Glycyrrhetinic acid and E.resveratroloside act as potential plant derived compounds against dopamine receptor D3 for Parkinson's disease: a pharmacoinformatics study

Parkinson’s disease (PD) is caused by loss in nigrostriatal dopaminergic neurons and is ranked as the second most common neurodegenerative disorder. Dopamine receptor D3 is considered as a potential target in drug development against PD because of its lesser side effects and higher degree of neuro-protection. One of the prominent therapies currently available for PD is the use of dopamine agonists which mimic the natural action of dopamine in the brain and stimulate dopamine receptors directly. Unfortunately, use of these pharmacological therapies such as bromocriptine, apomorphine, and ropinirole provides only temporary relief of the disease symptoms and is frequently linked with insomnia, anxiety, depression, and agitation. Thus, there is a need for an alternative treatment that not only hinders neurodegeneration, but also has few or no side effects. Since the past decade, much attention has been given to exploitation of phytochemicals and their use in alternative medicine research. This is because plants are a cheap, indispensable, and never ending resource of active compounds that are beneficial against various diseases. In the current study, 40 active phytochemicals against PD were selected through literature survey. These ligands were docked with dopamine receptor D3 using AutoDock and AutoDockVina. Binding energies were compared to docking results of drugs approved by the US Food and Drug Administration against PD. The compounds were further analyzed for their absorption, distribution, metabolism, and excretion-toxicity profile. From the study it is concluded that glycyrrhetinic acid and E.resveratroloside are potent compounds having high binding energies which should be considered as potential lead compounds for drug development against PD

Glycyrrhetinic acid and E.resveratroloside act as potential plant derived compounds against dopamine receptor D3 for Parkinson's disease: a pharmacoinformatics study

Parkinson’s disease (PD) is caused by loss in nigrostriatal dopaminergic neurons and is ranked as the second most common neurodegenerative disorder. Dopamine receptor D3 is considered as a potential target in drug development against PD because of its lesser side effects and higher degree of neuro-protection. One of the prominent therapies currently available for PD is the use of dopamine agonists which mimic the natural action of dopamine in the brain and stimulate dopamine receptors directly. Unfortunately, use of these pharmacological therapies such as bromocriptine, apomorphine, and ropinirole provides only temporary relief of the disease symptoms and is frequently linked with insomnia, anxiety, depression, and agitation. Thus, there is a need for an alternative treatment that not only hinders neurodegeneration, but also has few or no side effects. Since the past decade, much attention has been given to exploitation of phytochemicals and their use in alternative medicine research. This is because plants are a cheap, indispensable, and never ending resource of active compounds that are beneficial against various diseases. In the current study, 40 active phytochemicals against PD were selected through literature survey. These ligands were docked with dopamine receptor D3 using AutoDock and AutoDockVina. Binding energies were compared to docking results of drugs approved by the US Food and Drug Administration against PD. The compounds were further analyzed for their absorption, distribution, metabolism, and excretion-toxicity profile. From the study it is concluded that glycyrrhetinic acid and E.resveratroloside are potent compounds having high binding energies which should be considered as potential lead compounds for drug development against PD

SuperCYPsPred - a web server for the prediction of cytochrome activity

Cytochrome P450 enzymes (CYPs)-mediated drug metabolism influences drug pharmacokinetics and results in adverse outcomes in patients through drug-drug interactions (DDIs). Absorption, distribution, metabolism, excretion and toxicity (ADMET) issues are the leading causes for the failure of a drug in the clinical trials. As details on their metabolism are known for just half of the approved drugs, a tool for reliable prediction of CYPs specificity is needed. The SuperCYPsPred web server is currently focused on five major CYPs isoenzymes, which includes CYP1A2, CYP2C19, CYP2D6, CYP2C9 and CYP3A4 that are responsible for more than 80% of the metabolism of clinical drugs. The prediction models for classification of the CYPs inhibition are based on well-established machine learning methods. The models were validated both on cross-validation and external validation sets and achieved good performance. The web server takes a 2D chemical structure as input and reports the CYP inhibition profile of the chemical for 10 models using different molecular fingerprints, along with confidence scores, similar compounds, known CYPs information of drugs-published in literature, detailed interaction profile of individual cytochromes including a DDIs table and an overall CYPs prediction radar chart (http://insilico-cyp.charite.de/SuperCYPsPred/).The web server does not require log in or registration and is free to use

GIMLET: A Unified Graph-Text Model for Instruction-Based Molecule Zero-Shot Learning

Molecule property prediction has gained significant attention in recent years. The main bottleneck is the label insufficiency caused by expensive lab experiments. In order to alleviate this issue and to better leverage textual knowledge for tasks, this study investigates the feasibility of employing natural language instructions to accomplish molecule-related tasks in a zero-shot setting. We discover that existing molecule-text models perform poorly in this setting due to inadequate treatment of instructions and limited capacity for graphs. To overcome these issues, we propose GIMLET, which unifies language models for both graph and text data. By adopting generalized position embedding, our model is extended to encode both graph structures and instruction text without additional graph encoding modules. GIMLET also decouples encoding of the graph from tasks instructions in the attention mechanism, enhancing the generalization of graph features across novel tasks. We construct a dataset consisting of more than two thousand molecule tasks with corresponding instructions derived from task descriptions. We pretrain GIMLET on the molecule tasks along with instructions, enabling the model to transfer effectively to a broad range of tasks. Experimental results demonstrate that GIMLET significantly outperforms molecule-text baselines in instruction-based zero-shot learning, even achieving closed results to supervised GNN models on tasks such as toxcast and muv

