Language：English   Publishing type：Research paper (scientific journal)  

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.

DOI： 10.1093/bioinformatics/btz313

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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.

DOI： 10.1186/s12918-019-0691-1

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Language：English   Publishing type：Research paper (scientific journal)  

Developing drugs with anticancer activity and low toxic side-effects at low costs is a challenging issue for cancer chemotherapy. In this work, we propose to use molecular pathways as the therapeutic targets and develop a novel computational approach for drug repositioning for cancer treatment. We analyzed chemically induced gene expression data of 1112 drugs on 66 human cell lines and searched for drugs that inactivate pathways involved in the growth of cancer cells (cell cycle) and activate pathways that contribute to the death of cancer cells (e.g., apoptosis and p53 signaling). Finally, we performed a large-scale prediction of potential anticancer effects for all the drugs and experimentally validated the prediction results via three in vitro cellular assays that evaluate cell viability, cytotoxicity, and apoptosis induction. Using this strategy, we successfully identified several potential anticancer drugs. The proposed pathway-based method has great potential to improve drug repositioning research for cancer treatment.

DOI： 10.1021/acs.jmedchem.8b01044

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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.

DOI： 10.1038/s41598-018-29516-1

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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.

DOI： 10.1038/s41598-017-18315-9

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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.

DOI： 10.2142/biophysico.15.0_75

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Language：English   Publishing type：Research paper (scientific journal)  

The identification of the modes of action of bioactive compounds is a major challenge in chemical systems biology of diseases. Genome-wide expression profiling of transcriptional responses to compound treatment for human cell lines is a promising unbiased approach for the mode-of-action analysis. Here we developed a novel approach to elucidate the modes of action of bioactive compounds in a cell-specific manner using large-scale chemically-induced transcriptome data acquired from the Library of Integrated Network-based Cellular Signatures (LINCS), and analyzed 16,268 compounds and 68 human cell lines. First, we performed pathway enrichment analyses of regulated genes to reveal active pathways among 163 biological pathways. Next, we explored potential target proteins (including primary targets and off-targets) with cell-specific transcriptional similarity using chemical-protein interactome. Finally, we predicted new therapeutic indications for 461 diseases based on the target proteins. We showed the usefulness of the proposed approach in terms of prediction coverage, interpretation, and large-scale applicability, and validated the new prediction results experimentally by an in vitro cellular assay. The approach has a high potential for advancing drug discovery and repositioning.

DOI： 10.1038/srep40164

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"Life" is a particular state of matter, and matter is composed of various molecules. The state corresponding to "life" is ultimately determined by the genome sequence, and this sequence determines the conditions necessary for survival of the organism. In order to elucidate one parameter characterizing the state of "life", we analyzed the amino acid sequences encoded in the total genomes of 557 prokaryotes and 40 eukaryotes using a membrane protein prediction online tool called SOSUI. SOSUI uses only the physical parameters of the encoded amino acid sequences to make its predictions. The ratio of membrane proteins in a genome predicted by the SOSUI online tool was around 23% for all genomes, indicating that this parameter is controlled by some mechanism in cells. In order to identify the property of genome DNA sequences that is the possible cause of the constant ratio of membrane proteins, we analyzed the nucleotide compositions at codon positions and observed the existence of systematic biases distinct from those expected based on random distribution. We hypothesize that the constant ratio of membrane proteins is the result of random mutations restricted by the systematic biases inherent to nucleotide codon composition. A new approach to the biological sciences based on the holistic analysis of whole genomes is discussed in order to elucidate the principles underlying "life" at the biological system level.

DOI： 10.2142/biophysico.13.0_305

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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.

DOI： 10.1021/acs.jcim.5b00444

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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.

DOI： 10.1021/acs.jcim.5b00330

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BACKGROUND: Phenotype-based high-throughput screening is a useful technique for identifying drug candidate compounds that have a desired phenotype. However, the molecular mechanisms of the hit compounds remain unknown, and substantial effort is required to identify the target proteins associated with the phenotype. METHODS: In this study, we propose a new method to predict target proteins of drug candidate compounds based on drug-induced gene expression data in Connectivity Map and a machine learning classification technique, which we call the "transcriptomic approach." RESULTS: Unlike existing methods such as the chemogenomic approach, the transcriptomic approach enabled the prediction of target proteins without dependence on prior knowledge of compound chemical structures. The prediction accuracy of the chemogenomic approach was highly depended on compounds structure similarities in data sets. In contrast, the prediction accuracy of the transcriptomic approach was maintained at a sufficient level, even for benchmark data consisting of structurally diverse compounds. CONCLUSIONS: The transcriptomic approach reported here is expected to be a useful tool for structure-independent prediction of target proteins for drug candidate compounds.

