Wangwichmann8189

Comparative Binding Energy (COMBINE) analysis is an approach for deriving a target-specific scoring function to compute binding free energy, drug-binding kinetics, or a related property by exploiting the information contained in the three-dimensional structures of receptor-ligand complexes. Here, we describe the process of setting up and running COMBINE analysis to derive a Quantitative Structure-Kinetics Relationship (QSKR) for the dissociation rate constants (koff) of inhibitors of a drug target. The derived QSKR model can be used to estimate residence times (τ, τ=1/koff) for similar inhibitors binding to the same target, and it can also help to identify key receptor-ligand interactions that distinguish inhibitors with short and long residence times. Herein, we demonstrate the protocol for the application of COMBINE analysis on a dataset of 70 inhibitors of heat shock protein 90 (HSP90) belonging to 11 different chemical classes. The procedure is generally applicable to any drug target with known structural information on its complexes with inhibitors.Medicinal chemistry society has enough arguments to justify the usage of fragment-based drug design (FBDD) methodologies for the identification of lead compounds. Since the FDA approval of three kinase inhibitors - vemurafenib, venetoclax, and erdafitinib, FBDD has become a challenging alternative to high-throughput screening methods in drug discovery. The following protocol presents in silico drug design of selective histone deacetylase 6 (HDAC6) inhibitors through a fragment-based approach. To date, structural motifs that are important for HDAC inhibitory activity and selectivity are described as surface recognition group (CAP group), aliphatic or aromatic linker, and zinc-binding group (ZBG). The main idea of this FBDD method is to identify novel and target-selective CAP groups by virtual scanning of publicly available fragment databases. Template structure used to search for novel heterocyclic and carbocyclic fragments is 1,8-naphthalimide (CAP group of scriptaid, a potent HDAC inhibitor). Herein, the design of HDAC6 inhibitors is based on linking the identified fragments with the aliphatic or aromatic linker and hydroxamic acid (ZBG) moiety. Final selection of potential selective HDAC6 inhibitors is based on combined structure-based (molecular docking) and ligand-based (three-dimensional quantitative structure-activity relationships, 3D-QSAR) techniques. Designed compounds are docked in the active site pockets of human HDAC1 and HDAC6 isoforms, and their docking conformations used to predict their HDAC inhibitory and selectivity profiles through two developed 3D-QSAR models (describing HDAC1 and HDAC6 inhibitory activities).Molecular docking produces often lackluster results in real-life virtual screening assays that aim to discover novel drug candidates or hit compounds. The problem lies in the inability of the default docking scoring to properly estimate the Gibbs free energy of binding, which impairs the recognition of the best binding poses and the separation of active ligands from inactive compounds. Negative image-based rescoring (R-NiB) provides both effective and efficient way for re-ranking the outputted flexible docking poses to improve the virtual screening yield. Importantly, R-NiB has been shown to work with multiple genuine drug targets and six popular docking algorithms using demanding benchmark test sets. The effectiveness of the R-NiB methodology relies on the shape/electrostatics similarity between the target protein's ligand-binding cavity and the docked ligand poses. In this chapter, the R-NiB method is described with practical usability in mind.Rational drug discovery relies heavily on molecular docking-based virtual screening, which samples flexibly the ligand binding poses against the target protein's structure. The upside of flexible docking is that the geometries of the generated docking poses are adjusted to match the residue alignment inside the target protein's ligand-binding pocket. The downside is that the flexible docking requires plenty of computing resources and, regardless, acquiring a decent level of enrichment typically demands further rescoring or post-processing. Negative image-based screening is a rigid docking technique that is ultrafast and computationally light but also effective as proven by vast benchmarking and screening experiments. In the NIB screening, the target protein cavity's shape/electrostatics is aligned and compared against ab initio-generated ligand 3D conformers. In this chapter, the NIB methodology is explained at the practical level and both its weaknesses and strengths are discussed candidly.Interactions between enzymes and small molecules lie in the center of many fundamental biochemical processes. Their analysis using molecular dynamics simulations have high computational demands, geometric approaches fail to consider chemical forces, and molecular docking offers only static information. Recently, we proposed to combine molecular docking and geometric approaches in an application called CaverDock. CaverDock is discretizing enzyme tunnel into discs, iteratively docking with restraints into one disc after another and searching for a trajectory of the ligand passing through the tunnel. Here, we focus on the practical side of its usage describing the whole method from getting the application, and processing the data through a workflow, to interpreting the results. Moreover, we shared the best practices, recommended how to solve the most common issues, and demonstrated its application on three use cases.In silico rational drug design is one of the major pylons in the drug discovery process. Drugs usually act on specific targets such as proteins, DNA, and lipid bilayers. Thus, molecular docking is an essential part of the