Genomic Drug Discovery Research Articles

DTI-BERT: Identifying Drug-Target Interactions in Cellular Networking Based on BERT and Deep Learning Method.

Drug–target interactions (DTIs) are regarded as an essential part of genomic drug discovery, and computational prediction of DTIs can accelerate to find the lead drug for the target, which can make up for the lack of time-consuming and expensive wet-lab techniques. Currently, many computational methods predict DTIs based on sequential composition or physicochemical properties of drug and target, but further efforts are needed to improve them. In this article, we proposed a new sequence-based method for accurately identifying DTIs. For target protein, we explore using pre-trained Bidirectional Encoder Representations from Transformers (BERT) to extract sequence features, which can provide unique and valuable pattern information. For drug molecules, Discrete Wavelet Transform (DWT) is employed to generate information from drug molecular fingerprints. Then we concatenate the feature vectors of the DTIs, and input them into a feature extraction module consisting of a batch-norm layer, rectified linear activation layer and linear layer, called BRL block and a Convolutional Neural Networks module to extract DTIs features further. Subsequently, a BRL block is used as the prediction engine. After optimizing the model based on contrastive loss and cross-entropy loss, it gave prediction accuracies of the target families of G Protein-coupled receptors, ion channels, enzymes, and nuclear receptors up to 90.1, 94.7, 94.9, and 89%, which indicated that the proposed method can outperform the existing predictors. To make it as convenient as possible for researchers, the web server for the new predictor is freely accessible at: https://bioinfo.jcu.edu.cn/dtibert or http://121.36.221.79/dtibert/. The proposed method may also be a potential option for other DITs.

Abstract P260: Development and validation of a novel T-cell modulating, microbiome-based peptide for combination with immunotherapy

Abstract The gut microbiome is an important determinant for the success of anti-tumor therapies including chemotherapeutics and anti-checkpoint inhibitors. In this study we wanted to leverage Second Genome’s large and curated microbiome database coupled with its proprietary bioinformatics and machine learning tools to discover bioactive peptides from Bifidobacteria that have the potential to drive response to immunotherapy. The genome of Bifidobacterium (B.) breve and B. longum were analyzed for proteins which were potentially secreted, and had unknown functions. 50 peptides were chemically synthesized and then screened in cell based assays for T cell activation and cytokine secretion. In the present study we describe one such novel B. breve-derived 42-aa peptide (SG-3-0020). The peptide stimulated secretion of effector cytokines by in vitro-cultured T cells (IFNg, TNF-a, IL-10 and IL-2) and increased the expression of PD-1 on both CD4+ and CD8+ T cells when stimulated with low-dose anti-CD3 antibody. To identify the binding partners and mechanism of action of the peptide, Mass Spec and Single-cell RNA-seq was used. Mass spec analysis showed that SG-3-05308, a variant of SG-3-0020 binds to a transmembrane glycoprotein of the immunoglobulin superfamily. Silencing this gene via CRISPR-Cas significantly decreased PD1 levels, cell proliferation and IFNg production in human pan T cells. Single cell RNA-seq data showed that SG-3-0020 activates NF-kB signaling and modulates calcium signaling in T cells. The potency of SG-3-0020 was further optimized for binding to activated T cells by using alanine scanning, saturation, and combinatorial mutation libraries using phage display. The results of protein engineering demonstrated that the 13 amino acids from the C-terminus of SG-3-0020 were not critical for binding and a 29-aa long (lacking the C-term) peptide stimulated increased IFNg production in human T cells across multiple human donors in a dose dependent manner. The peptide with the highest potency SG-3-05429 was selected for further understanding of how it interacts with the identified glycoprotein target and activates downstream T cell signaling pathways . Collectively, these data suggest that SG-3-0020’s ability to up-regulate key co-stimulatory and checkpoint molecules on T cells provides a strong rationale for its potential future use in combination with IO. These results validate the capability of the Second Genome drug discovery platform to identify novel microbial peptides/proteins of potential therapeutic relevance in IO and demonstrate a unique approach that can identify microbial derived bioactive molecules involved in modulating immune cell effector functions and/or immune cell differentiation. Citation Format: Michelle Lin, Archana S. Nagaraja, Dhwani Haria, Yuliya Katlinskaya, Divya Ravichandar, Preston Williams, Roberta Hannibal, Todd Z. DeSantis, Bum-Yeol Hwang, Michi Wilcoxon, Toshi Takeuchi, Karim Dabbagh, Preeti Lal, Helena Kiefel. Development and validation of a novel T-cell modulating, microbiome-based peptide for combination with immunotherapy [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2021 Oct 7-10. Philadelphia (PA): AACR; Mol Cancer Ther 2021;20(12 Suppl):Abstract nr P260.