DOI： 10.1186/s12920-015-0158-1

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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.

DOI： 10.1021/ci500670q

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The identification of drug-target interactions, or interactions between drug candidate compounds and target candidate proteins, is a crucial process in genomic drug discovery. In silico chemogenomic methods are recently recognized as a promising approach for genome-wide scale prediction of drug-target interactions, but the prediction performance depends heavily on the descriptors and similarity measures of drugs and proteins. In this paper, we investigated the performance of various descriptors and similarity measures of drugs and proteins for the drug-target interaction prediction using a chemogenomic approach. We compared the prediction accuracy of 18 chemical descriptors of drugs (e.g., ECFP, FCFP,E-state, CDK, KlekotaRoth, MACCS, PubChem, Dragon, KCF-S, and graph kernels) and 4 descriptors of proteins (e.g., amino acid composition, domain profile, local sequence similarity, and string kernel) on about one hundred thousand drug-target interactions. We examined the combinatorial effects of drug descriptors and protein descriptors using the same benchmark data under several experimental conditions. Large-scale experiments showed that our proposed KCF-S descriptor worked the best in terms of prediction accuracy. The comparative results are expected to be useful for selecting chemical descriptors in various pharmaceutical applications.

DOI： 10.1002/minf.201400066

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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.

DOI： 10.1093/nar/gku337

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Membrane spanning regions can be used as markers for studying the robustness of biologically important units of proteins against evolutionary change (R. Sawada and S. Mitaku, Genes to Cells, 2010). We carried out computational experiments of extensive DNA mutations on the assumption of constant GC content or constant codon positional nucleotide biases. Randomized sequences were evaluated by membrane protein prediction systems SOSUI and SOSUIsignal. When all amino acid sequences from the total real genomes of 538 prokaryotes were analysed, ratios of membrane proteins to all genes in the total genomes were almost constant around a ratio of 22% with a standard deviation of 1.56. When the nucleotide sequences were randomized, keeping only the GC contents constant, the ratios of membrane proteins became highly diverse with a standard deviation of 10.1. When the codon positional nucleotide biases were taken into account; however, the diverse ratios of membrane proteins converged to a value of ~25% with a standard deviation of 3.55. These results suggest that codon compositional biases play an important role in the evolution of prokaryotes for maintaining a constant ratio of membrane proteins. Further detailed analysis suggested that non-uniform nucleotide compositional biases at the terminal regions are the reason for the small but significant deviation.

DOI： 10.1093/jb/mvr132

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Language：English   Publishing type：Research paper (scientific journal)  

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.

DOI： 10.1111/j.1365-2443.2010.01468.x

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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.

DOI： 10.1093/jb/mvn017

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Chem-Bio Informatics Society  

More than 36,000 open reading frames (ORFs) from the human genome were previously analyzed by the autocorrelation function of electric charge distribution, revealing the existence of many proteins with a charge periodicity of 28 residues (PCP28) (Ke et al., Jpn. J. Appl. Phys. 2007). The major component of PCP28 was located in the nucleus, and the nuclear PCP28 of ten vertebrate and seven invertebrate organisms were predicted with a novel software system (Sakiyama et al., CBI Journal 2007) for revealing the biological significance of nuclear PCP28. Retrieval of the features of the human nuclear PCP28 in Swiss-Prot revealed that almost 90% of nuclear PCP28 functions in transcriptional regulation, including hypothetical transcription factors. To study how nuclear PCP28 is increased in eukaryote genomes, we compared the number of all nuclear PCP28 in vertebrate and invertebrate genomes. The results showed that nuclear PCP28 is specifically increased in vertebrate genomes and that the ratio of other types of PCP28 is almost constant in all eukaryote genomes. These findings strongly suggest that nuclear PCP28 is an essential protein for vertebrate organisms. Copyright 2007 Chem-Bio Informatics Society.

