Abstract: Most cellular processes are regulated by groups of proteins interacting together to form protein complexes. Protein compositions vary between different tissues or disease conditions enabling or preventing certain protein-protein interactions and resulting in variations in the complexome. Quantitative and qualitative characterization of context-specific protein complexes will help to better understand context-dependent variations in the physiological behavior of cells. Here, we present SiComPre 1.0, a computational tool that predicts context-specific protein complexes by integrating multi-omics sources. SiComPre outperforms other protein complex prediction tools in qualitative predictions and is unique in giving quantitative predictions on the complexome depending on the specific interactions and protein abundances defined by the user. We provide tutorials and examples on the complexome prediction of common model organisms, various human tissues and how the complexome is affected by drug treatment.

Abstract: High mobility group box 1 (HMGB1) is a damage-associated molecular pattern (DAMP) protein that mediates inflammatory responses after infection or injury. Previously, we reported a peptide inhibitor of HMGB1 (P5779) that acts by directly interrupting HMGB1/MD-2 binding. Here, fingerprint similarity search and docking studies suggest folic acid derived-drugs function as P5779 mimetopes. Molecular dynamic (MD) simulation studies demonstrate that folic acid mimics the binding of P5779 at the TLR4 and MD-2 intersection. In surface plasmon resonance (SPR) studies, these drugs showed direct binding to TLR4/MD-2 but not HMGB1. Furthermore, these P5779 mimetopes inhibit HMGB1 and MD-2 binding and suppress HMGB1-induced TNF release in human macrophages in the nanomolar range. We assert from our findings that their demonstrated anti-inflammatory effects may be working through TLR4-dependent signaling.

Abstract: Herein, we describe the synthesis of N-aryl phthalimides by metal-free coupling of potassium phthalimide with unsymmetrical aryl(TMP)iodonium tosylate salts. The aryl transfer from the iodonium moiety occurs under electronic control with the electron-rich trimethoxyphenyl group acting as a competent dummy ligand. The yields of N-aryl phthalimides are moderate to high and the coupling reaction is compatible with electron-deficient and sterically encumbered aryl groups.

Abstract: Ouratea fieldingiana (Gardner) Engl is popularly used for wound healing. This study describes the main chemical compounds present in extracts of O. fieldingiana and evaluates their biological potential by investigating antifungal, antioxidant, and anticholinesterase activities. The action mechanism of main antifungal compound was investigated by molecular docking using the enzyme sterol 14-alpha demethylase, CYP51, required for ergosterol biosynthesis. The seeds and leaves were extracted with ethanol in a Soxhlet apparatus and by maceration, respectively. Both extracts were subjected to silica gel column chromatography for isolation of main constituents, followed by purification in sephadex. The structures of compounds were established by (1)H and (13)C-NMR spectroscopy and identified by comparison with literature data as amentoflavone and kaempferol 3-O-rutinoside, respectively. The antioxidant activities of the extracts were determined by the DPPH and ABTS free radical inhibition methods. In general, the extracts with the highest antioxidant activity corresponded to those with higher content of phenolic compounds and flavonoids. The ethanol extracts and two isolated compounds presented relevant antifungal activity against several Candida strains. The in silico findings revealed that the compound amentoflavone coupled with the CYP450 protein due to the low energy stabilization (-9.39 kcal/mol), indicating a possible mechanism of action by inhibition of the ergosterol biosynthesis of Candida fungi.

Abstract: Drug-target interaction (DTI) is the basis of drug discovery. However, it is time-consuming and costly to determine DTIs experimentally. Over the past decade, various computational methods were proposed to predict potential DTIs with high efficiency and low costs. These methods can be roughly divided into several categories, such as molecular docking-based, pharmacophore-based, similarity-based, machine learning-based, and network-based methods. Among them, network-based methods, which do not rely on three-dimensional structures of targets and negative samples, have shown great advantages over the others. In this article, we focused on network-based methods for DTI prediction, in particular our network-based inference (NBI) methods that were derived from recommendation algorithms. We first introduced the methodologies and evaluation of network-based methods, and then the emphasis was put on their applications in a wide range of fields, including target prediction and elucidation of molecular mechanisms of therapeutic effects or safety problems. Finally, limitations and perspectives of network-based methods were discussed. In a word, network-based methods provide alternative tools for studies in drug repurposing, new drug discovery, systems pharmacology and systems toxicology.

