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Jack
Scantlebury
,
Lucy
Vost
,
Anna
Carbery
,
Thomas E.
Hadfield
,
Oliver M.
Turnbull
,
Nathan
Brown
,
Vijil
Chenthamarakshan
,
Payel
Das
,
Harold
Grosjean
,
Frank
Von Delft
,
Charlotte M.
Deane
Open Access
Abstract: Over the past 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 data set 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 generalize to dissimilar targets. To test what a machine learning-based scoring function has learned, 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 data set 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 data set filtering and show that it achieves comparable performance on the Comparative Assessment of Scoring Functions, 2016 (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 learned 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.
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May 2023
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I04-1-Macromolecular Crystallography (fixed wavelength)
Krios II-Titan Krios II at Diamond
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Andre
Schutzer Godoy
,
Aline Minalli
Nakamura
,
Alice
Douangamath
,
Yun
Song
,
Gabriela
Dias Noske
,
Victor
Oliveira Gawriljuk
,
Rafaela
Sachetto Fernandes
,
Humberto
D'Muniz Pereira
,
Ketllyn irene
Zagato Oliveira
,
Daren
Fearon
,
Alexandre
Dias
,
Tobias
Krojer
,
Michael
Fairhead
,
Alisa
Powell
,
Louise
Dunnett
,
Jose
Brandao-Neto
,
Rachael
Skyner
,
Rod
Chalk
,
Dávid
Bajusz
,
Miklós
Bege
,
Anikó
Borbás
,
György Miklós
Keserű
,
Frank
Von Delft
,
Glaucius
Oliva
Diamond Proposal Number(s):
[27083, 27023]
Open Access
Abstract: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of coronavirus disease 2019 (COVID-19). The NSP15 endoribonuclease enzyme, known as NendoU, is highly conserved and plays a critical role in the ability of the virus to evade the immune system. NendoU is a promising target for the development of new antiviral drugs. However, the complexity of the enzyme's structure and kinetics, along with the broad range of recognition sequences and lack of structural complexes, hampers the development of inhibitors. Here, we performed enzymatic characterization of NendoU in its monomeric and hexameric form, showing that hexamers are allosteric enzymes with a positive cooperative index, and with no influence of manganese on enzymatic activity. Through combining cryo-electron microscopy at different pHs, X-ray crystallography and biochemical and structural analysis, we showed that NendoU can shift between open and closed forms, which probably correspond to active and inactive states, respectively. We also explored the possibility of NendoU assembling into larger supramolecular structures and proposed a mechanism for allosteric regulation. In addition, we conducted a large fragment screening campaign against NendoU and identified several new allosteric sites that could be targeted for the development of new inhibitors. Overall, our findings provide insights into the complex structure and function of NendoU and offer new opportunities for the development of inhibitors.
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Apr 2023
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I03-Macromolecular Crystallography
I24-Microfocus Macromolecular Crystallography
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Tamar
Skaist Mehlmam
,
Justin T.
Biel
,
Syeda Maryam
Azeem
,
Elliot R.
Nelson
,
Sakib
Hossain
,
Louise
Dunnett
,
Neil G.
Paterson
,
Alice
Douangamath
,
Romain
Talon
,
Danny
Axford
,
Helen
Orins
,
Frank
Von Delft
,
Daniel A.
Keedy
Diamond Proposal Number(s):
[15751, 18340, 23570]
Open Access
Abstract: 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 et al., 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 cryo-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.
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Mar 2023
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William
Mccorkindale
,
Kadi L.
Saar
,
Daren
Fearon
,
Melissa
Boby
,
Haim
Barr
,
Amir
Ben-Shmuel
,
Nir
London
,
Frank
Von Delft
,
John D.
Chodera
,
Alpha. A.
Lee
,
The
Covid Moonshot Consortium
Open Access
Abstract: A common challenge in drug design pertains to finding chemical modifications to a ligand that increases its affinity to the target protein. An underutilized advance is the increase in structural biology throughput, which has progressed from an artisanal endeavor to a monthly throughput of hundreds of 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 their binding activities. This allows us to design one-step library syntheses which improved the potency of two distinct micromolar hits by over 10-fold, arriving at a noncovalent and nonpeptidomimetic 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.
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Mar 2023
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I03-Macromolecular Crystallography
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Diamond Proposal Number(s):
[23459]
Open Access
Abstract: The worldwide public health and socioeconomic consequences caused by the COVID-19 pandemic highlight the importance of increasing preparedness for viral disease outbreaks by providing rapid disease prevention and treatment strategies. The NSP3 macrodomain of coronaviruses including SARS-CoV-2 is among the viral protein repertoire that was identified as a potential target for the development of antiviral agents, due to its critical role in viral replication and consequent pathogenicity in the host. By combining virtual and biophysical screening efforts, we discovered several experimental small molecules and FDA-approved drugs as inhibitors of the NSP3 macrodomain. Analogue characterisation of the hit matter and crystallographic studies confirming binding modes, including that of the antibiotic compound aztreonam, to the active site of the macrodomain provide valuable structure–activity relationship information that support current approaches and open up new avenues for NSP3 macrodomain inhibitor development.
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Feb 2023
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Stefan
Gahbauer
,
Galen J.
Correy
,
Marion
Schuller
,
Matteo P.
Ferla
,
Yagmur Umay
Doruk
,
Moira
Rachman
,
Taiasean
Wu
,
Morgan
Diolaiti
,
Siyi
Wang
,
R. Jeffrey
Neitz
,
Daren
Fearon
,
Dmytro S.
Radchenko
,
Yurii S.
Moroz
,
John J.
Irwin
,
Adam R.
Renslo
,
Jenny C.
