Selective disruption of NRF2-KEAP1 interaction leads to NASH resolution and reduction of liver fibrosis in mice.

Background & Aims: Oxidative stress is recognized as a major driver of non-alcoholic steatohepatitis (NASH) progression. The transcription factor NRF2 and its negative regulator KEAP1 are master regulators of redox, metabolic and protein homeostasis, as well as detoxification, and thus appear to be attractive targets for the treatment of NASH. Methods: Molecular modeling and X-ray crystallography were used to design S217879 - a small molecule that could disrupt the KEAP1-NRF2 interaction. S217879 was highly characterized using various molecular and cellular assays. It was then evaluated in two different NASH-relevant preclinical models, namely the methionine and choline-deficient diet (MCDD) and diet-induced obesity NASH (DIO NASH) models. Results: Molecular and cell-based assays confirmed that S217879 is a highly potent and selective NRF2 activator with marked anti-inflammatory properties, as shown in primary human peripheral blood mononuclear cells. In MCDD mice, S217879 treatment for 2 weeks led to a dose-dependent reduction in NAFLD activity score while significantly increasing liver Nqo1 mRNA levels, a specific NRF2 target engagement biomarker. In DIO NASH mice, S217879 treatment resulted in a significant improvement of established liver injury, with a clear reduction in both NAS and liver fibrosis. αSMA and Col1A1 staining, as well as quantification of liver hydroxyproline levels, confirmed the reduction in liver fibrosis in response to S217879. RNA-sequencing analyses revealed major alterations in the liver transcriptome in response to S217879, with activation of NRF2-dependent gene transcription and marked inhibition of key signaling pathways that drive disease progression. Conclusions: These results highlight the potential of selective disruption of the NRF2-KEAP1 interaction for the treatment of NASH and liver fibrosis. Impact and implications: We report the discovery of S217879 - a potent and selective NRF2 activator with good pharmacokinetic properties. By disrupting the KEAP1-NRF2 interaction, S217879 triggers the upregulation of the antioxidant response and the coordinated regulation of a wide spectrum of genes involved in NASH disease progression, leading ultimately to the reduction of both NASH and liver fibrosis progression in mice.

FastGrow: on-the-fly growing and its application to DYRK1A.

Fragment-based drug design is an established routine approach in both experimental and computational spheres. Growing fragment hits into viable ligands has increasingly shifted into the spotlight. FastGrow is an application based on a shape search algorithm that addresses this challenge at high speeds of a few milliseconds per fragment. It further features a pharmacophoric interaction description, ensemble flexibility, as well as geometry optimization to become a fully fledged structure-based modeling tool. All features were evaluated in detail on a previously reported collection of fragment growing scenarios extracted from crystallographic data. FastGrow was also shown to perform competitively versus established docking software. A case study on the DYRK1A kinase, using recently reported new chemotypes, illustrates FastGrow's features in practice and its ability to identify active fragments. FastGrow is freely available to the public as a web server at https://fastgrow.plus/ and is part of the SeeSAR 3D software package.

Shape-Based Descriptors for Efficient Structure-Based Fragment Growing.

Structure-based fragment growing is one of the key techniques in fragment-based drug design. Fragment growing is commonly practiced based on structural and biophysical data. Computational workflows are employed to predict which fragment elaborations could lead to high-affinity binders. Several such workflows exist but many are designed to be long running noninteractive systems. Shape-based descriptors have been proven to be fast and perform well at virtual-screening tasks. They could, therefore, be applied to the fragment-growing problem to enable an interactive fragment-growing workflow. In this work, we describe and analyze the use of specific shape-based directional descriptors for the task of fragment growing. The performance of these descriptors that we call ray volume matrices (RVMs) is evaluated on two data sets containing protein-ligand complexes. While the first set focuses on self-growing, the second measures practical performance in a cross-growing scenario. The runtime of screenings using RVMs as well as their robustness to three dimensional perturbations is also investigated. Overall, it can be shown that RVMs are useful to prefilter fragment candidates. For up to 84% of the 3299 generated self-growing cases and for up to 66% of the 326 generated cross-growing cases, RVMs could create poses with less than 2 Å root-mean-square deviation to the crystal structure with average query speeds of around 30,000 conformations per second. This opens the door for fast explorative screenings of fragment libraries.

Molecular docking performance evaluated on the D3R Grand Challenge 2015 drug-like ligand datasets.

