Abstract: Systems biology is a data-heavy field that focuses on systems-wide depictions of biological phenomena necessarily sacrificing a detailed characterization of individual components. As an example, genome-wide protein interaction networks are widely used in systems biology and are continuously extended and refined as new sources of evidence become available. Despite the vast amount of information about individual protein structures and protein complexes that has accumulated in the past fifty years in the Protein Data Bank (PDB), the data, computational tools and language of structural biology have not become an integral part of systems biology. Increasing effort has been devoted to this integration and the related literature is reviewed here. Relationships between proteins that are detected via structural similarity offer a rich source of information not available from sequence similarity, and homology modeling can be used to leverage PDB structures to produce 3D models for a significant fraction of many proteomes. A number of structure-informed genomic and cross-species (i.e. virus-host) interactomes will be described and the unique information they provide will be illustrated with a number of examples. Tissue- and tumor-specific interactomes have also been developed through computational strategies that exploit patient information and through genetic interactions available from increasingly sensitive screens. Strategies to integrate structural information with these alternate data sources will be described. Finally, efforts to link protein structure space with chemical compound space offer novel sources of information in drug design, off-target identification and the identification of targets for compounds found to be effective in phenotypic screens.

Abstract: Compared to traditional experimental approaches, computational modeling is a promising strategy to efficiently prioritize new candidates with low cost. In this study, we developed a novel data mining and computational modeling workflow proven to be applicable by screening new analgesic opioids. To this end, a large opioid data set was used as the probe to automatically obtain bioassay data from the PubChem portal. There were 114 PubChem bioassays selected to build quantitative structure-activity relationship (QSAR) models based on the testing results across the probe compounds. The compounds tested in each bioassay were used to develop 12 models using the combination of three machine learning approaches and four types of chemical descriptors. The model performance was evaluated by the coefficient of determination (R (2)) obtained from 5-fold cross-validation. In total, 49 models developed for 14 bioassays were selected based on the criteria and were identified to be mainly associated with binding affinities to different opioid receptors. The models for these 14 bioassays were further used to fill data gaps in the probe opioids data set and to predict general drug compounds in the DrugBank data set. This study provides a universal modeling strategy that can take advantage of large public data sets for computer-aided drug design (CADD).

Abstract: TRPC6 (transient receptor potential cation channels; canonical subfamily C, member 6) is widespread localized in mammalian tissues like kidney and lung and associated with progressive proteinuria and pathophysiological pulmonary alterations, e.g., reperfusion edema or lung fibrosis. However, the understanding of TRPC6 channelopathies is still at the beginning stages. Recently, by chemical diversification of (+)-larixol originating from Larix decidua resin traditionally used for inhalation, its methylcarbamate congener, named SH045, was obtained and identified in functional assays as a highly potent, subtype-selective inhibitor of TRPC6. To pave the way for use of SH045 in animal disease models, this study aimed at developing a capable bioanalytical method and to provide exploratory pharmacokinetic data for this promising derivative. According to international guidelines, a robust and selective LC-MS/MS method based on MRM detection in positive ion mode was established and validated for quantification of SH045 in mice plasma, whereby linearity and accuracy were demonstrated for the range of 2-1600 ng/mL. Applying this method, the plasma concentration time course of SH045 following single intraperitoneal administration (20 mg/kg body weight) revealed a short half-life of 1.3 h. However, the pharmacological profile of SH045 is promising, as five hours after administration, plasma levels still remained sufficiently higher than published low nanomolar IC50 values. Summarizing, the LC-MS/MS method and exploratory pharmacokinetic data provide essential prerequisites for experimental pharmacological TRPC6 modulation and translational treatment of TRPC6 channelopathies.

Abstract: PURPOSE: One knowledge gap related to subcutaneous (SC) delivery is unpredictable and variable bioavailability. This study was aimed to develop machine learning methods to predict whether mAb's bioavailability was >/=70% or below, without completely knowing the mechanism and causality between inputs and outputs. METHODS: A database of mAb SC products was built. The model training and validation were accomplished based on this database and a set of the inputs (product properties) were mapped to the output (bioavailability) using different machine learning algorithms. Dimensionality reduction was undertaken using principal component analysis (PCA). RESULTS: The bioavailability of the mAb products being investigated varied from 35% to 90%. The tree-based methods, including random forest (RF), Adaptive Boost (AdaBoost), and decision tree (DT) presented the best predictability and generalization power on bioavailability classification. The models based on Multi-layer perceptron (MLP), Gaussian Naive Bayes (GaussianNB), and k nearest neighbor (kNN) algorithms also provided acceptable prediction accuracy. CONCLUSION: Machine learning could be a potential tool to predict mAb's bioavailability. Since all input features were acquired using theoretical calculations and predictions rather than experiments, the models may be particularly applicable to some early-stage research activities such as mAb molecule triage, design/optimization, mutant screening, molecule selection, and formulation design.

