Sampling of conformational ensemble for virtual screening using molecular dynamics simulations and normal mode analysis

Aim: Molecular dynamics simulations and normal mode analysis are well-established approaches to generate receptor conformational ensembles (RCEs) for ligand docking and virtual screening. Here, we report new fast molecular dynamics-based and normal mode analysis-based protocols combined with conformational pocket classifications to efficiently generate RCEs. Materials \& methods: We assessed our protocols on two well-characterized protein targets showing local active site flexibility, dihydrofolate reductase and large collective movements, CDK2. The performance of the RCEs was validated by distinguishing known ligands of dihydrofolate reductase and CDK2 among a dataset of diverse chemical decoys. Results \& discussion: Our results show that different simulation protocols can be efficient for generation of RCEs depending on different kind of protein flexibility

Thermal and Dielectric Properties of High Performance Polymer/ZnO Nanocomposites

<p>ØZinc oxide (ZnO) filled high performance poly(aryletherketon) (PAEK) matrix nanocomposites were studied for the application in electronic applications. The nanocomposites were prepared using planetary ball milling process followed by hot pressing. Experimental density of the nanocomposites was close to those of theoretical density indicating porosity free samples. Scanning electron microscopy showed excellent dispersion of nano sized (< 100 nm) ZnO particles into the PAEK matrix. X-ray diffraction (XRD) confirmed that the size of ZnO crystallites is about 58 nm. Thermogravimetry analyzer (TGA) showed significant increase in thermal stability and char yield of the nanocomposites with increasing ZnO content in the matrix. The dielectric constants of the nanocomposites increased signi</p> <div><a> Save changes </a></div> <p>ficantly compared to those of pure PAEK.</p

Dataset for Conflicting Statements Detection in Text

<p>The files are from three different. One of the three datasets (SemEval) is downloaded from SemEval-2014 which was an international workshop on semantic evaluation conducted in Dublin (Ireland). Another dataset is same dataset (Stanford) as used by Marneffe et al. for their work on finding contradictions in text. Another dataset that we use is the PHEME RTE (Recognizing Textual Entailment). The attached dataset consists of annotated dataset into four different types of contradictions. It consists of intermediate results and feature values on our work on conflicting statements detection in text.</p