The D3R Grand Challenge 2015 was focused on two protein targets: Heat Shock Protein 90 (HSP90) and Mitogen-Activated Protein Kinase Kinase Kinase Kinase 4 (MAP4K4). We used a protocol involving a preliminary analysis of the available data in PDB and PubChem BioAssay, and then a docking/scoring step using more computationally demanding parameters that were required to provide more reliable predictions. We could evidence that different docking software and scoring functions can behave differently on individual ligand datasets, and that the flexibility of specific binding site residues is a crucial element to provide good predictions.

Blind Pose Prediction, Scoring, and Affinity Ranking of the CSAR 2014 Dataset.

The 2014 CSAR Benchmark Exercise was focused on three protein targets: coagulation factor Xa, spleen tyrosine kinase, and bacterial tRNA methyltransferase. Our protocol involved a preliminary analysis of the structural information available in the Protein Data Bank for the protein targets, which allowed the identification of the most appropriate docking software and scoring functions to be used for the rescoring of several docking conformations datasets, as well as for pose prediction and affinity ranking. The two key points of this study were (i) the prior evaluation of molecular modeling tools that are most adapted for each target and (ii) the increased search efficiency during the docking process to better explore the conformational space of big and flexible ligands.

Integrated structure- and ligand-based in silico approach to predict inhibition of cytochrome P450 2D6.

MOTIVATION: Cytochrome P450 (CYP) is a superfamily of enzymes responsible for the metabolism of drugs, xenobiotics and endogenous compounds. CYP2D6 metabolizes about 30% of drugs and predicting potential CYP2D6 inhibition is important in early-stage drug discovery. RESULTS: We developed an original in silico approach for the prediction of CYP2D6 inhibition combining the knowledge of the protein structure and its dynamic behavior in response to the binding of various ligands and machine learning modeling. This approach includes structural information for CYP2D6 based on the available crystal structures and molecular dynamic simulations (MD) that we performed to take into account conformational changes of the binding site. We performed modeling using three learning algorithms--support vector machine, RandomForest and NaiveBayesian--and we constructed combined models based on topological information of known CYP2D6 inhibitors and predicted binding energies computed by docking on both X-ray and MD protein conformations. In addition, we identified three MD-derived structures that are capable all together to better discriminate inhibitors and non-inhibitors compared with individual CYP2D6 conformations, thus ensuring complementary ligand profiles. Inhibition models based on classical molecular descriptors and predicted binding energies were able to predict CYP2D6 inhibition with an accuracy of 78% on the training set and 75% on the external validation set.

Rational design of small-molecule stabilizers of spermine synthase dimer by virtual screening and free energy-based approach.

Snyder-Robinson Syndrome (SRS) is a rare mental retardation disorder which is caused by the malfunctioning of an enzyme, the spermine synthase (SMS), which functions as a homo-dimer. The malfunctioning of SMS in SRS patients is associated with several identified missense mutations that occur away from the active site. This investigation deals with a particular SRS-causing mutation, the G56S mutation, which was shown computationally and experimentally to destabilize the SMS homo-dimer and thus to abolish SMS enzymatic activity. As a proof-of-concept, we explore the possibility to restore the enzymatic activity of the malfunctioning SMS mutant G56S by stabilizing the dimer through small molecule binding at the mutant homo-dimer interface. For this purpose, we designed an in silico protocol that couples virtual screening and a free binding energy-based approach to identify potential small-molecule binders on the destabilized G56S dimer, with the goal to stabilize it and thus to increase SMS G56S mutant activity. The protocol resulted in extensive list of plausible stabilizers, among which we selected and tested 51 compounds experimentally for their capability to increase SMS G56S mutant enzymatic activity. In silico analysis of the experimentally identified stabilizers suggested five distinctive chemical scaffolds. This investigation suggests that druggable pockets exist in the vicinity of the mutation sites at protein-protein interfaces which can be used to alter the disease-causing effects by small molecule binding. The identified chemical scaffolds are drug-like and can serve as original starting points for development of lead molecules to further rescue the disease-causing effects of the Snyder-Robinson syndrome for which no efficient treatment exists up to now.

In silico mechanistic profiling to probe small molecule binding to sulfotransferases.

