Hasil In Silico Senyawa Z12501572, Z00321025, SCB5631028 dan SCB13970547 dibandingkan Turunan Zerumbon terhadap Human Liver Glycogen Phosphorylase (1l5Q) sebagai Antidiabetes

Abstrak Human Liver Glycogen Phosphorylase (HLGP), suatu katalis glikogen yang mengontrol pelepasan glukosa-1-fosfat glikogen dari hati. Enzim ini mempunyai peran sentral dalam luaran glukosa hati sehingga menjadi target obat antidiabetik. Kajian docking dilakukan pada komputer dengan prosesor Intel Pentium, RAM 1 GB dan Windows 7. Ligan yang digunakan adalah senyawa obat (Z12501572, Z00321025, SCB5631028 dan SCB13970547), dataset pembanding aktif glycogen phosphorylase outer dimer site (PYGL-out) dan decoysdari www.dekois.com dan turunan zerumbon. Protein dipisahkan dari ligan nativ dan semua ligan beserta protein dikonversi menggunakan PyRx. Visualisasi interaksi ligan-protein dihasilkan dengan program Protein-Ligand Interaction Profiler (PLIP) dan PyMOL. Senyawa ZER11 memiliki binding energy terbaik, yaitu -7.11 kkal/mol (untuk metode LGA dan GA) dan -4.08 kkal/mol untuk metode SA. Nilai binding energy tersebut lebih rendah dari pada nilai untuk ligan native dan satu dari keempat senyawa obat, terlebih jika dibandingkan dengan bindingaffinity dari dataset dan decoys. Interaksi ligan-protein pada ketiga metode tersebut ditemukan sangat bervariasi. Hal berbeda terjadi untuk metode Vina, bindingenergy ZER11 (-9.9 kkal/mol) lebih baik dibandingkan dengan ligan native dan keempat senyawa obat. Senyawa ZER11 memiliki residu interaksi yang sama dengan ligan native pada TRP67 dan LYS191 untuk metode Vina. Kata kunci: PDBID-1L5Q, AutoDock, docking molekuler, vina, antidiabetes Abstract Human Liver Glycogen Phosphorylase (HLGP) can catalyze glycogen and control the release of glucose-1-phosphate of glycogen from the liver. This enzyme has a central role in output rule of liver glucose as it can be used as an antidiabetic drug targets. Docking studies were carried out on PC with Intel Pentium, 1 GB RAM, in environment of Windows 7. Ligands used are drug compounds (Z12501572, Z00321025, SCB5631028 and SCB13970547), the active dataset comparator wasglycogenphosphorylase outer dimer site (PYGL-out) and decoys from www.dekois.com andzerumbonederivates. Protein was separated from its native ligand and all ligands including the protein were converted to pdbqt using PyRx. The interaction of protein-ligand was visualized using software of PLIP and PyMOL. Compound of ZER11 had the best binding energy were -7.11 kcal/mol (LGA and GA) and -4.08 kcal/mol (SA). The binding energy value was lower than the ligand native and one of the four drug compounds, especially compared with the binding affinity of dataset and decoys. Vice versa, for Vina method, the value of ligand binding protein for ZER11 (-9.9 kcal/mol) was better than the ligand native and all of the fourth drugcompounds. Vina result showed that ZER11 had the same residual interaction as the ligand native, which are TRP67 and LYS191. Keyword: PDBID-1L5Q, AutoDock, molecular docking, vina, antidiabetic DOI: http://dx.doi.org/10.15408/jkv.v0i0.4170

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Spiro-acridine inhibiting tyrosinase enzyme: Kinetic, protein-ligand interaction and molecular docking studies

International Journal of Biological Macromolecules ◽

10.1016/j.ijbiomac.2018.10.175 ◽

2019 ◽

Vol 122 ◽

pp. 289-297 ◽

Cited By ~ 6

Author(s):

Thaís Meira Menezes ◽

Sinara Mônica Vitalino de Almeida ◽

Ricardo Olímpio de Moura ◽

Gustavo Seabra ◽

Maria do Carmo Alves de Lima ◽

...

