FMOe: Preprocessing and Visualizing Package of the Fragment Molecular Orbital Method for Molecular Operating Environment and Its Applications in Covalent Ligand and Metalloprotein Analyses.

The fragment molecular orbital (FMO) method is an efficient quantum chemical calculation technique for large biomolecules, dividing each into smaller fragments and providing interfragment interaction energies (IFIEs) that support our understanding of molecular recognition. The ab initio fragment MO method (ABINIT-MP), an FMO processing program, can automatically divide typical proteins and nucleic acids. In contrast, small molecules such as ligands and heterosystems must be manually divided. Thus, we developed a graphical user interface to easily handle such manual fragmentation as a library for the Molecular Operating Environment (MOE) that preprocesses and visualizes FMO calculations. We demonstrated fragmentation with IFIE analyses for the two following cases: (1) covalent cysteine-ligand bonding inside the SARS-CoV-2 main protease (Mpro) and nirmatrelvir (Paxlovid) complex and (2) the metal coordination inside a zinc-bound cyclic peptide. IFIE analysis successfully identified the key amino acid residues for the molecular recognition of nirmatrelvir with Mpro and the details of their interactions (e.g., hydrogen bonds and CH/π interactions) via ligand fragmentation of functional group units. In metalloproteins, we found an efficient and accurate scheme for the fragmentation of Zn2+ ions with four histidines coordinated to the ion. FMOe simplifies manual fragmentation, allowing users to experiment with various fragmentation patterns and perform in-depth IFIE analysis with high accuracy. In the future, our findings will provide valuable insight into complicated cases, such as ligand fragmentation in modality drug discovery, especially for medium-sized molecules and metalloprotein fragmentation around metals.

Docosahexaenoic acid enhances the treatment efficacy for castration-resistant prostate cancer by inhibiting autophagy through Atg4B inhibition.

Autophagy induction in cancer is involved in cancer progression and the acquisition of resistance to anticancer agents. Therefore, autophagy is considered a potential therapeutic target in cancer therapy. In this study, we found that long-chain fatty acids (LCFAs) have inhibitory effects on Atg4B, which is essential for autophagosome formation, through screening based on the pharmacophore of 21f, a recently developed Atg4B inhibitor. Among these fatty acids, docosahexaenoic acid (DHA), a polyunsaturated fatty acid, exhibited the most potent Atg4B inhibitory activity. DHA inhibited autophagy induced by androgen receptor signaling inhibitors (ARSI) in LNCaP and 22Rv1 prostate cancer cells and significantly increased apoptotic cell death. Furthermore, we investigated the effect of DHA on resistance to ARSI by establishing darolutamide-resistant prostate cancer 22Rv1 (22Rv1/Dar) cells, which had developed resistance to darolutamide, a novel ARSI. At baseline, 22Rv1/Dar cells showed a higher autophagy level than parental 22Rv1 cells. DHA significantly suppressed Dar-induced autophagy and sensitized 22Rv1/Dar cells by inducing apoptotic cell death through mitochondrial dysfunction. These results suggest that DHA supplementation may improve prostate cancer therapy with ARSI.

Structure-based development of novel substrate-type G9a inhibitors as epigenetic modulators for sickle cell disease treatment.

The discovery and development of structurally distinct lysine methyltransferase G9a inhibitors have been the subject of intense research in epigenetics. Structure-based optimization was conducted, starting with the previously reported seed compound 7a and lead to the identification of a highly potent G9a inhibitor, compound 7i (IC50 = 0.024 µM). X-ray crystallography for the ligand-protein interaction and kinetics study, along with surface plasmon resonance (SPR) analysis, revealed that compound 7i interacts with G9a in a unique binding mode. In addition, compound 7i caused attenuation of cellular H3K9me2 levels and induction of γ-globin mRNA expression in HUDEP-2 cells in a dose-dependent manner.

Prediction of Binding Pose and Affinity of Nelfinavir, a SARS-CoV-2 Main Protease Repositioned Drug, by Combining Docking, Molecular Dynamics, and Fragment Molecular Orbital Calculations.

