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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NONE-No attached Diamond beamline
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Open Access
Abstract: Fragment-based lead discovery (FBLD) is a powerful application for developing ligands as modulators of disease targets. This approach strategy involves identification of interactions between low-molecular weight compounds (100–300 Da) and their putative targets, often with low affinity (KD ~0.1–1 mM) interactions. The focus of this screening methodology is to optimize and streamline identification of fragments with higher ligand efficiency (LE) than typical high-throughput screening. The focus of this review is on the last half decade of fragment-based drug discovery strategies that have been used for antimicrobial drug discovery.
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Feb 2023
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I03-Macromolecular Crystallography
I04-1-Macromolecular Crystallography (fixed wavelength)
I04-Macromolecular Crystallography
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Diamond Proposal Number(s):
[26998, 22717, 28172]
Open Access
Abstract: NSP14 is a dual function enzyme containing an N-terminal exonuclease domain (ExoN) and C-terminal Guanine-N7-methyltransferase (N7-MTase) domain. Both activities are essential for the viral life cycle and may be targeted for anti-viral therapeutics. NSP14 forms a complex with NSP10, and this interaction enhances the nuclease but not the methyltransferase activity. We have determined the structure of SARS-CoV-2 NSP14 in the absence of NSP10 to 1.7 Å resolution. Comparisons with NSP14/NSP10 complexes reveal significant conformational changes that occur within the NSP14 ExoN domain upon binding of NSP10, including helix to coil transitions that facilitate the formation of the ExoN active site and provide an explanation of the stimulation of nuclease activity by NSP10. We have determined the structure of NSP14 in complex with cap analogue 7MeGpppG, and observe conformational changes within a SAM/SAH interacting loop that plays a key role in viral mRNA capping offering new insights into MTase activity. We perform an X-ray fragment screen on NSP14, revealing 72 hits bound to sites of inhibition in the ExoN and MTase domains. These fragments serve as excellent starting point tools for structure guided development of NSP14 inhibitors that may be used to treat COVID-19 and potentially other future viral threats.
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Jan 2023
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I04-1-Macromolecular Crystallography (fixed wavelength)
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Diamond Proposal Number(s):
[13385, 14379, 19758]
Open Access
Abstract: None of the current data processing pipelines for X-ray crystallography fragment-based lead discovery (FBLD) consults all the information available when deciding on the lattice and symmetry (i.e., the polymorph) of each soaked crystal. Often, X-ray crystallography FBLD pipelines either choose the polymorph based on cell volume and point-group symmetry of the X-ray diffraction data or leave polymorph attribution to manual intervention on the part of the user. Thus, when the FBLD crystals belong to more than one crystal polymorph, the discovery pipeline can be plagued by space group ambiguity, especially if the polymorphs at hand are variations of the same lattice and, therefore, difficult to tell apart from their morphology and/or their apparent crystal lattices and point groups. In the course of a fragment-based lead discovery effort aimed at finding ligands of the catalytic domain of UDP–glucose glycoprotein glucosyltransferase (UGGT), we encountered a mixture of trigonal crystals and pseudotrigonal triclinic crystals—with the two lattices closely related. In order to resolve that polymorphism ambiguity, we have written and described here a series of Unix shell scripts called CoALLA (crystal polymorph and ligand likelihood-based assignment). The CoALLA scripts are written in Unix shell and use autoPROC for data processing, CCP4-Dimple/REFMAC5 and BUSTER for refinement, and RHOFIT for ligand docking. The choice of the polymorph is effected by carrying out (in each of the known polymorphs) the tasks of diffraction data indexing, integration, scaling, and structural refinement. The most likely polymorph is then chosen as the one with the best structure refinement Rfree statistic. The CoALLA scripts further implement a likelihood-based ligand assignment strategy, starting with macromolecular refinement and automated water addition, followed by removal of the water molecules that appear to be fitting ligand density, and a final round of refinement after random perturbation of the refined macromolecular model, in order to obtain unbiased difference density maps for automated ligand placement. We illustrate the use of CoALLA to discriminate between H3 and P1 crystals used for an FBLD effort to find fragments binding to the catalytic domain of Chaetomium thermophilum UGGT.
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Dec 2022
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I04-1-Macromolecular Crystallography (fixed wavelength)
I04-Macromolecular Crystallography
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Gemma
Davison
,
Mathew P.
Martin
,
Shannon
Turberville
,
Selma
Dormen
,
Richard
Heath
,
Amy B.
Heptinstall
,
Marie
Lawson
,
Duncan C.
Miller
,
Yi Min
Ng
,
James N.
Sanderson
,
Ian
Hope
,
Daniel
Wood
,
Céline
Cano
,
Jane A.
Endicott
,
Ian R.
Hardcastle
,
Martin E. M.
Noble
,
Michael J.
