Kinases are an important class of drug targets since their dysregulation can cause severe diseases such as cancer, inflammation, and neurodegeneration. Finding selective drugs, however, is challenging due to the highly conserved binding sites across the roughly 500 human kinases, which can lead to unwanted side-effects. The underlying off-targets are often not trivial to predict or to explain from a sequence-based perspective.
In our KiSSim project, we explored kinase similarity from a physicochemical and structural point of view.
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Assessing kinase similarity, or how to avoid side effects? Kinases are highly conserved in their binding site, which presents a challenge since one ligand may bind not only the designated target (on-target) but also other targets (off-targets), causing mild to severe sides effects. Being able to assess kinase similarity could therefore give insight into potential side-effects.
The proposed pipeline, part of the TeachOpenCADD project, allows the study of kinase similarities from four different angles in an automated and modular fashion.
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Have you heard of TeachOpenCADD? Yes? Perfect, we have good news! We are back with more content (12 fresh new notebooks!), a new website, simplified installation options, and much, much more.
No, you haven’t? Well, we are happy you found your way here!
We will show you what TeachOpenCADD has to offer and tell you more about the exciting details on the new release!
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Are you tired of hearing that deep learning needs more data and that physico-chemical data sets are still scarce? Check out our latest publication Kimber, 2021 for some augmentation strategies you can apply to improve your predictions when training deep neural networks using SMILES.
You can even make predictions for novel compounds using the command-line interface. The code is open-source and available on GitHub at https://github.com/volkamerlab/maxsmi.
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Our collaborative work with the BfR - combining in vitro with in silico predictions - is now published in the Environment International Journal: “Quantitative high-throughput phenotypic screening for environmental estrogens using the E-Morph Screening Assay in combination with in silico predictions” (DOI: 10.1016/j.envint.2021.106947).
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Have a look at our PLIPify work (WIP)! PLIPify provides a wrapper around PLIP, which allows to digest multiple structures at once, performs the mapping of the individual profiles to fingerprints and reports protein-ligand interaction frequencies.
It has recently been applied to detect the main interaction within a set of roughly 400 MPro complex structures (large crystal-based fragment screen by Diamond Light Source) in the broader scope of the COVID Moonshot initiative.
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Save the date (Thu. 9. September 2021, 4pm - 5:30pm CET) for an upcoming virtual seminar series, Digital Science for Drug Discovery.
Focused on the Berlin region, the series aims to facilitate and enmesh relationships between researchers working within both academia and industry. The over-arching theme? Making efficient and creative use of the wealth of available and growing chemical and biological data combined with powerful computational means at our disposal.
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Interested in the new developments of deep learning in virtual screening ? Check out our review Kimber, 2021. We discuss
ligand, protein and complex encodings, deep learning models, data sets, and recent studies. If you want to generate different encodings for your figures, have a look at the GitHub repository!
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The new edition of the “Structural Alignment and Superposition” internship is now open for registration!
In this internship, an open-source structural alignment Python package for biopolymers, i.e. proteins and ligands, that we started implementing in 2020 will be further developed and released. While diverse structural alignment implementations can be found in visualization software such as PyMol, VMD or UCSF Chimera, a standalone package is currently missing in the Python ecosystem. We have started filling that gap with a modern Python package designed under the current best practices for development, testing and deployment.
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Our KinFragLib project is now published in the Journal of Chemical Modeling and Information: “KinFragLib: Exploring the Kinase Inhibitor Space Using Subpocket-Focused Fragmentation and Recombination” (DOI: 10.1021/acs.jcim.0c00839).
Kinases are one of the most studied drug targets, resulting in an amount of available data too large to be analyzed manually. In the KinFragLib project, a precise cartography of the ATP-binding site guides the fragmentation of cocrystallized kinase ligands by subpockets. The resulting kinase-focused fragment library allows the analysis of the chemical space by subpocket and is a rich source of inspiration for building novel kinase inhibitors.
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