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.

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.

Pharmacological intervention of cholesterol sulfate-mediated T cell exclusion promotes antitumor immunity.

Effective cancer immunotherapy requires physical contact of T cells with cancer cells. However, tumors often constitute special microenvironments that exclude T cells and resist immunotherapy. Cholesterol sulfate (CS) is a product of sulfotransferase SULT2B1b and acts as an endogenous inhibitor of DOCK2, a Rac activator essential for migration and activation of lymphocytes. We have recently shown that cancer-derived CS prevents tumor infiltration by effector T cells. Therefore, SULT2B1b may be a therapeutic target to dampen CS-mediated immune evasion. Here, we identified 3ß-hydroxy-5-cholenoic acid (3ß-OH-5-Chln) as a cell-active inhibitor of SULT2B1b. 3ß-OH-5-Chln inhibited the cholesterol sulfotransferase activity of SULT2B1b in vitro and suppressed CS production from cancer cells expressing SULT2B1b. In vivo administration of 3ß-OH-5-Chln locally reduced CS level in murine CS-producing tumors and increased infiltration of CD8+ T cells. When combined with immune checkpoint blockade or antigen-specific T cell transfer, 3ß-OH-5-Chln suppressed the growth of CS-producing tumors. These results demonstrate that pharmacological inhibition of SULT2B1b can promote antitumor immunity through suppressing CS-mediated T cell exclusion.

Molecular action of larvicidal flavonoids on ecdysteroidogenic glutathione S-transferase Noppera-bo in Aedes aegypti.

BACKGROUND: Mosquito control is a crucial global issue for protecting the human community from mosquito-borne diseases. There is an urgent need for the development of selective and safe reagents for mosquito control. Flavonoids, a group of chemical substances with variable phenolic structures, such as daidzein, have been suggested as potential mosquito larvicides with less risk to the environment. However, the mode of mosquito larvicidal action of flavonoids has not been elucidated. RESULTS: Here, we report that several flavonoids, including daidzein, inhibit the activity of glutathione S-transferase Noppera-bo (Nobo), an enzyme used for the biosynthesis of the insect steroid hormone ecdysone, in the yellow fever mosquito Aedes aegypti. The crystal structure of the Nobo protein of Ae. aegypti (AeNobo) complexed with the flavonoids and its molecular dynamics simulation revealed that Glu113 forms a hydrogen bond with the flavonoid inhibitors. Consistent with this observation, substitution of Glu113 with Ala drastically reduced the inhibitory activity of the flavonoids against AeNobo. Among the identified flavonoid-type inhibitors, desmethylglycitein (4',6,7-trihydroxyisoflavone) exhibited the highest inhibitory activity in vitro. Moreover, the inhibitory activities of the flavonoids correlated with the larvicidal activity, as desmethylglycitein suppressed Ae. aegypti larval development more efficiently than daidzein. CONCLUSION: Our study demonstrates the mode of action of flavonoids on the Ae. aegypti Nobo protein at the atomic, enzymatic, and organismal levels.

Special Features of COVID-19 in the FMODB: Fragment Molecular Orbital Calculations and Interaction Energy Analysis of SARS-CoV-2-Related Proteins.

SARS-CoV-2 is the causative agent of coronavirus (known as COVID-19), the virus causing the current pandemic. There are ongoing research studies to develop effective therapeutics and vaccines against COVID-19 using various methods and many results have been published. The structure-based drug design of SARS-CoV-2-related proteins is promising, however, reliable information regarding the structural and intra- and intermolecular interactions is required. We have conducted studies based on the fragment molecular orbital (FMO) method for calculating the electronic structures of protein complexes and analyzing their quantitative molecular interactions. This enables us to extensively analyze the molecular interactions in residues or functional group units acting inside the protein complexes. Such precise interaction data are available in the FMO database (FMODB) (https://drugdesign.riken.jp/FMODB/). Since April 2020, we have performed several FMO calculations on the structures of SARS-CoV-2-related proteins registered in the Protein Data Bank. We have published the results of 681 structures, including three structural proteins and 11 nonstructural proteins, on the COVID-19 special page (as of June 8, 2021). In this paper, we describe the entire COVID-19 special page of the FMODB and discuss the calculation results for various proteins. These data not only aid the interpretation of experimentally determined structures but also the understanding of protein functions, which is useful for rational drug design for COVID-19.

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.