Waring
Open Access
Abstract: The development of ligands for biological targets is critically dependent on the identification of sites on proteins that bind molecules with high affinity. A set of compounds, called FragLites, can identify such sites, along with the interactions required to gain affinity, by X-ray crystallography. We demonstrate the utility of FragLites in mapping the binding sites of bromodomain proteins BRD4 and ATAD2 and demonstrate that FragLite mapping is comparable to a full fragment screen in identifying ligand binding sites and key interactions. We extend the FragLite set with analogous compounds derived from amino acids (termed PepLites) that mimic the interactions of peptides. The output of the FragLite maps is shown to enable the development of ligands with leadlike potency. This work establishes the use of FragLite and PepLite screening at an early stage in ligand discovery allowing the rapid assessment of tractability of protein targets and informing downstream hit-finding.
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Nov 2022
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NONE-No attached Diamond beamline
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Patrizio
Di micco
,
Albert A.
Antolin
,
Costas
Mitsopoulos
,
Eloy
Villasclaras-Fernandez
,
Domenico
Sanfelice
,
Daniela
Dolciami
,
Pradeep
Ramagiri
,
Ioan l.
Mica
,
Joseph e.
Tym
,
Philip w.
Gingrich
,
Huabin
Hu
,
Paul
Workman
,
Bissan
Al-Lazikani
Open Access
Abstract: canSAR (https://cansar.ai) is the largest public cancer drug discovery and translational research knowledgebase. Now hosted in its new home at MD Anderson Cancer Center, canSAR integrates billions of experimental measurements from across molecular profiling, pharmacology, chemistry, structural and systems biology. Moreover, canSAR applies a unique suite of machine learning algorithms designed to inform drug discovery. Here, we describe the latest updates to the knowledgebase, including a focus on significant novel data. These include canSAR’s ligandability assessment of AlphaFold; mapping of fragment-based screening data; and new chemical bioactivity data for novel targets. We also describe enhancements to the data and interface.
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Nov 2022
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NONE-No attached Diamond beamline
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Open Access
Abstract: The substantial cost of new drug research and development has consistently posed a huge burden for both pharmaceutical companies and patients. In order to lower the expenditure and development failure rate, repurposing existing and approved drugs by identifying interactions between drug molecules and target proteins based on computational methods have gained growing attention. Here, we propose the DeepLPI, a novel deep learning-based model that mainly consists of ResNet-based 1-dimensional convolutional neural network (1D CNN) and bi-directional long short term memory network (biLSTM), to establish an end-to-end framework for protein–ligand interaction prediction. We first encode the raw drug molecular sequences and target protein sequences into dense vector representations, which go through two ResNet-based 1D CNN modules to derive features, respectively. The extracted feature vectors are concatenated and further fed into the biLSTM network, followed by the MLP module to finally predict protein–ligand interaction. We downloaded the well-known BindingDB and Davis dataset for training and testing our DeepLPI model. We also applied DeepLPI on a COVID-19 dataset for externally evaluating the prediction ability of DeepLPI. To benchmark our model, we compared our DeepLPI with the baseline methods of DeepCDA and DeepDTA, and observed that our DeepLPI outperformed these methods, suggesting the high accuracy of the DeepLPI towards protein–ligand interaction prediction. The high prediction performance of DeepLPI on the different datasets displayed its high capability of protein–ligand interaction in generalization, demonstrating that the DeepLPI has the potential to pinpoint new drug-target interactions and to find better destinations for proven drugs.
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Oct 2022
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NONE-No attached Diamond beamline
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Abstract: The COVID-19 pandemic created an unprecedented global healthcare emergency prompting the exploration of new therapeutic avenues, including drug repurposing. A large number of ongoing studies revealed pervasive issues in clinical research, such as the lack of accessible and organised data. Moreover, current shortcomings in clinical studies highlighted the need for a multi-faceted approach to tackle this health crisis. Thus, we set out to explore and develop new strategies for drug repositioning by employing computational pharmacology, data mining, systems biology, and computational chemistry to advance shared efforts in identifying key targets, affected networks, and potential pharmaceutical intervention options. Our study revealed that formulating pharmacological strategies should rely on both therapeutic targets and their networks. We showed how data mining can reveal regulatory patterns, capture novel targets, alert about side-effects, and help identify new therapeutic avenues. We also highlighted the importance of the miRNA regulatory layer and how this information could be used to monitor disease progression or devise treatment strategies. Importantly, our work bridged the interactome with the chemical compound space to better understand the complex landscape of COVID-19 drugs. Machine and deep learning allowed us to showcase limitations in current chemical libraries for COVID-19 suggesting that both in silico and experimental analyses should be combined to retrieve therapeutically valuable compounds. Based on the gathered data, we strongly advocate for taking this opportunity to establish robust practices for treating today's and future infectious diseases by preparing solid analytical frameworks.
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Sep 2022
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