A class of chemical compounds enhances clustering of muscle nicotinic acetylcholine receptor in cultured myogenic cells.

Neuromuscular signal transmission is affected in various diseases including myasthenia gravis, congenital myasthenic syndromes, and sarcopenia. We used an ATF2-luciferase system to monitor the phosphorylation of MuSK in HEK293 cells introduced with MUSK and LRP4 cDNAs to find novel chemical compounds that enhanced agrin-mediated acetylcholine receptor (AChR) clustering. Four compounds with similar chemical structures carrying benzene rings and heterocyclic rings increased the luciferase activities 8- to 30-folds, and two of them showed continuously graded dose dependence. The effects were higher than that of disulfiram, a clinically available aldehyde dehydrogenase inhibitor, which we identified to be the most competent preapproved drug to enhance ATF2-luciferase activity in the same assay system. In C2C12 myotubes, all the compounds increased the area, intensity, length, and number of AChR clusters. Three of the four compounds increased the phosphorylation of MuSK, but not of Dok7, JNK. ERK, or p38. Monitoring cell toxicity using the neurite elongation of NSC34 neuronal cells as a surrogate marker showed that all the compounds had no effects on the neurite elongation up to 1 µM. Extensive docking simulation and binding structure prediction of the four compounds with all available human proteins using AutoDock Vina and DiffDock showed that the four compounds were unlikely to directly bind to MuSK or Dok7, and the exact target remained unknown. The identified compounds are expected to serve as a seed to develop a novel therapeutic agent to treat defective NMJ signal transmission.

Revealing Comprehensive Food Functionalities and Mechanisms of Action through Machine Learning.

Foods possess a range of unexplored functionalities; however, fully identifying these functions through empirical means presents significant challenges. In this study, we have proposed an in silico approach to comprehensively predict the functionalities of foods, encompassing even processed foods. This prediction is accomplished through the utilization of machine learning on biomedical big data. Our focus revolves around disease-related protein pathways, wherein we statistically evaluate how the constituent compounds collaboratively regulate these pathways. The proposed method has been employed across 876 foods and 83 diseases, leading to an extensive revelation of both food functionalities and their underlying operational mechanisms. Additionally, this approach identifies food combinations that potentially affect molecular pathways based on interrelationships between food functions within disease-related pathways. Our proposed method holds potential for advancing preventive healthcare.

Design and structural optimization of thiadiazole derivatives with potent GLS1 inhibitory activity.

GLS1 is an attractive target not only as anticancer agents but also as candidates for various potential pharmaceutical applications such as anti-aging and anti-obesity treatments. We performed docking simulations based on the complex crystal structure of GLS1 and its inhibitor CB-839 and found that compound A bearing a thiadiazole skeleton exhibits GLS1 inhibition. Furthermore, we synthesized 27 thiadiazole derivatives in an effort to obtain a more potent GLS1 inhibitor. Among the synthesized derivatives, 4d showed more potent GLS1 inhibitory activity (IC50 of 46.7 µM) than known GLS1 inhibitor DON and A. Therefore, 4d is a very promising novel GLS1 inhibitor.

Differential effects of proton pump inhibitors and vonoprazan on vascular endothelial growth factor expression in cancer cells.

Proton pump inhibitors (PPIs) are potent inhibitors of gastric acid secretion, used as first-line agents in treating peptic ulcers. However, we have previously reported that PPIs may diminish the therapeutic effect of anti-vascular endothelial growth factor (VEGF) drugs in patients with cancer. In this study, we explored the effects of vonoprazan, a novel gastric acid secretion inhibitor used for the treatment of peptic ulcers, on the secretion of VEGF in cancer cells and attempted to propose it as an alternative PPI for cancer chemotherapy. The effects of PPI and vonoprazan on VEGF expression in cancer cells were compared by real-time reverse transcription-polymerase chain reaction and ELISA. The interaction of vonoprazan and PPIs with transcriptional regulators by docking simulation analysis. In various cancer cell lines, including the human colorectal cancer cell line (LS174T), PPI increased VEGF messenger RNA expression and VEGF protein secretion, while this effect was not observed with vonoprazan. Molecular docking simulation analysis showed that vonoprazan had a lower binding affinity for estrogen receptor alpha (ER-α), one of the transcriptional regulators of VEGF, compared to PPI. Although the PPI-induced increase in VEGF expression was counteracted by pharmacological ER-α inhibition, the effect of vonoprazan on VEGF expression was unchanged. Vonoprazan does not affect VEGF expression in cancer cells, which suggests that vonoprazan might be an alternative to PPIs, with no interference with the therapeutic effects of anti-VEGF cancer chemotherapy.

