Quantum Chemical Interaction Analysis between SARS-CoV-2 Main Protease and Ensitrelvir Compared with Its Initial Screening Hit.

A non-covalent oral drug targeting SARS-CoV-2 main protease (Mpro), ensitrelvir (Xocova), has been developed using structure-based drug design (SBDD). To elucidate the factors responsible for enhanced inhibitory activities from an in silico screening hit compound to ensitrelvir, we analyzed the interaction energies of the inhibitors with each residue of Mpro using fragment molecular orbital (FMO) calculations. This analysis reveals that functional group conversion for P1' and P1 parts in the inhibitors increases the strength of existing interactions with Mpro and also provides novel interactions for ensitrelvir; the associated changes in the conformation of Mpro induce further interactions for ensitrelvir in other parts, including hydrogen bonds, a halogen bond, and π-orbital interactions. Thus, we illuminate the promising strategies of SBDD for leading ensitrelvir to get higher activity against Mpro by elucidating microscopic interactions through FMO-based analysis. These detailed mechanism findings, including water cross-linkings, will help to design novel inhibitors in SBDD.

Identifying antibiotics based on structural differences in the conserved allostery from mitochondrial heme-copper oxidases.

Antimicrobial resistance (AMR) is a global health problem. Despite the enormous efforts made in the last decade, threats from some species, including drug-resistant Neisseria gonorrhoeae, continue to rise and would become untreatable. The development of antibiotics with a different mechanism of action is seriously required. Here, we identified an allosteric inhibitory site buried inside eukaryotic mitochondrial heme-copper oxidases (HCOs), the essential respiratory enzymes for life. The steric conformation around the binding pocket of HCOs is highly conserved among bacteria and eukaryotes, yet the latter has an extra helix. This structural difference in the conserved allostery enabled us to rationally identify bacterial HCO-specific inhibitors: an antibiotic compound against ceftriaxone-resistant Neisseria gonorrhoeae. Molecular dynamics combined with resonance Raman spectroscopy and stopped-flow spectroscopy revealed an allosteric obstruction in the substrate accessing channel as a mechanism of inhibition. Our approach opens fresh avenues in modulating protein functions and broadens our options to overcome AMR.

New antimalarials identified by a cell-based phenotypic approach: Structure-activity relationships of 2,3,4,9-tetrahydro-1H-ß-carboline derivatives possessing a 2-((coumarin-5-yl)oxy)alkanoyl moiety.

The identification, structure-activity relationships (SARs), and biological effects of new antimalarials consisting of a 2,3,4,9-tetrahydro-1H-ß-carboline core, a coumarin ring, and an oxyalkanoyl linker are described. A cell-based phenotypic approach was employed in this search for novel antimalarial drugs with unique modes of action. Our screening campaign of the RIKEN compound library succeeded in the identification of the known tetrahydro-ß-carboline derivative (4e) as a hit compound showing significant in vitro activity. SAR studies on this chemical series led to the discovery of compound 4h having a (R)-methyl group on the oxyacetyl linker with potent inhibition of parasite growth (IC50 = 2.0 nM). Compound 4h was also found to exhibit significant in vivo antimalarial effects in mouse models. Furthermore, molecular modeling studies on 4e, 4h, and its diastereomer (4j) suggested that the (R)-methyl group of 4h forces the preferential adoption of a specific conformer which is considered to be an active conformer.

The ubiquinone synthesis pathway is a promising drug target for Chagas disease.

Chagas disease is caused by infection with the protozoan parasite Trypanosoma cruzi (T. cruzi). It was originally a Latin American endemic health problem, but now is expanding worldwide as a result of increasing migration. The currently available drugs for Chagas disease, benznidazole and nifurtimox, provoke severe adverse effects, and thus the development of new drugs is urgently required. Ubiquinone (UQ) is essential for respiratory chain and redox balance in trypanosomatid protozoans, therefore we aimed to provide evidence that inhibitors of the UQ biosynthesis have trypanocidal activities. In this study, inhibitors of the human COQ7, a key enzyme of the UQ synthesis, were tested for their trypanocidal activities because they were expected to cross-react and inhibit trypanosomal COQ7 due to their genetic homology. We show the trypanocidal activity of a newly found human COQ7 inhibitor, an oxazinoquinoline derivative. The structurally similar compounds were selected from the commercially available compounds by 2D and 3D ligand-based similarity searches. Among 38 compounds selected, 12 compounds with the oxazinoquinoline structure inhibited significantly the growth of epimastigotes of T. cruzi. The most effective 3 compounds also showed the significant antitrypanosomal activity against the mammalian stage of T. cruzi at lower concentrations than benznidazole, a commonly used drug today. We found that epimastigotes treated with the inhibitor contained reduced levels of UQ9. Further, the growth of epimastigotes treated with the inhibitors was partially rescued by UQ10 supplementation to the culture medium. These results suggest that the antitrypanosomal mechanism of the oxazinoquinoline derivatives results from inhibition of the trypanosomal UQ synthesis leading to a shortage of the UQ pool. Our data indicate that the UQ synthesis pathway of T. cruzi is a promising drug target for Chagas disease.

