Together these examples hint at the vast number of covalent inhibitors accessible through our protocol.

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↗

bioRxiv2020

Dataset Augmentation Allows Deep Learning-Based Virtual Screening To Better Generalise To Unseen Target Classes, And Highlight Important Binding Interactions

Current deep learning methods for structure-based virtual screening take the structures of both the protein and the ligand as input but make little or no use of the protein structure when predicting ligand binding. Here we show how a relatively simple method of dataset augmentation forces such deep learning methods to take into account information from the protein. Models trained in this way are more generalisable (make better predictions on protein-ligand complexes from a different distribution to the training data). They also assign more meaningful importance to the protein and ligand atoms involved in binding. Overall, our results show that dataset augmentation can help deep learning based virtual screening to learn physical interactions rather than dataset biases. Graphical TOC Entry O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=81 SRC="FIGDIR/small/979625v2_ufig1.gif" ALT="Figure 1"> View larger version (14K): org.highwire.dtl.DTLVardef@609f5forg.highwire.dtl.DTLVardef@17f2cbborg.highwire.dtl.DTLVardef@51e854org.highwire.dtl.DTLVardef@16c111f_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2019

The Low-Cost, Semi-Automated Shifter Microscope Stage Transforms Speed and Robustness of Manual Protein Crystal Harvesting

Despite the tremendous success of x-ray cryocrystallography over recent decades, the transfer of crystals from the drops where they grow to diffractometer sample mounts, remains a manual process in almost all laboratories. Here we describe the Shifter, a semi-automated microscope stage that offers an accessible and scalable approach to crystal mounting that exploits on the strengths of both humans and machines. The Shifter control software manoeuvres sample drops beneath a hole in a clear protective cover, for human mounting under a microscope. By allowing complete removal of film seals the tedium of cutting or removing the seal is eliminated. The control software also automatically captures experimental annotations for uploading to the users data repository, removing the overhead of manual documentation. The Shifter facilitates mounting rates of 100-240 crystals per hour, in a more controlled process than manual mounting, which greatly extends the lifetime of drops and thus allows for a dramatic increase in the number of crystals retrievable from any given drop, without loss of X-ray diffraction quality. In 2015 the first in a series of three Shifter devices was deployed as part of the XChem fragment screening facility at Diamond Light Source (DLS), where they have since facilitated the mounting of over 100,000 crystals. The Shifter was engineered to be simple, allowing for a low-cost device to be commercialised and thus potentially transformative as many research initiatives as possible. SynopsisA motorised X/Y microscope stage is presented that combines human fine motor control with machine automation and automated experiment documentation, to transform productivity in protein crystal harvesting.

biophysics↗

bioRxiv2019

Structural consequences of BMPR2 kinase domain mutations causing pulmonary arterial hypertension

Bone morphogenetic proteins (BMPs) are secreted ligands of the transforming growth factor-{beta} (TGF-{beta}) family that control embryonic patterning, as well as tissue development and homeostasis. Loss of function mutations in the type II BMP receptor BMPR2 are the leading cause of pulmonary arterial hypertension (PAH), a rare disease of vascular occlusion and heart hypertrophy. To understand the structural consequences of these mutations, we determined the crystal structure of the human BMPR2 kinase domain at 2.35 [A] resolution. The structure revealed an activate conformation of the catalytic domain that formed canonical interactions with the bound ligand Mg-ADP. Disease-associated missense mutations were mapped throughout the protein structure, but clustered predominantly in the larger kinase C-lobe. Modelling revealed that the mutations will destabilize the protein structure by varying extents consistent with their previously reported functional heterogeneity. The most severe mutations introduced steric clashes in the hydrophobic protein core, whereas those found on the protein surface were less destabilizing and potentially most favorable for therapeutic rescue strategies currently under clinical investigation.

molecular biology↗

bioRxiv2019

Strategies to improve scFvs as crystallization chaperones suggested by analysis of a complex with the human PHD-bromodomain SP140