Predicting drug-induced transcriptome responses of a wide range of human cell lines by a novel tensor-train decomposition algorithm.

MOTIVATION: Genome-wide identification of the transcriptomic responses of human cell lines to drug treatments is a challenging issue in medical and pharmaceutical research. However, drug-induced gene expression profiles are largely unknown and unobserved for all combinations of drugs and human cell lines, which is a serious obstacle in practical applications. RESULTS: Here, we developed a novel computational method to predict unknown parts of drug-induced gene expression profiles for various human cell lines and predict new drug therapeutic indications for a wide range of diseases. We proposed a tensor-train weighted optimization (TT-WOPT) algorithm to predict the potential values for unknown parts in tensor-structured gene expression data. Our results revealed that the proposed TT-WOPT algorithm can accurately reconstruct drug-induced gene expression data for a range of human cell lines in the Library of Integrated Network-based Cellular Signatures. The results also revealed that in comparison with the use of original gene expression profiles, the use of imputed gene expression profiles improved the accuracy of drug repositioning. We also performed a comprehensive prediction of drug indications for diseases with gene expression profiles, which suggested many potential drug indications that were not predicted by previous approaches. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DINIES: drug-target interaction network inference engine based on supervised analysis.

DINIES (drug-target interaction network inference engine based on supervised analysis) is a web server for predicting unknown drug-target interaction networks from various types of biological data (e.g. chemical structures, drug side effects, amino acid sequences and protein domains) in the framework of supervised network inference. The originality of DINIES lies in prediction with state-of-the-art machine learning methods, in the integration of heterogeneous biological data and in compatibility with the KEGG database. The DINIES server accepts any 'profiles' or precalculated similarity matrices (or 'kernels') of drugs and target proteins in tab-delimited file format. When a training data set is submitted to learn a predictive model, users can select either known interaction information in the KEGG DRUG database or their own interaction data. The user can also select an algorithm for supervised network inference, select various parameters in the method and specify weights for heterogeneous data integration. The server can provide integrative analyses with useful components in KEGG, such as biological pathways, functional hierarchy and human diseases. DINIES (http://www.genome.jp/tools/dinies/) is publicly available as one of the genome analysis tools in GenomeNet.

Target-Based Drug Repositioning Using Large-Scale Chemical-Protein Interactome Data.

Drug repositioning, or the identification of new indications for known drugs, is a useful strategy for drug discovery. In this study, we developed novel computational methods to predict potential drug targets and new drug indications for systematic drug repositioning using large-scale chemical-protein interactome data. We explored the target space of drugs (including primary targets and off-targets) based on chemical structure similarity and phenotypic effect similarity by making optimal use of millions of compound-protein interactions. On the basis of the target profiles of drugs, we constructed statistical models to predict new drug indications for a wide range of diseases with various molecular features. The proposed method outperformed previous methods in terms of interpretability, applicability, and accuracy. Finally, we conducted a comprehensive prediction of the drug-target-disease association network for 8270 drugs and 1401 diseases and showed biologically meaningful examples of newly predicted drug targets and drug indications. The predictive model is useful to understand the mechanisms of the predicted drug indications.

Large-Scale Prediction of Beneficial Drug Combinations Using Drug Efficacy and Target Profiles.

The identification of beneficial drug combinations is a challenging issue in pharmaceutical and clinical research toward combinatorial drug therapy. In the present study, we developed a novel computational method for large-scale prediction of beneficial drug combinations using drug efficacy and target profiles. We designed an informative descriptor for each drug-drug pair based on multiple drug profiles representing drug-targeted proteins and Anatomical Therapeutic Chemical Classification System codes. Then, we constructed a predictive model by learning a sparsity-induced classifier based on known drug combinations from the Orange Book and KEGG DRUG databases. Our results show that the proposed method outperforms the previous methods in terms of the accuracy of high-confidence predictions, and the extracted features are biologically meaningful. Finally, we performed a comprehensive prediction of novel drug combinations for 2,639 approved drugs, which predicted 142,988 new potentially beneficial drug-drug pairs. We showed several examples of successfully predicted drug combinations for a variety of diseases.

