%0 Journal Article %1 Okwei2023-pg %A Okwei, Elleansar %A Smith, Shannon T %A Bender, Brian J %A Allison, Brittany %A Ganguly, Soumya %A Geanes, Alexander %A Zhang, Xuan %A Ledwitch, Kaitlyn %A Meiler, Jens %D 2023 %J Biochemistry %K topic_lifescience %N 3 %P 700--709 %T Rosetta's predictive ability for low-affinity ligand binding in fragment-based drug discovery %V 62 %X Fragment-based drug discovery begins with the identification of small molecules with a molecular weight of usually less than 250 Da which weakly bind to the protein of interest. This technique is challenging for computational docking methods as binding is determined by only a few specific interactions. Inaccuracies in the energy function or slight deviations in the docking pose can lead to the prediction of incorrect binding or difficulties in ranking fragments in in silico screening. Here, we test RosettaLigand by docking a series of fragments to a cysteine-depleted variant of the TIM-barrel protein, HisF (UniProtKB Q9X0C6). We compare the computational results with experimental NMR spectroscopy screens. NMR spectroscopy gives details on binding affinities of individual ligands, which allows assessment of the ligand-ranking ability using RosettaLigand and also provides feedback on the location of the binding pocket, which serves as a reliable test of RosettaLigand's ability to identify plausible binding poses. From a library screen of 3456 fragments, we identified a set of 31 ligands with intrinsic affinities to HisF with dissociation constants as low as 400 $\mu$M. The same library of fragments was blindly screened in silico. RosettaLigand was able to rank binders before non-binders with an area under the curve of the receiver operating characteristics of 0.74. The docking poses observed for binders agreed with the binding pocket identified by NMR chemical shift perturbations for all fragments. Taken together, these results provide a baseline performance of RosettaLigand in a fragment-based drug discovery setting.

@article{Okwei2023-pg, abstract = {Fragment-based drug discovery begins with the identification of small molecules with a molecular weight of usually less than 250 Da which weakly bind to the protein of interest. This technique is challenging for computational docking methods as binding is determined by only a few specific interactions. Inaccuracies in the energy function or slight deviations in the docking pose can lead to the prediction of incorrect binding or difficulties in ranking fragments in in silico screening. Here, we test RosettaLigand by docking a series of fragments to a cysteine-depleted variant of the TIM-barrel protein, HisF (UniProtKB Q9X0C6). We compare the computational results with experimental NMR spectroscopy screens. NMR spectroscopy gives details on binding affinities of individual ligands, which allows assessment of the ligand-ranking ability using RosettaLigand and also provides feedback on the location of the binding pocket, which serves as a reliable test of RosettaLigand's ability to identify plausible binding poses. From a library screen of 3456 fragments, we identified a set of 31 ligands with intrinsic affinities to HisF with dissociation constants as low as 400 $\mu$M. The same library of fragments was blindly screened in silico. RosettaLigand was able to rank binders before non-binders with an area under the curve of the receiver operating characteristics of 0.74. The docking poses observed for binders agreed with the binding pocket identified by NMR chemical shift perturbations for all fragments. Taken together, these results provide a baseline performance of RosettaLigand in a fragment-based drug discovery setting.}, added-at = {2024-09-10T10:41:24.000+0200}, author = {Okwei, Elleansar and Smith, Shannon T and Bender, Brian J and Allison, Brittany and Ganguly, Soumya and Geanes, Alexander and Zhang, Xuan and Ledwitch, Kaitlyn and Meiler, Jens}, biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/2fa54a159559d89f2d3962f3bcc301d08/scadsfct}, interhash = {4b94a3e7fc75d4dec7253a402abf5f99}, intrahash = {fa54a159559d89f2d3962f3bcc301d08}, journal = {Biochemistry}, keywords = {topic_lifescience}, language = {en}, month = feb, number = 3, pages = {700--709}, timestamp = {2024-09-10T12:00:15.000+0200}, title = {Rosetta's predictive ability for low-affinity ligand binding in fragment-based drug discovery}, volume = 62, year = 2023 }