Drug metabolizing enzymes play a key role in the metabolism, elimination and detoxification of xenobiotics, drugs and endogenous molecules. While their principal role is to detoxify organisms by modifying compounds, such as pollutants or drugs, for a rapid excretion, in some cases they render their substrates more toxic thereby inducing severe side effects and adverse drug reactions, or their inhibition can lead to drug-drug interactions. We focus on sulfotransferases (SULTs), a family of phase II metabolizing enzymes, acting on a large number of drugs and hormones and showing important structural flexibility. Here we report a novel in silico structure-based approach to probe ligand binding to SULTs. We explored the flexibility of SULTs by molecular dynamics (MD) simulations in order to identify the most suitable multiple receptor conformations for ligand binding prediction. Then, we employed structure-based docking-scoring approach to predict ligand binding and finally we combined the predicted interaction energies by using a QSAR methodology. The results showed that our protocol successfully prioritizes potent binders for the studied here SULT1 isoforms, and give new insights on specific molecular mechanisms for diverse ligands' binding related to their binding sites plasticity. Our best QSAR models, introducing predicted protein-ligand interaction energy by using docking, showed accuracy of 67.28%, 78.00% and 75.46%, for the isoforms SULT1A1, SULT1A3 and SULT1E1, respectively. To the best of our knowledge our protocol is the first in silico structure-based approach consisting of a protein-ligand interaction analysis at atomic level that considers both ligand and enzyme flexibility, along with a QSAR approach, to identify small molecules that can interact with II phase dug metabolizing enzymes.

Advances in molecular modeling of human cytochrome P450 polymorphism.

Cytochrome P450 (CYP) is a supergene family of metabolizing enzymes involved in the phase I metabolism of drugs and endogenous compounds. CYP oxidation often leads to inactive drug metabolites or to highly toxic or carcinogenic metabolites involved in adverse drug reactions (ADR). During the last decade, the impact of CYP polymorphism in various drug responses and ADR has been demonstrated. Of the drugs involved in ADR, 56% are metabolized by polymorphic phase I metabolizing enzymes, 86% among them being CYP. Here, we review the major CYP polymorphic forms, their impact for drug response and current advances in molecular modeling of CYP polymorphism. We focus on recent studies exploring CYP polymorphism performed by the use of sequence-based and/or protein-structure-based computational approaches. The importance of understanding the molecular mechanisms related to CYP polymorphism and drug response at the atomic level is outlined.

Computational analysis of protein-protein interfaces involving an alpha helix: insights for terphenyl-like molecules binding.

BACKGROUND: Protein-Protein Interactions (PPIs) are key for many cellular processes. The characterization of PPI interfaces and the prediction of putative ligand binding sites and hot spot residues are essential to design efficient small-molecule modulators of PPI. Terphenyl and its derivatives are small organic molecules known to mimic one face of protein-binding alpha-helical peptides. In this work we focus on several PPIs mediated by alpha-helical peptides. METHOD: We performed computational sequence- and structure-based analyses in order to evaluate several key physicochemical and surface properties of proteins known to interact with alpha-helical peptides and/or terphenyl and its derivatives. RESULTS: Sequence-based analysis revealed low sequence identity between some of the analyzed proteins binding alpha-helical peptides. Structure-based analysis was performed to calculate the volume, the fractal dimension roughness and the hydrophobicity of the binding regions. Besides the overall hydrophobic character of the binding pockets, some specificities were detected. We showed that the hydrophobicity is not uniformly distributed in different alpha-helix binding pockets that can help to identify key hydrophobic hot spots. CONCLUSIONS: The presence of hydrophobic cavities at the protein surface with a more complex shape than the entire protein surface seems to be an important property related to the ability of proteins to bind alpha-helical peptides and low molecular weight mimetics. Characterization of similarities and specificities of PPI binding sites can be helpful for further development of small molecules targeting alpha-helix binding proteins.

Toward in silico structure-based ADMET prediction in drug discovery.

Quantitative structure-activity relationship (QSAR) methods and related approaches have been used to investigate the molecular features that influence the absorption, distribution, metabolism, excretion and toxicity (ADMET) of drugs. As the three-dimensional structures of several major ADMET proteins become available, structure-based (docking-scoring) computations can be carried out to complement or to go beyond QSAR studies. Applying docking-scoring methods to ADMET proteins is a challenging process because they usually have a large and flexible binding cavity; however, promising results relating to metabolizing enzymes have been reported. After reviewing current trends in the field we applied structure-based methods in the context of receptor flexibility in a case study involving the phase II metabolizing sulfotransferases. Overall, the explored concepts and results suggested that structure-based ADMET profiling will probably join the mainstream during the coming years.