Protein ligand interaction analysis against new CaMKK2 inhibitors by use of X-ray crystallography and the fragment molecular orbital (FMO) method.

CaMKK2 (calcium/calmodulin dependent protein kinase kinase 2) is a serine/threonine protein kinase that regulates phosphorylation of CaM kinases (CaMKs) such as CaMKI, CaMKIV, and AMP-activated protein kinase (AMPK). From a pathological perspective, CaMKK2 plays a role in obesity, diabetes, and prostate cancer. Therefore, CaMKK2 is an attractive target protein for drug design. Here, we tried to find new CaMKK2 inhibitors by using ligand-based and structure-based drug design approaches. From the in silico hit compounds, we identified new inhibitors by using a CaMKK2 kinase assay. We solved X-ray crystallography structures of the CaMKK2-inhibitor complexes and performed Fragment Molecular Orbital (FMO) calculations to analyze the protein-ligand interactions, identify the key residues in inhibitor binding, and quantitatively measure their contribution. We experimentally determined five CaMKK2-inhibitor structures and calculated the binding energies of the inhibitors by the FMO method plus MM-PBSA (Molecular Mechanics Poisson-Boltzmann Surface Area) approach. The results showed a high correlation (R = -0.89) between experimentally measured inhibitory activity (pIC50) and the predicted ligand binding energy. We then quantitatively evaluated the contribution of each binding site residue in CaMKK2 by the IFIE (Inter-fragment Interaction Energy)/PIEDA (Pair Interaction Energy Decomposition Analysis) method. The IFIE values indicated that Lys194 and Glu236, which formed hydrogen bonds with the carboxylate groups of the inhibitors, were key residues for ligand binding. PIEDA revealed that the dispersion interaction of inhibitors with hydrophobic residues, such as Ile171, Phe267, and Leu319, contributed highly to ligand binding; we considered that this was due to CH-π interactions with methoxy groups and/or aromatic rings contained in our CaMKK2 inhibitor. These results from the quantitative interaction analysis by the FMO method are useful not only for future CaMMK2 inhibitor development but for application of the FMO method to in silico drug design.

Interruption of Vif/Elongin C interaction: In silico and experimental elucidation of the underlying molecular mechanism of benzimidazole-based APOBEC3G stabilizers.

In 2014, two novel and promising benzimidazole-based APOBEC3G stabilizers MM-1 and MM-2 (MMs) were uncovered with an elusive mechanism of action. Vif-APOBEC3G axis has been recognized as a novel therapeutic target for anti HIV-1 drug development. The unexplored mechanism of MMs hindered their further development into lead compounds. To recognize their underlying mechanism we adopted an exhaustive in silico workflow by which we tested their ability to interrupt Vif complex network formation. The preliminary outcome guided us to a high likelihood of MMs interaction within Elongin C binding site, which in turn, perturbs Vif/Elongin C binding and ultimately undermines Vif action. To validate our estimation, we synthesized only MM-1 as a model to complement our study by in vitro assay for a real-time understanding. An immunoprecipitation experiment confirmed the capacity of MM-1 to interrupt Vif/Elongin C interaction. This is an integral study that lies at the interface between theoretical and experimental approaches showing the potential of molecular modelling to address issues related to drug development.

Characterization of crystal water molecules in a high-affinity inhibitor and hematopoietic prostaglandin D synthase complex by interaction energy studies.

Hematopoietic prostaglandin D synthase (H-PGDS) is one of the two enzymes that catalyze prostaglandin D2 synthesis and a potential therapeutic target of allergic and inflammatory responses. To reveal key molecular interactions between a high-affinity ligand and H-PGDS, we designed and synthesized a potent new inhibitor (KD: 0.14 nM), determined the crystal structure in complex with human H-PGDS, and quantitatively analyzed the ligand-protein interactions by the fragment molecular orbital calculation method. In the cavity, 10 water molecules were identified, and the interaction energy calculation indicated their stable binding to the surface amino acids in the cavity. Among them, 6 water molecules locating from the deep inner cavity to the peripheral part of the cavity contributed directly to the ligand binding by forming hydrogen bonding interactions. Arg12, Gly13, Gln36, Asp96, Trp104, Lys112 and an essential co-factor glutathione also had strong interactions with the ligand. A strong repulsive interaction between Leu199 and the ligand was canceled out by forming a hydrogen bonding network with the adjacent conserved water molecule. Our quantitative studies including crystal water molecules explained that compounds with an elongated backbone structure to fit from the deep inner cavity to the peripheral part of the cavity would have strong affinity to human H-PGDS.

