Fragmenstein: predicting protein-ligand structures of compounds derived from known crystallographic fragment hits using a strict conserved-binding–based methodology

Ferla, Matteo P.; Sánchez-García, Rubén; Skyner, Rachael E.; Gahbauer, Stefan; Taylor, Jenny C.; von Delft, Frank; Marsden, Brian D.; Deane, Charlotte M.

doi:10.26434/chemrxiv-2024-17w01

Cited by 5 publications

References 36 publications

(52 reference statements)

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Identification of Potent CHK2 Inhibitors‐Modulators for Therapeutic Application in Cancer: A Machine Learning Integrated Fragment‐Based Drug Design Approach

Sudhir Kolpe,

Sardarsinh Suryawanshi,

Eldesoky

et al. 2024

ChemistrySelect

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The CHK2 protein regulates the cell division cycle and responds to DNA damage. Additionally, it facilitates the repair of DNA damage and maintains the integrity of its biological processes. Dysregulation of the CHK2 protein is associated with a predisposition to harmful diseases. The current research protocol was designed to identify novel hit molecules as CHK2 inhibitors and disrupt the normal biological function of the CHK2 protein via a fragment‐based drug discovery approach. The protocol involved generating fragments using the MacFrag tool, followed by a chemical similarity search utilizing RDKit to identify fragment molecules analogous to previously established CHK2 inhibitor scaffolds. The bioactive molecules were constructed using the Fragmenstein tool, followed by molecular docking simulations to investigate their binding affinity. In addition, pharmacokinetic properties were analyzed, and a molecular dynamics simulation study was conducted to assess the stability of selected compounds with CHK2 protein. Finally, five novel compounds were identified as excellent CHK2 inhibitors through the FBDD and show good binding interactions at active sites of CHK2 with beneficial ADMET properties. This research work presents novel CHK2 inhibitor molecules that have the potential to be utilized in drug discovery, serving as key leads for future advancements in healthcare industries and sectors.

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Identification of Potent CHK2 Inhibitors‐Modulators for Therapeutic Application in Cancer: A Machine Learning Integrated Fragment‐Based Drug Design Approach

Sudhir Kolpe,

Sardarsinh Suryawanshi,

Eldesoky

et al. 2024

ChemistrySelect

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Crystallographic fragment screen of Enterovirus D68 3C protease and iterative design of lead-like compounds using structure-guided expansions

Lithgo,

Tomlinson,

Fairhead

et al. 2024

Preprint

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The development of effective broad-spectrum antivirals forms an important part of preparing for future pandemics. One cause for concern is the currently emerging pathogen Enterovirus D68 (EV-D68) which primarily spreads through respiratory routes causing mostly mild to severe respiratory illness but, in severe cases, acute flaccid myelitis. The 3C protease of EV-D68 (3Cpro) is a potential target for the development of antiviral drugs due to its essential role in the viral life cycle and high sequence conservation amongst family members. In this study, we describe the identification of fragments which bind to3Cpro using crystallographic screening and the expansion of these into more lead-like compounds. The hits revealed interesting directions for hit-to-lead progression, specifically the importance of the pocket occupied by the conserved glutamine sidechain of the substrates and the interactions formed. Additionally, two pockets could be joined by not following the backbone of the native substrates, thus circumventing the screening issues arising from the flexibility of the catalytic triad. These observations of the novel binding modes of the chemical matter found by this screen can help shape future drug design campaigns against 3C proteases.

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Expanding the scope of a catalogue search to bioisosteric fragment merges using a graph database approach

Wills,

Sanchez-Garcia,

Roughley

et al. 2024

Preprint

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The efficiency of fragment-to-lead optimization could be improved by automated workflows for the design of follow-up compounds. Pipelines that are able to fully exploit the interaction opportunities identified from the crystal structures of bound fragments would greatly aid this goal. To do so, these pipelines need to require minimal intervention from the user and be computationally efficient. In this work, we describe an updated version of our fragment merging methodology, which provides several feature enhancements, primarily by expanding the chemical space searched, allowing the identification of more diverse follow-up compounds, thus maximizing the chances of finding successful hits. While the original method focused on finding ‘perfect merges’, meaning compounds that directly incorporate substructures from the original fragments, here we expand the search to what we term ‘bioisosteric merges’, involving the incorporation of substructures that replicate the pharmacophoric features of the original fragments but may not be exactly identical. Unlike existing pharmacophore and shape-based descriptors used for virtual screening, this approach combines the search for these properties with the incorporation of novelty, which is necessary when searching for ways to link together distinct substructures. Compared with ‘perfect merging’, our new approach is able to find compounds that are directly informed by structures within the original fragments but are more chemically diverse. We contrast our approach with the use of a pharmacophore-constrained docking pipeline, run in parallel for select fragment pairs, and show that our method requires between 1.1-45.9-fold less computational time for conformer generation per merging ‘hit’ identified, referring to compounds that show a favourable degree of shape and colour overlap and recapitulation of original fragment interactions. Overall, our results show that our method has potential to be used to generate designs inspired by all fragments within a given pocket.

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Fragmenstein: predicting protein-ligand structures of compounds derived from known crystallographic fragment hits using a strict conserved-binding–based methodology

Cited by 5 publications

References 36 publications

Identification of Potent CHK2 Inhibitors‐Modulators for Therapeutic Application in Cancer: A Machine Learning Integrated Fragment‐Based Drug Design Approach

Identification of Potent CHK2 Inhibitors‐Modulators for Therapeutic Application in Cancer: A Machine Learning Integrated Fragment‐Based Drug Design Approach

Crystallographic fragment screen of Enterovirus D68 3C protease and iterative design of lead-like compounds using structure-guided expansions

Expanding the scope of a catalogue search to bioisosteric fragment merges using a graph database approach

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