Keyword(s):

Molecular Docking ◽

Docking Studies ◽

Enzyme Kinetic ◽

Ligand Interaction ◽

Molecular Docking Studies ◽

Protein Ligand Interaction ◽

Tyrosinase Enzyme

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Compound2Drug – a Machine/deep Learning Tool for Predicting the Bioactivity of PubChem Compounds

10.26434/chemrxiv.13052951 ◽

2020 ◽

Author(s):

Ben Geoffrey A S ◽

Pavan Preetham Valluri ◽

Akhil Sanker ◽

Rafal Madaj ◽

Host Antony Davidd ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Molecular Docking ◽

Drug Target ◽

Drug Targets ◽

Learning Algorithms ◽

Network Data ◽

Ligand Interaction ◽

Pubchem Compound ◽

Protein Ligand Interaction

Network data is composed of nodes and edges. Successful application of machine learning/deep learning algorithms on network data to make node classification and link prediction has been shown in the area of social networks through which highly customized suggestions are offered to social network users. Similarly one can attempt the use of machine learning/deep learning algorithms on biological network data to generate predictions of scientific usefulness. In the present work, compound-drug target interaction data set from bindingDB has been used to train machine learning/deep learning algorithms which are used to predict the drug targets for any PubChem compound queried by the user. The user is required to input the PubChem Compound ID (CID) of the compound the user wishes to gain information about its predicted biological activity and the tool outputs the RCSB PDB IDs of the predicted drug target. The tool also incorporates a feature to perform automated In Silico modelling for the compounds and the predicted drug targets to uncover their protein-ligand interaction profiles. The programs fetches the structures of the compound and the predicted drug targets, prepares them for molecular docking using standard AutoDock Scripts that are part of MGLtools and performs molecular docking, protein-ligand interaction profiling of the targets and the compound and stores the visualized results in the working folder of the user. The program is hosted, supported and maintained at the following GitHub repository <a href="https://github.com/bengeof/Compound2Drug">https://github.com/bengeof/Compound2Drug</a>

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SuCOS is Better than RMSD for Evaluating Fragment Elaboration and Docking Poses

10.26434/chemrxiv.8100203.v1 ◽

2019 ◽

Cited By ~ 1

Author(s):

Susan Leung ◽

Michael Bodkin ◽

Frank von Delft ◽

Paul Brennan ◽

Garrett Morris

Keyword(s):

Binding Mode ◽

Docking Studies ◽

Binding Modes ◽

False Negatives ◽

Ligand Interaction ◽

Cross Docking ◽

Protein Ligand Interaction ◽

Feature Overlap ◽

Better Than ◽

Fragment Based Drug Discovery

One of the fundamental assumptions of fragment-based drug discovery is that the fragment’s binding mode will be conserved upon elaboration into larger compounds. The most common way of quantifying binding mode similarity is Root Mean Square Deviation (RMSD), but Protein Ligand Interaction Fingerprint (PLIF) similarity and shape-based metrics are sometimes used. We introduce SuCOS, an open-source shape and chemical feature overlap metric. We explore the strengths and weaknesses of RMSD, PLIF similarity, and SuCOS on a dataset of X-ray crystal structures of paired elaborated larger and smaller molecules bound to the same protein. Our redocking and cross-docking studies show that SuCOS is superior to RMSD and PLIF similarity. When redocking, SuCOS produces fewer false positives and false negatives than RMSD and PLIF similarity; and in cross-docking, SuCOS is better at differentiating experimentally-observed binding modes of an elaborated molecule given the pose of its non-elaborated counterpart. Finally we show that SuCOS performs better than AutoDock Vina at differentiating actives from decoy ligands using the DUD-E dataset. SuCOS is available at https://github.com/susanhleung/SuCOS .

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SuCOS is Better than RMSD for Evaluating Fragment Elaboration and Docking Poses

10.26434/chemrxiv.8100203 ◽

2019 ◽

Author(s):

Susan Leung ◽

Michael Bodkin ◽

Frank von Delft ◽

Paul Brennan ◽

Garrett Morris

Keyword(s):

Binding Mode ◽

Docking Studies ◽

Binding Modes ◽

False Negatives ◽

Ligand Interaction ◽

Cross Docking ◽

Protein Ligand Interaction ◽

Feature Overlap ◽

Better Than ◽

Fragment Based Drug Discovery

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van der Waals interactions govern C -β- d -glucopyranosyl triazoles’ nM inhibitory potency in human liver glycogen phosphorylase

Journal of Structural Biology ◽

10.1016/j.jsb.2017.05.001 ◽

2017 ◽

Vol 199 (1) ◽

pp. 57-67 ◽

Cited By ~ 9

Author(s):

Anastassia L. Kantsadi ◽

George A. Stravodimos ◽

Efthimios Kyriakis ◽

Demetra S.M. Chatzileontiadou ◽

Theodora G.A. Solovou ◽

...