Systematic drug repositioning for a wide range of diseases with integrative analyses of phenotypic and molecular data.

Drug repositioning, or the application of known drugs to new indications, is a challenging issue in pharmaceutical science. In this study, we developed a new computational method to predict unknown drug indications for systematic drug repositioning in a framework of supervised network inference. We defined a descriptor for each drug-disease pair based on the phenotypic features of drugs (e.g., medicinal effects and side effects) and various molecular features of diseases (e.g., disease-causing genes, diagnostic markers, disease-related pathways, and environmental factors) and constructed a statistical model to predict new drug-disease associations for a wide range of diseases in the International Classification of Diseases. Our results show that the proposed method outperforms previous methods in terms of accuracy and applicability, and its performance does not depend on drug chemical structure similarity. Finally, we performed a comprehensive prediction of a drug-disease association network consisting of 2349 drugs and 858 diseases and described biologically meaningful examples of newly predicted drug indications for several types of cancers and nonhereditary diseases.

Predicting therapeutic and side effects from drug binding affinities to human proteome structures.

Evaluation of the binding affinities of drugs to proteins is a crucial process for identifying drug pharmacological actions, but it requires three dimensional structures of proteins. Herein, we propose novel computational methods to predict the therapeutic indications and side effects of drug candidate compounds from the binding affinities to human protein structures on a proteome-wide scale. Large-scale docking simulations were performed for 7,582 drugs with 19,135 protein structures revealed by AlphaFold (including experimentally unresolved proteins), and machine learning models on the proteome-wide binding affinity score (PBAS) profiles were constructed. We demonstrated the usefulness of the method for predicting the therapeutic indications for 559 diseases and side effects for 285 toxicities. The method enabled to predict drug indications for which the related protein structures had not been experimentally determined and to successfully extract proteins eliciting the side effects. The proposed method will be useful in various applications in drug discovery.

A network-based trans-omics approach for predicting synergistic drug combinations.

BACKGROUND: Combination therapy can offer greater efficacy on medical treatments. However, the discovery of synergistic drug combinations is challenging. We propose a novel computational method, SyndrumNET, to predict synergistic drug combinations by network propagation with trans-omics analyses. METHODS: The prediction is based on the topological relationship, network-based proximity, and transcriptional correlation between diseases and drugs. SyndrumNET was applied to analyzing six diseases including asthma, diabetes, hypertension, colorectal cancer, acute myeloid leukemia (AML), and chronic myeloid leukemia (CML). RESULTS: Here we show that SyndrumNET outperforms the previous methods in terms of high accuracy. We perform in vitro cell survival assays to validate our prediction for CML. Of the top 17 predicted drug pairs, 14 drug pairs successfully exhibits synergistic anticancer effects. Our mode-of-action analysis also reveals that the drug synergy of the top predicted combination of capsaicin and mitoxantrone is due to the complementary regulation of 12 pathways, including the Rap1 signaling pathway. CONCLUSIONS: The proposed method is expected to be useful for discovering synergistic drug combinations for various complex diseases.

Adding drug treatments together can sometimes produce better results for patients. We introduced a new computer-based method called SyndrumNET, designed to identify effective drug combinations for treating diseases. The method uses data about how diseases and drugs interact at a molecular level to predict which drugs work well together. Tested on six different diseases, such as asthma and different types of cancer, SyndrumNET proved to be more accurate than previous approaches. For example, most of the drug combinations predicted by SyndrumNET to rank highly have shown better combination effects on leukemia cells. This method also helped understand why certain drug combinations work better by analyzing their effects on cellular pathways. The findings suggest that SyndrumNET could be a valuable tool in developing more effective treatment for various complex diseases.

A trial of topiramate for patients with hereditary spinocerebellar ataxia.

In an open pilot trial, six patients with various hereditary forms of spinocerebellar ataxia (SCA) were assigned to topiramate (50 mg/day) for 24 weeks. Four patients completed the protocol without adverse events. Of these four patients, topiramate was effective for three patients. Some patients with SCA could respond to treatment with topiramate.

How are exons encoding transmembrane sequences distributed in the exon-intron structure of genes?