Atrial Fibrillation Genomics: Discovery and Translation.

Our understanding of the fundamental cellular and molecular factors leading to atrial fibrillation (AF) remains stagnant despite significant advancement in ablation and device technologies. Diagnosis and prevention strategies fall behind that of treatment, but expanding knowledge in AF genetics holds the potential to drive progress. We aim to review how an understanding of the genetic contributions to AF can guide an approach to individualized risk stratification and novel avenues in drug discovery. Rare familial forms of AF identified monogenic contributions to the development of AF. Genome-wide association studies (GWAS) further identified single-nucleotide polymorphisms (SNPs) suggesting polygenic and multiplex nature of this common disease. Polygenic risk scores accounting for the multitude of associated SNPs that each confer mildly elevated risk have been developed to translate genetic information into clinical practice, though shortcomings remain. Additionally, novel laboratory methods have been empowered by recent genetic findings to enhance drug discovery efforts. AF is increasingly recognized as a disease with a significant genetic component. With expanding sequencing technologies and accessibility, polygenic risk scores can help identify high risk individuals. Advancement in digital health tools, artificial intelligence and machine learning based on standard electrocardiograms, and genomic driven drug discovery may be integrated to deliver a sophisticated level of precision medicine in this modern era of emphasis on prevention. Randomized, prospective studies to demonstrate clinical benefits of these available tools are needed to validate this approach.

Incorporating chemical sub-structures and protein evolutionary information for inferring drug-target interactions

Accumulating evidence has shown that drug-target interactions (DTIs) play a crucial role in the process of genomic drug discovery. Although biological experimental technology has made great progress, the identification of DTIs is still very time-consuming and expensive nowadays. Hence it is urgent to develop in silico model as a supplement to the biological experiments to predict the potential DTIs. In this work, a new model is designed to predict DTIs by incorporating chemical sub-structures and protein evolutionary information. Specifically, we first use Position-Specific Scoring Matrix (PSSM) to convert the protein sequence into the numerical descriptor containing biological evolutionary information, then use Discrete Cosine Transform (DCT) algorithm to extract the hidden features and integrate them with the chemical sub-structures descriptor, and finally utilize Rotation Forest (RF) classifier to accurately predict whether there is interaction between the drug and the target protein. In the 5-fold cross-validation (CV) experiment, the average accuracy of the proposed model on the benchmark datasets of Enzymes, Ion Channels, GPCRs and Nuclear Receptors reached 0.9140, 0.8919, 0.8724 and 0.8111, respectively. In order to fully evaluate the performance of the proposed model, we compare it with different feature extraction model, classifier model, and other state-of-the-art models. Furthermore, we also implemented case studies. As a result, 8 of the top 10 drug-target pairs with the highest prediction score were confirmed by related databases. These excellent results indicate that the proposed model has outstanding ability in predicting DTIs and can provide reliable candidates for biological experiments.

Drug-target interaction prediction using Multi Graph Regularized Nuclear Norm Minimization

The identification of potential interactions between drugs and target proteins is crucial in pharmaceutical sciences. The experimental validation of interactions in genomic drug discovery is laborious and expensive; hence, there is a need for efficient and accurate in-silico techniques which can predict potential drug-target interactions to narrow down the search space for experimental verification. In this work, we propose a new framework, namely, Multi-Graph Regularized Nuclear Norm Minimization, which predicts the interactions between drugs and target proteins from three inputs: known drug-target interaction network, similarities over drugs and those over targets. The proposed method focuses on finding a low-rank interaction matrix that is structured by the proximities of drugs and targets encoded by graphs. Previous works on Drug Target Interaction (DTI) prediction have shown that incorporating drug and target similarities helps in learning the data manifold better by preserving the local geometries of the original data. But, there is no clear consensus on which kind and what combination of similarities would best assist the prediction task. Hence, we propose to use various multiple drug-drug similarities and target-target similarities as multiple graph Laplacian (over drugs/targets) regularization terms to capture the proximities exhaustively. Extensive cross-validation experiments on four benchmark datasets using standard evaluation metrics (AUPR and AUC) show that the proposed algorithm improves the predictive performance and outperforms recent state-of-the-art computational methods by a large margin. Software is publicly available at https://github.com/aanchalMongia/MGRNNMforDTI.

