Periodic Reporting for period 1 - Fragment-Screen (From fragments to high affinity binders interfacing integrated structural biology, medicinal chemistry and artificial intelligence)
Reporting period: 2023-02-01 to 2024-01-31
Fragment-Screen Context
Fragment-based drug discovery (FBDD) is revolutionising drug discovery campaigns. FBDD aims to discover very small chemical entities (fragments) in focussed libraries containing no more than a few thousand molecules. This is in comparison to traditional drug discovery routes, which often screen libraries of hundreds of thousands of molecules. The fragments uncovered by FBDD bind the specific target with low affinity but provide original, chemically tractable starting points for generating drugs by extending or combining fragments. Prior to this project, FBDD has generated drugs that have entered the clinic and obtained FDA approval.
Overall Objectives
The Fragment-Screen project aims to develop innovative instrumentation, workflows and experimental and computational methodologies for FBDD. By enabling access to early phase structure-based drug discovery for all biological targets, the project will have a direct impact on the drug discovery pipeline, identifying more potential targets at the early stage which can then be developed for more detailed analysis. As structural biology is fundamental to FBDD, the established workflows will use structural biology insights and associated data to feed artificial intelligence (AI) methodology to guide medicinal chemistry in drug development.
The new instrumentation and workflows will be available at European RIs, while the new instruments will be commercialised to increase the technological competitiveness of European industry in drug design and the attractiveness of structural biology RIs for the pharmaceutical and biotech sectors.
Pathway to impact
There is a clear impact route for the project, both directly and indirectly. Fragment-Screen will directly advance FBDD workflow, increasing the speed and accuracy of target identification. With greater accuracy in these initial stages, the entire drug discovery pipeline will become more focused, with the potential to greatly improve output and yield of drug discovery research. Indirectly, this will help European RIs to grow and better support their research communities in the field of drug discovery and can help secure more targeted funding and collaborations down the line.
Fragment-based drug discovery (FBDD) is revolutionising drug discovery campaigns. FBDD aims to discover very small chemical entities (fragments) in focussed libraries containing no more than a few thousand molecules. This is in comparison to traditional drug discovery routes, which often screen libraries of hundreds of thousands of molecules. The fragments uncovered by FBDD bind the specific target with low affinity but provide original, chemically tractable starting points for generating drugs by extending or combining fragments. Prior to this project, FBDD has generated drugs that have entered the clinic and obtained FDA approval.
Overall Objectives
The Fragment-Screen project aims to develop innovative instrumentation, workflows and experimental and computational methodologies for FBDD. By enabling access to early phase structure-based drug discovery for all biological targets, the project will have a direct impact on the drug discovery pipeline, identifying more potential targets at the early stage which can then be developed for more detailed analysis. As structural biology is fundamental to FBDD, the established workflows will use structural biology insights and associated data to feed artificial intelligence (AI) methodology to guide medicinal chemistry in drug development.
The new instrumentation and workflows will be available at European RIs, while the new instruments will be commercialised to increase the technological competitiveness of European industry in drug design and the attractiveness of structural biology RIs for the pharmaceutical and biotech sectors.
Pathway to impact
There is a clear impact route for the project, both directly and indirectly. Fragment-Screen will directly advance FBDD workflow, increasing the speed and accuracy of target identification. With greater accuracy in these initial stages, the entire drug discovery pipeline will become more focused, with the potential to greatly improve output and yield of drug discovery research. Indirectly, this will help European RIs to grow and better support their research communities in the field of drug discovery and can help secure more targeted funding and collaborations down the line.
FS has engaged in several developments aiming at enhancing the quality, and throughput of X-Ray based Fragment screening pipelines. 1) a proof-of-concept for high-density fragment screening was established to facilitate the screening of larger fragments. 2) Protocols have been established to allow routine collection of X-ray fragment screening data at room that provides important information relevant to ligand binding. 3) Finally, the cloud-based Fragalysis platform, was upgraded to improve fragment screening data analysis
For NMR-Based fragment screening, the project is working on the development of new NMR screening pipelines including the development and the testing of 2 new NMR probes, which have now been partially finalised and that should significantly improve efficiency of NMR ligand-based screening
In the nascent field of Cryo-EM fragment screening, Fragment-Screen partners have now developed and tested new technologies to facilitate automated sample preparation of cryo-EM fragment screening campaigns based on EasyGrid platform.
These fragment screening technologies are orthogonally validated by mass spectrometry (MS)and project partners have begun the work for medium to high-throughput fragment screening at extreme resolution using MS. Kinetic methods have been validated against more classic ones for determining dissociation constants and the use native top-down MS/MS approaches based on FT-ICR/MRMS with analysis of model proteins has been successfully initiated.
