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Cambridge MedChem Consulting

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.

NICE reaches important milestone in the UK’s efforts to tackle antimicrobial resistance.

Two new antimicrobial drugs - cefiderocol and ceftazidime–avibactam - are close to becoming the first to be made available as part of the UK’s innovative subscription-style payment model after NICE published draft guidance estimating their value to the NHS.

https://www.nice.org.uk/news/article/nice-reaches-important-milestone-in-the-uk-s-efforts-to-tackle-antimicrobial-resistance

Cefiderocol is a cephalosporin antibiotic that is coupled to a siderophore that binds to iron and aids cell entry.

Cefiderocol

Ceftazidime–avibactam is a fixed-dose combination medication composed of ceftazidime, a cephalosporin antibiotic, and avibactam, a β-lactamase inhibitor. Bacterial resistance to cephalosporins is often due to bacterial production of β-lactamase enzymes that deactivate these antibiotics. Avibactam inhibits bacterial β-lactamases.

Ceftazidime_and_avibactam.svg

Investment in new antimicrobials, especially those that target multi-drug-resistant pathogens, is not commercially attractive because they are subject to strict controls to restrict their use to slow the development of resistance. This means sales could be low. The new payment method overcomes this by ensuring a fixed annual fee is paid to the company regardless of how many prescriptions are issued.

Ultra large Chemical Libraries

In a recent blog post Derek Lowe talked about "Virtual Screening Versus the Numbers" https://www.science.org/content/blog-post/virtual-screening-versus-numbers highlighting some of the issues around ultra large chemical libraries.

It seems quite timely that RSC CICAG is organising a meeting on Ultra Large Chemical libraries 10 August 2022 10:00-17:00, Burlington House, London, United Kingdom.

A decade ago a chemical library of a million compounds was considered large but over the last few years there has been a period of continuous growth in the size of both physical and virtual chemical libraries. As the libraries have grown the conventional search technologies have become unsustainable and new technologies are needed. This meeting will look at the challenges and solutions used to design, create, compare and search these ultra-large chemical libraries.

There are more details and registration here https://www.rsc.org/events/detail/73675/ultra-large-chemical-libraries.

It is now open for abstract submission (oral due by May 1st, posters June 2nd).

Registration fees

Delegate member early £95
Delegate non-member early £115
Delegate member std £120
Delegate non-member std £145
Student member early £65
Student non-member early £85
Student member std £90
Student non-member std £110

Open-Source Tools workshops

Registration for the next batch of Open-Source Tools workshops run by the RSC Chemical Information and Computer Applications Group is now open.

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

These workshops have been enormously popular and the interactions with the instructors have been especially valuable. Details of the next 3 workshops are described below.

All meetings start at 2 pm UK time (5 min break after 1 hour). All run using Zoom Webinar

21 April 2022 PDBe Knowledge Base (David Armstrong)

This workshop explores the Protein Data Bank in Europe Knowledge Base (PDBe-KB https://www.ebi.ac.uk/pdbe/) resource and its tools for the investigation, analysis, and interpretation of biomacromolecular structures. PDBe-KB brings together data from all PDB entries and displays this data as aggregated information for individual proteins, including ligand binding sites, macromolecular interactions and more. Furthermore, this community-led resource brings together structural and functional information from a host of other related resources. In this workshop, you will learn how to use the PDBe-KB aggregated views for proteins to investigate structural and function information for proteins and their associated ligands. We will also demonstrate effective use of novel visualisation components of large-scale structural data on these pages, including 3D visualisation of superposed protein structures with their bound ligands.

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 (https://klifs.net) 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.

23 June 2022 Scoring of shape and ESP similarity (Ester Heid)

Electrostatic effects along with volume restrictions play a major role in enzyme and receptor recognition. Evaluating electrostatic and shape similarities of pairs of molecules such as proposed versus known ligands can therefore be valuable indicators of prospective binding affinities. This workshop will demonstrate how to compute electrostatic and shape similarities using the open-source tool ESP-Sim (github.com/hesther/espsim, doi.org/10.26434/chemrxiv-2021-sqvv9-v3). Available options for comparing electrostatics will be discussed interactively on selected examples of public datasets, along with advice on embedding and aligning molecules prior to computing similarities.

Dame Carol Robinson BMCS Hall of Fame inductee

I'm really delighted to see that Dame Carol Robinson is the 2021 inductee to the RSC BMCS Hall of Fame. Her group's work on using Mass Spec to investigate biomolecular process is stunning science and her Keynote at the 2021 Cambridge MedChem meeting was one of the highlights of the meeting.