Targeting Ras-Driven Cancer Cell Survival and Invasion through Selective Inhibition of DOCK1.

Oncogenic Ras plays a key role in cancer initiation but also contributes to malignant phenotypes by stimulating nutrient uptake and promoting invasive migration. Because these latter cellular responses require Rac-mediated remodeling of the actin cytoskeleton, we hypothesized that molecules involved in Rac activation may be valuable targets for cancer therapy. We report that genetic inactivation of the Rac-specific guanine nucleotide exchange factor DOCK1 ablates both macropinocytosis-dependent nutrient uptake and cellular invasion in Ras-transformed cells. By screening chemical libraries, we have identified 1-(2-(3'-(trifluoromethyl)-[1,1'-biphenyl]-4-yl)-2-oxoethyl)-5-pyrrolidinylsulfonyl-2(1H)-pyridone (TBOPP) as a selective inhibitor of DOCK1. TBOPP dampened DOCK1-mediated invasion, macropinocytosis, and survival under the condition of glutamine deprivation without impairing the biological functions of the closely related DOCK2 and DOCK5 proteins. Furthermore, TBOPP treatment suppressed cancer metastasis and growth in vivo in mice. Our results demonstrate that selective pharmacological inhibition of DOCK1 could be a therapeutic approach to target cancer cell survival and invasion.

HCV NS3 protease enhances liver fibrosis via binding to and activating TGF-ß type I receptor.

Viruses sometimes mimic host proteins and hijack the host cell machinery. Hepatitis C virus (HCV) causes liver fibrosis, a process largely mediated by the overexpression of transforming growth factor (TGF)-ß and collagen, although the precise underlying mechanism is unknown. Here, we report that HCV non-structural protein 3 (NS3) protease affects the antigenicity and bioactivity of TGF-ß2 in (CAGA)9-Luc CCL64 cells and in human hepatic cell lines via binding to TGF-ß type I receptor (TßRI). Tumor necrosis factor (TNF)-α facilitates this mechanism by increasing the colocalization of TßRI with NS3 protease on the surface of HCV-infected cells. An anti-NS3 antibody against computationally predicted binding sites for TßRI blocked the TGF-ß mimetic activities of NS3 in vitro and attenuated liver fibrosis in HCV-infected chimeric mice. These data suggest that HCV NS3 protease mimics TGF-ß2 and functions, at least in part, via directly binding to and activating TßRI, thereby enhancing liver fibrosis.

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.

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.

A new method for induced fit docking (GENIUS) and its application to virtual screening of novel HCV NS3-4A protease inhibitors.

Hepatitis C virus (HCV) is an etiologic agent of chronic liver disease, and approximately 170 million people worldwide are infected with the virus. HCV NS3-4A serine protease is essential for the replication of this virus, and thus has been investigated as an attractive target for anti-HCV drugs. In this study, we developed our new induced-fit docking program (genius), and applied it to the discovery of a new class of NS3-4A protease inhibitors (IC(50)=1-10 µM including high selectivity index). The new inhibitors thus identified were modified, based on the docking models, and revealed preliminary structure-activity relationships. Moreover, the genius in silico screening performance was validated by using an enrichment factor. We believe our designed scaffold could contribute to the improvement of HCV chemotherapy.

Structural basis for compound C inhibition of the human AMP-activated protein kinase α2 subunit kinase domain.

AMP-activated protein kinase (AMPK) is a serine/threonine kinase that functions as a sensor to maintain energy balance at both the cellular and the whole-body levels and is therefore a potential target for drug design against metabolic syndrome, obesity and type 2 diabetes. Here, the crystal structure of the phosphorylated-state mimic T172D mutant kinase domain from the human AMPK α2 subunit is reported in the apo form and in complex with a selective inhibitor, compound C. The AMPK α2 kinase domain exhibits a typical bilobal kinase fold and exists as a monomer in the crystal. Like the wild-type apo form, the T172D mutant apo form adopts the autoinhibited structure of the `DFG-out' conformation, with the Phe residue of the DFG motif anchored within the putative ATP-binding pocket. Compound C binding dramatically alters the conformation of the activation loop, which adopts an intermediate conformation between DFG-out and DFG-in. This induced fit forms a compound-C binding pocket composed of the N-lobe, the C-lobe and the hinge of the kinase domain. The pocket partially overlaps with the putative ATP-binding pocket. These three-dimensional structures will be useful to guide drug discovery.

