Performance of protein-ligand docking with simulated chemical shift perturbations

A Step toward NRF2-DNA Interaction Inhibitors by Fragment-Based NMR Methods

The NRF2 transcription factor is a key regulator in cellular oxidative stress response, and acts as a tumor suppressor. Aberrant activation of NRF2 has been implicated in promoting chemo-resistance, tumor growth, and metastasis by activating its downstream target genes. Hence, inhibition of NRF2 promises to be an attractive therapeutic strategy to suppress cell proliferation and enhance cell apoptosis in cancer. Direct targeting of NRF2 with small-molecules to discover protein-DNA interaction inhibitors is challenging as it is a largely intrinsically disordered protein. To discover molecules that bind to NRF2 at the DNA binding interface, we performed an NMR-based fragment screen against its DNA-binding domain. We discovered several weakly binding fragment hits that bind to a region overlapping with the DNA binding site. Using SAR by catalogue we developed an initial structure-activity relationship for the most interesting initial hit series. By combining NMR chemical shift perturbations and data-driven docking, binding poses which agreed with NMR information and the observed SAR were elucidated. The herein discovered NRF2 hits and proposed binding modes form the basis for future structure-based optimization campaigns on this important but to date 'undrugged' cancer driver.

Automated Fragmentation QM/MM Calculation of NMR Chemical Shifts for Protein-Ligand Complexes

In this study, the automated fragmentation quantum mechanics/molecular mechanics (AF-QM/MM) method was applied for NMR chemical shift calculations of protein-ligand complexes. In the AF-QM/MM approach, the protein binding pocket is automatically divided into capped fragments (within ~200 atoms) for density functional theory (DFT) calculations of NMR chemical shifts. Meanwhile, the solvent effect was also included using the Poission-Boltzmann (PB) model, which properly accounts for the electrostatic polarization effect from the solvent for protein-ligand complexes. The NMR chemical shifts of neocarzinostatin (NCS)-chromophore binding complex calculated by AF-QM/MM accurately reproduce the large-sized system results. The 1H chemical shift perturbations (CSP) between apo-NCS and holo-NCS predicted by AF-QM/MM are also in excellent agreement with experimental results. Furthermore, the DFT calculated chemical shifts of the chromophore and residues in the NCS binding pocket can be utilized as molecular probes to identify the correct ligand binding conformation. By combining the CSP of the atoms in the binding pocket with the Glide scoring function, the new scoring function can accurately distinguish the native ligand pose from decoy structures. Therefore, the AF-QM/MM approach provides an accurate and efficient platform for protein-ligand binding structure prediction based on NMR derived information.

Predictive Power of Different Types of Experimental Restraints in Small Molecule Docking: A Review

Incorporating experimental restraints is a powerful method of increasing accuracy in computational protein small molecule docking simulations. Different algorithms integrate distinct forms of biochemical data during the docking and/or scoring stages. These so-called hybrid methods make use of receptor-based information such as nuclear magnetic resonance (NMR) restraints or small molecule-based information such as structure-activity relationships (SARs). A third class of methods directly interrogates contacts between the protein receptor and the small molecule. This work reviews the current state of using such restraints in docking simulations, evaluates their feasibility across broad systems, and identifies potential areas of algorithm development.

NMR in structure-based drug design

NMR spectroscopy is a powerful technique that can provide valuable structural information for drug discovery endeavors. Here, we discuss the strengths (and limitations) of NMR applications to structure-based drug discovery, highlighting the different levels of resolution and throughput obtainable. Additionally, the emerging field of paramagnetic NMR in drug discovery and recent developments in approaches to speed up and automate protein-observed NMR data collection and analysis are discussed.

Increased hydrophobic surface exposure in the cataract-related G18V variant of human γS-crystallin

BACKGROUND

The objective of this study was to determine whether the cataract-related G18V variant of human γS-crystallin has increased exposure of hydrophobic residues that could explain its aggregation propensity and/or recognition by αB-crystallin.

METHODS

We used an ANS fluorescence assay and NMR chemical shift perturbation to experimentally probe exposed hydrophobic surfaces. These results were compared to flexible docking simulations of ANS molecules to the proteins, starting with the solution-state NMR structures of γS-WT and γS-G18V.

RESULTS

γS-G18V exhibits increased ANS fluorescence, suggesting increased exposed hydrophobic surface area. The specific residues involved in ANS binding were mapped by NMR chemical shift perturbation assays, revealing ANS binding sites in γS-G18V that are not present in γS-WT. Molecular docking predicts three binding sites that are specific to γS-G18V corresponding to the exposure of a hydrophobic cavity located at the interdomain interface, as well as two hydrophobic patches near a disordered loop containing solvent-exposed cysteines, all but one of which is buried in γS-WT.

CONCLUSIONS

Although both proteins display non-specific binding, more residues are involved in ANS binding to γS-G18V, and the affected residues are localized in the N-terminal domain and the nearby interdomain interface, proximal to the mutation site.

GENERAL SIGNIFICANCE

Characterization of changes in exposed hydrophobic surface area between wild-type and variant proteins can help elucidate the mechanisms of aggregation propensity and chaperone recognition, presented here in the context of cataract formation. Experimental data and simulations provide complementary views of the interactions between proteins and the small molecule probes commonly used to study aggregation. This article is part of a Special Issue entitled Crystallin Biochemistry in Health and Disease.

On-the-Fly Integration of Data from a Spin-Diffusion-Based NMR Experiment into Protein-Ligand Docking

INPHARMA (interligand nuclear Overhauser enhancement for pharmacophore mapping) determines the relative orientation of two competitive ligands in the protein binding pocket. It is based on the observation of interligand transferred NOEs mediated by spin diffusion through protons of the protein and is, therefore, sensitive to the specific interactions of each of the two ligands with the protein. We show how this information can be directly included into a protein-ligand docking program to guide the prediction of the complex structures. Agreement between the experimental and back-calculated spectra based on the full relaxation matrix approach is translated into a score contribution that is combined with the scoring function ChemPLP of our docking tool PLANTS. This combined score is then used to predict the poses of five weakly bound cAMP-dependent protein kinase (PKA) ligands. After optimizing the setup, which finally also included trNOE data and optimized protonation states, very good success rates were obtained for all combinations of three ligands. For one additional ligand, no conclusive results could be obtained due to the ambiguous electron density of the ligand in the X-ray structure, which does not disprove alternative ligand poses. The failures of the remaining ligand are caused by suboptimal locations of specific protein side chains. Therefore, side-chain flexibility should be included in an improved INPHARMA-PLANTS version. This will reduce the strong dependence on the used protein input structure leading to improved scores overall, not only for this last ligand.