Thraneparrish1770

Purpose Mutation-induced variation of protein-ligand binding affinity is the key to many genetic diseases and the emergence of drug resistance, and therefore predicting such mutation impacts is of great importance. In this work, we aim to predict the mutation impacts on protein-ligand binding affinity using efficient structure-based, computational methods. Methods Relying on consolidated databases of experimentally determined data we characterize the affinity change upon mutation based on a number of local geometrical features and monitor such feature differences upon mutation during molecular dynamics (MD) simulations. The differences are quantified according to average difference, trajectory-wise distance or time-vary differences. Machine-learning methods are employed to predict the mutation impacts using the resulting conventional or time-series features. Predictions based on estimation of energy and based on investigation of molecular descriptors were conducted as benchmarks. Results Our method (machine-learning techniques using time-series features) outperformed the benchmark methods, especially in terms of the balanced F1 score. Particularly, deep-learning models led to the best prediction performance with distinct improvements in balanced F1 score and a sustained accuracy. Conclusion Our work highlights the effectiveness of the characterization of affinity change upon mutations. Furthermore, deep-learning techniques are well designed for handling the extracted time-series features. This study can lead to a deeper understanding of mutation-induced diseases and resistance, and further guide the development of innovative drug design. © 2020 The Authors.Drug combinations are frequently used for the treatment of cancer patients in order to increase efficacy, decrease adverse side effects, or overcome drug resistance. Given the enormous number of drug combinations, it is cost- and time-consuming to screen all possible drug pairs experimentally. Currently, it has not been fully explored to integrate multiple networks to predict synergistic drug combinations using recently developed deep learning technologies. In this study, we proposed a Graph Convolutional Network (GCN) model to predict synergistic drug combinations in particular cancer cell lines. Specifically, the GCN method used a convolutional neural network model to do heterogeneous graph embedding, and thus solved a link prediction task. The graph in this study was a multimodal graph, which was constructed by integrating the drug-drug combination, drug-protein interaction, and protein-protein interaction networks. We found that the GCN model was able to correctly predict cell line-specific synergistic drug combinations from a large heterogonous network. The majority (30) of the 39 cell line-specific models show an area under the receiver operational characteristic curve (AUC) larger than 0.80, resulting in a mean AUC of 0.84. Moreover, we conducted an in-depth literature survey to investigate the top predicted drug combinations in specific cancer cell lines and found that many of them have been found to show synergistic antitumor activity against the same or other cancers in vitro or in vivo. Selleckchem SecinH3 Taken together, the results indicate that our study provides a promising way to better predict and optimize synergistic drug pairs in silico. © 2020 The Authors.Nanotubes are miniature materials with significant potential applications in nanotechnological, medical, biological and material sciences. The quest for manufacturing methods of nano-mechanical modules is in progress. For example, the application of carbon nanotubes has been extensively investigated due to the precise width control, but the precise length control remains challenging. Here we report two approaches for the one-pot self-assembly of RNA nanotubes. For the first approach, six RNA strands were used to assemble the nanotube by forming a 11 nm long hollow channel with the inner diameter of 1.7 nm and the outside diameter of 6.3 nm. For the second approach, six RNA strands were designed to hybridize with their neighboring strands by complementary base pairing and formed a nanotube with a six-helix hollow channel similar to the nanotube assembled by the first approach. The fabricated RNA nanotubes were characterized by gel electrophoresis and atomic force microscopy (AFM), confirming the formation of nanotube-shaped RNA nanostructures. Cholesterol molecules were introduced into RNA nanotubes to facilitate their incorporation into lipid bilayer. Incubation of RNA nanotube complex with the free-standing lipid bilayer membrane under applied voltage led to discrete current signatures. Addition of peptides into the sensing chamber revealed discrete steps of current blockage. Polyarginine peptides with different lengths can be detected by current signatures, suggesting that the RNA-cholesterol complex holds the promise of achieving single molecule sensing of peptides.Surgical resection followed by concurrent radiation therapy and temozolomide (TMZ) chemotherapy is the current standard treatment for glioblastoma multiforme (GBM). The present metaanalysis investigated the impact of prolonged TMZ maintenance therapy (more than 6 cycles) in comparison with standard TMZ maintenance therapy (exactly six cycles) on overall survival (OS) and progression-free survival (PFS) of patients with GBM. A meta-analysis of the literature was conducted using Medline, PubMed, EMBASE and the Cochrane Library in accordance with PRISMA guidelines. Seven articles involving 1018 patients were included. The overall survival was higher in the case group (>6 cycles TMZ) compared to the control group (6 cycles TMZ) (Z=2.375, P=0.018). The lower and upper limits were between 1.002-10.467 months. The case group had higher progression-free survival compared with the control group (Z=3.84; P less then 0.001). The lower and upper limits were between 2.559-7.894 months. Evidence from this meta-analysis suggests that prolonged TMZ therapy compared to the standard 6-cycle TMZ therapy was associated with higher survival in patients with glioblastoma. ©Copyright the Author(s), 2020.