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Cambridge MedChem Consulting

3rd In Silico Toxicology Conference

The 3rd In Silico Toxicology Conference, supported by the British Toxicology Society (BTS), the Royal Society of Chemistry (RSC) CICAG Group, Lhasa Ltd., and the Cambridge Alliance on Medicines Safety (CAMS) will take place online on 29 September 2022; attendance is free and open to everyone interested.

Topics will include In Silico Toxicology Consortia, Cell Painting, Gene Expression Data, Biomarkers, Interpreting Neural Networks, Drug-Induced Liver Injury/DILI, Skin Sensitization, Animal Histopathology Data, Species Concordance, In Vivo Pharmacokinetics (PK), Molecular Initiating Events (MIEs), Chemicals, Pharma, Food, Read-Across, ... and beyond (see website for the full programme and registration).

http://drugdiscovery.net/tox2022/.

Submit a phenotypic assay

This sounds like an interesting opportunity.

Xcellomics is seeking novel, in vitro or ex vivo pathologically relevant cellular phenotypes that have the potential to be developed into small molecule High Content or CRISPR screens. We are encouraging members of the research community to submit proposals through the Xcellomics applications portal in the following disease areas….

Full details here https://www.xcellomics.com/calls

Xcellomics is a partnership between Exscientia and the University of Oxford Target Discovery Institute (TDI).

ChEMBL 31 is released

The latest release of the absolutely invaluable ChEMBL database is available.

chembl31

This version of the database, prepared on 12/07/2022 contains:

    2,967,627 compound records
    2,331,700 compounds (of which 2,304,875 have mol files)
    19,780,369 activities
    1,498,681 assays
    15,072 targets
    85,431 documents

Available from the downloads page https://chembl.gitbook.io/chembl-interface-documentation/downloads

AlphaFold predicts structure of almost every catalogued protein known to science

A little over a year ago I highlighted the AlphaFold Protein Structure Database in which AlphaFold DB provided open access to protein structure predictions for the human proteome and 20 other key organisms to accelerate scientific research. Well things have moved on.

DeepMind and EMBL’s European Bioinformatics Institute (EMBL-EBI) have made AI-powered predictions of the three-dimensional structures of nearly all catalogued proteins known to science freely and openly available to the scientific community, via the AlphaFold Protein Structure Database.

The database is being expanded by approximately 200 times, from nearly 1 million protein structures to over 200 million, covering almost every organism on Earth that has had its genome sequenced. The expansion of the database includes predicted structures for a wide range of species, including plants, bacteria, animals, and other organisms.

The full dataset of all predictions is available at no cost and under a CC-BY-4.0 licence from Google Cloud Public Datasets. We've grouped this by single-species for ease of downloading subsets or all of the data. We suggest that you only download the full dataset if you need to process all the data with local computing resources (the size of the dataset is 23 TiB, ~1M tar files).

Downloads can be found here https://alphafold.ebi.ac.uk/download#full-dataset-section.

It is worth noting that AlphaFold2 is not the only protein structure prediction tool available, there is also RoseTTAFold, OpenFold, and FastFold.

Bioisosteres pages updated

I've been updating the Drug Discovery Resources. Over the last few days I've been expanding the section on bioisosteres.

A bioisostere is a molecule resulting from the exchange of an atom or of a group of atoms with an alternative, broadly similar, atom or group of atoms. The objective of a bioisosteric replacement is to create a new molecule with similar biological properties to the parent compound. The bioisosteric replacement may be physicochemically or topologically based. The replacement can attenuate toxicity, modify activity of lead, and/or alter pharmacokinetics or the toxicity of the lead.

Bioisosteres are an essential element in the Medicinal Chemists toolbox and the increasing variety reported is a testimony to the creativity of medicinal chemists.

An interesting opportunity

Postdoctoral Scientist for Protein Crystallography SRF Centre for Medicines Discovery (CMD), Biochemistry Phase II, South Parks Road, OX1 3QU We are seeking to appoint a Postdoctoral fellow for the Protein Crystallography Small Research Facility (PX-SRF), under the supervision of Professor Frank von Delft.

