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178 items found (0 items not approved), displaying 1 to 10.[First/Prev] 1, 2, 3, 4, 5, 6, 7, 8 [Next/Last]

Instruments / Technical Area	Publication Details	Published
NONE-No attached Diamond beamline	Fragment-based lead discovery strategies in antimicrobial drug discovery Monika I. Konaklieva , Balbina J. Plotkin Antibiotics 12 DOI: 10.3390/antibiotics12020315 Open Access Show Abstract ▼ 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.	Feb 2023
NONE-No attached Diamond beamline	canSAR: update to the cancer translational research and drug discovery knowledgebase 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 Nucleic Acids Research 40 DOI: 10.1093/nar/gkac1004 Open Access Show Abstract ▼ 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.	Nov 2022
NONE-No attached Diamond beamline	DeepLPI: a novel deep learning-based model for protein–ligand interaction prediction for drug repurposing Bomin Wei , Yue Zhang , Xiang Gong Scientific Reports 12 DOI: 10.1038/s41598-022-23014-1 Open Access Show Abstract ▼ 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.	Oct 2022
NONE-No attached Diamond beamline	Computational pharmacology: New avenues for COVID-19 therapeutics search and better preparedness for future pandemic crises Austė Kanapeckaitė , Asta Mažeikienė , Liesbet Geris , Neringa Burokienė , Graeme S. Cottrell , Darius Widera Biophysical Chemistry 43 DOI: 10.1016/j.bpc.2022.106891 Show Abstract ▼ 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.	Sep 2022
NONE-No attached Diamond beamline	Facilitating uniform large-scale MoS2, WS2 monolayers, and their heterostructures through van der Waals epitaxy Chung-Che Huang , He Wang , Yameng Cao , Ed Weatherby , Filipe Richheimer , Sebastian Wood , Shan Jiang , Daqing Wei , Yongkang Dong , Xiaosong Lu , Pengfei Wang , Tomas Polcar , Daniel W. Hewak Acs Applied Materials & Interfaces DOI: 10.1021/acsami.2c12174 Open Access Show Abstract ▼ Abstract: The fabrication process for the uniform large-scale MoS2, WS2 transition-metal dichalcogenides (TMDCs) monolayers, and their heterostructures has been developed by van der Waals epitaxy (VdWE) through the reaction of MoCl5 or WCl6 precursors and the reactive gas H2S to form MoS2 or WS2 monolayers, respectively. The heterostructures of MoS2/WS2 or WS2/MoS2 can be easily achieved by changing the precursor from WCl6 to MoCl5 once the WS2 monolayer has been fabricated or switching the precursor from MoCl5 to WCl6 after the MoS2 monolayer has been deposited on the substrate. These VdWE-grown MoS2, WS2 monolayers, and their heterostructures have been successfully deposited on Si wafers with 300 nm SiO2 coating (300 nm SiO2/Si), quartz glass, fused silica, and sapphire substrates using the protocol that we have developed. We have characterized these TMDCs materials with a range of tools/techniques including scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), micro-Raman analysis, photoluminescence (PL), atomic force microscopy (AFM), transmission electron microscopy (TEM), energy-dispersive X-ray spectroscopy (EDX), and selected-area electron diffraction (SAED). The band alignment and large-scale uniformity of MoS2/WS2 heterostructures have also been evaluated with PL spectroscopy. This process and resulting large-scale MoS2, WS2 monolayers, and their heterostructures have demonstrated promising solutions for the applications in next-generation nanoelectronics, nanophotonics, and quantum technology.	Sep 2022
NONE-No attached Diamond beamline	Advancing simulation methods for molecular design and drug discovery Matthew Hurley Show Abstract ▼ Abstract: Investigating interactions between proteins and small molecules at an atomic scale is fundamental towards understanding biological processes and designing novel candidates during the pre-clinical stages of drug discovery. By optimizing the methods used to study these interactions in terms of accuracy and computational cost, we can accelerate this aspect of biological research and contribute more readily to therapeutic design. While biological assays and other experimental techniques are invaluable in quantitatively determining in vitro and in vivo inhibition activity, as well as validating computational predictions, there