SummarySite-directed spin labeling of proteins via non-canonical amino acids (ncAAs) is a non-traditional method for the measurement of pseudocontact shifts (PCSs) by nuclear magnetic resonance (NMR) spectroscopy. PCSs provide long-range distance and orientational information between a paramagnetic center and protein nuclei that can be used as restraints for computational structural modeling techniques. Here, we present the first experimental structure of an ncAA chemically linked to a lanthanide tag conjugated to the protein, T4-Lysozyme (T4L). T4L was crystallized with a cyclen-based C3 tag coordinated to the paramagnetic ion terbium (Tb3+). The paramagnetic C3-lanthanide tag generated PCSs measured at four different ncAA sites. We show that the addition of these restraints improves structure prediction protocols for T4L using the RosettaNMR framework. Generated models provide insight into T4L conformational flexibility sampled in solution. This integrative modeling protocol is readily transferable to larger proteins. Methods to predict protein structures are advancing into an exciting arena such that reliable experimental data will play important roles for evaluating the biophysical relevance of predicted structural models. Our contribution here caters to the growing interest in using ncAAs for a range of biophysical studies, and these methods can be readily transferred to larger protein systems of interest.

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 TIMbarrel 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 μM. 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.

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A single experimental method alone often fails to provide the resolution, accuracy, and coverage needed to model integral membrane proteins (IMPs). Integrating computation with experimental data is a powerful approach to supplement missing structural information with atomic detail. We combine RosettaNMR with experimentally-derived paramagnetic NMR restraints to guide membrane protein structure prediction. We demonstrate this approach using the disulfide bond formation protein B (DsbB), an α-helical IMP. We attached a cyclen-based paramagnetic lanthanide tag to an engineered noncanonical amino acid (ncAA) using a coppercatalyzed azide-alkyne cycloaddition (CuAAC) click chemistry reaction. Using this tagging strategy, we collected 203 backbone H N pseudocontact shifts (PCSs) for three different labeling sites and used these as input to guide de novo membrane protein structure prediction protocols in Rosetta. We find that this sparse PCS dataset combined with 44 long-range NOEs as restraints in our calculations improves structure prediction of DsbB by enhancements in model accuracy, sampling, and scoring. The most accurate DsbB models generated in this case gave Cα-RMSD values over the transmembrane region of 2.11 Å (best-RMSD) and 3.23 Å (best-scoring).

Fragment-based drug discovery begins with the identification of small molecules with a molecular weight of usually less than 250 Da that 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. 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 by 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 μM. 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 (AUC) of the receiver operating characteristics (ROC) 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.