Notes

Digital Coherence and also Consistent Dephasing inside the Eye Control of Electrons within Graphene.
Molecular docking is commonly used for identification of drug candidates targeting a specified protein of known structure. With the increasing emphasis on drug repurposing over recent decades, molecular inverse docking has been widely applied to prediction of the potential protein targets of a specified molecule. In practice, inverse docking has many advantages, including early supervision of drugs' side effects and toxicity. MDock developed from our laboratory is a protein-ligand docking software based on a knowledge-based scoring function and has numerous applications to lead identification. In addition to its computational efficiency on ensemble docking for multiple protein conformations, MDock is well suited for inverse docking. In this chapter, we focus on introducing the protocol of inverse docking with MDock. For academic users, the MDock package is freely available at http//zoulab.dalton.missouri.edu/mdock.htm .Bionoi is a new software to generate Voronoi representations of ligand-binding sites in proteins for machine learning applications. Unlike many other deep learning models in biomedicine, Bionoi utilizes off-the-shelf convolutional neural network architectures, reducing the development work without sacrificing the performance. When initially generating images of binding sites, users have the option to color the Voronoi cells based on either one of six structural, physicochemical, and evolutionary properties, or a blend of all six individual properties. Encouragingly, after inputting images generated by Bionoi into the convolutional autoencoder, the network was able to effectively learn the most salient features of binding pockets. Elenbecestat concentration The accuracy of the generated model is evaluated both visually and numerically through the reconstruction of binding site images from the latent feature space. The generated feature vectors capture well various properties of binding sites and thus can be applied in a multitude of machine learning projects. As a demonstration, we trained the ResNet-18 architecture from Microsoft on Bionoi images to show that it is capable to effectively classify nucleotide- and heme-binding pockets against a large dataset of control pockets binding a variety of small molecules. Bionoi is freely available to the research community at https//github.com/CSBG-LSU/BionoiNet.Designing drugs that directly interact with multiple targets is a promising approach for treating complicated diseases. In order to successfully bind to multiple targets of different families and achieve the desired ligand efficiency, multi-target-directed ligands (MTDLs) require a higher level of diversity and complexity. De novo design strategies for creating more diverse chemical entities with desired properties may present an improved approach for developing MTDLs. In this chapter, we describe a computational protocol for developing MTDLs using the first reported multi-target de novo program, LigBuilder 3, which combines a binding site prediction module with de novo drug design and optimization modules. As an illustration of each detailed procedure, we design dual-functional compounds of two well-characterized virus enzymes, HIV protease and reverse transcriptase (PR and RT, respectively), using fragments extracted from known inhibitors. LigBuilder 3 is accessible at http//www.pkumdl.cn/ligbuilder3/ .Although science and technology have progressed rapidly, de novo drug development has been a costly and time-consuming process over the past decades. In this scenario, drug repurposing has appeared as an alternative tool to accelerate the drug development process. Herein, we applied such an approach to the highly popular human Carbonic Anhydrase (hCA) VA drug target, that is involved in ureagenesis, gluconeogenesis, lipogenesis, and in the metabolism regulation. Albeit several hCA inhibitors have been designed and are currently in clinical use, serious drug interactions have been reported due to their poor selectivity. In this perspective, the drug repurposing approach could be a useful tool for investigating the drug promiscuity/polypharmacology profile. In this chapter, we describe a combination of virtual screening techniques and in vitro assays aimed to identify novel selective hCA VA inhibitors and to repurpose drugs known for other clinical indications.Molecular dynamics simulations can now routinely access the microsecond timescale, making feasible direct sampling of ligand association events. While Markov State Model (MSM) approaches offer a useful framework for analyzing such trajectory data to gain insight into binding mechanisms, accurate modeling of ligand association pathways and kinetics must be done carefully. We describe methods and good practices for constructing MSMs of ligand binding from unbiased trajectory data and discuss how to use time-lagged independent component analysis (tICA) to build informative models, using as an example recent simulation work to model the binding of phenylalanine to the regulatory ACT domain dimer of phenylalanine hydroxylase. We describe a variety of methods for estimating association rates from MSMs and discuss how to distinguish between conformational selection and induced-fit mechanisms using MSMs. In addition, we review some examples of MSMs constructed to elucidate the mechanisms by which p53 transactivation domain (TAD) and related peptides bind the oncoprotein MDM2.Three-dimensional pharmacophore models have been proven extremely valuable in exploring novel chemical space through virtual screening. However, traditional pharmacophore-based approaches need ligand information and rely on static snapshots of highly dynamic systems. In this chapter, we describe PyRod, a novel tool to generate three-dimensional pharmacophore models based on water traces of a molecular dynamics simulation of an apo-protein.The protocol described herein was successfully applied for the discovery of novel drug-like inhibitors of West Nile virus NS2B-NS3 protease. By using this recent example, we highlight the key steps of the generation and validation of PyRod-derived pharmacophore models and their application for virtual screening.
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