These new and enhanced technologies and methods across experimental techniques require new data management solutions to ensure FAIRness and interoperability of the data generated as food for advanced AI algorithms and reuse by academia and industry. Within Fragment-Screen, development has begun on a tool “FandanGO” to enable researchers and facilities to link collected data to access projects once they have been completed, generating an accessible webpage per visit, which stores the collected data, and collecting the critical metadata necessary to describe the data. Associated work on LIMS, metadata and data standards for fragment screening data and its elucidation in public repositories are also ongoing.
Ultimately, to demonstrate the power of new AI and technology developments in Fragment-Screen to accelerate FBDD four fragment-to-lead campaigns were initiated on four SARS-CoV-2 targets with the guide of AI technology. AI-supported fragment-to-lead optimisation workflows have been established and the early stages of the design-synthesise-test-analyse-cycle were implemented by research teams. Libraries of possible structures with potential activity against each target have been generated, and several candidate compounds have been synthesised and characterised.
For NMR-Based fragment screening, the project is working on the development of new NMR screening pipelines including the development and the testing of 2 new NMR probes, which have now been partially finalised and that should significantly improve efficiency of NMR ligand-based screening
In the nascent field of Cryo-EM fragment screening, Fragment-Screen partners have now developed and tested new technologies to facilitate automated sample preparation of cryo-EM fragment screening campaigns based on EasyGrid platform.
These fragment screening technologies are orthogonally validated by mass spectrometry (MS)and project partners have begun the work for medium to high-throughput fragment screening at extreme resolution using MS. Kinetic methods have been validated against more classic ones for determining dissociation constants and the use native top-down MS/MS approaches based on FT-ICR/MRMS with analysis of model proteins has been successfully initiated.
These new and enhanced technologies and methods across experimental techniques require new data management solutions to ensure FAIRness and interoperability of the data generated as food for advanced AI algorithms and reuse by academia and industry. Within Fragment-Screen, development has begun on a tool “FandanGO” to enable researchers and facilities to link collected data to access projects once they have been completed, generating an accessible webpage per visit, which stores the collected data, and collecting the critical metadata necessary to describe the data. Associated work on LIMS, metadata and data standards for fragment screening data and its elucidation in public repositories are also ongoing.
Ultimately, to demonstrate the power of new AI and technology developments in Fragment-Screen to accelerate FBDD four fragment-to-lead campaigns were initiated on four SARS-CoV-2 targets with the guide of AI technology. AI-supported fragment-to-lead optimisation workflows have been established and the early stages of the design-synthesise-test-analyse-cycle were implemented by research teams. Libraries of possible structures with potential activity against each target have been generated, and several candidate compounds have been synthesised and characterised.
Proof of concept has been successfully established for high-density approaches for ligand screening via X-ray methods, when fully implemented this approach will enable a very thorough exploration of the chemical space available around a target for drug target for drug design. New pipelines for diffraction data collection at room temperature have been implemented. These new services are now available to Research Infrastructure users and will facilitate the analysis of protein-small molecule interactions are near physiological temperature with potential impact in drug design and the understanding of protein dynamics.
An OEM high precision liquid handling system has been integrated into the EasyGrid automated Cryo-EM sample grid preparation machine. The liquid handling system includes a temperature regulated, picolitre drop dispenser head, and 96 well plate dock for sample storage during preparation. The preparation process has been tested with model proteins of which high resolution experimental maps have been obtained. This fully automated sample grid preparation system will soon be available for user access. The EasyGrid platform equipped with the high precision liquid handling modules will provide a reliable and repeatable sample preparation procedure, an essential asset for fragment screening applications.
AI approaches have been used to conduct hit-to-lead optimisation campaigns and made progress toward a feedback-driven discovery process. Fragment-Screen aims to efficiently optimise fragment hits in an iterative design-synthesise-test-analyse-cycle capitalising from technology and method developments across the project. Moreover, software and data management solutions developed within the project facilitate not only metadata generation and deposition, but will link to raw facility data, promoting FAIR data principles and full exploitation of results from fragment-screening campaigns.
An OEM high precision liquid handling system has been integrated into the EasyGrid automated Cryo-EM sample grid preparation machine. The liquid handling system includes a temperature regulated, picolitre drop dispenser head, and 96 well plate dock for sample storage during preparation. The preparation process has been tested with model proteins of which high resolution experimental maps have been obtained. This fully automated sample grid preparation system will soon be available for user access. The EasyGrid platform equipped with the high precision liquid handling modules will provide a reliable and repeatable sample preparation procedure, an essential asset for fragment screening applications.
AI approaches have been used to conduct hit-to-lead optimisation campaigns and made progress toward a feedback-driven discovery process. Fragment-Screen aims to efficiently optimise fragment hits in an iterative design-synthesise-test-analyse-cycle capitalising from technology and method developments across the project. Moreover, software and data management solutions developed within the project facilitate not only metadata generation and deposition, but will link to raw facility data, promoting FAIR data principles and full exploitation of results from fragment-screening campaigns.