The exon-intron structure of eukaryotic genes raises a question about the distribution of transmembrane regions in membrane proteins. Were exons that encode transmembrane regions formed simply by inserting introns into preexisting genes or by some kind of exon shuffling? To answer this question, the exon-per-gene distribution was analyzed for all genes in 40 eukaryotic genomes with a particular focus on exons encoding transmembrane segments. In 21 higher multicellular eukaryotes, the percentage of multi-exon genes (those containing at least one intron) within all genes in a genome was high (>70%) and with a mean of 87%. When genes were grouped by the number of exons per gene in higher eukaryotes, good exponential distributions were obtained not only for all genes but also for the exons encoding transmembrane segments, leading to a constant ratio of membrane proteins independent of the exon-per-gene number. The positional distribution of transmembrane regions in single-pass membrane proteins showed that they are generally located in the amino or carboxyl terminal regions. This nonrandom distribution of transmembrane regions explains the constant ratio of membrane proteins to the exon-per-gene numbers because there are always two terminal (i.e., the amino and carboxyl) regions - independent of the length of sequences.

Prediction of the Health Effects of Food Peptides and Elucidation of the Mode-of-action Using Multi-task Graph Convolutional Neural Network.

Food proteins work not only as nutrients but also modulators for the physiological functions of the human body. The physiological functions of food proteins are basically regulated by peptides encrypted in food protein sequences (food peptides). In this study, we propose a novel deep learning-based method to predict the health effects of food peptides and elucidate the mode-of-action. In the algorithm, we estimate potential target proteins of food peptides using a multi-task graph convolutional neural network, and predict its health effects using information about therapeutic targets for diseases. We constructed predictive models based on 21,103 peptide-protein interactions involving 10,950 peptides and 2,533 proteins, and applied the models to food peptides (e. g., lactotripeptide, isoleucyltyrosine and sardine peptide) defined in food for specified health use. The models suggested potential effects such as blood-pressure lowering effects, blood glucose level lowering effects, and anti-cancer effects for several food peptides. The interactions of food peptides with target proteins were confirmed by docking simulations.

In silico systems for predicting chemical-induced side effects using known and potential chemical protein interactions, enabling mechanism estimation.

In silico models for predicting chemical-induced side effects have become increasingly important for the development of pharmaceuticals and functional food products. However, existing predictive models have difficulty in estimating the mechanisms of side effects in terms of molecular targets or they do not cover the wide range of pharmacological targets. In the present study, we constructed novel in silico models to predict chemical-induced side effects and estimate the underlying mechanisms with high general versatility by integrating the comprehensive prediction of potential chemical-protein interactions (CPIs) with machine learning. First, the potential CPIs were comprehensively estimated by chemometrics based on the known CPI data (1,179,848 interactions involving 3,905 proteins and 824,143 chemicals). Second, the predictive models for 61 side effects in the cardiovascular system (CVS), gastrointestinal system (GIS), and central nervous system (CNS) were constructed by sparsity-induced classifiers based on the known and potential CPI data. The cross validation experiments showed that the proposed CPI-based models had a higher or comparable performance than the traditional chemical structure-based models. Moreover, our enrichment analysis indicated that the highly weighted proteins derived from predictive models could be involved in the corresponding functions of the side effects. For example, in CVS, the carcinogenesis-related pathways (e.g., prostate cancer, PI3K-Akt signal pathway), which were recently reported to be involved in cardiovascular side effects, were enriched. Therefore, our predictive models are biologically valid and would be useful for predicting side effects and novel potential underlying mechanisms of chemical-induced side effects.

Network-based characterization of drug-protein interaction signatures with a space-efficient approach.

BACKGROUND: Characterization of drug-protein interaction networks with biological features has recently become challenging in recent pharmaceutical science toward a better understanding of polypharmacology. RESULTS: We present a novel method for systematic analyses of the underlying features characteristic of drug-protein interaction networks, which we call "drug-protein interaction signatures" from the integration of large-scale heterogeneous data of drugs and proteins. We develop a new efficient algorithm for extracting informative drug-protein interaction signatures from the integration of large-scale heterogeneous data of drugs and proteins, which is made possible by space-efficient representations for fingerprints of drug-protein pairs and sparsity-induced classifiers. CONCLUSIONS: Our method infers a set of drug-protein interaction signatures consisting of the associations between drug chemical substructures, adverse drug reactions, protein domains, biological pathways, and pathway modules. We argue the these signatures are biologically meaningful and useful for predicting unknown drug-protein interactions and are expected to contribute to rational drug design.