A novel in silico drug design procedure is described targeting the Main protease (Mpro) of the SARS-CoV-2 virus. The procedure combines molecular docking, molecular dynamics (MD), and fragment molecular orbital (FMO) calculations. The binding structure and properties of Mpro were predicted for Nelfinavir (NFV), which had been identified as a candidate compound through drug repositioning, targeting Mpro. Several poses of the Mpro and NFV complexes were generated by docking, from which four docking poses were selected by scoring with FMO energy. Then, each pose was subjected to MD simulation, 100 snapshot structures were sampled from each of the generated MD trajectories, and the structures were evaluated by FMO calculations to rank the pose based on binding energy. Several residues were found to be important in ligand recognition, including Glu47, Asp48, Glu166, Asp187, and Gln189, all of which interacted strongly with NFV. Asn142 is presumably regarded to form hydrogen bonds or CH/π interaction with NFV; however, in the present calculation, their interactions were transient. Moreover, the tert-butyl group of NFV had no interaction with Mpro. Identifying such strong and weak interactions provides candidates for maintaining and substituting ligand functional groups and important suggestions for drug discovery using drug repositioning. Besides the interaction between NFV and the amino acid residues of Mpro, the desolvation effect of the binding pocket also affected the ranking order. A similar procedure of drug design was applied to Lopinavir, and the calculated interaction energy and experimental inhibitory activity value trends were consistent. Our approach provides a new guideline for structure-based drug design starting from a candidate compound whose complex crystal structure has not been obtained.

AI-driven molecular generation of not-patented pharmaceutical compounds using world open patent data.

Developing compounds with novel structures is important for the production of new drugs. From an intellectual perspective, confirming the patent status of newly developed compounds is essential, particularly for pharmaceutical companies. The generation of a large number of compounds has been made possible because of the recent advances in artificial intelligence (AI). However, confirming the patent status of these generated molecules has been a challenge because there are no free and easy-to-use tools that can be used to determine the novelty of the generated compounds in terms of patents in a timely manner; additionally, there are no appropriate reference databases for pharmaceutical patents in the world. In this study, two public databases, SureChEMBL and Google Patents Public Datasets, were used to create a reference database of drug-related patented compounds using international patent classification. An exact structure search system was constructed using InChIKey and a relational database system to rapidly search for compounds in the reference database. Because drug-related patented compounds are a good source for generative AI to learn useful chemical structures, they were used as the training data. Furthermore, molecule generation was successfully directed by increasing and decreasing the number of generated patented compounds through incorporation of patent status (i.e., patented or not) into learning. The use of patent status enabled generation of novel molecules with high drug-likeness. The generation using generative AI with patent information would help efficiently propose novel compounds in terms of pharmaceutical patents. Scientific contribution: In this study, a new molecule-generation method that takes into account the patent status of molecules, which has rarely been considered but is an important feature in drug discovery, was developed. The method enables the generation of novel molecules based on pharmaceutical patents with high drug-likeness and will help in the efficient development of effective drug compounds.

Identification of small-molecule inhibitors against the interaction of RNA-binding protein PSF and its target RNA for cancer treatment.

Diverse cellular activities are modulated through a variety of RNAs, including long noncoding RNAs (lncRNAs), by binding to certain proteins. The inhibition of oncogenic proteins or RNAs is expected to suppress cancer cell proliferation. We have previously demonstrated that PSF interaction with its target RNAs, such as androgen-induced lncRNA CTBP1-AS, is critical for hormone therapy resistance in prostate and breast cancers. However, the action of protein-RNA interactions remains almost undruggable to date. High-throughput screening (HTS) has facilitated the discovery of drugs for protein-protein interactions. In the present study, we developed an in vitro alpha assay using Flag peptide-conjugated lncRNA, CTBP1-AS, and PSF. We then constructed an effective HTS screening system to explore small compounds that inhibit PSF-RNA interactions. Thirty-six compounds were identified and dose-dependently inhibited PSF-RNA interaction in vitro. Moreover, chemical optimization of these lead compounds and evaluation of cancer cell proliferation revealed two promising compounds, N-3 and C-65. These compounds induced apoptosis and inhibited cell growth in prostate and breast cancer cells. By inhibiting PSF-RNA interaction, N-3 and C-65 up-regulated signals that are repressed by PSF, such as the cell cycle signals by p53 and p27. Furthermore, using a mouse xenograft model for hormone therapy-resistant prostate cancer, we revealed that N-3 and C-65 can significantly suppress tumor growth and downstream target gene expression, such as the androgen receptor (AR). Thus, our findings highlight a therapeutic strategy through the development of inhibitors for RNA-binding events in advanced cancers.