FMODB: The World's First Database of Quantum Mechanical Calculations for Biomacromolecules Based on the Fragment Molecular Orbital Method.

We developed the world's first web-based public database for the storage, management, and sharing of fragment molecular orbital (FMO) calculation data sets describing the complex interactions between biomacromolecules, named FMO Database (https://drugdesign.riken.jp/FMODB/). Each entry in the database contains relevant background information on how the data was compiled as well as the total energy of each molecular system and interfragment interaction energy (IFIE) and pair interaction energy decomposition analysis (PIEDA) values. Currently, the database contains more than 13 600 FMO calculation data sets, and a comprehensive search function implemented at the front-end. The procedure for selecting target proteins, preprocessing the experimental structures, construction of the database, and details of the database front-end were described. Then, we demonstrated a use of the FMODB by comparing IFIE value distributions of hydrogen bond, ion-pair, and XH/π interactions obtained by FMO method to those by molecular mechanics approach. From the comparison, the statistical analysis of the data provided standard reference values for the three types of interactions that will be useful for determining whether each interaction in a given system is relatively strong or weak compared to the interactions contained within the data in the FMODB. In the final part, we demonstrate the use of the database to examine the contribution of halogen atoms to the binding affinity between human cathepsin L and its inhibitors. We found that the electrostatic term derived by PIEDA greatly correlated with the binding affinities of the halogen containing cathepsin L inhibitors, indicating the importance of QM calculation for quantitative analysis of halogen interactions. Thus, the FMO calculation data in FMODB will be useful for conducting statistical analyses to drug discovery, for conducting molecular recognition studies in structural biology, and for other studies involving quantum mechanics-based interactions.

Combined inhibition of XIAP and BCL2 drives maximal therapeutic efficacy in genetically diverse aggressive acute myeloid leukemia.

Aggressive therapy-resistant and refractory acute myeloid leukemia (AML) has an extremely poor outcome. By analyzing a large number of genetically complex and diverse, primary high-risk poor-outcome human AML samples, we identified specific pathways of therapeutic vulnerability. Through drug screens followed by extensive in vivo validation and genomic analyses, we found inhibition of cytosolic and mitochondrial anti-apoptotic proteins XIAP, BCL2 and MCL1, and a key regulator of mitosis, AURKB, as a vulnerability hub based on patient-specific genetic aberrations and transcriptional signatures. Combinatorial therapeutic inhibition of XIAP with an additional patient-specific vulnerability eliminated established AML in vivo in patient-derived xenografts (PDXs) bearing diverse genetic aberrations, with no signs of recurrence during off-treatment follow-up. By integrating genomic profiling and drug-sensitivity testing, this work provides a platform for a precision-medicine approach for treating aggressive AML with high unmet need.

Design and Discovery of an Orally Efficacious Spiroindolinone-Based Tankyrase Inhibitor for the Treatment of Colon Cancer.

Tankyrases (TNKS/TNKS2) belong to the poly(ADP-ribose) polymerase family. Inhibition of their enzymatic activities attenuates the Wnt/ß-catenin signaling, which plays an important role in cancer pathogenesis. We previously reported the discovery of RK-287107, a spiroindoline-based, highly selective, potent tankyrase inhibitor. Herein we describe the optimization process of RK-287107 leading to RK-582, which exhibits a markedly improved robust tumor growth inhibition in a COLO-320DM mouse xenograft model when orally administered. In addition to the dose-dependent elevation and attenuation of the levels of biomarkers AXIN2 and ß-catenin, respectively, results of the TCF reporter and cell proliferation studies on COLO-320DM are discussed.

Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II.

