Biological evaluation and energetic analyses of novel GSK-3ß inhibitors.

Glycogen synthase kinase-3 beta (GSK-3ß) is involved in multiple signaling pathways. Consistent with its critical roles in normal cells, abnormalities in GSK-3ß activity have been implicated in diabetes, heart disease, Parkinson disease, and Alzheimer's disease. In this study, a series of new scaffolds of small molecule inhibitors of GSK-3ß were identified by virtual screening and bioassay. Candidates that adhere to drug-like criteria from a virtual library of compounds were tested using computational docking studies. Twenty selected compounds were tested, which led to the discovery of two hits. Compound 14 (IC50 = 8.48 µM) and compound 19 (IC50 = 2.19 µM) were identified with high affinity. Molecular dynamics (MD) simulations, in conjunction with molecular mechanics/Poisson-Boltzmann surface area binding free-energy analysis, were employed to gain insight into the binding modes and energetics of GSK-3ß inhibitors. The detailed analysis of molecular dynamics results shows that Ile62, Val70, Tyr134, and Leu188 in GSK-3ß are key residues responsible to the binding of compound 14 and compound 19. Importantly, our results also validated this combined virtual screening and biophysical technique approach to discovery kinase inhibitors, which may be applied for future inhibitor discovery work for GSK-3ß.

A chromosome-level genome assembly of Artocarpus nanchuanensis (Moraceae), an extremely endangered fruit tree.

Artocarpus nanchuanensis (Moraceae), which is naturally distributed in China, is a representative and extremely endangered tree species. In this study, we obtained a high-quality chromosome-scale genome assembly and annotation information for A. nanchuanensis using integrated approaches, including Illumina, Nanopore sequencing platform, and Hi-C. A total of 128.71 Gb of raw Nanopore reads were generated from 20-kb libraries, and 123.38 Gb of clean reads were obtained after filtration with 160.34× coverage depth and a 17.48-kb average read length. The final assembled A. nanchuanensis genome was 769.44 Mb with a 2.09 Mb contig N50, and 99.62% (766.50 Mb) of the assembled data was assigned to 28 pseudochromosomes. In total, 39,596 genes (95.10%, 39,596/41,636) were successfully annotated, and 129 metabolic pathways were detected. Plants disease resistance/insect resistance genes, plant-pathogen interaction metabolic pathways, and abundant biosynthesis pathways of vitamins, flavonoid, and gingerol were detected. Unigene reveals the basis of species-specific functions, and gene family in contraction and expansion generally implies strong functional differences in the evolution. Compared with other related species, a total of 512 unigenes, 309 gene families in contraction, and 559 gene families in expansion were detected in A. nanchuanensis. This A. nanchuanensis genome information provides an important resource to expand our understanding of the unique biological processes, nutritional and medicinal benefits, and evolutionary relationship of this species. The study of gene function and metabolic pathway in A. nanchuanensis may reveal the theoretical basis of a special trait in A. nanchuanensis and promote the study and utilization of its rare medicinal value.

Discovering Proangiogenic Drugs in Ischemic Stroke Based on the Relationship between Protein Domain and Drug Substructure.

As an important protective mechanism against cerebral ischemia, angiogenesis has become a topic of interest in the treatment of ischemic stroke with the challenge that few drugs promote angiogenesis. Previous studies of the identification of drug-target interactions mainly focused on the overall structures of drugs and proteins, which limited the discovery of novel structure drugs. In this article, we proposed a new strategy for discovering proangiogenic drugs based on the assumption that drug-protein interaction is mediated by substructure and domain. First, we identified substructure-domain relationships according to the known drug-protein interactions and established the drug-substructure-domain-protein relationships of genes that are proangiogenic in brain tissue and expressed significantly during ischemic stroke. Then we quantified the intensity of interaction between each drug and each protein. Finally, we obtained 540 interactive relationships between 238 drugs and 54 genes, establishing a drug-gene network with two patterns of independent and complex drug-gene interactions. Both of the patterns facilitated finding not only drugs with the same overall structure but also drugs with a different overall structure based on the same or a similar protein spectrum. In addition, we analyzed the mechanism of action of each predicted drug and extracted drugs with similar mechanisms. In vitro, our results showed that azelnidipine, azilsartan, lercanidipine, nafcillin, and vortioxetine enhanced bEnd.3 cell proliferation, migration, tube formation, and the expression of angiogenic marker PCNA. Azelnidipine, azilsartan, lercanidipine, and nafcillin increased the level of expression of proangiogenic factor VEGF. Unlike previous studies focusing on the overall structures of drugs, our research highlighted local structural similarity, which has great potential in the search for more proangiogenic drugs with novel structures.

