Antiviral Molnupiravir Reduced the Risk of Hospitalization or Death by Approximately 50 Percent Compared to Placebo for Patients with Mild or Moderate COVID-19
Merck have just announced that the Investigational Oral Antiviral Molnupiravir Reduced the Risk of Hospitalization or Death by Approximately 50 Percent Compared to Placebo for Patients with Mild or Moderate COVID-19 in Positive Interim Analysis of Phase 3 Study.
Link to report https://www.merck.com/news/merck-and-ridgebacks-investigational-oral-antiviral-molnupiravir-reduced-the-risk-of-hospitalization-or-death-by-approximately-50-percent-compared-to-placebo-for-patients-with-mild-or-moderat/
Great news, but bear in mind this is only an interim study, so keep those fingers crossed.
Molnupiravir (development codes MK-4482 and EIDD-2801) is an experimental antiviral drug which is orally active and was developed for the treatment of influenza. It is a prodrug of the synthetic nucleoside derivative N4-hydroxycytidine, and exerts its antiviral action through introduction of copying errors during viral RNA replication.
A great start to the week.
The COVID Moonshot, a non-profit, open-science consortium of scientists from around the world dedicated to the discovery of globally affordable and easily-manufactured antiviral drugs against COVID-19 and future viral pandemics has received key funding of £8 million from Wellcome, on behalf of the Covid-19 Therapeutics Accelerator.
Nominations are now open for the 2022 RSC Medicinal Chemistry Emerging Investigator Lectureship and close on 08 October 2021. The lectureship is open to candidates who received their PhD in 2012 or later and who have made a significant contribution to medicinal chemistry in their early career.
The RSC welcome nominations from anyone, to be sent to our journal inbox at email@example.com, but ask that nominations include the name and affiliation of the researcher, along with at least one paragraph explaining their achievements and why they should be considered (a CV is not required, but is helpful if sent along with the nomination). Additionally, self-nominations are welcomed if accompanied by a letter of support from the nominees’ institute.
There are more details about nominations on our webpage here: https://www.rsc.org/journals-books-databases/about-journals/rsc-medicinal-chemistry/#Lectureship_MChemCom.
CYPlebrity: Machine learning models for the prediction of inhibitors of cytochrome P450 enzymes DOI, https://nerdd.univie.ac.at/cyplebrity/, structures can be submitted in SMILES format or drawn using the sketcher. The calculation takes a few second per compound and the results are displayed as shown below.
Added to the section on CYP Interactions.
Thanks to fantastic work from the folks at UniProt, the Open Targets Platform target profile pages now feature DeepMind’s AlphaFold data.
Can be easily linked to disease associations.
The AlphaFold Protein Structure Database Developed by DeepMind and EMBL-EBI is now available online.
AlphaFold DB provides open access to protein structure predictions for the human proteome and 20 other key organisms to accelerate scientific research.
AlphaFold DB currently provides predicted structures for the organisms listed below and includes human, laboratory species, and key pathogens. All the predictions for all the species can be downloaded from the EBI FTP site ftp://ftp.ebi.ac.uk/pub/databases/alphafold.
|Species||Common Name||Reference Proteome||Predicted Structures||Download|
|Arabidopsis thaliana||Arabidopsis||UP000006548||27,434||Download (3642 MB)|
|Caenorhabditis elegans||Nematode worm||UP000001940||19,694||Download (2601 MB)|
|Candida albicans||C. albicans||UP000000559||5,974||Download (965 MB)|
|Danio rerio||Zebrafish||UP000000437||24,664||Download (4141 MB)|
|Dictyostelium discoideum||Dictyostelium||UP000002195||12,622||Download (2150 MB)|
|Drosophila melanogaster||Fruit fly||UP000000803||13,458||Download (2174 MB)|
|Escherichia coli||E. coli||UP000000625||4,363||Download (448 MB)|
|Glycine max||Soybean||UP000008827||55,799||Download (7142 MB)|
|Homo sapiens||Human||UP000005640||23,391||Download (4784 MB)|
|Leishmania infantum||L. infantum||UP000008153||7,924||Download (1481 MB)|
|Methanocaldococcus jannaschii||M. jannaschii||UP000000805||1,773||Download (171 MB)|
|Mus musculus||Mouse||UP000000589||21,615||Download (3547 MB)|
|Mycobacterium tuberculosis||M. tuberculosis||UP000001584||3,988||Download (421 MB)|
|Oryza sativa||Asian rice||UP000059680||43,649||Download (4416 MB)|
|Plasmodium falciparum||P. falciparum||UP000001450||5,187||Download (1132 MB)|
|Rattus norvegicus||Rat||UP000002494||21,272||Download (3404 MB)|
|Saccharomyces cerevisiae||Budding yeast||UP000002311||6,040||Download (960 MB)|
|Schizosaccharomyces pombe||Fission yeast||UP000002485||5,128||Download (776 MB)|
|Staphylococcus aureus||S. aureus||UP000008816||2,888||Download (268 MB)|
|Trypanosoma cruzi||T. cruzi||UP000002296||19,036||Download (2905 MB)|
|Zea mays||Maize||UP000007305||39,299||Download (5014 MB)|
The search bar at the top of the query page accepts queries based on protein name, gene name, UniProt identifier, or organism name. At present you can't search using a sequence and look for similar proteins. You would first need to do a BLAST search and use the results from that as queries.
Here I searched for Plasmodium falciparum carbonic anhydrase (Q8IHW5) a potential Malaria target. As you can see there is no crystal structure in the PDB. Whilst the active site is predicted with high confidence there are clearly regions for which there is very low confidence.