Full details are here

FIRST-IN-CLASS ANTIBIOTIC NOSO-502

It has been a real pleasure to be involved with the GNA NOW Consortium (https://amr-accelerator.eu/project/gna-now/) and I'm really delighted to share this news.

NOSOPHARM AND GNA NOW ANNOUNCE POSITIVE RESULTS FOR THE LATE PRECLINICAL DEVELOPMENT OF THE FIRST-IN-CLASS ANTIBIOTIC NOSO-502 An important milestone has been reached for the GNA NOW Consortium with the completion of the GLP toxicology studies for the NOSO-502 program. The results allow for the further development of the program to Phase 1.

Full details are here https://www.lygature.org/news/gna-now-consortium-announces-positive-results-late-preclinical-development-first-class.

The NOSO-502 program received a unanimous recommendation from the internal and external experts of the GNA NOW Consortium to start preparing for clinical trials. This is of particular importance as novel classes of antibiotics with efficacy against the WHO critical priority Gram-negative pathogens are very rare. No novel class of antibiotics with efficacy against these pathogens has been introduced into clinical use for more than 40 years. Furthermore, according to a very recent and comprehensive analysis of the antibacterial drug pipeline, there is no first-in-class Gram-negative antibiotic with a novel target or a novel mode of action in clinical development. If successful, the introduction into the clinical use of NOSO-502 will give a new option to the physician for the treatment of patients suffering from life-threatening bacterial infections, avoiding a therapeutic dead-end. This way, NOSO-502 will strengthen the therapeutic arsenal against Gram-negative infections.

Fantastic news to all involved in the program and great news for patients.

Open science ASAP antiviral discovery center

The National Institute of Allergy and Infectious Diseases (NIAID), part of the National Institutes of Health, has awarded approximately $577 million to establish nine Antiviral Drug Discovery (AViDD) Centers for Pathogens of Pandemic Concern. https://www.nih.gov/news-events/news-releases/nih-announces-antiviral-drug-development-awards. One of the centres is the AI-Driven Structure-Enabled Antiviral Platform (ASAP) https://asapdiscovery.org.

ASAP uses artificial intelligence and computational chemistry to accelerate structure-based open science antiviral drug discovery and deliver oral antivirals for pandemics with the goal of global, equitable, and affordable access.

ASAP Overview

ASAP-overview-twitter

They are currently recruiting for multiple positions, so if you would like to be involved in this exciting programme head over to https://asapdiscovery.org/jobs/.

CASP15 details

The details of the latest Critical Assessment of Structure Prediction (CASP) experiment to determine and advance the state of the art in modeling biomolecular structures have been published https://predictioncenter.org/casp15/index.cgi.

Modeling categories

The core of CASP remains the same: blind testing of methods with independent assessment against experiment to establish the state-of-art in modeling proteins and protein complexes. CASP15 will include following categories.

