Fragmenstein: predicting protein–ligand structures of compounds derived from known crystallographic fragment hits using a strict conserved-binding–based methodology
Abstract Current strategies centred on either merging or linking initial hits from fragment-based drug design (FBDD) crystallographic screens generally do not fully leaverage 3D structural information. We show that an algorithmic approach (Fragmenstein) that ‘stitches’ the ligand atoms from this str...
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| Format: | Article |
| Language: | English |
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2025-01-01
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| Series: | Journal of Cheminformatics |
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| Online Access: | https://doi.org/10.1186/s13321-025-00946-0 |
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| author | Matteo P. Ferla Rubén Sánchez-García Rachael E. Skyner Stefan Gahbauer Jenny C. Taylor Frank von Delft Brian D. Marsden Charlotte M. Deane |
| author_facet | Matteo P. Ferla Rubén Sánchez-García Rachael E. Skyner Stefan Gahbauer Jenny C. Taylor Frank von Delft Brian D. Marsden Charlotte M. Deane |
| author_sort | Matteo P. Ferla |
| collection | DOAJ |
| description | Abstract Current strategies centred on either merging or linking initial hits from fragment-based drug design (FBDD) crystallographic screens generally do not fully leaverage 3D structural information. We show that an algorithmic approach (Fragmenstein) that ‘stitches’ the ligand atoms from this structural information together can provide more accurate and reliable predictions for protein–ligand complex conformation than general methods such as pharmacophore-constrained docking. This approach works under the assumption of conserved binding: when a larger molecule is designed containing the initial fragment hit, the common substructure between the two will adopt the same binding mode. Fragmenstein either takes the atomic coordinates of ligands from a experimental fragment screen and combines the atoms together to produce a novel merged virtual compound, or uses them to predict the bound complex for a provided molecule. The molecule is then energy minimised under strong constraints to obtain a structurally plausible conformer. The code is available at https://github.com/oxpig/Fragmenstein . Scientific contribution This work shows the importance of using the coordinates of known binders when predicting the conformation of derivative molecules through a retrospective analysis of the COVID Moonshot data. This method has had a prior real-world application in hit-to-lead screening, yielding a sub-micromolar merger from parent hits in a single round. It is therefore likely to further benefit future drug design campaigns and be integrated in future pipelines. Graphical Abstract |
| format | Article |
| id | doaj-art-18c7e526aa8f4bd1b8bea85110a64659 |
| institution | Kabale University |
| issn | 1758-2946 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Cheminformatics |
| spelling | doaj-art-18c7e526aa8f4bd1b8bea85110a646592025-01-19T12:37:03ZengBMCJournal of Cheminformatics1758-29462025-01-0117111310.1186/s13321-025-00946-0Fragmenstein: predicting protein–ligand structures of compounds derived from known crystallographic fragment hits using a strict conserved-binding–based methodologyMatteo P. Ferla0Rubén Sánchez-García1Rachael E. Skyner2Stefan Gahbauer3Jenny C. Taylor4Frank von Delft5Brian D. Marsden6Charlotte M. Deane7Oxford Protein Informatics Group, Department of Statistics, University of OxfordOxford Protein Informatics Group, Department of Statistics, University of OxfordDiamond Light Source, Science and Technology Facilities CouncilDepartment of Pharmaceutical Chemistry, University of California San FranciscoWellcome Centre for Human Genetics, NIHR Oxford BRC Genomic Medicine, University of OxfordCentre for Medicine Discoveries, Nuffield Department of Medicine, University of OxfordCentre for Medicine Discoveries, Nuffield Department of Medicine, University of OxfordOxford Protein Informatics Group, Department of Statistics, University of OxfordAbstract Current strategies centred on either merging or linking initial hits from fragment-based drug design (FBDD) crystallographic screens generally do not fully leaverage 3D structural information. We show that an algorithmic approach (Fragmenstein) that ‘stitches’ the ligand atoms from this structural information together can provide more accurate and reliable predictions for protein–ligand complex conformation than general methods such as pharmacophore-constrained docking. This approach works under the assumption of conserved binding: when a larger molecule is designed containing the initial fragment hit, the common substructure between the two will adopt the same binding mode. Fragmenstein either takes the atomic coordinates of ligands from a experimental fragment screen and combines the atoms together to produce a novel merged virtual compound, or uses them to predict the bound complex for a provided molecule. The molecule is then energy minimised under strong constraints to obtain a structurally plausible conformer. The code is available at https://github.com/oxpig/Fragmenstein . Scientific contribution This work shows the importance of using the coordinates of known binders when predicting the conformation of derivative molecules through a retrospective analysis of the COVID Moonshot data. This method has had a prior real-world application in hit-to-lead screening, yielding a sub-micromolar merger from parent hits in a single round. It is therefore likely to further benefit future drug design campaigns and be integrated in future pipelines. Graphical Abstracthttps://doi.org/10.1186/s13321-025-00946-0Fragment-based drug designHit discoveryDe novo designFragment mergingFragment linkingFragment elaboration |
| spellingShingle | Matteo P. Ferla Rubén Sánchez-García Rachael E. Skyner Stefan Gahbauer Jenny C. Taylor Frank von Delft Brian D. Marsden Charlotte M. Deane Fragmenstein: predicting protein–ligand structures of compounds derived from known crystallographic fragment hits using a strict conserved-binding–based methodology Journal of Cheminformatics Fragment-based drug design Hit discovery De novo design Fragment merging Fragment linking Fragment elaboration |
| title | Fragmenstein: predicting protein–ligand structures of compounds derived from known crystallographic fragment hits using a strict conserved-binding–based methodology |
| title_full | Fragmenstein: predicting protein–ligand structures of compounds derived from known crystallographic fragment hits using a strict conserved-binding–based methodology |
| title_fullStr | Fragmenstein: predicting protein–ligand structures of compounds derived from known crystallographic fragment hits using a strict conserved-binding–based methodology |
| title_full_unstemmed | Fragmenstein: predicting protein–ligand structures of compounds derived from known crystallographic fragment hits using a strict conserved-binding–based methodology |
| title_short | Fragmenstein: predicting protein–ligand structures of compounds derived from known crystallographic fragment hits using a strict conserved-binding–based methodology |
| title_sort | fragmenstein predicting protein ligand structures of compounds derived from known crystallographic fragment hits using a strict conserved binding based methodology |
| topic | Fragment-based drug design Hit discovery De novo design Fragment merging Fragment linking Fragment elaboration |
| url | https://doi.org/10.1186/s13321-025-00946-0 |
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