Bioinformatics based Ligand-Docking and in-silico screening.

We report a novel method, ChooseLD (CHOOse biological information Semi-Empirically on the Ligand Docking), which uses simulated annealing (SA) based on bioinformatics for protein-ligand flexible docking. The fingerprint alignment score (FPAScore) value is used to determine the docking conformation of the ligand. This method includes the matching of chemical descriptors such as fingerprints (FPs) and the root mean square deviation (rmsd) calculation of the coordinates of atoms of the chemical descriptors. Here, the FPAScore optimization for the translation and rotation of a rigid body is performed using the Metropolis Monte Carlo method. Our ChooseLD method will find wide application in the field of biochemistry and medicine to improve the search for new drugs targeting various proteins implicated in diseases.

Fams-ace: a combined method to select the best model after remodeling all server models.

During Critical Assessment of Protein Structure Prediction (CASP7, Pacific Grove, CA, 2006), fams-ace was entered in the 3D coordinate prediction category as a human expert group. The procedure can be summarized by the following three steps. (1) All the server models were refined and rebuilt utilizing our homology modeling method. (2) Representative structures were selected from each server, according to a model quality evaluation, based on a 3D1D profile score (like Verify3D). (3) The top five models were selected and submitted in the order of the consensus-based score (like 3D-Jury). Fams-ace is a fully automated server and does not require human intervention. In this article, we introduce the methodology of fams-ace and discuss the successes and failures of this approach during CASP7. In addition, we discuss possible improvements for the next CASP.

The SKE-DOCK server and human teams based on a combined method of shape complementarity and free energy estimation.

We participated in rounds 6-12 of the critical assessment of predicted interaction (CAPRI) contest as the SKE-DOCK server and human teams. The SKE-DOCK server is based on simple geometry docking and a knowledge base scoring function. The procedure is summarized in the following three steps: (1) protein docking according to shape complementarity, (2) evaluating complex models, and (3) repacking side-chain of models. The SKE-DOCK server did not make use of biological information. On the other hand, the human team tried various intervention approaches. In this article, we describe in detail the processes of the SKE-DOCK server, together with results and reasons for success and failure. Good predicted models were obtained for target 25 by both the SKE-DOCK server and human teams. When the modeled receptor proteins were superimposed on the experimental structures, the smallest Ligand-rmsd values corresponding to the rmsd between the model and experimental structures were 3.307 and 3.324 A, respectively. Moreover, the two teams obtained 4 and 2 acceptable models for target 25. The overall result for both the SKE-DOCK server and human teams was medium accuracy for one (Target 25) out of nine targets.

FAMS complex: a fully automated homology modeling system for protein complex structures.

The formation of a protein-protein complex is responsible for many biological functions; therefore, three-dimensional structures of protein complexes are essential for deeper understandings of protein functions and the mechanisms of diseases at the atomic level. However, compared with individual proteins, complex structures are difficult to solve experimentally because of technical limitations. Thus a method that can predict protein complex structures would be invaluable. In this study, we developed new software, FAMS Complex; a fully automated homology modeling system for protein complex structures consisting of two or more molecules. FAMS Complex requires only sequences and alignments of the target protein as input and constructs all molecules simultaneously and automatically. FAMS Complex is likely to become an essential tool for structure-based drug design, such as in silico screening to accelerate drug discovery before an experimental structure is solved. Moreover, in this post-genomic era when huge amounts of protein sequence information are available, a major goal is the determination of protein-protein interaction networks on a genomic scale. FAMS Complex will contribute to this goal, because its procedure is fully automated and so is suited for large-scale genome wide modeling.

Protein structure prediction in CASP6 using CHIMERA and FAMS.

In CASP6, the CHIMERA-group predicted full-atom models of all targets using SKE-CHIMERA, a Web-user interface system for protein structure prediction that allows human intervention at necessary stages; we used a lot of information from our own data and from publicly available data. Using SKE-CHIMERA, we iterated manual step (template selection and alignment by the in-house program CHIMERA) and automatic step (three-dimensional model building by the in-house program FAMS). The official CASP6 assessment showed that CHIMERA-group was one of the most successful predictors in homology modeling, especially for FR/H (Fold Recognition/Homologous). In this article, we introduce the method of CHIMERA-group and discuss its successes and failures in CASP6.