rational drug design process. Molecular docking uses specific algorithms and scoring functions to reveal the strength of the interaction of the ligand to its target. AutoDock is a molecular docking suite that offers a variety of algorithms to tackle specific problems. These algorithms include Monte Carlo Simulated Annealing (SA), a Genetic Algorithm (GA), and a hybrid local search GA, also known as the Lamarckian Genetic Algorithm (LGA). This chapter aims to acquaint the reader with the docking process using AutoDockTools (GUI of AutoDock). Furthermore, herein is described the docking process of calf thymus DNA with three metal complexes, as a potential metallo-therapeutics as also the docking process of the plant flavonoid quercetin to the antiapoptotic protein BcL-xL.The mechanism of action of covalent drugs involves the formation of a bond between their electrophilic warhead group and a nucleophilic residue of the protein target. The recent advances in covalent drug discovery have accelerated the development of computational tools for the design and characterization of covalent binders. Covalent docking algorithms can predict the binding mode of covalent ligands by modeling the bonds and interactions formed at the reaction site. Their scoring functions can estimate the relative binding affinity of ligands towards the target of interest, thus allowing virtual screening of compound libraries. However, most of the scoring schemes have no specific terms for the bond formation, and therefore it prevents the direct comparison of warheads with different intrinsic reactivity. Herein, we describe a protocol for the binding mode prediction of covalent ligands, a typical virtual screening of compound sets with a single warhead chemistry, and an alternative approach to screen libraries that include various warhead types, as applied in recently validated studies.The interaction between a protein and its ligands is one of the basic and most important processes in biological chemistry. Docking methods aim to predict the molecular 3D structure of protein-ligand complexes starting from coordinates of the protein and the ligand separately. They are widely used in both industry and academia, especially in the context of drug development projects. AutoDock4 is one of the most popular docking tools and, as for any docking method, its performance is highly system dependent. Knowledge about specific protein-ligand interactions on a particular target can be used to successfully overcome this limitation. Here, we describe how to apply the AutoDock Bias protocol, a simple and elegant strategy that allows users to incorporate target-specific information through a modified scoring function that biases the ligand structure towards those poses (or conformations) that establish selected interactions. We discuss two examples using different bias sources. PI3K inhibitor In the first, we show how to steer dockings towards interactions derived from crystal structures of the receptor with different ligands; in the second example, we define and apply hydrophobic biases derived from Molecular Dynamics simulations in mixed solvents. Finally, we discuss general concepts of biased docking, its performance in pose prediction, and virtual screening campaigns as well as other potential applications.Molecular descriptors encode a variety of molecular representations for computer-assisted drug discovery. Here, we focus on the Weighted Holistic Atom Localization and Entity Shape (WHALES) descriptors, which were originally designed for scaffold hopping from natural products to synthetic molecules. WHALES descriptors capture molecular shape and partial charges simultaneously. We introduce the key aspects of the WHALES concept and provide a step-by-step guide on how to use these descriptors for virtual compound screening and scaffold hopping. The results presented can be reproduced by using the code freely available from URL github.com/ETHmodlab/scaffold_hopping_whales .This chapter provides a brief overview of the applications of ZINClick virtual library. In the last years, we have investigated the click-chemical space covered by molecules containing the triazole ring and generated a database of 1,2,3-triazoles called ZINClick, starting from literature reported alkynes and azides synthesizable in no more than three synthetic steps from commercially available products. This combinatorial database contains millions of 1,4-disubstituted 1,2,3-triazoles that are easily synthesizable. The library is regularly updated and can be freely downloaded from http//www.ZINClick.org . This virtual library is a good starting point to explore a new portion of chemical space.Many studies have reported attentional biases based on feature-reward associations. However, the effects of location-reward associations on attentional selection remain less well-understood. Unlike feature cases, a previous study that induced participants' awareness of the location-reward association by instructing them to look for a high-reward location has suggested the critical role of goal-driven manipulations in such associations. In this study, we investigated whether the reward effect occurred without goal-driven manipulations if participants were spontaneously aware of the location-reward association. We conducted three experiments using a visual search task that included four circles where participants received rewards; one possible target location was associated with a high reward, and another with a low reward. In Experiment 1, the target was presented among distractors, and participants had to search for the target. The results showed a faster reaction time in the high-reward rather than the low-reward locations only in participants aware of the location-reward association, even if they were not required to look for the association.