Antibody fragments have great potential as crystallization chaperones for structural biology due to their ability to either stabilise targets, trap certain conformations and/or promote crystal packing. Here we present an example of using a single-chain variable fragment (scFv) to determine the previously unsolved structure of the multidomain protein SP140. This nuclear leukocyte-specific protein contains domains related to chromatin-mediated gene expression and has been implicated in various disease states. The structure of two of the domains (PHD-bromodomain) was solved by crystallizing them as a complex with a scFv generated by phage display technology. SP140 maintains a similar overall fold to previous PHD-bromodomains and the scFv CDR loops predominately interact with the PHD, while the framework regions of the scFv makes numerous interactions with the bromodomain. Analysis of our and other complex structures suggest various protein engineering strategies that might be employed to improve the usefulness of scFvs as crystallization chaperones.

biochemistry↗

bioRxiv2018

Rapid covalent-probe discovery by electrophile fragment screening

Covalent probes can display unmatched potency, selectivity and duration of action, however, their discovery is challenging. In principle, fragments that can irreversibly bind their target can overcome the low affinity that limits reversible fragment screening. Such electrophilic fragments were considered non-selective and were rarely screened. We hypothesized that mild electrophiles might overcome the selectivity challenge, and constructed a library of 993 mildly electrophilic fragments. We characterized this library by a new high-throughput thiol-reactivity assay and screened them against ten cysteine-containing proteins. Highly reactive and promiscuous fragments were rare and could be easily eliminated. By contrast, we found selective hits for most targets. Combination with high-throughput crystallography allowed rapid progression to potent and selective probes for two enzymes, the deubiquitinase OTUB2, and the pyrophosphatase NUDT7. No inhibitors were previously known for either. This study highlights the potential of electrophile fragment screening as a practical and efficient tool for covalent ligand discovery.

biochemistry↗

bioRxiv2017

New routes for PTP1B allosteric inhibition by multitemperature crystallography, fragment screening and covalent tethering

Allostery is an inherent feature of proteins and provides alternative routes to regulating function. Small-molecule allosteric inhibitors are often desirable; however, it remains challenging to identify surface sites in proteins which can bind small molecules and modulate function. We identified new allosteric sites in protein tyrosine phosphatase 1B (PTP1B) by combining multiple-temperature X-ray crystallography experiments and structure determination from hundreds of individual small-molecule fragment soaks. New modeling approaches reveal \"hidden\" low-occupancy conformational states for protein and ligands. Our results converge on a new allosteric site that is conformationally coupled to the active-site WPD loop, a hotspot for fragment binding, not conserved in the closest homolog, and distinct from other recently reported allosteric sites in PTP1B. Targeting this site with covalently tethered molecules allosterically inhibits enzyme activity. Overall, this work demonstrates how the ensemble nature of macromolecular structure revealed by multitemperature crystallography can be exploited for developing allosteric modulators.

biophysics↗

bioRxiv2016

Gentle, fast and effective crystal soaking by acoustic dispensing

SynopsisA high-throughput method is described for crystal soaking using acoustic droplet ejection, and its effectiveness demonstrated.\n\nAbstractBright light sources, agile robotics, and fast detectors are continually reducing the time it takes to perform an X-ray diffraction experiment, making high throughput experiments more feasible than ever. But this is also pushing the upstream bottleneck towards sample preparation, even for robust and well characterised crystal systems. Crystal soaking is routinely used to generate protein-ligand complex structures, yet protein crystals are often sensitive to changes in solvent composition, and frequently require gentle or careful stepwise soaking techniques, limiting overall throughput. Here, we describe the use of acoustic droplet ejection for soaking of protein crystals with small molecules, and show that it is both gentle on crystals and allows very high throughput, with 1000 unique soaks easily performed in under 10 minutes. In addition to having very low compound consumption (tens of nanolitres per sample), the positional precision of acoustic droplet ejection enables targeted placement of the compound/solvent away from crystals and towards drop edges, allowing for gradual diffusion of solvent across the drop. This ensures both an improvement in reproducibility of X-ray diffraction and an increased solvent tolerance of the crystals, thus enabling higher effective compound soaking concentrations. We detail the technique here with examples from the protein target JMJD2D, a histone lysine demethylase, having roles in cancer and the focus of active structure based drug design efforts.

biophysics↗