Taylor
,
Jason E.
Gestwicki
,
Frank
Von Delft
,
Alan
Ashworth
,
Ivan
Ahel
,
Brian K.
Shoichet
,
James S.
Fraser
Open Access
Abstract: 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 153 Mac1-ligand complex crystal structures were determined, typically to 1 Å resolution or better. Our analyses discovered selective and cell-permeable molecules, unexpected ligand-mediated conformational changes within the active site, and key inhibitor motifs that will template future drug development against Mac1.
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Jan 2023
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Open Access
Abstract: Compound availability is a critical property for design prioritization across the drug discovery pipeline. Historically, and despite their multiple limitations, compound-oriented synthetic accessibility scores have been used as proxies for this problem. However, the size of the catalogues of commercially available molecules has dramatically increased over the last decade, redefining the problem of compound accessibility as a matter of budget. In this paper we show that if compound prices are the desired proxy for compound availability, then synthetic accessibility scores are not effective strategies for us in selection. Our approach, CoPriNet, is a retrosynthesis-free deep learning model trained on 2D graph representations of compounds alongside their prices extracted from the Mcule catalogue. We show that CoPriNet provides price predictions that correlate far better with actual compound prices than any synthetic accessibility score. Moreover, unlike standard retrosynthesis methods, CoPriNet is rapid, with execution times comparable to popular synthetic accessibility metrics, and thus is suitable for high-throughput experiments including virtual screening and de novo compound generation. While the Mcule catalogue is a proprietary dataset, the CoPriNet source code and the model trained on the proprietary data as well as the fraction of the catalogue (100 K compound/prices) used as test dataset have been made publicly available at https://github.com/oxpig/CoPriNet.
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Nov 2022
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I04-1-Macromolecular Crystallography (fixed wavelength)
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Open Access
Abstract: Current fragment-based drug design relies on the efficient exploration of chemical space by using structurally diverse libraries of small fragments. However, structurally dissimilar compounds can exploit the same interactions and thus be functionally similar. Using three-dimensional structures of many fragments bound to multiple targets, we examined if a better strategy for selecting fragments for screening libraries exists. 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 the amounts recovered by other methods of selection. Using these results, we design small functionally efficient libraries that can give significantly more information about new protein targets than similarly sized structurally diverse libraries. By covering more functional space, we can generate more diverse sets of drug leads.
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Aug 2022
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I04-1-Macromolecular Crystallography (fixed wavelength)
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Diamond Proposal Number(s):
[18145]
Open Access
Abstract: Primary hyperoxaluria type I (PH1) is caused by AGXT gene mutations that decrease the functional activity of alanine:glyoxylate aminotransferase. A build-up of the enzyme’s substrate, glyoxylate, results in excessive deposition of calcium oxalate crystals in the renal tract, leading to debilitating renal failure. Oxidation of glycolate by glycolate oxidase (or hydroxy acid oxidase 1, HAO1) is a major cellular source of glyoxylate, and siRNA studies have shown phenotypic rescue of PH1 by the knockdown of HAO1, representing a promising inhibitor target. Here, we report the discovery and optimization of six low-molecular-weight fragments, identified by crystallography-based fragment screening, that bind to two different sites on the HAO1 structure: at the active site and an allosteric pocket above the active site. The active site fragments expand known scaffolds for substrate-mimetic inhibitors to include more chemically attractive molecules. The allosteric fragments represent the first report of non-orthosteric inhibition of any hydroxy acid oxidase and hold significant promise for improving inhibitor selectivity. The fragment hits were verified to bind and inhibit HAO1 in solution by fluorescence-based activity assay and surface plasmon resonance. Further optimization cycle by crystallography and biophysical assays have generated two hit compounds of micromolar (44 and 158 µM) potency that do not compete with the substrate and provide attractive starting points for the development of potent and selective HAO1 inhibitors.
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May 2022
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I04-1-Macromolecular Crystallography (fixed wavelength)
I04-Macromolecular Crystallography
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Diamond Proposal Number(s):
[16816, 18582, 19190, 25586, 30588]
Open Access
Abstract: Polyomaviruses are a family of ubiquitous double-stranded DNA viruses many of which are human pathogens. These include BK polyomavirus which causes severe urinary tract infection in immunocompromised patients and Merkel cell polyomavirus associated with aggressive cancers. The small genome of polyomaviruses lacks conventional drug targets, and no specific drugs are available at present. Here we focus on the main structural protein VP1 of BK polyomavirus which is responsible for icosahedral capsid formation. To provide a foundation towards rational drug design, we crystallized truncated VP1 pentamers and subjected them to a high-throughput screening for binding drug-like fragments through a direct X-ray analysis. To enable a highly performant screening, rigorous optimization of the crystallographic pipeline and processing with the latest generation PanDDA2 software were necessary. As a result, a total of 144 binding hits were established. Importantly, the hits are well clustered in six surface pockets. Three pockets are located on the outside of the pentamer and map on the regions where the ‘invading’ C-terminal arm of another pentamer is attached upon capsid assembly. Another set of three pockets is situated within the wide pore along the five-fold axis of the VP1 pentamer. These pockets are situated at the interaction interface with the minor capsid protein VP2 which is indispensable for normal functioning of the virus. Here we systematically analyse the three outside pockets which are highly conserved across various polyomaviruses, while point mutations in these pockets are detrimental for viral replication. We show that one of the pockets can accommodate antipsychotic drug trifluoperazine. For each pocket, we derive pharmacophore features which enable the design of small molecules preventing the interaction between VP1 pentamers and therefore inhibiting capsid assembly. Our data lay a foundation towards a rational development of first-in-class drugs targeting polyomavirus capsid.
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May 2022
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