Synthetic Lethality-based Identification of Targets for Anticancer Drugs in the Human Signaling Network.

Chemotherapy agents can cause serious adverse effects by attacking both cancer tissues and normal tissues. Therefore, we proposed a synthetic lethality (SL) concept-based computational method to identify specific anticancer drug targets. First, a 3-step screening strategy (network-based, frequency-based and function-based screening) was proposed to identify the SL gene pairs by mining 697 cancer genes and the human signaling network, which had 6306 proteins and 62937 protein-protein interactions. The network-based screening was composed of a stability score constructed using a network information centrality measure (the average shortest path length) and the distance-based screening between the cancer gene and the non-cancer gene. Then, the non-cancer genes were extracted and annotated using drug-target interaction and drug description information to obtain potential anticancer drug targets. Finally, the human SL data in SynLethDB, the existing drug sensitivity data and text-mining were utilized for target validation. We successfully identified 2555 SL gene pairs and 57 potential anticancer drug targets. Among them, CDK1, CDK2, PLK1 and WEE1 were verified by all three aspects and could be preferentially used in specific targeted therapy in the future.

Prediction on the risk population of idiosyncratic adverse reactions based on molecular docking with mutant proteins.

Idiosyncratic adverse drug reactions are drug reactions that occur rarely and unpredictably among the population. These reactions often occur after a drug is marketed, which means that they are strongly related to the genotype of the population. The prediction of such adverse reactions is a major challenge because of the lack of appropriate test models during the drug development process. In this study, we chose withdrawn drugs because the reasons why they were withdrawn and from which countries or regions is easily obtained. We selected Dilevalol and its chiral drug (Labetalol) as the investigatory drugs, as they have been withdrawn from a European market (Britain) because of serious hepatotoxicity. First, we searched for and obtained the Dilevalol-induced- liver-injury related protein, multidrug resistance protein 1 (MDR1), from the Comparative Toxicogenomics Database (CTD). Then, we searched and extracted 477 non-synonymous single nucleotide polymorphisms (nsSNP) on MDR1 in the dbSNP database. Second, we used the VarMod tool to predict the functional changes of MDR1 induced by these nsSNPs, from which we extracted the nsSNPs that significantly change the functions of this protein. Third, we built the three-dimensional structures of those variant proteins and used AutoDock to perform a docking study, choosing the best model to determine the sites of nsSNPs. Finally, we used the data from the 1000 Genomes Project to verify the dominant population distribution of the risk SNP. We applied the same strategy to the post-marketing drug-induced liver injury drugs to further test the feasibility of our method.

Designing of dual inhibitors for GSK-3ß and CDK5: Virtual screening and in vitro biological activities study.

Alzheimer's disease is a multifactorial neurodegenerative disorder with many drug targets contributing to its etiology. Despite the devastating effects of this disease, therapeutic methods for treating Alzheimer's disease remain limited. The multifactorial nature of Alzheimer's disease strongly supports a multi-target rationale as a drug design strategy. Glycogen synthase kinase-3 beta and cyclin-dependent kinase 5 have been identified as being involved in the pathological hyperphosphorylation of tau proteins, which leads to the formation of neurofibrillary tangles and causes Alzheimer's disease. In this study, using a molecular docking method to screen a virtual library, we discovered molecules that can simultaneously inhibit Glycogen synthase kinase-3 beta and cyclin-dependent kinase 5 as lead compounds for the treatment of Alzheimer's disease. The docking results revealed the key residues in the substrate binding sites of both Glycogen synthase kinase-3 beta and cyclin-dependent kinase 5. A receiver operating characteristic curve indicated that the docking model consistently and selectively scored the majority of active compounds above decoys. The pre-treatment of cells with screened compounds protected them against Aß25-35- induced cell death by up to 80%. Collectively, these findings suggest that some compounds have potential to be promising multifunctional agents for Alzheimer's disease treatment.