Abstract: Damnacanthus indicus C.F.Gaertn is known as Huci in traditional Chinese medicine. It contains a component having anthraquinone-like structure which is a part of the many used anticancer drugs. This study was to collect the evidence of disease-modulatory activities of Huci by analyzing the published literature on the chemicals and drugs. A list of its compounds and direct protein targets is predicted by using Bioinformatics Analysis Tool for Molecular Mechanism of TCM. A protein-protein interaction network using links between its directed targets and the other known targets was constructed. The DPT-associated genes in net were scrutinized by WebGestalt. Exploring the cancer genomics data related to Huci through cBio Portal. Survival analysis for the overlap genes is done by using UALCAN. We got 16 compounds and it predicts 62 direct protein targets and 100 DPTs and they were identified for these compounds. DPT-associated genes were analyzed by WebGestalt. Through the enrichment analysis, we got top 10 identified KEGG pathways. Refined analysis of KEGG pathways showed that one of these ten pathways is linked to Rap1 signaling pathway and another one is related to breast cancer. The survival analysis for the overlap genes shows the significant negative effect of these genes on the breast cancer patients. Through the research results of Damnacanthus indicus C.F.Gaertn, it is shown that medicine network pharmacology may be regarded as a new paradigm for guiding the future studies of the traditional Chinese medicine in different fields.

Abstract: The interactions between drugs and their target proteins induce altered expression of genes involved in complex intracellular networks. The properties of these functional network modules are critical for the identification of drug targets, for drug repurposing, and for understanding the underlying mode of action of the drug. The topological modules generated by a computational approach are defined as functional clusters. However, the functions inferred for these topological modules extracted from a large-scale molecular interaction network, such as a protein-protein interaction (PPI) network, could differ depending on different cluster detection algorithms. Moreover, the dynamic gene expression profiles among tissues or cell types causes differential functional interaction patterns between the molecular components. Thus, the connections in the PPI network should be modified by the transcriptomic landscape of specific cell lines before producing topological clusters. Here, we systematically investigated the clusters of a cell-based PPI network by using four cluster detection algorithms. We subsequently compared the performance of these algorithms for target gene prediction, which integrates gene perturbation data with the cell-based PPI network using two drug target prioritization methods, shortest path and diffusion correlation. In addition, we validated the proportion of perturbed genes in clusters by finding candidate anti-breast cancer drugs and confirming our predictions using literature evidence and cases in the ClinicalTrials.gov. Our results indicate that the Walktrap (CW) clustering algorithm achieved the best performance overall in our comparative study.

Abstract: OBJECTIVE: To explore the molecular-level mechanism on the hematopoiesis effect of Angelicae sinensis Radix (ASR) with systems-based interactome analysis. METHODS: This systems-based interactome analysis was designed to enforce the workflow of "ASR (herb)-->compound-->target protein-->internal protein actions-->ending regulated protein for hematopoiesis". This workflow was deployed with restrictions on regulated proteins expresses in bone marrow and anemia disease and futher validated with experiments. RESULTS: The hematopoiesis mechanism of ASR might be accomplished through regulating pathways of cell proliferation towards hemopoiesis with cross-talking agents of spleen tyrosine kinase (SYK), Janus kinase 2 (JAK2), and interleukin-2-inducible T-cell kinase (ITK). The hematopoietic function of ASR was also validated by colony-forming assay performed on mice bone marrow cells. As a result, SYK, JAK2 and ITK were activated. CONCLUSION: This study provides a new approach to systematically study and predict the therapeutic mechanism for ASR based on interactome analysis towards biological process with experimental validations.

Abstract: Crz1p regulates Calcineurin, a serine-threonine-specific protein phosphatase, in Rhizoctonia solani. It has attracted consideration as a novel target of antifungal therapy based on studies in numerous pathogenic fungi, including, Cryptococcus neoformans, Candida albicans and Aspergillus fumigatus. To investigate whether Calcineurin can be a useful target for the treatment of Crz1 protein in R. solani causing wet root rot in Chickpea. The work presented here reports the in-silico studies of Crz1 protein against natural compounds. This study Comprises of quantitative structure-toxicity relationship (QSTR) and quantitative structure-activity relationship (QSAR). All compounds showed high binding energy for Crz1 protein through molecular docking. Further, a pharmacokinetic study revealed that these compounds had minimal side effects. Biological activity spectrum prediction of these compounds showed potential antifungal properties by showing significant interaction with Crz1. Hence, these compounds can be used for the prevention and treatment of wet root rot in Chickpea.