Abstract 576: A novel intestinal microbiome-derived peptide modulates host T cell activation

Abstract The microbiome shapes the metabolic and immunological landscape of individuals in health and disease and represents a new robust source of bioactive molecules with therapeutic potential. Second Genome’s large and curated microbiome database coupled with its proprietary bioinformatics and machine learning pipeline enables the discovery of novel microbiome-derived drug candidates across multiple disease areas, including immuno-oncology (IO). Microbial genus Bifidobacterium previously showed a positive association with antitumor T cell responses in mouse models and was overrepresented in melanoma patients responding to immunotherapy with anti-PD-1 antibody. Our hypothesis is that bioactive molecules derived from these Bifidobacteria help drive these antitumor responses and could function as cancer therapeutics or adjuvants for cancer immunotherapy. Thus, we nominated protein and peptide candidates with high scores of secretability, uniqueness, stability, and expressability from Bifidobacterium breve and Bifidobacterium longum genomes for screening in cell-based assays. Initial evaluation of more than 50 Bifidobacterium peptides revealed candidates capable of inducing immune activation. Here, we describe a B. breve-derived peptide (termed SG-B) that induced up-regulation of co-stimulatory (OX40 and ICOS) and inhibitory (PD-1) molecules on CD4+ and CD8+ T cells in both: i) a purified human pan-T cell system; and ii) PBMC cultures stimulated with low-dose anti-CD3 antibody. Furthermore, SG-B stimulated secretion of effector cytokines by in vitro-cultured purified T cells (TNF-a, IL-2, IFN-g, and IL-10) and PBMCs (IL-1b, IL-6, and IL-8). These effects were dose-dependent and evident across multiple human blood donors. In the CT26 syngeneic tumor model, peri-tumoral administration of SG-B induced an increase in the proportion of NK cells and CD4+ T cells within the tumor. The peptide also induced up-regulation of activation marker on CD8+ T cells (CD25, Ki67, and OX40). Collectively, these data suggest that SG-B can modulate adaptive immunity via T cell potentiation and may enhance tumor inflammation in vivo. The peptide’s ability to up-regulate key co-stimulatory and checkpoint molecules on T cells provides a strong rationale for its potential future use in combination with approved or IO agents in current development. These results validate the capability of the Second Genome drug discovery platform to identify novel microbial agents of potential therapeutic relevance in IO and demonstrate a unique approach that can identify microbial factors involved in modulating immune cell effector functions and/or immune cell differentiation. Citation Format: Yuliya V. Katlinskaya, Dhwani Haria, Lily McLaughlin, Sunit Jain, Shoko Iwai, Todd DeSantis, Thomas Weinmaier, Toshi Takeuchi, Anu Hoey, Karim Dabbagh, Kareem Graham, Helena Kiefel. A novel intestinal microbiome-derived peptide modulates host T cell activation [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 576.

Next generation zebrafish-based drug discovery and precision medicine

Even after entering the era of genomic drug discovery in the 21st century, development of a breakthrough therapeutic drug (first-in-class) for intractable diseases (unmet medical needs) has been extremely difficult, but to the US FDA 62% of the approved first-in-class drugs are found by phenotypic screening. The next-generation zebrafish drug discovery enables high-throughput quantitative live in vivo phenotypic screening, and has been impacting global drug discovery strategies now. Compared to severe immunodeficient mice, zebrafish is expected to become a true individualized medical tool as a clinical ex vivo diagnostic system because of the high efficiency and speed of engraftment of patient-derived cancer xenotransplantation. Phenomics-based personalized medicine with the patient-derived cancer xenograft zebrafish in addition to conventional omics platform of individualized medicine is a true next-generation precision medicine to utilize for selection of therapeutic drugs and decision of their doses for the patient, and emerging paradigm shift is realizing in this century.