Vertebrate genomes code excess proteins with charge periodicity of 28 residues.

All amino acid sequences derived from 248 prokaryotic genomes, 10 invertebrate genomes (plants and fungi) and 10 vertebrate genomes were analysed by the autocorrelation function of charge sequences. The analysis of the total amino acid sequences derived from the 268 biological genomes showed that a significant periodicity of 28 residues is observable for the vertebrate genomes, but not for the other genomes. When proteins with a charge periodicity of 28 residues (PCP28) were selected from the total proteomes, we found that PCP28 in fact exists in all proteomes, but the number of PCP28 is much larger for the vertebrate proteomes than for the other proteomes. Although excess PCP28 in the vertebrate proteomes are only poorly characterized, a detailed inspection of the databases suggests that most excess PCP28 are nuclear proteins.

Biological meaning of "habitable zone" in nucleotide composition space.

Organisms generally display two contrasting properties: large biodiversity and a uniform state of "life". In this study, we focused on the question of how genome sequences describe "life" where a large number of biomolecules are harmonized. We analyzed the whole genome sequence of 2664 organisms, paying attention to the nucleotide composition which is an intensive parameter from the genome sequence. The results showed that all organisms were plotted in narrow regions of the nucleotide composition space of the first and second letters of the codon. Since all genome sequences overlap irrespective of the living environment, it can be called a "habitable zone". The habitable zone deviates by 500 times the standard deviation from the nucleotide composition expected from the random sequence, indicating that unexpectedly rare sequences are realized. Furthermore, we found that the habitable zones at the first and second letters of the codon serve as the background mechanisms for the functional network of biological systems. The habitable zone at the second letter of the codon controls the formation of transmembrane regions and the habitable zone at the first letter controls the formation of molecular recognition unit. These analyses showed that the habitable zone of the nucleotide composition space and the exquisite arrangement of amino acids in the codon table are conjugated to form biological systems. Finally, we discussed the evolution of the higher order of genome sequences.

Predicting inhibitory and activatory drug targets by chemically and genetically perturbed transcriptome signatures.

Genome-wide identification of all target proteins of drug candidate compounds is a challenging issue in drug discovery. Moreover, emerging phenotypic effects, including therapeutic and adverse effects, are heavily dependent on the inhibition or activation of target proteins. Here we propose a novel computational method for predicting inhibitory and activatory targets of drug candidate compounds. Specifically, we integrated chemically-induced and genetically-perturbed gene expression profiles in human cell lines, which avoided dependence on chemical structures of compounds or proteins. Predictive models for individual target proteins were simultaneously constructed by the joint learning algorithm based on transcriptomic changes in global patterns of gene expression profiles following chemical treatments, and following knock-down and over-expression of proteins. This method discriminates between inhibitory and activatory targets and enables accurate identification of therapeutic effects. Herein, we comprehensively predicted drug-target-disease association networks for 1,124 drugs, 829 target proteins, and 365 human diseases, and validated some of these predictions in vitro. The proposed method is expected to facilitate identification of new drug indications and potential adverse effects.

KampoDB, database of predicted targets and functional annotations of natural medicines.

Natural medicines (i.e., herbal medicines, traditional formulas) are useful for treatment of multifactorial and chronic diseases. Here, we present KampoDB ( http://wakanmoview.inm.u-toyama.ac.jp/kampo/ ), a novel platform for the analysis of natural medicines, which provides various useful scientific resources on Japanese traditional formulas Kampo medicines, constituent herbal drugs, constituent compounds, and target proteins of these constituent compounds. Potential target proteins of these constituent compounds were predicted by docking simulations and machine learning methods based on large-scale omics data (e.g., genome, proteome, metabolome, interactome). The current version of KampoDB contains 42 Kampo medicines, 54 crude drugs, 1230 constituent compounds, 460 known target proteins, and 1369 potential target proteins, and has functional annotations for biological pathways and molecular functions. KampoDB is useful for mode-of-action analysis of natural medicines and prediction of new indications for a wide range of diseases.