Cheminformatics
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Computational exploration of chemical space is crucial in modern cheminformatics research for accelerating the discovery of new biologically active compounds. In this study, we present a detailed analysis of the chemical library of potential glucocorticoid receptor (GR) ligands generated by the molecular generator, Molpher. To generate the targeted GR library and construct the classification models, structures from the ChEMBL database as well as from the internal IMG library, which was experimentally screened for biological activity in the primary luciferase reporter cell assay, were utilized. The composition of the targeted GR ligand library was compared with a reference library that randomly samples chemical space. A random forest model was used to determine the biological activity of ligands, incorporating its applicability domain using conformal prediction. It was demonstrated that the GR library is significantly enriched with GR ligands compared to the random library. Furthermore, a prospective analysis demonstrated that Molpher successfully designed compounds, which were subsequently experimentally confirmed to be active on the GR. A collection of 34 potential new GR ligands was also identified. Moreover, an important contribution of this study is the establishment of a comprehensive workflow for evaluating computationally generated ligands, particularly those with potential activity against targets that are challenging to dock.
- MeSH
- knihovny malých molekul * farmakologie chemie MeSH
- lidé MeSH
- ligandy MeSH
- receptory glukokortikoidů * metabolismus chemie MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
A novel series of proflavine ureas, derivatives 11a-11i, were synthesized on the basis of molecular modeling design studies. The structure of the novel ureas was obtained from the pharmacological model, the parameters of which were determined from studies of the structure-activity relationship of previously prepared proflavine ureas bearing n-alkyl chains. The lipophilicity (LogP) and the changes in the standard entropy (ΔS°) of the urea models, the input parameters of the pharmacological model, were determined using quantum mechanics and cheminformatics. The anticancer activity of the synthesized derivatives was evaluated against NCI-60 human cancer cell lines. The urea derivatives azepyl 11b, phenyl 11c and phenylethyl 11f displayed the highest levels of anticancer activity, although the results were only a slight improvement over the hexyl urea, derivative 11j, which was reported in a previous publication. Several of the novel urea derivatives displayed GI50 values against the HCT-116 cancer cell line, which suggest the cytostatic effect of the compounds azepyl 11b-0.44 μM, phenyl 11c-0.23 μM, phenylethyl 11f-0.35 μM and hexyl 11j-0.36 μM. In contrast, the novel urea derivatives 11b, 11c and 11f exhibited levels of cytotoxicity three orders of magnitude lower than that of hexyl urea 11j or amsacrine.
- MeSH
- chemické jevy MeSH
- entropie * MeSH
- fibroblasty cytologie účinky léků MeSH
- inhibiční koncentrace 50 MeSH
- kinetika MeSH
- lidé MeSH
- močovina chemická syntéza chemie farmakologie MeSH
- molekulární modely MeSH
- proflavin chemická syntéza chemie farmakologie MeSH
- Check Tag
- lidé MeSH
- mužské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
The problem of the efficient treatment of acute organophosphorus (OP) poisoning needs more efforts in the development of a versatile antidote, applicable for treatment of the injuries of both peripheral and central nervous systems. A series of N-H, N-methyl, N-butyl, and N-phenyl derivatives of benzhydroxamic (1a-1d), 3-methoxybenzhydroxamic (2a-2d), 4-methoxybenzhydroxamic (3a-3d) acids, and corresponding salycilhydroxamates (4a-4d) was prepared. Their predicted hydrophobicity (log P) was evaluated as regards to ВВВ score by the open access cheminformatics tools; prediction of the passive transport across the BBB was found by means on the parallel artificial membrane permeability assay (PAMPA). The data on reactivation capacity of human acetylcholinesterase (HssAChE) inhibited by GB, VX, and paraoxon was supported by molecular docking study on binding to the active site of the AChE, viability study against mammalian cells (Chinese hamster ovary CHO-K1), and biodegradability (Closed Bottle test OECD 301D). Among the studied compounds, N-butyl derivatives have better balanced combination of properties; among them, N-butylsalicylhydroxamic acid is most promising. The studied compounds demonstrate modest reactivation capacity; change of N-H by N-Me ensures the reactivation capacity in studied concentrations on all studied OP substrates; among N-butyl derivatives, the N-butylsalicylhydroxamic acid demonstrates most promising results within the series. The found regularities may lead to selection of perspective structures to complement current formulations for medical countermeasures against poisoning by organophosphorus toxicants.
- MeSH
- acetylcholinesterasa metabolismus MeSH
- antidota farmakologie MeSH
- CHO buňky MeSH
- cholinesterasové inhibitory chemie farmakologie MeSH
- Cricetulus MeSH
- křečci praví MeSH
- lidé MeSH
- otrava organofosfáty * MeSH
- oximy chemie MeSH
- reaktivátory cholinesterasy * chemie farmakologie MeSH
- simulace molekulového dockingu MeSH
- vztahy mezi strukturou a aktivitou MeSH
- zvířata MeSH
- Check Tag
- křečci praví MeSH
- lidé MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
Building reliable and robust quantitative structure-property relationship (QSPR) models is a challenging task. First, the experimental data needs to be obtained, analyzed and curated. Second, the number of available methods is continuously growing and evaluating different algorithms and methodologies can be arduous. Finally, the last hurdle that researchers face is to ensure the reproducibility of their models and facilitate their transferability into practice. In this work, we introduce QSPRpred, a toolkit for analysis of bioactivity data sets and QSPR modelling, which attempts to address the aforementioned challenges. QSPRpred's modular Python API enables users to intuitively describe different parts of a modelling workflow using a plethora of pre-implemented components, but also integrates customized implementations in a "plug-and-play" manner. QSPRpred data sets and models are directly serializable, which means they can be readily reproduced and put into operation after training as the models are saved with all required data pre-processing steps to make predictions on new compounds directly from SMILES strings. The general-purpose character of QSPRpred is also demonstrated by inclusion of support for multi-task and proteochemometric modelling. The package is extensively documented and comes with a large collection of tutorials to help new users. In this paper, we describe all of QSPRpred's functionalities and also conduct a small benchmarking case study to illustrate how different components can be leveraged to compare a diverse set of models. QSPRpred is fully open-source and available at https://github.com/CDDLeiden/QSPRpred .Scientific ContributionQSPRpred aims to provide a complex, but comprehensive Python API to conduct all tasks encountered in QSPR modelling from data preparation and analysis to model creation and model deployment. In contrast to similar packages, QSPRpred offers a wider and more exhaustive range of capabilities and integrations with many popular packages that also go beyond QSPR modelling. A significant contribution of QSPRpred is also in its automated and highly standardized serialization scheme, which significantly improves reproducibility and transferability of models.
