Small Molecule Fragment Hotspot Analyses to Drive Semi-Automated Design of Selective Molecules across a Protein Family
Lead Research Organisation:
University of Oxford
Department Name: SABS CDT
Abstract
This project falls within the Chemical biology/biological chemistry, biological informatics, computational chemistry research areas, as outlined on the EPSRC website.
Companies/collaborators: SGC (Structural Genomics Consortium, Oxford)/XChem, CCDC (Cambridge Crystallographic Data Centre), Exscientia
Summary of the proposed project:
The past decade has seen an explosion in the availability of genomic and structural data for a great number of biomolecular disease targets. Rational drug discovery aims to create potent and selective compounds against these targets, with the aim of developing not only drugs, but also highly selective probes to investigate protein function. This has proven to be a challenging and expensive endeavour, and considerable efforts have been made to automate this process. Currently, automated methods such as fragment screening by X-ray crystallography output a wealth of structural data on low molecular weight molecules in complex with protein targets. Interpreting this data presents a complex problem, so computational tools that can distill it into suggestions for compound elaboration are needed. While considerable effort has been put into developing tools to predict binding affinity, less is known about the ways in which selectivity can be characterised and predicted.
Fragment hotspot analysis is a new and promising method that can highlight the specific interactions a protein makes with a compound to drive its binding. Building on work from the rotation project, the DPhil project will initially focus on using fragment hotspot analysis to describe and look for selectivity when designing compounds against a family of related targets. During the rotation, I looked at ways to combine hotspot information across an ensemble of X-ray structures of the same protein into a hotspot "ensemble map", then subtracted the ensemble maps for two different proteins to highlight differences in the binding pockets of the two proteins. To extend this work, it needs to be applied to a wider variety of proteins and protein families, and further work will cluster and extract important features from the ensemble maps. In addition, differences between ensemble maps for apo- , fragment-bound and ligand-bound structures will be investigated.
In the longer term, the project will attempt to develop novel methods or approaches to ranking fragment hits from X-ray crystallography screening campaigns, focussing on combining information from both computational and experimental methods.
Companies/collaborators: SGC (Structural Genomics Consortium, Oxford)/XChem, CCDC (Cambridge Crystallographic Data Centre), Exscientia
Summary of the proposed project:
The past decade has seen an explosion in the availability of genomic and structural data for a great number of biomolecular disease targets. Rational drug discovery aims to create potent and selective compounds against these targets, with the aim of developing not only drugs, but also highly selective probes to investigate protein function. This has proven to be a challenging and expensive endeavour, and considerable efforts have been made to automate this process. Currently, automated methods such as fragment screening by X-ray crystallography output a wealth of structural data on low molecular weight molecules in complex with protein targets. Interpreting this data presents a complex problem, so computational tools that can distill it into suggestions for compound elaboration are needed. While considerable effort has been put into developing tools to predict binding affinity, less is known about the ways in which selectivity can be characterised and predicted.
Fragment hotspot analysis is a new and promising method that can highlight the specific interactions a protein makes with a compound to drive its binding. Building on work from the rotation project, the DPhil project will initially focus on using fragment hotspot analysis to describe and look for selectivity when designing compounds against a family of related targets. During the rotation, I looked at ways to combine hotspot information across an ensemble of X-ray structures of the same protein into a hotspot "ensemble map", then subtracted the ensemble maps for two different proteins to highlight differences in the binding pockets of the two proteins. To extend this work, it needs to be applied to a wider variety of proteins and protein families, and further work will cluster and extract important features from the ensemble maps. In addition, differences between ensemble maps for apo- , fragment-bound and ligand-bound structures will be investigated.
In the longer term, the project will attempt to develop novel methods or approaches to ranking fragment hits from X-ray crystallography screening campaigns, focussing on combining information from both computational and experimental methods.
Organisations
People |
ORCID iD |
Brian Marsden (Primary Supervisor) | |
Mihaela Smilova (Student) |
Publications
![publication icon](/resources/img/placeholder-60x60.png)
Smilova MD
(2022)
Fragment Hotspot Mapping to Identify Selectivity-Determining Regions between Related Proteins.
in Journal of chemical information and modeling
Description | Since the start of my PhD project, I have been working on extending and validating an existing method for protein binding site mapping called fragment hotspot maps. I have worked on applying this method to summarise information from an ensemble of structures of the same protein, and comparing two ensembles of related proteins. This is relevant in the very early stages when designing drugs or chemical probe molecules, as it can provide clues on how to improve the binding affinity of within-protein-family selectivity of these agents. I have compiled a number of retrospective examples to validate the methods. This work was included as an open access publication in 2022. In addition, I worked on validating the open-source implementation of a molecular-dynamics based method for estimating the structural stability of a protein-ligand complex. I then used this method, together with the ensemble and selectivity maps developed previously, as part of a computational workflow to suggest molecules to buy for three ongoing medicinal chemistry campaigns in our department. Experiments undertaken by researchers at the department showed that some of these molecules had weak, but detectable activity against their target protein. |
Exploitation Route | This will mostly be of use to computational (bio)chemists and medicinal chemists who are interested in characterising binding sites and looking for differences between related proteins that could be exploited for designing selective ligands. The full semi-automated workflow aims to allow a user with a background in drug discovery, but who is not an expert medicinal or computational chemist, to quickly generate suggestions for compounds that could show an improved fit to the protein's binding site and exploit putative selective regions. |
Sectors | Pharmaceuticals and Medical Biotechnology |
URL | https://www.eurekalert.org/news-releases/945081 |
Description | CCDC Discovery Meeting |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Industry/Business |
Results and Impact | Presented a talk on the Fragment Hotspot Maps method, including the selectivity and ensemble maps developed as a result of this award, at the CCDC Discovery Science Meeting. The recorded talk is freely available on Youtube. The meeting was virtual and freely accessible (registration required). The audience consisted mainly of experts in structure-based drug discovery (both from industry and academia) and students from a number of countries. |
Year(s) Of Engagement Activity | 2021 |
URL | https://www.youtube.com/watch?v=Kvo1yr_-Abo |
Description | Ensemble and Selectivity Maps Press Release |
Form Of Engagement Activity | A magazine, newsletter or online publication |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Industry/Business |
Results and Impact | Press release describing the paper Mihaela D. Smilova, Peter R. Curran, Chris J. Radoux, Frank von Delft, Jason C. Cole, Anthony R. Bradley, and Brian D. Marsden, J Chem Inf Model, 2022 62 (2), 284-294. DOI: 10.1021/acs.jcim.1c00823 published in AAAS's EurekAlert site, the CCDC Website (https://www.ccdc.cam.ac.uk/News/List/Hotspot-mapping-for-compound-selectivity-across-protein-families-in-drug-discovery/). Written by Ashley Moreno. |
Year(s) Of Engagement Activity | 2022 |
URL | https://www.eurekalert.org/news-releases/945081 |