High-throughput binding affinity calculations at extreme scales

BackgroundResistance to chemotherapy and molecularly targeted therapies is a major factor in limiting the effectiveness of cancer treatment. In many cases, resistance can be linked to genetic changes in target proteins, either pre-existing or evolutionarily selected during treatment. Key to overcoming this challenge is an understanding of the molecular determinants of drug binding. Using multi-stage pipelines of molecular simulations we can gain insights into the binding free energy and the residence time of a ligand, which can inform both stratified and personal treatment regimes and drug development. To support the scalable, adaptive and automated calculation of the binding free energy on high-performance computing resources, we introduce the High-throughput Binding Affinity Calculator (HTBAC). HTBAC uses a building block approach in order to attain both workflow flexibility and performance.ResultsWe demonstrate close to perfect weak scaling to hundreds of concurrent multi-stage binding affinity calculation pipelines. This permits a rapid time-to-solution that is essentially invariant of the calculation protocol, size of candidate ligands and number of ensemble simulations.ConclusionsAs such, HTBAC advances the state of the art of binding affinity calculations and protocols. HTBAC provides the platform to enable scientists to study a wide range of cancer drugs and candidate ligands in order to support personalized clinical decision making based on genome sequencing and drug discovery.

Abstract B15: Comprehensive cistromic characterization of SOX10, a lineage-specific genetic dependency in melanoma, via the integration of functional genomic approaches

Abstract Melanoma accounts for less than 5% of skin cancers, but a majority of skin cancer deaths. Genomic studies and drug discovery programs have led to the development of targeted therapies, including BRAF and MEK inhibitors, but the inevitable resistance to these targeted agents necessitates a greater understanding of melanoma biology and genetics. By integrating genome-wide functional genomic and transcriptomic data, we have identified SOX10 as a lineage-specific genetic dependency in melanoma independent of BRAF or NRAS mutation status. Subsequent analyses indicate that SOX10 dependency is highly correlated with SOX10 gene expression and functional in vitro experiments have confirmed the role of SOX10 in the proliferation and growth of melanoma cell lines regardless of their sensitivity to MAPK pathway inhibitors. Chromatin immunoprecipitation of endogenous SOX10 followed by sequencing (SOX10 ChIP-seq) combined with transcriptomic profiling (RNA-seq) following SOX10 depletion by shRNA in a panel of melanoma cell lines determined the SOX10 cistrome, the set of SOX10 target genes across the melanoma genome. Integration of the SOX10 cistrome with genetic dependency data may lead to novel therapeutic approaches in melanoma. Citation Format: Terence Wong, Cory Johannessen, Jingyu Fan, Henry Long, Shirley Liu, Levi Garraway. Comprehensive cistromic characterization of SOX10, a lineage-specific genetic dependency in melanoma, via the integration of functional genomic approaches [abstract]. In: Proceedings of the AACR Precision Medicine Series: Opportunities and Challenges of Exploiting Synthetic Lethality in Cancer; Jan 4-7, 2017; San Diego, CA. Philadelphia (PA): AACR; Mol Cancer Ther 2017;16(10 Suppl):Abstract nr B15.

Benchmarking a Wide Range of Chemical Descriptors for Drug-Target Interaction Prediction Using a Chemogenomic Approach.

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.

From the era of genome analysis to the era of genomic drug discovery: a pioneering example of rheumatoid arthritis.

Although we have obtained comprehensive catalogs of genetic risk loci that are linked to human diseases, little is known regarding how to devise a systematic strategy to integrate genetic study results with diverse biological resources. Such strategies will be crucial for providing novel insights into disease biology and for aiding drug discovery as an ultimate goal. Here we describe the current progress in this field using a pioneering example of large-scale genetic association studies on rheumatoid arthritis (RA), an autoimmune disease characterized by inflammation and destruction of joints. Through functional and bioinformatic annotations of risk single nucleotide polymorphisms (SNPs) and genes from >100 RA risk loci identified by genome-wide association study (GWAS) meta-analysis, we found novel biological insights into RA pathogenicity. Further, by integrating RA genetic findings with the complete catalog of approved drugs for RA and other diseases, we provide empirical data to indicate that human genetic-based approaches may be useful for supporting 'genetics-driven genomic drug discovery' efforts in complex human traits and suggest that further development of integrative approaches should be undertaken.

Drug-target and disease networks: polypharmacology in the post-genomic era.