Keyword(s):

Human Liver ◽

Glycogen Phosphorylase ◽

Van Der Waals ◽

Liver Glycogen ◽

Van Der Waals Interactions ◽

Inhibitory Potency

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Docking, Binding Free Energy Estimation, and MD Simulation of Newly Designed CQ and HCQ Analogues Against the Spike-ACE2 Complex of SARS-CoV-2

International Journal of Quantitative Structure-Property Relationships ◽

10.4018/ijqspr.2021100105 ◽

2021 ◽

Vol 6 (4) ◽

pp. 77-89

Author(s):

Anjoomaara H. Patel ◽

Riya B. Patel ◽

MahammadHussain J. Memon ◽

Samiya S. Patel ◽

Sharav A. Desai ◽

...

Keyword(s):

Free Energy ◽

Md Simulation ◽

Binding Free Energy ◽

Docking Studies ◽

Radius Of Gyration ◽

Ligand Interaction ◽

Energy Estimation ◽

Efficacious Treatment ◽

Protein Ligand Interaction ◽

Derivatives Of

The coronavirus disease 2019 (COVID-19) virus has been spreading rapidly, and scientists are endeavouring to discover drugs for its efficacious treatment. Chloroquine phosphate, an old drug for treatment of malaria, has shown to have apparent efficacy and acceptable safety against COVID-19. As a part of Drug Discovery Hackathon-2020, in this study, the authors have tried making the derivatives of CQ and HCQ using MarvinSketch by ChemAxon. Molecular docking studies of these ligands were performed using Glide by Schrodinger, and ADME profiles were obtained by using QikProp. The obtained results after data analysis demonstrated that ligands HCQ_imidazoll, choloroquine_3c, HCQ_pyrrolC had good binding affinity and complied with all the ADME parameters. The molecular dynamic simulation of these ligands in complex with the 2019-nCoV RBD/ACE-2-B0AT1 complex PDB ID: 6M17 were carried out, and the parameters like RMSD, RMSF, and radius of gyration were observed to understand the fluctuations and protein-ligand interaction.

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Synthesis of 5-chloro-N-aryl-1H-indole-2-carboxamide derivatives as inhibitors of human liver glycogen phosphorylase a

Bioorganic & Medicinal Chemistry ◽

10.1016/j.bmc.2008.04.010 ◽

2008 ◽

Vol 16 (10) ◽

pp. 5452-5464 ◽

Cited By ~ 16

Author(s):

Kenichi Onda ◽

Takayuki Suzuki ◽

Ryota Shiraki ◽

Yasuhiro Yonetoku ◽

Kenji Negoro ◽

...

Keyword(s):

Human Liver ◽

Glycogen Phosphorylase ◽

Liver Glycogen

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Human liver glycogen phosphorylase inhibitors bind at a new allosteric site

Chemistry & Biology ◽

10.1016/s1074-5521(00)00004-1 ◽

2000 ◽

Vol 7 (9) ◽

pp. 677-682 ◽

Cited By ~ 81

Author(s):

Virginia L Rath ◽

Mark Ammirati ◽

Dennis E Danley ◽

Jennifer L Ekstrom ◽

E Michael Gibbs ◽

...

Keyword(s):

Human Liver ◽

Glycogen Phosphorylase ◽

Liver Glycogen ◽

Allosteric Site ◽

Glycogen Phosphorylase Inhibitors

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Computational design of novel cyclic urea as HIV-1 protease inhibitor

Open Chemistry ◽

10.2478/s11532-007-0040-x ◽

2007 ◽

Vol 5 (4) ◽

pp. 1064-1072 ◽

Cited By ~ 3

Author(s):

Manga Vijjulatha ◽

S. Kanth

Keyword(s):

Biological Activity ◽

Interaction Energy ◽

Model Equation ◽

Computational Design ◽

Docking Studies ◽

Computational Techniques ◽

Ligand Interaction ◽

Protein Ligand Interaction ◽

Adme Properties ◽

Hiv 1

AbstractA series of novel cyclic urea molecules 5,6-dihydroxy-1,3-diazepane-2,4,7-trione as HIV-1 protease inhibitors were designed using computational techniques. The designed molecules were compared with the known cyclic urea molecules by performing docking studies, calculating their ADME (Absorption, Distribution, Metabolism, and Excretion) properties and protein ligand interaction energy. These novel molecules were designed by substituting the P 1/P′ 1 positions (4th and 7th position of 1, 3-diazepan-2-one) with double bonded oxygens. This reduces the molecular weight and increases the bioavailability, indicating better ADME properties. The docking studies showed good binding affinity towards HIV-1 protease. The biological activity of these inhibitors were predicted by a model equation generated by the regression analysis between biological activity (log 1/K i ) of known inhibitors and their protein ligand interaction energy. The synthetic studies are in progress.

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