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.

Discovery of Novel Substrate-Competitive Lysine Methyltransferase G9a Inhibitors as Anticancer Agents.

Identification of structurally novel inhibitors of lysine methyltransferase G9a has been a subject of intense research in cancer epigenetics. Starting with the high-throughput screening (HTS) hit rac-10a obtained from the chemical library of the University of Tokyo Drug Discovery Initiative, the structure-activity relationship of the unique substrate-competitive inhibitors was established with the help of X-ray crystallography and fragment molecular orbital (FMO) calculations for the ligand-protein interaction. Further optimization of the in vitro characteristics and drug metabolism and pharmacokinetics (DMPK) properties led to the identification of 26j (RK-701), which is a structurally distinct potent inhibitor of G9a/GLP (IC50 = 27/53 nM). Compound 26j exhibited remarkable selectivity against other related methyltransferases, dose-dependent attenuation of cellular H3K9me2 levels, and tumor growth inhibition in MOLT-4 cells in vitro. Moreover, compound 26j showed inhibition of tumor initiation and growth in a carcinogen-induced hepatocellular carcinoma (HCC) in vivo mouse model without overt acute toxicity.

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.

Structural basis of transcription regulation by CNC family transcription factor, Nrf2.

Several basic leucine zipper (bZIP) transcription factors have accessory motifs in their DNA-binding domains, such as the CNC motif of CNC family or the EHR motif of small Maf (sMaf) proteins. CNC family proteins heterodimerize with sMaf proteins to recognize CNC-sMaf binding DNA elements (CsMBEs) in competition with sMaf homodimers, but the functional role of the CNC motif remains elusive. In this study, we report the crystal structures of Nrf2/NFE2L2, a CNC family protein regulating anti-stress transcriptional responses, in a complex with MafG and CsMBE. The CNC motif restricts the conformations of crucial Arg residues in the basic region, which form extensive contact with the DNA backbone phosphates. Accordingly, the Nrf2-MafG heterodimer has approximately a 200-fold stronger affinity for CsMBE than canonical bZIP proteins, such as AP-1 proteins. The high DNA affinity of the CNC-sMaf heterodimer may allow it to compete with the sMaf homodimer on target genes without being perturbed by other low-affinity bZIP proteins with similar sequence specificity.

Selective Inhibitor Design for Kinase Homologs Using Multiobjective Monte Carlo Tree Search.

Designing highly selective molecules for a drug target protein is a challenging task in drug discovery. This task can be regarded as a multiobjective problem that simultaneously satisfies criteria for various objectives, such as selectivity for a target protein, pharmacokinetic endpoints, and drug-like indices. Recent breakthroughs in artificial intelligence have accelerated the development of molecular structure generation methods, and various researchers have applied them to computational drug designs and successfully proposed promising drug candidates. However, designing efficient selective inhibitors with releasing activities against various homologs of a target protein remains a difficult issue. In this study, we developed a de novo structure generator based on reinforcement learning that is capable of simultaneously optimizing multiobjective problems. Our structure generator successfully proposed selective inhibitors for tyrosine kinases while optimizing 18 objectives consisting of inhibitory activities against 9 tyrosine kinases, 3 pharmacokinetics endpoints, and 6 other important properties. These results show that our structure generator and optimization strategy for selective inhibitors will contribute to the further development of practical structure generators for drug designs.

N-(3,4-dimethoxyphenethyl)-6-methyl-2,3,4,9-tetrahydro-1H-carbazol-1-amine inhibits bladder cancer progression by suppressing YAP1/TAZ.

Bladder cancer (BlC) is the fourth most common cancer in males worldwide, but few systemic chemotherapy options for its effective treatment exist. The development of new molecularly-targeted agents against BlC is therefore an urgent issue. The Hippo signaling pathway, with its upstream LATS kinases and downstream transcriptional co-activators YAP1 and TAZ, plays a pivotal role in diverse cell functions, including cell proliferation. Recent studies have shown that overexpression of YAP1 occurs in advanced BlCs and is associated with poor patient prognosis. Accessing data from our previous screening of a chemical library of compounds targeting the Hippo pathway, we identified DMPCA (N-(3,4-dimethoxyphenethyl)-6-methyl-2,3,4,9-tetrahydro-1H-carbazol-1-amine) as an agent able to induce the phosphorylation of LATS1 and YAP1/TAZ in BlC cells, thereby suppressing their viability both in vitro and in mouse xenografts. Our data indicate that DMPCA has a potent anti-tumor effect, and raise the possibility that this agent may represent a new and effective therapeutic option for BlC.