Assessing the hERG liability in the early stages of drug discovery programs is important. The recent increase of hERG-related information in public databases enabled various successful applications of machine learning techniques to predict hERG inhibition. However, most of these researches constructed the datasets from only one database, limiting the predictability and scope of the models. In this study, a hERG classification model was constructed using the largest dataset for hERG inhibition built by integrating multiple databases. The integrated dataset consisted of more than 291,000 structurally diverse compounds derived from ChEMBL, GOSTAR, PubChem, and hERGCentral. The prediction model was built by support vector machine (SVM) with descriptor selection based on Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to optimize the descriptor set for maximum prediction performance with the minimal number of descriptors. The SVM classification model using 72 selected descriptors and ECFP_4 structural fingerprints recorded kappa statistics of 0.733 and accuracy of 0.984 for the test set, substantially outperforming the prediction performance of the current commercial applications for hERG prediction. Finally, the applicability domain of the prediction model was assessed based on the molecular similarity between the training set and test set compounds.

Discovery of Novel Spiroindoline Derivatives as Selective Tankyrase Inhibitors.

The canonical WNT pathway plays an important role in cancer pathogenesis. Inhibition of poly(ADP-ribose) polymerase catalytic activity of the tankyrases (TNKS/TNKS2) has been reported to reduce the Wnt/ß-catenin signal by preventing poly ADP-ribosylation-dependent degradation of AXIN, a negative regulator of Wnt/ß-catenin signaling. With the goal of investigating the effects of tankyrase and Wnt pathway inhibition on tumor growth, we set out to find small-molecule inhibitors of TNKS/TNKS2 with suitable drug-like properties. Starting from 1a, a high-throughput screening hit, the spiroindoline derivative 40c (RK-287107) was discovered as a potent TNKS/TNKS2 inhibitor with >7000-fold selectivity against the PARP1 enzyme, which inhibits WNT-responsive TCF reporter activity and proliferation of human colorectal cancer cell line COLO-320DM. RK-287107 also demonstrated dose-dependent tumor growth inhibition in a mouse xenograft model. These observations suggest that RK-287107 is a promising lead compound for the development of novel tankyrase inhibitors as anticancer agents.

Construction of an integrated database for hERG blocking small molecules.

The inhibition of the hERG potassium channel is closely related to the prolonged QT interval, and thus assessing this risk could greatly facilitate the development of therapeutic compounds and the withdrawal of hazardous marketed drugs. The recent increase in SAR information about hERG inhibitors in public databases has led to many successful applications of machine learning techniques to predict hERG inhibition. However, most of these reports constructed their prediction models based on only one SAR database because the differences in the data format and ontology hindered the integration of the databases. In this study, we curated the hERG-related data in ChEMBL, PubChem, GOSTAR, and hERGCentral, and integrated them into the largest database about hERG inhibition by small molecules. Assessment of structural diversity using Murcko frameworks revealed that the integrated database contains more than twice as many chemical scaffolds for hERG inhibitors than any of the individual databases, and covers 18.2% of the Murcko framework-based chemical space occupied by the compounds in ChEMBL. The database provides the most comprehensive information about hERG inhibitors and will be useful to design safer compounds for drug discovery. The database is freely available at http://drugdesign.riken.jp/hERGdb/.

Theoretical Analysis of Activity Cliffs among Benzofuranone-Class Pim1 Inhibitors Using the Fragment Molecular Orbital Method with Molecular Mechanics Poisson-Boltzmann Surface Area (FMO+MM-PBSA) Approach.

Significant activity changes due to small structural changes (i.e., activity cliffs) of serine/threonine kinase Pim1 inhibitors were studied theoretically using the fragment molecular orbital method with molecular mechanics Poisson-Boltzmann surface area (FMO+MM-PBSA) approach. This methodology enables quantum-chemical calculations for large biomolecules with solvation. In the course of drug discovery targeting Pim1, six benzofuranone-class inhibitors were found to differ only in the position of the indole-ring nitrogen atom. By comparing the various qualities of complex structures based on X-ray, classical molecular mechanics (MM)-optimized, and quantum/molecular mechanics (QM/MM)-optimized structures, we found that the QM/MM-optimized structures provided the best correlation (R2 = 0.85) between pIC50 and the calculated FMO+MM-PBSA binding energy. Combining the classical solvation energy with the QM binding energy was important to increase the correlation. In addition, decomposition of the interaction energy into various physicochemical components by pair interaction energy decomposition analysis suggested that CH-π and electrostatic interactions mainly caused the activity differences.

