Search

Abstract Heart failure (HF) has become an important social problem that seriously threatens human health due to its high morbidity and mortality. G Protein-Coupled Receptors Kinase 2 (GRK2), as a novel target of heart failure, is overexpressed in the pathogenesis and progression of HF. In this study, we conducted a multistep virtual screening process from the ZINC database to discover potential GRK2 inhibitors. First, by using 6 known GRK2 inhibitors with high bioactivity and diverse structures as the template molecules, 7499 compounds were obtained from ligand-based similarity screening by 4 types of fingerprints. Subsequently, these hits were further screened by three quantitative structure-activity relationship (QSAR) models, which were built by 197 known GRK2 compounds and represented by two-dimensional MOE descriptors (MOE2D), molecular fragments (MACCS) and 2- dimensional pharmacophore (CATS) based on random forest algorithm, respectively. These hit compounds were then filtered by three molecular docking packages (MOE, GOLD and GLIDE) to improve the docking accuracy for avoid losing candidate compounds. The compounds with high docking scores were selected for the evaluation of absorption, distribution, metabolism, excretion, and toxicity (ADMET). Finally, 17 compounds were identified the binding affinity to GRK2 by using SPR assay, three of which were considered as novel GRK2 inhibitors, and their binding modes with GRK2 active sites were analyzed. As new potential GRK2 inhibitors, COM5(K D = 0.172 μM) and COM17(K D = 0.595 μM) with the KD value less than 1 μM could be directly used in the next study, while the remaining hit COM4 (K D = 4.72 μM) whose K D over 1 μM can be optimized into the required low nanomole range for further study. Highlights • GRK2 is considered as an important potential pharmaceutical target for the treatment of heart failure. • To discover novel GRK2 inhibitors, a multi-step computational study was conducted, including similarity screening based on fingerprints, QSAR models screening based on random forest algorithm, docking studies, ADMET filtering, and the final activity validation. • Three hit compounds with novelty structures were identified as the potential GRK2 inhibitors, its binding affinity were COM5(KD=0.172µM), COM17(KD=0.595µM), and COM4 (KD =4.72µM). [ABSTRACT FROM AUTHOR]

The behavior of a chemical in human or environment mostly depends on its several key physicochemical properties, such as aqueous solubility, octanol-water partition coefficient (logP), boiling point (BP), density, flash point (FP), viscosity, surface tension (ST), vapor pressure (VP) and melting point (MP). Commonly, these properties are important for the environmental sciences and drug discovery, such as the absorption, distribution, metabolism, excretion, and toxicity (ADMET) for medicinal compounds and the common risk assessment for problematic chemicals. At present, the quantitative structure-property relationship (QSPR) model was widely applied to save time and money investment in the early stage of chemical research. Although some satisfactory models were already obtained, most of them are not available for the public researchers and thus cannot be directly applied to practical research projects. Herein, in this study, we developed a user-friendly web server named ChemBCPP that can be used to predict aforementioned 8 important physicochemical properties and calculate several other commonly used properties just by uploading a molecular structure or file. In addition, for a new chemical entity, users can not only get its predicted value but also obtain a leverage value ( h value) which can be used to evaluate the reliability of predictive result. We believe that ChemBCPP could be widely applied in environmental science, chemical synthesis and drug ADMET fields with the demand for high quality of chemical properties. ChemBCPP could be freely available via http://chembcpp.scbdd.com . [ABSTRACT FROM AUTHOR]