2021-Hall-of-Fame-Winner-Announcement-FINAL

Longitudinal analysis reveals high prevalence of Epstein-Barr virus associated with multiple sclerosis

The link between Epstein-Barr virus and multiple sclerosis has been suggested for some time but this publication in Science really underlines the importance.

"Longitudinal analysis reveals high prevalence of Epstein-Barr virus associated with multiple sclerosis" DOI

Multiple sclerosis (MS) is a chronic inflammatory demyelinating disease of the central nervous system of unknown etiology. We tested the hypothesis that MS is caused by Epstein-Barr virus (EBV) in a cohort comprising more than 10 million young adults on active duty in the US military, 955 of whom were diagnosed with MS during their period of service. Risk of MS increased 32-fold after infection with EBV but was not increased after infection with other viruses, including the similarly transmitted cytomegalovirus. Serum levels of neurofilament light chain, a biomarker of neuroaxonal degeneration, increased only after EBV seroconversion. These findings cannot be explained by any known risk factor for MS and suggest EBV as the leading cause of MS.

Almost everyone gets exposed to EBV (Human gammaherpesvirus 4) and it is the cause of glandular fever (aka infectious mononucleosis), after you get an EBV infection, the virus becomes latent (inactive) in your body. In some cases, the virus may reactivate. EBV infects the B cells of the immune system and epithelial cells. Once EBV's initial lytic infection is brought under control, EBV latency persists in the individual's B cells for the rest of their life.

EBV has been implicated in a variety of other diseases including various cancers.

This publication will certainly added increased interest in the Moderna the Phase I Eclipse clinical trial of its Epstein-Barr Virus (EBV) vaccine candidate, mRNA-1189. https://www.clinicaltrialsarena.com/news/moderna-ebv-vaccine-trial/.

Annual site review

As 2022 starts I'd like to wish you all a Happy New Year and hope that 2022 marks the start of the recovery from the pandemic.

The Drug Discovery Resources website continues to increase in popularity with 193,322 page views, an increase of 31% over the figure for 2020. The pages were visited by over 95,308 viewers and around 20% of the visitors come back on multiple occasions suggesting they find it useful. The visitors come from 180 different countries with the top countries being

  • United States (25%)
  • United Kingdom (14%)
  • India (13%)
  • Germany (3.6%)
  • Canada (3%)
  • South Korea (2.5%)

Perhaps not unexpectedly one of the popular pages was COVID-19 and the Identification of "Drug Candidates" a checklist for those using virtual screening to identify potential hits for COVID-19 targets.

The other most viewed pages were

Looking at the operating systems 54% are Windows users, 20% Mac users, 13% Android, 9% iOS and 2% Linux.

I don't know how comprehensive the analytics software is but there is approximately a 50:50 M:F split for Gender.

Seasons Greetings

It has been a mild winter here so far but some lovely still mornings. Take care everyone and have a great break and a successful New Year. As ever monies saved on cards will be donated to the MS Society. https://www.mssociety.org.uk.

IMG_3257

Reproducibility Project: Cancer Biology

I've been waiting for this for a while. Reproducibility Project: Cancer Biology

The Reproducibility Project: Cancer Biology was an 8-year effort to replicate experiments from high-impact cancer biology papers published between 2010 and 2012. The project was a collaboration between the Center of Open Science and Science Exchange with all papers published as part of this project available in a collection at eLife and all replication data, code, and digital materials for the project available in a collection on OSF.

The work tried to repeat 193 experiments from 53 papers and found a significant number of challenges.

ReproducibiltyReport

In summary

  • Replication effect sizes were 85% smaller on average than the original findings
  • 46% of effects replicated successfully on more criteria than they failed
  • Original positive results were half as likely to replicate successfully (40%) than original null results (80%)

This quote from In the Pipeline is perhaps a useful reminder.

A robust result can probably be reproduced even if you switch to a different buffer, or if your cell lines have been passaged a different number of times, or if the concentration of the test molecule is a bit off, etc. The more persnickity and local the conditions have to be, the less robust your result is, and in general (sad to say) the lower the odds of it having a real-world impact in drug discovery. There are certainly important things that can only be demonstrated under very precise conditions, don’t get me wrong – but when you’re expecting umpteen thousand patients to take your drug candidate and show real effects, your underlying hypothesis needs to be able to take a good kicking and still come through.