Abstract: Identifying effective pharmacological treatments for heart failure (HF) patients remains critically important. Given that the development of drugs de novo is expensive and time consuming, drug repositioning has become an increasingly important branch. In the present study, we propose a two-step drug repositioning pipeline and investigate the novel therapeutic effects of existing drugs approved by the US Food and Drug Administration to discover potential therapeutic drugs for HF. In the first step, we compared the gene expression pattern of HF patients with drug-induced gene expression profiles to obtain preliminary candidates. In the second step, we performed a systems biology approach based on the known protein-protein interaction information and targets of drugs to narrow down preliminary candidates to obtain final candidates. Drug set enrichment analysis and literature search were applied to assess the performance of our repositioning approach. We also constructed a mode of action network for each candidate and performed pathway analysis for each candidate using genes contained in their mode of action network to uncover pathways that potentially reflect the mechanisms of candidates' therapeutic efficacy to HF. We discovered numerous preliminary candidates, some of which are used in clinical practice and supported by the literature. The final candidates contained nearly all of the preliminary candidates supported by previous studies. Drug set enrichment analysis and literature search support the validity of our repositioning approach. The mode of action network for each candidate not only displayed the underlying mechanisms of drug efficacy but also uncovered potential biomarkers and therapeutic targets for HF. Our two-step drug repositioning approach is efficient to find candidates with potential therapeutic efficiency to HF supported by the literature and might be of particular use in the discovery of novel effective pharmacological therapies for HF.

Abstract: Prostate cancer (PCa) is the second leading cause of cancer-related death for men in the United States. Approximately 35% of PCa recurs and is often transformed to castration-resistant prostate cancer (CRPCa), the most deadly and aggressive form of PCa. However, the CRPCa standard-of-care treatment (enzalutamide with abiraterone) usually has limited efficacy. Herein, we report a novel molecule (PAWI-2) that inhibits cellular proliferation of androgen-sensitive and androgen-insensitive cells (LNCaP and PC-3, respectively). In vivo studies in a PC-3 xenograft model showed that PAWI-2 (20 mg/kg per day i.p., 21 days) inhibited tumor growth by 49% compared with vehicle-treated mice. PAWI-2 synergized currently clinically used enzalutamide in in vitro inhibition of PCa cell viability and resensitized inhibition of in vivo PC-3 tumor growth. Compared with vehicle-treated mice, PC-3 xenograft studies also showed that PAWI-2 (20 mg/kg per day i.p., 21 days) and enzalutamide (5 mg/kg per day i.p., 21 days) inhibited tumor growth by 63%. Synergism was mainly controlled by the imbalance of prosurvival factors (e.g., Bcl-2, Bcl-xL, Mcl-1) and antisurvival factors (e.g., Bax, Bak) induced by affecting mitochondrial membrane potential/mitochondria dynamics. Thus, PAWI-2 utilizes a distinct mechanism of action to inhibit PCa growth independently of androgen receptor signaling and overcomes enzalutamide-resistant CRPCa. SIGNIFICANCE STATEMENT: Castration-resistant prostate cancer (CRPCa) is the most aggressive human prostate cancer (PCa) but standard chemotherapies for CRPCa are largely ineffective. PAWI-2 potently inhibits PCa proliferation in vitro and in vivo regardless of androgen receptor status and uses a distinct mechanism of action. PAWI-2 has greater utility in treating CRPCa than standard-of-care therapy. PAWI-2 possesses promising therapeutic potency in low-dose combination therapy with a clinically used drug (e.g., enzalutamide). This study describes a new approach to address the overarching challenge in clinical treatment of CRPCa.