Plasma protein binding affinity of a drug compound has a strong influence on its pharmacodynamic behavior because it can affect the drug uptake and distribution. In this study, we collected a sizeable dataset consisting of 1830 drug compounds from several accessible sources. A descriptor pool composed of four different types of descriptors (2-D, 3-D, Estate and MACCS) was firstly built and non-dominated sorting genetic algorithm (NSGA-II) combining partial least square (PLS) regression was applied to select important descriptors for model building. Finally, we obtained a consensus model (for five-fold cross-validation: Q 2 = 0.750; RMSE = 16.151) based on five different predictive models built using random forest (RF), support vector machine (SVM), Cubist, Gaussian process (GP), and Boosting. Further, a test set and two external validation datasets were applied to validate its robustness and practicality. For the test set, R T 2 = 0.787 and RMSE T = 14.154; when two external datasets were applied, R Ex 2 = 0.704 and 0.703, RMSE Ex = 18.194 and 17.233 respectively. Additionally, according to OECD principles, y-randomization, Williams plot and scaffold analyses were proposed to validate the reliability and practical application domain of our predictive model. Overall, our consensus model shows a good prediction performance and generalization ability in predicting plasma protein binding (PPB). After analyzing those important descriptors selected by NSGA-II and RF, we concluded that the PPB of a drug compound is mainly related to its lipophilicity, aromatic rings, and partial charge properties. In summary, this study developed a robust and practical consensus model for PPB prediction and it could be used to the distribution evaluation and risk assessment in the early stage of drug development. [ABSTRACT FROM AUTHOR]

There is a great need to assess the harmful effects or toxicities of chemicals to which man is exposed. In the present paper, the kernel fusion technique, together with the state-of-the-art support vector machine (SVM) algorithm, was developed to classify the toxicity of chemicals from Distributed Structure-Searchable Toxicity (DSSTox) database network. In this method, different kernels were firstly constructed by applying different molecular fingerprint systems, including FP2, FP4 and MACCS, and then these kernels were integrated to form a new fused kernel strictly under the algorithmic framework of kernel methods. The fused kernel can accurately measure the similarities of molecules for the toxicity classification, taking advantage of the complementarity in multiple kernels and therefore improving the prediction performance. Two model validation approaches, five-fold cross-validation and independent validation set, were used for assessing the predictive capability of our developed models. The obtained results indicate that the kernel fusion-based SVM gave the best prediction ability compared to single fingerprint kernels, and therefore could be regarded as a very promising and alternative modeling approach for potential toxicity prediction of chemicals. [ABSTRACT FROM AUTHOR]

The p90 ribosomal S6 kinase 2 (RSK2) belongs to Ser/Thr kinase family which is involved in a variety of cellular processes, such as cell proliferation, survival and transformation. Recent studies suggest that RSK2 also is a critical proliferative signaling effector of oncogenic tyrosine kinases, which represents targeting RSK2 would be a potential anticancer therapy. Therefore, it would have great clinical significance to discover more novel RSK2 inhibitors by using an in silico strategy that combined ligand-based and structure-based virtual screening (VS). Quantitative structure-activity relationship (QSAR) models were developed based on the structures of known RSK2 bioactivity inhibitors and the verified models were used for screening SPECS database. By molecular docking, the virtual hit compounds were further filtered to obtained compounds with strong binding affinity. Finally, 30 molecules were selected and purchased for in vitro inhibition and SPR binding assays after ADMET evaluation. 5 hit compounds exhibited breast cancer or melanoma cell inhibitory activities with IC 50 values from 5.7 to 28.4 ​μM, and the following surface plasmon resonance (SPR) assay suggested that 5 compounds showed strong affinity to RSK2 with the K D values in the range 4.76 ​× ​10−6 to 3.17 ​× ​10−7 ​M, wherein, C2 as the most active compound displayed an IC 50 of 5.7 ​μM against melanoma, and with an K D of 856 ​nM against RSK2. Therefore, these compounds can provide us a valuable insight for further optimization and development of more potent RSK2 inhibitors. • RSK2 belongs to Ser/Thr kinase family involved in various physiological and pathological cell activities. • RSK2 is a critical signaling effector of oncogenic tyrosine kinases for a potential anticancer target. • 5 novel RSK2 inhibitors were discovered by silico strategies exhibiting potent bioactivities on breast cancer or melanoma. [ABSTRACT FROM AUTHOR]