With the growing understanding of complex diseases, the focus of drug discovery has shifted away from the well-accepted “one target, one drug” model, to a new “multi-target, multi-drug” model, aimed at systemically modulating multiple targets. Identification of the interaction between drugs and target proteins plays an important role in genomic drug discovery, in order to discover new drugs or novel targets for existing drugs. Due to the laborious and costly experimental process of drug-target interaction prediction, in silico prediction could be an efficient way of providing useful information in supporting experimental interaction data. An important notion that has emerged in post-genomic drug discovery is that the large-scale integration of genomic, proteomic, signaling and metabolomic data can allow us to construct complex networks of the cell that would provide us with a new framework for understanding the molecular basis of physiological or pathophysiological states. An emerging paradigm of polypharmacology in the post-genomic era is that drug, target and disease spaces can be correlated to study the effect of drugs on different spaces and their interrelationships can be exploited for designing drugs or cocktails which can effectively target one or more disease states. The future goal, therefore, is to create a computational platform that integrates genome-scale metabolic pathway, protein–protein interaction networks, gene transcriptional analysis in order to build a comprehensive network for multi-target multi-drug discovery.

Genome-Scale Screening of Drug-Target Associations Relevant to Ki Using a Chemogenomics Approach

The identification of interactions between drugs and target proteins plays a key role in genomic drug discovery. In the present study, the quantitative binding affinities of drug-target pairs are differentiated as a measurement to define whether a drug interacts with a protein or not, and then a chemogenomics framework using an unbiased set of general integrated features and random forest (RF) is employed to construct a predictive model which can accurately classify drug-target pairs. The predictability of the model is further investigated and validated by several independent validation sets. The built model is used to predict drug-target associations, some of which were confirmed by comparing experimental data from public biological resources. A drug-target interaction network with high confidence drug-target pairs was also reconstructed. This network provides further insight for the action of drugs and targets. Finally, a web-based server called PreDPI-Ki was developed to predict drug-target interactions for drug discovery. In addition to providing a high-confidence list of drug-target associations for subsequent experimental investigation guidance, these results also contribute to the understanding of drug-target interactions. We can also see that quantitative information of drug-target associations could greatly promote the development of more accurate models. The PreDPI-Ki server is freely available via: http://sdd.whu.edu.cn/dpiki.

Regulatory Authorities Expect Innovative Drug Delivery Systems (DDS)

The Japanese Ministry of Health, Labour and Welfare (MHLW) and the Pharmaceuticals and Medical Devices Agency (PMDA) are responsible for appropriately implementing regulations and providing necessary instructions and advice so that patients have access to safer, more effective drugs. These responsibilities are essential missions of the MHLW/PMDA, although restrictions on drug use or development might be considered to be purely regulatory matters. In the genomic drug discovery era of the 21st century, it is expected that new, innovative drugs will be developed, although the reality can be slightly disturbing. The number of approvals of new molecular entities (NMEs) is only approximately 20 per year both in Japan and the USA and may reach an even lower level. In light of current drug development trends, drug delivery systems (DDS) for targeted therapy or personalized medicines as well as NMEs should be explored more proactively. To promote the development and evaluation of innovative DDS, the MHLW/PMDA considers it important to communicate smoothly among industry-government-academia from the very early stage of development. To promote this, the MHLW/PMDA launched regulatory affairs consultations on R&D strategy for drugs in July 2011. Innovative DDS require not only cutting-edge technology or materials but also extensions of existing pharmaceutical technology. It is most important for innovative DDS to benefit patients in practical clinical settings. The MHLW/PMDA encourages the relevant parties to develop a far-sighted strategy with this goal in mind.

Large-scale prediction of drug–target interactions using protein sequences and drug topological structures

The identification of interactions between drugs and target proteins plays a key role in the process of genomic drug discovery. It is both consuming and costly to determine drug–target interactions by experiments alone. Therefore, there is an urgent need to develop new in silico prediction approaches capable of identifying these potential drug–target interactions in a timely manner. In this article, we aim at extending current structure–activity relationship (SAR) methodology to fulfill such requirements. In some sense, a drug–target interaction can be regarded as an event or property triggered by many influence factors from drugs and target proteins. Thus, each interaction pair can be represented theoretically by using these factors which are based on the structural and physicochemical properties simultaneously from drugs and proteins. To realize this, drug molecules are encoded with MACCS substructure fingerings representing existence of certain functional groups or fragments; and proteins are encoded with some biochemical and physicochemical properties. Four classes of drug–target interaction networks in humans involving enzymes, ion channels, G-protein-coupled receptors (GPCRs) and nuclear receptors, are independently used for establishing predictive models with support vector machines (SVMs). The SVM models gave prediction accuracy of 90.31%, 88.91%, 84.68% and 83.74% for four datasets, respectively. In conclusion, the results demonstrate the ability of our proposed method to predict the drug–target interactions, and show a general compatibility between the new scheme and current SAR methodology. They open the way to a host of new investigations on the diversity analysis and prediction of drug–target interactions.