is an inherent benefit in the possible throughput provided by molecular dynamics (MD) simulations and related computational methods. These calculations provide researchers with unparalleled access to large amounts of all-atom sampling of biological systems, including non-physical pathways and other enhanced sampling methods. This dissertation presents research into advancing the application of expanded ensemble and other simulation-based methods of ligand design towards reliable and efficient absolute free energy of binding calculations on the scale of hundreds to thousands of small molecule ligands. This culminates in a combined workflow that allows for an automated approach to the force-field parameterization of custom systems, simulation preparation, optimization of the restraint and sampling protocols, production free energy simulations, and analysis that has facilitated the computation of absolute binding free energy predictions. Specifically highlighted is our ongoing effort to discover novel inhibitors of the main protease (Mpro) of SARS-CoV-2 as well as participation in the SAMPL9 Host-Guest Challenge.	Aug 2022
NONE-No attached Diamond beamline	Allosteric hotspots in the main protease of SARS-CoV-2 Léonie Strömich , Nan Wu , Mauricio Barahona , Sophia N. Yaliraki Journal Of Molecular Biology 579 DOI: 10.1016/j.jmb.2022.167748 Open Access Show Abstract ▼ Abstract: Inhibiting the main protease of SARS-CoV-2 is of great interest in tackling the COVID-19 pandemic caused by the virus. Most efforts have been centred on inhibiting the binding site of the enzyme. However, considering allosteric sites, distant from the active or orthosteric site, broadens the search space for drug candidates and confers the advantages of allosteric drug targeting. Here, we report the allosteric communication pathways in the main protease dimer by using two novel fully atomistic graph-theoretical methods: Bond-to-bond propensity, which has been previously successful in identifying allosteric sites in extensive benchmark data sets without a priori knowledge, and Markov transient analysis, which has previously aided in finding novel drug targets in catalytic protein families. Using statistical bootstrapping, we score the highest ranking sites against random sites at similar distances, and we identify four statistically significant putative allosteric sites as good candidates for alternative drug targeting.	Jul 2022
NONE-No attached Diamond beamline	Pathogen-sugar interactions revealed by universal saturation transfer analysis Charles J. Buchanan , Ben Gaunt , Peter J. Harrison , Yun Yang , Jiwei Liu , Aziz Khan , Andrew M. Giltrap , Audrey Le Bas , Philip N. Ward , Kapil Gupta , Maud Dumoux , Tiong Kit Tan , Lisa Schimaski , Sergio Daga , Nicola Picchiotti , Margherita Baldassarri , Elisa Benetti , Chiara Fallerini , Francesca Fava , Annarita Giliberti , Panagiotis I. Koukos , Matthew J. Davy , Abirami Lakshminarayanan , Xiaochao Xue , Georgios Papadakis , Lachlan P. Deimel , Virgínia Casablancas-Antràs , Timothy D. W. Claridge , Alexandre M. J. J. Bonvin , Quentin J. Sattentau , Simone Furini , Marco Gori , Jiandong Huo , Raymond J. Owens , Christiane Schaffitzel , Imre Berger , Alessandra Renieri , James H. Naismith , Andrew J. Baldwin , Benjamin G. Davis Science 479 DOI: 10.1126/science.abm3125 Open Access Show Abstract ▼ Abstract: Many pathogens exploit host cell-surface glycans. However, precise analyses of glycan ligands binding with heavily-modified pathogen proteins can be confounded by overlapping sugar signals and/or compound with known experimental constraints. ‘Universal saturation transfer analysis’ (uSTA) builds on existing nuclear magnetic resonance spectroscopy to provide an automated workflow for quantitating protein-ligand interactions. uSTA reveals that early-pandemic, B-origin lineage SARS-CoV-2 spike trimer binds sialoside sugars in an ‘end-on’ manner. uSTA-guided modelling and a high-resolution cryo-electron microscopy structure implicate the spike N-terminal domain (NTD) and confirm end-on binding. This finding rationalizes the effect of NTD mutations that abolish sugar-binding in SARS CoV 2 variants of concern. Together with genetic variance analyses in early pandemic patient cohorts, this binding implicates a sialylated polylactosamine motif found on tetraantennary N-linked glycoproteins in deeper human lung as potentially relevant to virulence and/or zoonosis.	Jun 2022