Computational methods and tools to predict cytochrome P450 metabolism for drug discovery

In this review, we present important, recent developments in the computational prediction of cytochrome P450 (CYP) metabolism in the context of drug discovery. We discuss in silico models for the various aspects of CYP metabolism prediction, including CYP substrate and inhibitor predictors, site of metabolism predictors (i.e., metabolically labile sites within potential substrates) and metabolite structure predictors. We summarize the different approaches taken by these models, such as rule‐based methods, machine learning, data mining, quantum chemical methods, molecular interaction fields, and docking. We highlight the scope and limitations of each method and discuss future implications for the field of metabolism prediction in drug discovery.publishedVersio

Exploring the effects of polymorphic variation on the stability and function of human cytochrome P450 enzymes in silico and in vitro

Includes bibliographical references.Cytochrome P450s are highly polymorphic enzymes responsible for the Phase I metabolism of over 80% of pharmaceutical drugs. Polymorphic variation can result in altered drug efficacy as well as adverse drug reactions so the lack of understanding of the effects of single amino acid substitutions on cytochrome P450 drug metabolism is a major problem for drug development. In order to begin to address this problem, this thesis describes an in silico analysis of over 300 nonsynonymous single nucleotide polymorphisms found across nine of the major human drug metabolising cytochrome P450 isoforms. Information from functional studies - in which regions of the cytochrome P450 structure important for substrate recognition, substrate and product access and egress and interaction with the cytochrome P450 reductase were delineated - was combined with in silico calculations on the effect of mutations on protein stability in order to establish the likely causes of altered drug metabolism observed for cytochrome P450 variants in functional assays carried out to date. This study revealed that 75% of all cytochrome P450 mutations showing altered activity in vitro are either predicted to be damaging to protein structure or are found within regions predicted to be important for catalytic activity. Furthermore, this study showed that 70% of the mutations that showed similar activity to the wild-type enzyme in in vitro studies lie outside of functional regions important for catalytic activity and are predicted to have no effect on protein stability. Based on these results, a cytochrome P450 polymorphic variant map was created that should find utility in predicting the functional effect of uncharacterised variants on drug metabolism. To further test the accuracy of the in silico predictions, in vitro assays were performed on a panel of CYP3A4 and CYP2C9 variants heterogeneously expressed in E.coli. All mutations predicted to alter protein function by stabilising or destabilising the apo-protein structure in silico were found to significantly alter the thermostability of the holo-protein in solution. Thermostability assays also suggest that other mutations may affect stability by disrupting haem binding, changing protein conformation or altering oligomer formation. The utility of a fluorescence-based functional P450 protein microarray platform, previously developed in our laboratory, for generating kinetic data for multiple CYP450 variants in parallel was also examined. Since the microarray platform in its current stage of development was found to be unsuitable for this purpose, kinetic data for the full panel of CYP3A4 and CYP2C9 variants was generated using solution phase assays, revealing several variants with altered catalytic turnover and/or binding affinity for fluorescent substrates

Antioxidant and cytotoxic activities of different solvent fractions from Murraya koenigii shoots: HPLC quantification And molecular docking of identified phenolics with anti-apoptotic proteins

ABSTRACT. Murraya koenigii is known for its health benefits against constipation, diarrhea, bacterial infections, wounds and skin related diseases. Aim of this project is to determine cytotoxic aptitude of antioxidant compounds present in M. koenigii. The fractionation of M. koenigii shoots methanol extract was carried out with different solvents followed by determination of total phenolic content, radical scavenging potential along with phenolic profile. M. koenigii shoot fractions were analyzed for their cytotoxic potential by MTT assay besides evaluating molecular interactions between identified phenolics with Bcl-2, Bcl-xl and MCL-1. The results revealed that butanol fraction contains maximum amount of quercetin, 4-hydroxy-3-methoxy benzoic acid and trans-4-hydroxy-3-methoxy cinnamic acid. Ferulic acid is abundant in water fraction whereas n-hexane fractions contain sinapic and vanillic acids. The ethyl acetate fraction possess the highest level of phenolics as well as radical scavenging potential. HPLC results show that 9 organic acids are present in ethyl acetate and butanol fractions. The highest cytotoxic activity was exhibited by n-hexane and ethyl acetate fractions. Molecular docking studies supports that ethyl acetate and n-hexane fractions are the major sources of antioxidant and cytotoxic compounds. Also, molecular interactions exist between identified phenolics from plant shoots fractions with anti-apoptotic proteins Bcl-2, Bcl-xl and MCL-1.   KEY WORDS: Morraya koenigii, Fractionation, Antioxidant, Cytotoxic, Molecular docking Bull. Chem. Soc. Ethiop. 2022, 36(3), 651-666.                                                               DOI: https://dx.doi.org/10.4314/bcse.v36i3.14                                                     &nbsp