Abstract: Discovering drug-target (protein) interactions (DTIs) is of great significance for researching and developing novel drugs, having a tremendous advantage to pharmaceutical industries and patients. However, the prediction of DTIs using wet-lab experimental methods is generally expensive and time-consuming. Therefore, different machine learning-based methods have been developed for this purpose, but there are still substantial unknown interactions needed to discover. Furthermore, data imbalance and feature dimensionality problems are a critical challenge in drug-target datasets, which can decrease the classifier performances that have not been significantly addressed yet. This paper proposed a novel drug-target interaction prediction method called PreDTIs. First, the feature vectors of the protein sequence are extracted by the pseudo-position-specific scoring matrix (PsePSSM), dipeptide composition (DC) and pseudo amino acid composition (PseAAC); and the drug is encoded with MACCS substructure fingerings. Besides, we propose a FastUS algorithm to handle the class imbalance problem and also develop a MoIFS algorithm to remove the irrelevant and redundant features for getting the best optimal features. Finally, balanced and optimal features are provided to the LightGBM Classifier to identify DTIs, and the 5-fold CV validation test method was applied to evaluate the prediction ability of the proposed method. Prediction results indicate that the proposed model PreDTIs is significantly superior to other existing methods in predicting DTIs, and our model could be used to discover new drugs for unknown disorders or infections, such as for the coronavirus disease 2019 using existing drugs compounds and severe acute respiratory syndrome coronavirus 2 protein sequences.

Abstract: Improved and cheaper molecular diagnostics allow the shift from "one size fits all" therapies to personalised treatments targeting the individual tumor. However, the wealth of potential targets based on comprehensive sequencing remains a yet unsolved challenge that prevents its routine use in clinical practice. Thus, we designed a workflow that selects the most promising treatment targets based on multi-omics sequencing and in silico drug prediction. In this study we demonstrate the workflow with focus on bladder cancer (BLCA), as there are, to date, no reliable diagnostics available to predict the potential benefit of a therapeutic approach. Within the TCGA-BLCA cohort, our workflow identified a panel of 21 genes and 72 drugs that suggested personalized treatment for 95% of patients-including five genes not yet reported as prognostic markers for clinical testing in BLCA. The automated predictions were complemented by manually curated data, thus allowing for accurate sensitivity- or resistance-directed drug response predictions. We discuss potential improvements of drug-gene interaction databases on the basis of pitfalls that were identified during manual curation.

Abstract: The world is reeling under severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, and it will be frightening if compounded by other co-existing infections. The co-occurrence of the Dengue virus (DENV) and Chikungunya virus (CHIKV) has been into existence, but recently the co-infection of DENV and SARS-CoV-2 has been reported. Thus, the possibility of DENV, CHIKV, and SARS-CoV-2 co-infection could be predicted in the future with enhanced vulnerability. It is essential to elucidate the host interactors and the connected pathways to understand the biological insights. The in silico approach using Cytoscape was exploited to elucidate the common human proteins interacting with DENV, CHIKV, and SARS-CoV-2 during their probable co-infection. In total, 17 interacting host proteins were identified showing association with envelope, structural, non-structural, and accessory proteins. Investigating the functional and biological behaviour using PANTHER, UniProtKB, and KEGG databases uncovered their association with several cellular pathways including, signaling pathways, RNA processing and transport, cell cycle, ubiquitination, and protein trafficking. Withal, exploring the DrugBank and Therapeutic Target Database, total seven druggable host proteins were predicted. Among all integrin beta-1, histone deacetylase-2 (HDAC2) and microtubule affinity-regulating kinase-3 were targeted by FDA approved molecules/ drugs. Furthermore, HDAC2 was predicted to be the most significant target, and some approved drugs are available against it. The predicted druggable targets and approved drugs could be investigated to obliterate the identified interactions that could assist in inhibiting viral infection.