NONE-No attached Diamond beamline	Advanced removal of Reactive Yellow 84 azo dye using functionalised amorphous calcium carbonates as adsorbent Loredana Brinza , Andreea Elena Maftei , Sorin Tascu , Florin Brinza , Mariana Neamtu Scientific Reports 12 DOI: 10.1038/s41598-022-07134-2 Open Access Show Abstract ▼ Abstract: Two environmentally friendly organics (ethylenediaminetetraacetic acid, EDTA and its easier biodegradabe isomer, ethylenediamine-N, N′-disuccinic acid, EDDS) were used to dope calcium carbonate (CC) nanoparticles intending to increase their adsorptive properties and evaluate adsorption performance (uptake capacity and removal efficiency) for the persistent Reactive Yellow 84 azo dye. Easily synthesized nanomaterials were fully characterized (morphology and size, mineralogy, organic content, surface area, pore size and hydrodynamic diameter). RY84 removal was performed using two consecutive processes: photodegradation after adsorption. The CC-EDTA particles were most efficient for dye removal as compared to the plain and CC-EDDS particles. Adsorption kinetics and isotherms were considered for the CC-EDTA system. 99% removal occurred via adsorption on 1 g/L of adsorbent at 5 mg/L dye concentration and pH of 8 and it decreased to 48% at 60 mg/L. Maximum uptake capacity as described by Langmuir is 39.53 mg/g. As post-adsorption, under UVA irradiation, in the presence of 40 mmol/L H2O2, at dye concentration of 10 mg/L the highest degradation was 49.11%. Substantial decrease of adsorption (ca. 4 times) and photodegradation (ca. 5 times) efficiencies were observed in wastewater effluent as compared to distilled water. The results have important implications to wastewater treatments and appropriate decisions making for the choice of treatment process, process optimization and scaling up to pilot and industrial levels.	Feb 2022
NONE-No attached Diamond beamline	Discovery of SARS-CoV-2 Mpro peptide inhibitors from modelling substrate and ligand binding H. T. Henry Chan , Marc A. Moesser , Rebecca K. Walters , Tika R. Malla , Rebecca M. Twidale , Tobias John , Helen M. Deeks , Tristan Johnston-Wood , Victor Mikhailov , Richard B. Sessions , William Dawson , Eidarus Salah , Petra Lukacik , Claire Strain-Damerell , C. David Owen , Takahito Nakajima , Katarzyna Świderek , Alessio Lodola , Vicent Moliner , David R. Glowacki , James Spencer , Martin A. Walsh , Christopher J. Schofield , Luigi Genovese , Deborah K. Shoemark , Adrian J. Mulholland , Fernanda Duarte , Garrett M. Morris Chemical Science 12, 13686 - 13703 DOI: 10.1039/D1SC03628A Open Access Show Abstract ▼ Abstract: The main protease (Mpro) of SARS-CoV-2 is central to viral maturation and is a promising drug target, but little is known about structural aspects of how it binds to its 11 natural cleavage sites. We used biophysical and crystallographic data and an array of biomolecular simulation techniques, including automated docking, molecular dynamics (MD) and interactive MD in virtual reality, QM/MM, and linear-scaling DFT, to investigate the molecular features underlying recognition of the natural Mpro substrates. We extensively analysed the subsite interactions of modelled 11-residue cleavage site peptides, crystallographic ligands, and docked COVID Moonshot-designed covalent inhibitors. Our modelling studies reveal remarkable consistency in the hydrogen bonding patterns of the natural Mpro substrates, particularly on the N-terminal side of the scissile bond. They highlight the critical role of interactions beyond the immediate active site in recognition and catalysis, in particular plasticity at the S2 site. Building on our initial Mpro-substrate models, we used predictive saturation variation scanning (PreSaVS) to design peptides with improved affinity. Non-denaturing mass spectrometry and other biophysical analyses confirm these new and effective ‘peptibitors’ inhibit Mpro competitively. Our combined results provide new insights and highlight opportunities for the development of Mpro inhibitors as anti-COVID-19 drugs.	Oct 2021

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