DOI： 10.1273/cbij.7.69

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The numbers of membrane proteins in the current genomes of various organisms provide an important clue about how the protein world has evolved from the aspect of membrane proteins. Numbers of membrane proteins were estimated by analyzing the total proteomes of 248 prokaryota, using the SOSUI system for membrane proteins (Hirokawa et al., Bioinformatics, 1998) and SOSUI-signal for signal peptides (Gomi et al., CBIJ, 2004). The results showed that the ratio of membrane proteins to total proteins in these proteomes was almost constant: 0.228. When amino acid sequences were randomized, setting the probability of occurrence of all amino acids to 5%, the membrane protein/total protein ratio decreased to about 0.085. However, when the same simulation was carried out, but using the amino acid composition of the above proteomes, this ratio was 0.218, which is nearly the same as that of the real proteomic systems. This fact is consistent with the birth, death and innovation (BDI) model for membrane proteins, in which transmembrane segments emerge and disappear in accordance with random mutation events.

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Chem-Bio Informatics Society  

Secretory proteins function as agents for numerous cell-cell interactions and determine the survival strategies adopted by organisms. Using the SOSUI system for membrane proteins (Hirokawa et al., Bioinformatics, 1998) and SOSUIsignal for signal peptides (Gomi et al., CBIJ, 2004), we undertook predictive analyses of secretory proteins from 248 prokaryota using all of the amino acid sequences coded by their respective genomes. The number of secretory proteins exhibited a strong positive correlation with the number of total open reading frames, with analysis of these correlations revealing that prokaryotic organisms could be placed into several groups. Symbiotic or obligate parasitic organisms in eukaryotic cells with less than 1200 open reading frames exhibited a single linear relationship between the number of secretory proteins and the total number of open reading frames. Conversely, free-living organisms with more than 2500 open reading frames could be grouped into three linear relationships. The intercept with the axis of the number of open reading frames in the linear relationships was approximately 300 genes for the survival of symbiotic or obligate parasitic organisms and approximately 700 for the free-living organisms. The factor responsible for distinguishing between the different categories of organisms appeared to be G+C content and the number of open reading frames. The roles of secretory proteins and membrane proteins were discussed on the basis of the ratios of those proteins. The list of all predicted secretory proteins for 248 prokaryota is available through the internet at the URL: http://bp.nuap.nagoya-u.ac.jp/ sosui/sosuisignal/SOSUIsignalDB/. Copyright 2005 Chem-Bio Informatics Society.

DOI： 10.1273/cbij.5.56

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Language：Japanese   Publisher：日本計算機統計学会  

DOI： 10.20551/jscstaikai.32.0_76

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Language：English  

Proteins with a charge periodicity of 28 residues (PCP28) were found recently in the human proteome, and many of the annotated PCP28 were located in the nucleus (Ke et al., Jpn. J. Appl. Phys. 2007). The physical properties of the amino acid sequences were analyzed to detect the difference in the physicochemistry between the nuclear and cytoplasmic PCP28 and develop a software system to classify the two types of PCP28. A significant difference in the global parameters from the entire sequence and the local parameters around a segment with the highest positive charge density was found between the nuclear and cytoplasmic PCP28. The global classification score included the densities of proline and cysteine, and the negative charge density, while the local score included the symmetry.of the charge distribution, the density of cysteine, and the positive charge density. A prediction system was developed using the global and local scores, which possessed a sensitivity and specificity of 92% and 88%, respectively. The mechanism of translocation of proteins to the nucleus is discussed using the parameters relevant to the predictive system. Copyright 2007 Chem-Bio Informatics Society.

DOI： 10.1273/cbij.7.35

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Language：English   Publisher：JAPAN SOC APPLIED PHYSICS  

The human genome includes more than 36,000 open reading frames that are translated to amino acid sequences of proteins. When the charge distribution in amino acid sequences from the total human genome was analyzed by the autocorrelation function, a surprisingly sharp periodicity of 28 residues was observed. Every protein with the charge periodicity of 28 residues (PCP28) could be discriminated by a simple algorithm, and the number of PCP28 amounted to about 3% of the total open reading frames of the human genome. The net charge of most PCP28 was highly positive. The possible structural and functional features of this type of protein were discussed in terms of the electric repulsion within molecules.

DOI： 10.1143/JJAP.46.6083

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