Using Compound Similarity and Functional Domain Composition for Prediction of Drug-Target Interaction Networks

Study of interactions between drugs and target proteins is an essential step in genomic drug discovery. It is very hard to determine the compound-protein interactions or drug-target interactions by experiment alone. As supplementary, effective prediction model using machine learning or data mining methods can provide much help. In this study, a prediction method based on Nearest Neighbor Algorithm and a novel metric, which was obtained by combining compound similarity and functional domain composition, was proposed. The target proteins were divided into the following groups: enzymes, ion channels, G protein-coupled receptors, and nuclear receptors. As a result, four predictors with the optimal parameters were established. The overall prediction accuracies, evaluated by jackknife cross-validation test, for four groups of target proteins are 90.23%, 94.74%, 97.80%, and 97.51%, respectively, indicating that compound similarity and functional domain composition are very effective to predict drug-target interaction networks.

Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework

Motivation: In silico prediction of drug–target interactions from heterogeneous biological data is critical in the search for drugs and therapeutic targets for known diseases such as cancers. There is therefore a strong incentive to develop new methods capable of detecting these potential drug–target interactions efficiently.Results: In this article, we investigate the relationship between the chemical space, the pharmacological space and the topology of drug–target interaction networks, and show that drug–target interactions are more correlated with pharmacological effect similarity than with chemical structure similarity. We then develop a new method to predict unknown drug–target interactions from chemical, genomic and pharmacological data on a large scale. The proposed method consists of two steps: (i) prediction of pharmacological effects from chemical structures of given compounds and (ii) inference of unknown drug–target interactions based on the pharmacological effect similarity in the framework of supervised bipartite graph inference. The originality of the proposed method lies in the prediction of potential pharmacological similarity for any drug candidate compounds and in the integration of chemical, genomic and pharmacological data in a unified framework. In the results, we make predictions for four classes of important drug–target interactions involving enzymes, ion channels, GPCRs and nuclear receptors. Our comprehensively predicted drug–target interaction networks enable us to suggest many potential drug–target interactions and to increase research productivity toward genomic drug discovery.Supplementary information: Datasets and all prediction results are available at http://cbio.ensmp.fr/~yyamanishi/pharmaco/.Availability: Softwares are available upon request.Contact: yoshihiro.yamanishi@ensmp.fr

Prediction of drug–target interaction networks from the integration of chemical and genomic spaces

Motivation: The identification of interactions between drugs and target proteins is a key area in genomic drug discovery. Therefore, there is a strong incentive to develop new methods capable of detecting these potential drug–target interactions efficiently.Results: In this article, we characterize four classes of drug–target interaction networks in humans involving enzymes, ion channels, G-protein-coupled receptors (GPCRs) and nuclear receptors, and reveal significant correlations between drug structure similarity, target sequence similarity and the drug–target interaction network topology. We then develop new statistical methods to predict unknown drug–target interaction networks from chemical structure and genomic sequence information simultaneously on a large scale. The originality of the proposed method lies in the formalization of the drug–target interaction inference as a supervised learning problem for a bipartite graph, the lack of need for 3D structure information of the target proteins, and in the integration of chemical and genomic spaces into a unified space that we call ‘pharmacological space’. In the results, we demonstrate the usefulness of our proposed method for the prediction of the four classes of drug–target interaction networks. Our comprehensively predicted drug–target interaction networks enable us to suggest many potential drug–target interactions and to increase research productivity toward genomic drug discovery.Availability: Softwares are available upon request.Contact: Yoshihiro.Yamanishi@ensmp.frSupplementary information: Datasets and all prediction results are available at http://web.kuicr.kyoto-u.ac.jp/supp/yoshi/drugtarget/.

Construction of 2D and 3D biomolecules structures using fs lasers

Bio-microarray fabrication has been the focus of much research in recent years, as they are envisaged to play an important part in genomic studies, protein identification, drug discovery and screening. We demonstrate both 2D and 3D biomolecules patterning using techniques which enable the construction of arbitrary two and three-dimensional shapes, not restricted to array-based shapes. For the 2D printing, Laser-Induced Forward Transfer (LIFT) is employed to deposit controlled and viable micropatterns of the Horseradish Peroxidase (HRP) and the functionality of the transferred materials is shown. For the 3D printing, firstly microstructures are made employing multiphoton polymerisation and then biotin is subsequently immobilised on the surface of the structures by excimer laser photo-activation of photobiotin and further exposed to fluorescently labelled streptavidin. The specificity of the binding is demonstrated.