You can then download the structure in PDB or mmCIF format.
I made a homology model (in purple below) of this protein a while back and it has little sequence similarity with any proteins in the PDB. Despite not including a Zinc the Alphafold Predicted Structure includes histidines in positions to potentially coordinate to the Zinc. If it is possible to include the Zinc in the structure prediction I'd be interested in finding out.
Overall I'd say this is a very useful starting point.
As PROTACs have become more widespread the obvious question is which proteins are best suited to modulation by Protacs? A recent publication provides useful guidelines The PROTACtable genome DOI. The workflow is based on a method developed by a group at GSK, subsequently expanded and now integrated into the Open Targets Platform. Using publicly available data sources, the new method assesses whether a protein could be targeted using a PROTAC, based on the protein’s sequence, location, natural turnover rate in the cell, and evidence from published literature. The framework will help drug discovery researchers to gauge the PROTACtability of their protein of interest, and to prioritise their research accordingly.
More details on PROTACs here
Session 1: Session Chair: Professor Jeremy Frey (University of Southampton)
An AI solution to the protein folding problem: what is it, how did it happen, and some implications Professor John Moult (University of Maryland)
Session 2: Session Chair: Dr Melanie Vollmar (Diamond)
So you predicted a protein structure – What now? Dr Thomas Steinbrecher (Schrödinger)
Deep Learning enhanced prediction of protein structure and dynamics Dr Martina Audagnotto (AstraZeneca)
Fireflies-Lévy Flights algorithm for peptides conformational optimization Dr Zied Hosni (University of Sheffield)
Session 3: Session Chair: Dr Chris Swain (Cambridge MedChem Consulting)
How good are protein structure prediction methods at predicting folding pathways? Mr Carlos Outeiral Rubiera (University of Oxford)
Protein-Ligand Structure Prediction for GPCR Drug Design Dr Chris De Graaf (Sosei Heptares)
Session 4: Session Chair: Dr Márton Vass
Using icospherical input data in machine learning on the protein-binding problem Dr Ella Gale (University of Bristol)
Biological sequence design with machine learning Professor Debora Marks (Harvard University)
Session 5: Session Chair: Dr Simone Fulle (Novo Nordisk)
Lessons learned from generative models of biological sequences Professor Aleksej Zelezniak (Chalmers University of Technology)
DeepDock: a deep learning approach to predict ligand binding conformations Dr Oscar Méndez-Lucio (Janssen Pharmaceuticals)
Finding new in silico-based therapeutic strategies for IAHSP Dr Matteo Rossi Sebastiano (University of Turin)
Session 6: Session Chair: Professor Jonathan Goodman (University of Cambridge)
Designing molecular models by machine learning and experimental data Professor Cecilia Clementi (Freie Universität Berlin)
The “almost druggable” genome Professor Tudor Oprea (University of New Mexico)
Session 7: Session Chair: Dr Lucy Colwell (University of Cambridge)
General Effects of AI on Drug Discovery Dr Derek Lowe (Novartis)
Open Access Data: A Cornerstone for Artificial Intelligence Approaches to Protein Structure Prediction Professor Stephen Burley (RCSB PDB, Rutgers University, UCSD)
The videos of the presentations are now available on YouTube and you can access the playlist here https://www.youtube.com/playlist?list=PLBQwbn0mPhvWyTLnN6eFsbIwb5FByrs.
For those wanting a hype free insight into the impact AI might make on Drug Discovery then the presentation by Derek Lowe is well worth watching.
Open Targets Platform 21.06 has been released
The Open Targets Platform is a comprehensive tool that supports systematic identification and prioritisation of potential therapeutic drug targets. By integrating publicly available datasets including data generated by the Open Targets consortium, the Platform builds and scores target-disease associations to assist in drug target identification and prioritisation. It also integrates relevant annotation information about targets, diseases, phenotypes, and drugs, as well as their most relevant relationships.
Currently there are:-
Evidence strings 13,267,236
Covalent Inhibitors are an increasingly important class of therapeutic agents.
A computational pipeline has been described by the London lab to predict suggest covalent analogs of non-covalent ligands DOI.
Designing covalent inhibitors is increasingly important, although it remains challenging. Here, we present covalentizer, a computational pipeline for identifying irreversible inhibitors based on structures of targets with non-covalent binders. Through covalent docking of tailored focused libraries, we identify candidates that can bind covalently to a nearby cysteine while preserving the interactions of the original molecule. We found ∼11,000 cysteines proximal to a ligand across 8,386 complexes in the PDB. Of these, the protocol identified 1,553 structures with covalent predictions. In a prospective evaluation, five out of nine predicted covalent kinase inhibitors showed half-maximal inhibitory concentration (IC50) values between 155 nM and 4.5 μM. Application against an existing SARS-CoV Mpro reversible inhibitor led to an acrylamide inhibitor series with low micromolar IC50 values against SARS-CoV-2 Mpro. The docking was validated by 12 co-crystal structures. Together these examples hint at the vast number of covalent inhibitors accessible through our protocol.
RDKit was used for 2D molecular handling, conformation generation and RMSD calculation. RDKit: Open-source cheminformatics; version 2018.09.3; RDKit.org. Marvin was used in the process of preparing the molecules for docking, Marvin 17.21.0, ChemAxon (https://www.chemaxon.com). OpenBabel (http:// openbabel.org/wiki/Main_Page) was used to switch between molecular file formats. DOCKovalent (London et al., 2014) was used for virtual covalent docking. The Covalentizer code is available at https://github.com/LondonLab/Covalentizer.