Abstract: Rheumatoid arthritis (RA) is a commonly occurring autoimmune disease. Its defining pathological characteristic is the excessive proliferation of fibroblastlike synoviocytes (FLS), which is similar to tumor cells and results in a range of clinical problems. As a commonly used antipyretic, analgesic and antiinflammatory drug, aspirin is the firstline treatment for RA. However, its mechanism of action has not been well explained. The goal is to investigate the biological effects of aspirin on primary RAFLS and its underlying mechanisms. In this experiment we treated cells with various concentrations of aspirin (0, DMSO, 1, 2, 5, 10 mM). Cell proliferation activity was detected with CCK8 assays. Apoptosis and cell cycle distribution were detected via flow cytometry. Apoptosis and cell cycleassociated proteins (Bcl2, Bax, PRAP1, Cyclin D1, P21), as well as the key proteins and their phosphorylation levels of the NFkappaB and JAK/STAT3 signaling pathways, were detected via western blot analysis. Bioinformatics prediction revealed that aspirin was closely associated with cell proliferation and apoptosis, including the p53 and NFkappaB signaling pathways. By stimulating with aspirin, cell viability decreased, while the proportion of apoptotic cells increased, and the number of cells arrested in the G0/G1 phase increased in a dosedependent manner. The expression of Bax increased with aspirin stimulation, while the levels of Bcl2, PRAP1, Cyclin D1 and P21 decreased; pSTAT3, pP65 and p50 levels also decreased while STAT3, P65, P50, pP105 and P105 remained unchanged. From our data, it can be concluded that aspirin is able to promote apoptosis and inhibit the proliferation of RAFLS through blocking the JAK/STAT3 and NFkappaB signaling pathways.

Abstract: Knowledge of the bioactive conformations of small molecules or the ability to predict them with theoretical methods is of key importance to the design of bioactive compounds such as drugs, agrochemicals, and cosmetics. Using an elaborate cheminformatics pipeline, which also evaluates the support of individual atom coordinates by the measured electron density, we compiled a complete set ("Sperrylite Dataset") of high-quality structures of protein-bound ligand conformations from the PDB. The Sperrylite Dataset consists of a total of 10,936 high-quality structures of 4,548 unique ligands. Based on this dataset, we assessed the variability of the bioactive conformations of 91 small molecules-each represented by a minimum of ten structures-and found it to be largely independent of the number of rotatable bonds. Sixty-nine molecules had at least two distinct conformations (defined by an RMSD greater than 1 A). For a representative subset of 17 approved drugs and cofactors we observed a clear trend for the formation of few clusters of highly similar conformers. Even for proteins that share a very low sequence identity, ligands were regularly found to adopt similar conformations. For cofactors, a clear trend for extended conformations was measured, although in few cases also coiled conformers were observed. The Sperrylite Dataset is available for download from http://www.zbh.uni-hamburg.de/sperrylite_dataset.