Hyperbaric exposure with high oxygen concentration inhibits growth‐associated increase in the glucose level of diabetic Goto‐Kakizaki rats

Diabetes, Obesity and MetabolismVolume 8, Issue 6 p. 714-715 Hyperbaric exposure with high oxygen concentration inhibits growth-associated increase in the glucose level of diabetic Goto-Kakizaki rats K. Yasuda, K. Yasuda Department of Endocrinology, Metabolism and DiabetesMellitus, Kinki University School of Medicine, Osaka-Sayama, JapanSearch for more papers by this authorN. Aoki, N. Aoki Department of Endocrinology, Metabolism and DiabetesMellitus, Kinki University School of Medicine, Osaka-Sayama, JapanSearch for more papers by this authorT. Adachi, T. Adachi Department of Genomic Drug Discovery Science, Graduate School of Pharmaceutical Sciences,Kyoto University,Kyoto, JapanSearch for more papers by this authorG. Tsujimoto, G. Tsujimoto Department of Genomic Drug Discovery Science, Graduate School of Pharmaceutical Sciences,Kyoto University,Kyoto, JapanSearch for more papers by this authorN. Gu, N. Gu Laboratory of Metabolism, Graduate School ofHuman and Environmental Studies, KyotoUniversity, Kyoto, JapanSearch for more papers by this authorT. Matsunaga, T. Matsunaga Laboratory of Metabolism, Graduate School ofHuman and Environmental Studies, KyotoUniversity, Kyoto, JapanSearch for more papers by this authorN. Kikuchi, N. Kikuchi Laboratory of Metabolism, Graduate School ofHuman and Environmental Studies, KyotoUniversity, Kyoto, JapanSearch for more papers by this authorK. Tsuda, K. Tsuda Laboratory of Metabolism, Graduate School ofHuman and Environmental Studies, KyotoUniversity, Kyoto, JapanSearch for more papers by this authorA. Ishihara, A. Ishihara Laboratory of Neurochemistry, GraduateSchool of Human and EnvironmentalStudies, Kyoto University, Kyoto, JapanSearch for more papers by this author K. Yasuda, K. Yasuda Department of Endocrinology, Metabolism and DiabetesMellitus, Kinki University School of Medicine, Osaka-Sayama, JapanSearch for more papers by this authorN. Aoki, N. Aoki Department of Endocrinology, Metabolism and DiabetesMellitus, Kinki University School of Medicine, Osaka-Sayama, JapanSearch for more papers by this authorT. Adachi, T. Adachi Department of Genomic Drug Discovery Science, Graduate School of Pharmaceutical Sciences,Kyoto University,Kyoto, JapanSearch for more papers by this authorG. Tsujimoto, G. Tsujimoto Department of Genomic Drug Discovery Science, Graduate School of Pharmaceutical Sciences,Kyoto University,Kyoto, JapanSearch for more papers by this authorN. Gu, N. Gu Laboratory of Metabolism, Graduate School ofHuman and Environmental Studies, KyotoUniversity, Kyoto, JapanSearch for more papers by this authorT. Matsunaga, T. Matsunaga Laboratory of Metabolism, Graduate School ofHuman and Environmental Studies, KyotoUniversity, Kyoto, JapanSearch for more papers by this authorN. Kikuchi, N. Kikuchi Laboratory of Metabolism, Graduate School ofHuman and Environmental Studies, KyotoUniversity, Kyoto, JapanSearch for more papers by this authorK. Tsuda, K. Tsuda Laboratory of Metabolism, Graduate School ofHuman and Environmental Studies, KyotoUniversity, Kyoto, JapanSearch for more papers by this authorA. Ishihara, A. Ishihara Laboratory of Neurochemistry, GraduateSchool of Human and EnvironmentalStudies, Kyoto University, Kyoto, JapanSearch for more papers by this author First published: 04 October 2006 https://doi.org/10.1111/j.1463-1326.2005.00555.xCitations: 20Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article.Citing Literature Volume8, Issue6November 2006Pages 714-715 RelatedInformation