Abstract: Importance: Observational studies have reported associations between antihypertensive medication and psychiatric disorders, although the reported direction of association appears to be dependent on drug class. Objective: To estimate the potential effect of different antihypertensive drug classes on schizophrenia, bipolar disorder, and major depressive disorder. Design, Setting, and Participants: This 2-sample mendelian randomization study assessed the association between a single-nucleotide variant (SNV) and drug target gene expression derived from existing expression quantitative trait loci (eQTL) data in blood (sample 1) and the SNV-disease association from published case-control genome-wide association studies (sample 2). Significant associations were corroborated using published brain eQTL and protein QTL data. Participants included 40675 patients with schizophrenia and 64643 controls, 20352 patients with bipolar disorder and 31358 controls, and 135458 patients with major depressive disorder and 344901 controls. Blood eQTL levels were measured in 31684 individuals from 37 cohorts (eQTLGen consortium); prefrontal cortex eQTLs were measured from the PsychENCODE resource in 1387 individuals; and protein QTLs were measured in cerebral spinal fluid from 544 individuals and plasma from 818 individuals. Data were collected from October 4, 2019, to June 1, 2020, and analyzed from October 14, 2019, to June 6, 2020. Exposures: Expression levels of antihypertensive drug target genes as proxies for drug exposure, and genetic variants robustly associated with the expression of these genes as mendelian randomization instruments. Main Outcomes and Measures: Risk for schizophrenia, bipolar disorder, and major depressive disorder. Results: A 1-SD lower expression of the angiotensin-converting enzyme (ACE) gene in blood was associated with lower systolic blood pressure of 4.0 (95% CI, 2.7-5.3) mm Hg, but increased risk of schizophrenia (odds ratio [OR], 1.75; 95% CI, 1.28-2.38; P = 3.95 x 10-4). A concordant direction of association was also observed between ACE expression in prefrontal cortex (OR, 1.33; 95% CI, 1.13-1.56) and ACE protein levels in cerebral spinal fluid (OR per 1-SD decrease, 1.12; 95% CI, 1.05-1.19) and plasma (OR per 1-SD decrease, 1.04; 95% CI, 1.01-1.07). We found no evidence for an association between genetically estimated SBP and schizophrenia risk. Conclusions and Relevance: Findings suggest an adverse association of lower ACE messenger RNA and protein levels with schizophrenia risk. These findings warrant greater pharmacovigilance and further investigation into the effect of ACE inhibitors, particularly those that are centrally acting, on psychiatric symptoms in patients with schizophrenia, as well as the role of ACE inhibitor use in late-onset schizophrenia.

Abstract: The insulin-like growth factors (IGFs)/insulin resistance (IR) axis is the major metabolic hormonal pathway mediating the biologic mechanism of several complex human diseases, including type 2 diabetes (T2DM) and cancers. The genomewide association study (GWAS)-based approach has neither fully characterized the phenotype variation nor provided a comprehensive understanding of the regulatory biologic mechanisms. We applied systematic genomics to integrate our previous GWAS data for IGF-I and IR with multi-omics datasets, e.g., whole-blood expression quantitative loci, molecular pathways, and gene network, to capture the full range of genetic functionalities associated with IGF-I/IR and key drivers (KDs) in gene-regulatory networks. We identified both shared (e.g., T2DM, lipid metabolism, and estimated glomerular filtration signaling) and IR-specific (e.g., mechanistic target of rapamycin, phosphoinositide 3-kinases, and erb-b2 receptor tyrosine kinase 4 signaling) molecular biologic processes of IGF-I/IR axis regulation. Next, by using tissue-specific gene-gene interaction networks, we identified both well-established (e.g., IRS1 and IGF1R) and novel (e.g., AKT1, HRAS, and JAK1) KDs in the IGF-I/IR-associated subnetworks. Our results, if validated in additional genomic studies, may provide robust, comprehensive insights into the mechanisms of IGF-I/IR regulation and highlight potential novel genetic targets as preventive and therapeutic strategies for the associated diseases, e.g., T2DM and cancers.

Abstract: Recent political unrest has highlighted the importance of understanding the short- and long-term effects of gamma-radiation exposure on human health and survivability. In this regard, effective treatment for acute radiation syndrome (ARS) is a necessity in cases of nuclear disasters. Here, we propose 20 therapeutic targets for ARS identified using a systematic approach that integrates gene coexpression networks obtained under radiation treatment in humans and mice, drug databases, disease-gene association, radiation-induced differential gene expression, and literature mining. By selecting gene targets with existing drugs, we identified potential candidates for drug repurposing. Eight of these genes (BRD4, NFKBIA, CDKN1A, TFPI, MMP9, CBR1, ZAP70, IDH3B) were confirmed through literature to have shown radioprotective effect upon perturbation. This study provided a new perspective for the treatment of ARS using systems-level gene associations integrated with multiple biological information. The identified genes might provide high confidence drug target candidates for potential drug repurposing for ARS.