Abstract: Multi-components Traditional Chinese Medicine (TCM) treats various complex diseases (multi-etiologies and multi-symptoms) via herbs interactions to exert curative efficacy with less adverse effects. However, the ancient Chinese compatibility theory of herbs formula still remains ambiguous. Presently, this combination principle is dissected through a systems pharmacology study on the mechanism of action of a representative TCM formula, Huo-xiang-zheng-qi (HXZQ) prescription, on the treatment of functional dyspepsia (FD), a chronic or recurrent clinical disorder of digestive system, as typical gastrointestinal (GI) diseases which burden human physical and mental health heavily and widely. In approach, a systems pharmacology platform which incorporates the pharmacokinetic and pharmaco-dynamics evaluation, target fishing and network pharmacological analyses is employed. As a result, 132 chemicals and 48 proteins are identified as active compounds and FD-related targets, and the mechanism of HXZQ formula for the treatment of GI diseases is based on its three function modules of anti-inflammation, immune protection and gastrointestinal motility regulation mainly through four, i.e., PIK-AKT, JAK-STAT, Toll-like as well as Calcium signaling pathways. In addition, HXZQ formula conforms to the ancient compatibility rule of "Jun-Chen-Zuo-Shi" due to the different, while cooperative roles that herbs possess, specifically, the direct FD curative effects of GHX (serving as Jun drug), the anti-bacterial efficacy and major accompanying symptoms-reliving bioactivities of ZS and BZ (as Chen), the detoxication and ADME regulation capacities of GC (as Shi), as well as the minor symptoms-treating efficacy of the rest 7 herbs (as Zuo). This work not only provides an insight of the therapeutic mechanism of TCMs on treating GI diseases from a multi-scale perspective, but also may offer an efficient way for drug discovery and development from herbal medicine as complementary drugs.

Abstract: Atherosclerosis-predominant vasculopathy is a common complication of diabetes with high morbidity and high mortality, which is ruining the patient's daily life. As is known to all, traditional Chinese medicine (TCM) SHENQI compound and western medicine rosiglitazone play an important role in the treatment of diabetes. In particular, SHENQI compound has a significant inhibitory effect on vascular lesions. Here, to explore and compare the therapeutic mechanism of SHENQI compound and rosiglitazone on diabetic vasculopathy, we first built 7 groups of mouse models. The behavioral, physiological and pathological morphological characteristics of these mice showed that SHENQI compound has a more comprehensive curative effect than rosiglitazone and has a stronger inhibitory effect on vascular lesions. While rosiglitazone has a more effective but no significant effect on hypoglycemic. Further, based on the gene expression of mice in each group, we performed differential expression analysis. The functional enrichment analysis of these differentially expressed genes (DEGs) revealed the potential pathogenesis and treatment mechanisms of diabetic angiopathy. In addition, we found that SHENQI compound mainly exerts comprehensive effects by regulating MCM8, IRF7, CDK7, NEDD4L by pivot regulator analysis, while rosiglitazone can rapidly lower blood glucose levels by targeting PSMD3, UBA52. Except that, we also identified some pivot TFs and ncRNAs for these potential disease-causing DEG modules, which may the mediators bridging drugs and modules. Finally, similar to pivot regulator analysis, we also identified the regulation of some drugs (e.g. bumetanide, disopyramide and glyburide etc.) which have been shown to have a certain effect on diabetes or diabetic angiopathy, proofing the scientific and objectivity of this study. Overall, this study not only provides an in-depth comparison of the efficacy of SHENQI compound and rosiglitazone in the treatment of diabetic vasculopathy, but also provides clinicians and drug designers with valuable theoretical guidance.

Abstract: The interplay between drugs and cell metabolism is a key factor in determining both compound potency and toxicity. In particular, how and to what extent transmembrane transporters affect drug uptake and disposition is currently only partially understood. Most transporter proteins belong to two protein families: the ATP-Binding Cassette (ABC) transporter family, whose members are often involved in xenobiotic efflux and drug resistance, and the large and heterogeneous family of solute carriers (SLCs). We recently argued that SLCs are collectively a rather neglected gene group, with most of its members still poorly characterized, and thus likely to include many yet-to-be-discovered associations with drugs. We searched publicly available resources and literature to define the currently known set of drugs transported by ABCs or SLCs, which involved approximately 500 drugs and more than 100 transporters. In order to extend this set, we then mined the largest publicly available pharmacogenomics dataset, which involves approximately 1,000 molecularly annotated cancer cell lines and their response to 265 anti-cancer compounds, and used regularized linear regression models (Elastic Net, LASSO) to predict drug responses based on SLC and ABC data (expression levels, SNVs, CNVs). The most predictive models included both known and previously unidentified associations between drugs and transporters. To our knowledge, this represents the first application of regularized linear regression to this set of genes, providing an extensive prioritization of potentially pharmacologically interesting interactions.