Abstract: Due to the high heterogeneity and complexity of cancer, it is still a challenge to predict the prognosis of cancer patients. In this work, we used a clustering algorithm to divide patients into different subtypes in order to reduce the heterogeneity of the cancer patients in each subtype. Based on the hypothesis that the gene co-expression network may reveal relationships among genes, some communities in the network could influence the prognosis of cancer patients and all the prognosis-related communities could fully reveal the prognosis of cancer patients. To predict the prognosis for cancer patients in each subtype, we adopted an ensemble classifier based on the gene co-expression network of the corresponding subtype. Using the gene expression data of ovarian cancer patients in TCGA (The Cancer Genome Atlas), three subtypes were identified. Survival analysis showed that patients in different subtypes had different survival risks. Three ensemble classifiers were constructed for each subtype. Leave-one-out and independent validation showed that our method outperformed control and literature methods. Furthermore, the function annotation of the communities in each subtype showed that some communities were cancer-related. Finally, we found that the current drug targets can partially support our method.

Abstract: The computational prediction of interactions between drugs and targets is a standing challenge in drug discovery. State-of-the-art methods for drug-target interaction prediction are primarily based on supervised machine learning with known label information. However, in biomedicine, obtaining labeled training data is an expensive and a laborious process. This paper proposes a semi-supervised generative adversarial networks (GANs)-based method to predict binding affinity. Our method comprises two parts, two GANs for feature extraction and a regression network for prediction. The semi-supervised mechanism allows our model to learn proteins drugs features of both labeled and unlabeled data. We evaluate the performance of our method using multiple public datasets. Experimental results demonstrate that our method achieves competitive performance while utilizing freely available unlabeled data. Our results suggest that utilizing such unlabeled data can considerably help improve performance in various biomedical relation extraction processes, for example, Drug-Target interaction and protein-protein interaction, particularly when only limited labeled data are available in such tasks. To our best knowledge, this is the first semi-supervised GANs-based method to predict binding affinity.

Abstract: The biomedical data landscape is fragmented with several isolated, heterogeneous data and knowledge sources, which use varying formats, syntaxes, schemas, and entity notations, existing on the Web. Biomedical researchers face severe logistical and technical challenges to query, integrate, analyze, and visualize data from multiple diverse sources in the context of available biomedical knowledge. Semantic Web technologies and Linked Data principles may aid toward Web-scale semantic processing and data integration in biomedicine. The biomedical research community has been one of the earliest adopters of these technologies and principles to publish data and knowledge on the Web as linked graphs and ontologies, hence creating the Life Sciences Linked Open Data (LSLOD) cloud. In this paper, we provide our perspective on some opportunities proffered by the use of LSLOD to integrate biomedical data and knowledge in three domains: (1) pharmacology, (2) cancer research, and (3) infectious diseases. We will discuss some of the major challenges that hinder the wide-spread use and consumption of LSLOD by the biomedical research community. Finally, we provide a few technical solutions and insights that can address these challenges. Eventually, LSLOD can enable the development of scalable, intelligent infrastructures that support artificial intelligence methods for augmenting human intelligence to achieve better clinical outcomes for patients, to enhance the quality of biomedical research, and to improve our understanding of living systems.

Abstract: B-cell lymphoma/leukaemia 11A (BCL11A) is a modulator of foetal-to-adult globin switching and is involved in brain development and normal lymphopoiesis. The three-dimensional structure of BCL11A and its structural domains had not yet been completely determined; hence, this study aimed to elucidate the structural domains of BCL11A. Molecular modelling and dynamics simulation studies were conducted using in silico tools with the templates selected based on Basic Local Alignment Search Tool (BLAST) searches of the Protein Data Bank (PDB). Ten protein models were generated using the MODELLER software, and the best model was selected according to the Discrete Optimised Protein Energy (DOPE) score and validated using the RAMPAGE server by evaluation of the Ramachandran plot. More than 93% of the amino acid residues of the best model of BCL11A were found to be in the favoured and allowed regions. The best model was validated using a 100-ns time span molecular dynamics simulation. The root-mean-square deviation, root-mean-square fluctuation, and radius of gyration values were found to explain the stability of the best BCL11A protein molecular model generated in the study. The validated best model of the BCL11A protein may be useful for effective modulator studies on foetal-to-adult globin switching and related research.