  • Single Protein and Domain Modeling As in previous CASPs, the accuracy of single proteins and where appropriate single protein domains will be assessed, using the established metrics. Two changes will be the elimination of the distinction between template-based and template-free modeling, and an emphasis on the fine-grained accuracy of models, such as local main chain motifs and side chains. Because of the high accuracy of the new modeling methods, we expect assessment against high resolution experimental structures will be most informative.
  • Assembly As in recent CASPs, the ability of current methods to correctly model domain-domain, subunit-subunit, and protein-protein interactions will be assessed. We will again work in close collaboration with our CAPRI partners. Because of the promising deep learning results reported so far, substantial progress is expected.
  • Accuracy Estimation Members of the community will be invited to submit accuracy estimates for multimeric complexes and inter-subunit interfaces. There will no longer be a category for estimating the accuracy of single protein models, since it has become clear these cannot compete with modeling method specific estimates. Instead, there will be increased emphasis on assessment of self-reported accuracy estimates at the atomic level. Note the units will now be pLDDT, not Angstroms.
  • RNA structures and complexes There will be a pilot experiment to assess the accuracy of modeling for RNA models and protein-RNA complexes. The assessment will be done in collaboration with the RNA-Puzzles and Marta Szachniuk's group in Poznan.
  • Protein-ligand complexes Subject to the availability of adequate resources, there will also be a pilot experiment in this area. Deep-learning is already having an impact here, and there is high interest because of the relevance to drug design.
  • Data Assisted As in recent CASPs, there will be assessment of the extent to which the accuracy of models can be increased by the provision of sparse data, particularly that provided by SAXS and mass spectroscopy/chemical crosslinking. Only targets where these low-resolution data are likely to be useful will be considered, that is, large single proteins and complexes. As previously, we will work with collaborators to obtain the necessary experimental data. Targets will initially be released without the experimental data, followed by a second round of prediction including those data.
  • Protein conformational ensembles Following the success of deep-learning methods for single structures, it is increasingly important to assess methods for predicting structure ensembles. This is a huge area, ranging from the many conformations of disordered regions to the small number of conformations that may be involved in allosteric transitions and enzyme excited states to local protein dynamics. While it is clear that deep learning and other methods have the potential to generate ensembles in some circumstances, the difficulty is in finding cases where there are sufficiently accurate and extensive experimental data to allow rigorous assessment. One promising avenue is modeling sets of conformations in regions of cryo-EM structures where there is evidence of local conformational heterogeneity. If suitable cases arise, we will present these as a special type of sub-target. First requesting conformational ensembles that will be evaluated against the election density map and then in a possible second stage providing the map for data assisted ensemble prediction. A second possibility is for cases where detailed NMR data have already established the structure of two or more conformations. We have a good lead for a few targets of this type. In addition to this, we are considering a non-blind experiment (a departure from normal CASP practice), where we will first ask those interested to reproduce the known conformations. We will also ask participants to identify any additional conformations that appear to be present. It may then be possible to test these against existing or new experimental data.

Details of the targets will be made available over the next week https://predictioncenter.org/casp15/targetlist.cgi.

Do you work with kinases?

If you work with kinases then this free workshop run by RSC CICAG is must for you. There is now a wealth of public domain information about kinases but it is scattered over a multitude of publications and databases. The Kinase–Ligand Interaction Fingerprints and Structures database provides a central repository for all this information. This workshop will guide you through accessing this information.

Register here https://www.eventbrite.com/e/open-source-tools-for-chemistry-tickets-294585512197?.

19 May 2022 KILFS database (Albert Jelke Kooistra, Andrea Volkamer )

Over the past three decades, six thousand structures of the catalytic kinase domain have been made publicly available via the Protein Data Bank. But to what extent are we making use of this wealth of information? In order to harness this data in a better way and to make it readily available for all to use in their research, KLIFS was constructed. KLIFS, i.e. the Kinase–Ligand Interaction Fingerprints and Structures database, is a structural kinase database that systematically collects and processes all structures of the catalytic kinase domain. With the database, you can - for example - easily get a complete overview of all structures, search for ligands with a specific binding mode, identify analogs or your ligands of interest, collect data for your data mining and machine learning applications.

For this workshop, the developers of KLIFS have teamed up with the Volkamer Lab and therefore the workshop will be divided into two segments. First, Albert J. Kooistra will give an introduction to KLIFS and demonstrate different functionalities of the KLIFS website and the integration of KLIFS in KNIME via the 3D-e-Chem nodes. In the second half, Andrea Volkamer and Dominique Sydow will demonstrate, based on their new kinase-focused TeachOpenCADD workflow, how to assess kinase similarity from different data perspectives. They will emphasize their Python package KiSSim – a KLIFS-based kinase structural similarity fingerprint, and OpenCADD-KLIFS – a Python module to facilitate the integration of KLIFS data into kinase research workflows.

These workshops are supported by Liverpool ChiroChem.