- Publikační typ
- časopisecké články MeSH
Molecular dynamics simulations serve as a prevalent approach for investigating the dynamic behaviour of proteins and protein-ligand complexes. Due to its versatility and speed, GROMACS stands out as a commonly utilized software platform for executing molecular dynamics simulations. However, its effective utilization requires substantial expertise in configuring, executing, and interpreting molecular dynamics trajectories. Existing automation tools are constrained in their capability to conduct simulations for large sets of compounds with minimal user intervention, or in their ability to distribute simulations across multiple servers. To address these challenges, we developed a Python-based tool that streamlines all phases of molecular dynamics simulations, encompassing preparation, execution, and analysis. This tool minimizes the required knowledge for users engaging in molecular dynamics simulations and can efficiently operate across multiple servers within a network or a cluster. Notably, the tool not only automates trajectory simulation but also facilitates the computation of free binding energies for protein-ligand complexes and generates interaction fingerprints across the trajectory. Our study demonstrated the applicability of this tool on several benchmark datasets. Additionally, we provided recommendations for end-users to effectively utilize the tool.Scientific contributionThe developed tool, StreaMD, is applicable to different systems (proteins, ligands and their complexes including co-factors) and requires a little user knowledge to setup and run molecular dynamics simulations. Other features of StreaMD are seamless integration with calculation of MM-GBSA/PBSA binding free energies and protein-ligand interaction fingerprints, and running of simulations within distributed environments. All these will facilitate routine and massive molecular dynamics simulations.
- Publikační typ
- časopisecké články MeSH
Tunnels in enzymes with buried active sites are key structural features allowing the entry of substrates and the release of products, thus contributing to the catalytic efficiency. Targeting the bottlenecks of protein tunnels is also a powerful protein engineering strategy. However, the identification of functional tunnels in multiple protein structures is a non-trivial task that can only be addressed computationally. We present a pipeline integrating automated structural analysis with an in-house machine-learning predictor for the annotation of protein pockets, followed by the calculation of the energetics of ligand transport via biochemically relevant tunnels. A thorough validation using eight distinct molecular systems revealed that CaverDock analysis of ligand un/binding is on par with time-consuming molecular dynamics simulations, but much faster. The optimized and validated pipeline was applied to annotate more than 17,000 cognate enzyme-ligand complexes. Analysis of ligand un/binding energetics indicates that the top priority tunnel has the most favourable energies in 75% of cases. Moreover, energy profiles of cognate ligands revealed that a simple geometry analysis can correctly identify tunnel bottlenecks only in 50% of cases. Our study provides essential information for the interpretation of results from tunnel calculation and energy profiling in mechanistic enzymology and protein engineering. We formulated several simple rules allowing identification of biochemically relevant tunnels based on the binding pockets, tunnel geometry, and ligand transport energy profiles.Scientific contributionsThe pipeline introduced in this work allows for the detailed analysis of a large set of protein-ligand complexes, focusing on transport pathways. We are introducing a novel predictor for determining the relevance of binding pockets for tunnel calculation. For the first time in the field, we present a high-throughput energetic analysis of ligand binding and unbinding, showing that approximate methods for these simulations can identify additional mutagenesis hotspots in enzymes compared to purely geometrical methods. The predictor is included in the supplementary material and can also be accessed at https://github.com/Faranehhad/Large-Scale-Pocket-Tunnel-Annotation.git . The tunnel data calculated in this study has been made publicly available as part of the ChannelsDB 2.0 database, accessible at https://channelsdb2.biodata.ceitec.cz/ .
- Publikační typ
- časopisecké články MeSH
Docking of large compound collections becomes an important procedure to discover new chemical entities. Screening of large sets of compounds may also occur in de novo design projects guided by molecular docking. To facilitate these processes, there is a need for automated tools capable of efficiently docking a large number of molecules using multiple computational nodes within a reasonable timeframe. These tools should also allow for easy integration of new docking programs and provide a user-friendly program interface to support the development of further approaches utilizing docking as a foundation. Currently available tools have certain limitations, such as lacking a convenient program interface or lacking support for distributed computations. In response to these limitations, we have developed a module called EasyDock. It can be deployed over a network of computational nodes using the Dask library, without requiring a specific cluster scheduler. Furthermore, we have proposed and implemented a simple model that predicts the runtime of docking experiments and applied it to minimize overall docking time. The current version of EasyDock supports popular docking programs, namely Autodock Vina, gnina, and smina. Additionally, we implemented a supplementary feature to enable docking of boron-containing compounds, which are not inherently supported by Vina and smina, and demonstrated its applicability on a set of 55 PDB protein-ligand complexes.
- Publikační typ
- časopisecké články MeSH