Structure-Activity Relationship and In Silico Evaluation of cis- and trans-PCPA-Derived Inhibitors of LSD1 and LSD2.

trans-2-Phenylcycloproylamine (trans-PCPA) has been used as the scaffold to develop covalent-binding inhibitors against lysine-specific demethylase 1 (LSD1/KDM1A), a therapeutic target for several cancers. However, the effects of different structural moieties on the inhibitory activity, selectivity, and reactivity of these derivatives, including the cis isomers, against LSD1 and its paralogue LSD2/KDM1B are not fully understood. Here we synthesized 65 cis- and trans-PCPA derivatives and evaluated their inhibitory activity against LSD1 and LSD2. One of the derivatives, 7c (cis-4-Br-2,5-F2-PCPA; S1024), inhibited LSD1 and LSD2 with K i values of 0.094 µM and 8.4 µM, respectively, and increased the level of dimethylated histone H3 at K4 in CCRF-CEM cells. A machine learning-based regression model (Q 2 = 0.61) to predict LSD1-inhibitory activity was also constructed and showed a good prediction accuracy (R 2 = 0.81) for 12 test-set compounds, including 7c. The present methodology would be useful when designing covalent-binding inhibitors for other enzymes.

Utilizing public and private sector data to build better machine learning models for the prediction of pharmacokinetic parameters.

One solution to compensate for the shortage of publicly available data is to collect more quality-controlled data from the private sector through public-private partnerships. However, several issues must be resolved before implementing such a system. Here, we review the technical aspects of public-private partnerships using our initiative in Japan as an example. In particular, we focus on the procedure for collecting data from multiple private sector companies and building prediction models and discuss how merging public and private sector datasets will help to improve the chemical space coverage and prediction performance. Teaser: Japan's first public-private consortium in pharmacokinetics has incorporated data from multiple pharmaceutical companies to create useful predictive models.

Protein-ligand binding affinity prediction of cyclin-dependent kinase-2 inhibitors by dynamically averaged fragment molecular orbital-based interaction energy.

Fragment molecular orbital (FMO) method is a powerful computational tool for structure-based drug design, in which protein-ligand interactions can be described by the inter-fragment interaction energy (IFIE) and its pair interaction energy decomposition analysis (PIEDA). Here, we introduced a dynamically averaged (DA) FMO-based approach in which molecular dynamics simulations were used to generate multiple protein-ligand complex structures for FMO calculations. To assess this approach, we examined the correlation between the experimental binding free energies and DA-IFIEs of six CDK2 inhibitors whose net charges are zero. The correlation between the experimental binding free energies and snapshot IFIEs for X-ray crystal structures was R2  = 0.75. Using the DA-IFIEs, the correlation significantly improved to 0.99. When an additional CDK2 inhibitor with net charge of -1 was added, the DA FMO-based scheme with the dispersion energies still achieved R2  = 0.99, whereas R2 decreased to 0.32 employing all the energy terms of PIEDA.

Design and Synthesis of Tranylcypromine-Derived LSD1 Inhibitors with Improved hERG and Microsomal Stability Profiles.

Lysine-specific demethylase 1 (LSD1/KDM1A) is a promising therapeutic target for the treatment of cancers. Several derivatives of tranylcypromine (trans-2-phenylcyclopropylamine) have been developed as LSD1 inhibitors. One such derivative is S2157; however, this compound has a high hERG channel inhibitory activity and a low microsomal stability, making it unsuitable as a drug candidate. Here, using an in silico hERG inhibition prediction model, we designed, synthesized, and evaluated a novel series of S2157 derivatives characterized by modifications of the benzyloxy and piperazine groups. Among the synthesized derivatives, a compound possessing 2-fluoropyridine and 2,8-diaza-spiro[4.5]decane groups (compound 10) showed the most desirable activities, and its eutomer, S1427, was isolated by the optical resolution of 10. In addition to potent LSD1 inhibitory activity, S1427 exhibited desirable hERG channel inhibition and microsomal stability profiles.

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.