Identification of pyrrolo[2,3-d]pyrimidines as potent HCK and FLT3-ITD dual inhibitors.

A series of novel pyrrolo[2,3-d]pyrimidines were synthesized by introducing 15 different amino acids to 7-cyclohexyl-5-(4-phenoxyphenyl)-7H-pyrrolo[2,3-d]pyrimidine-4-amine. Compounds with potent activities against HCK and FLT3-ITD were evaluated in viability studies with acute myeloid leukemia cell line MV4-11. Our structure activity relationship analyses lead to the identification of compound 31, which exhibited potent HCK and FLT3-ITD inhibition and activity against the MV4-11 cell line.

Activity cliff for 7-substituted pyrrolo-pyrimidine inhibitors of HCK explained in terms of predicted basicity of the amine nitrogen.

We previously reported the structure-based design of a highly potent hematopoietic cell kinase (HCK) inhibitor, a pyrrolo-pyrimidine compound designated RK-20449, for treatment of recurrent leukemia. Herein we report the synthesis and structure-activity relationships of some amino acid derivatives of 7-substituted pyrrolo-pyrimidine. Although these derivatives had the same predicted binding conformation as RK-20449, their IC50 values were 100-1000 times larger than that of the parent compound. We assumed that the basicity of the amine nitrogen, which formed an ionic bond with Asp348 of HCK, markedly affected inhibitory activity against HCK. The pKa values of the nitrogen were predicted by means of an ab initio quantum mechanical method, and complexes of the derivatives with HCK were analyzed by X-ray crystallography. We observed a significant correlation between the predicted pKa and IC50 values, and the crystal structures of the less potent derivatives showed various types of defects around the ionic bond.

A pyrrolo-pyrimidine derivative targets human primary AML stem cells in vivo.

Leukemia stem cells (LSCs) that survive conventional chemotherapy are thought to contribute to disease relapse, leading to poor long-term outcomes for patients with acute myeloid leukemia (AML). We previously identified a Src-family kinase (SFK) member, hematopoietic cell kinase (HCK), as a molecular target that is highly differentially expressed in human primary LSCs compared with human normal hematopoietic stem cells (HSCs). We performed a large-scale chemical library screen that integrated a high-throughput enzyme inhibition assay, in silico binding prediction, and crystal structure determination and found a candidate HCK inhibitor, RK-20449, a pyrrolo-pyrimidine derivative with an enzymatic IC50 (half maximal inhibitory concentration) in the subnanomolar range. A crystal structure revealed that RK-20449 bound the activation pocket of HCK. In vivo administration of RK-20449 to nonobese diabetic (NOD)/severe combined immunodeficient (SCID)/IL2rg(null) mice engrafted with highly aggressive therapy-resistant AML significantly reduced human LSC and non-stem AML burden. By eliminating chemotherapy-resistant LSCs, RK-20449 may help to prevent relapse and lead to improved patient outcomes in AML.

Prediction of ligand-induced structural polymorphism of receptor interaction sites using machine learning.

Protein functions are closely related to their three-dimensional structures. Various degrees of conformational changes in the main and side chains occur when binding with other molecules, such as small ligands or proteins. The ligand-induced structural polymorphism of proteins is also referred to as "induced-fit", and it plays an important role in the recognition of a particular class of ligands as well as in signal transduction. We have developed new prediction models that discriminate conformationally fluctuant residues caused by ligand-binding. The training and test data sets were obtained from the Protein Data Bank. The induced-fit residues were judged based on the Z values of the Cα atom distances in each protein cluster. Moreover, we introduced various descriptors, such as the number of residues, accessible surface area (ASA), depth of the residue, and position-specific scoring matrix (PSSM), which were obtained from the 2D- or 3D-structural information for the protein. After the optimization of the parameters by 5-fold cross validation, the best prediction model was applied to some well-known induced-fit target proteins to verify its effectiveness. Especially in the validation for the DFG motif of a protein kinase family, we succeeded in the prediction of the DFG-out possibility from only the DFG-in conformation of each kinase structure.

Insights from Pim1 structure for anti-cancer drug design.

INTRODUCTION: Pim1 is a unique, constitutively active serine/threonine kinase, and is a potent oncogene overexpressed in a range of hematologic malignancies and solid cancers. It functions as a signaling regulator in cell proliferation and survival pathways through substrate phosphorylation. More than 400 small molecular Pim1 inhibitors with IC(50)/Ki < 10 µM have been registered in the ChEMBL. However, no drug targeting Pim1 has been launched at this present time. AREAS COVERED: The authors provide a review of Pim1 inhibitors from the viewpoint of the structural analysis of the Pim1-inhibitor complexes. PDB data and PubMed literature searches were performed. The inhibitors have been classified and summarized by their interactions with Pim1 residues, to facilitate desirable inhibitor design. EXPERT OPINION: To obtain a potent and selective Pim1 inhibitor as a lead compound, the authors propose the development of compounds which simultaneously interact with both the ATP binding site (Lys67, Glu121 and Phe49) and substrate binding residues (Asp128, Asp131 and Glu171). The development of Pim1 inhibitors could lead to new therapeutic options for a number of hematological malignancies and prostate cancer in the future.

Flexibility of the P-loop of Pim-1 kinase: observation of a novel conformation induced by interaction with an inhibitor.

The serine/threonine kinase Pim-1 is emerging as a promising target for cancer therapeutics. Much attention has recently been focused on identifying potential Pim-1 inhibitor candidates for the treatment of haematopoietic malignancies. The outcome of a rational drug-design project has recently been reported [Nakano et al. (2012), J. Med. Chem. 55, 5151-5156]. The report described the process of optimization of the structure-activity relationship and detailed from a medicinal chemistry perspective the development of a low-potency and nonselective compound initially identified from in silico screening into a potent, selective and metabolically stable Pim-1 inhibitor. Here, the structures of the initial in silico hits are reported and the noteworthy features of the Pim-1 complex structures are described. A particular focus was placed on the rearrangement of the glycine-rich P-loop region that was observed for one of the initial compounds, (Z)-7-(azepan-1-ylmethyl)-2-[(1H-indol-3-yl)methylidene]-6-hydroxy-1-benzofuran-3(2H)-one (compound 1), and was also found in all further derivatives. This novel P-loop conformation, which appears to be stabilized by an additional interaction with the ß3 strand located above the binding site, is not usually observed in Pim-1 structures.

Identification of novel drug-resistant EGFR mutant inhibitors by in silico screening using comprehensive assessments of protein structures.

EGFR is a target protein for the treatment of non small cell lung cancer (NSCLC). The mutations associated with the activation of EGFR kinase activity, such as L858R and G719S, destabilize the inactive conformation of EGFR and are closely linked with the development of NSCLC. The additional T790M mutation reportedly causes drug resistance against the commercially available EGFR inhibitors, gefitinib and erlotinib. In this study, we searched for novel G719S/T790M EGFR inhibitors by a new in silico screening strategy, using two datasets. The results of in silico screening using protein-ligand docking are affected by the selection of 3D structure of the target protein. As the first strategy, we chose the 3D structures for in silico screening by test dockings using the G719S/T790M crystal structure, its molecular dynamics snapshots, and known inhibitors of the drug-resistant EGFR. In the second strategy, we selected the 3D structures by test dockings using all of the EGFR structures, regardless of the mutations, and all of the known EGFR inhibitors. Using each of the 3D structures selected by the strategies, 1000 compounds were chosen from the 71,588 compounds. Kinase assays identified 15 G719S/T790M EGFR inhibitors, including two compounds with novel scaffolds. Analyses of their structure-activity relationships revealed that interactions with the mutated Met790 residue specifically increase the inhibitory activity against G719S/T790M EGFR.

Application of support vector machine to three-dimensional shape-based virtual screening using comprehensive three-dimensional molecular shape overlay with known inhibitors.

In this study, machine learning using support vector machine was combined with three-dimensional (3D) molecular shape overlay, to improve the screening efficiency. Since the 3D molecular shape overlay does not use fingerprints or descriptors to compare two compounds, unlike 2D similarity methods, the application of machine learning to a 3D shape-based method has not been extensively investigated. The 3D similarity profile of a compound is defined as the array of 3D shape similarities with multiple known active compounds of the target protein and is used as the explanatory variable of support vector machine. As the measures of 3D shape similarity for our new prediction models, the prediction performances of the 3D shape similarity metrics implemented in ROCS, such as ShapeTanimoto and ScaledColor, were validated, using the known inhibitors of 15 target proteins derived from the ChEMBL database. The learning models based on the 3D similarity profiles stably outperformed the original ROCS when more than 10 known inhibitors were available as the queries. The results demonstrated the advantages of combining machine learning with the 3D similarity profile to process the 3D shape information of plural active compounds.