scholarly journals Repurposing the Trypanosomatidic GSK Kinetobox for the Inhibition of Parasitic Pteridine and Dihydrofolate Reductases

2021 ◽  
Vol 14 (12) ◽  
pp. 1246
Author(s):  
Matteo Santucci ◽  
Rosaria Luciani ◽  
Eleonora Gianquinto ◽  
Cecilia Pozzi ◽  
Flavio di Pisa ◽  
...  
Keyword(s):  
Selective Inhibition ◽  
Docking Studies ◽  
Biological Profile ◽  
Ligand Interaction ◽  
Dihydrofolate Reductases ◽  
Dual Inhibitors ◽  
Protein Ligand Interaction ◽  
Screening Assays ◽  
Dhfr Inhibitors ◽  
New Treatments

Three open-source anti-kinetoplastid chemical boxes derived from a whole-cell phenotypic screening by GlaxoSmithKline (Tres Cantos Anti-Kinetoplastid Screening, TCAKS) were exploited for the discovery of a novel core structure inspiring new treatments of parasitic diseases targeting the trypansosmatidic pteridine reductase 1 (PTR1) and dihydrofolate reductase (DHFR) enzymes. In total, 592 compounds were tested through medium-throughput screening assays. A subset of 14 compounds successfully inhibited the enzyme activity in the low micromolar range of at least one of the enzymes from both Trypanosoma brucei and Lesihmania major parasites (pan-inhibitors), or from both PTR1 and DHFR-TS of the same parasite (dual inhibitors). Molecular docking studies of the protein–ligand interaction focused on new scaffolds not reproducing the well-known antifolate core clearly explaining the experimental data. TCMDC-143249, classified as a benzenesulfonamide derivative by the QikProp descriptor tool, showed selective inhibition of PTR1 and growth inhibition of the kinetoplastid parasites in the 5 μM range. In our work, we enlarged the biological profile of the GSK Kinetobox and identified new core structures inhibiting selectively PTR1, effective against the kinetoplastid infectious protozoans. In perspective, we foresee the development of selective PTR1 and DHFR inhibitors for studies of drug combinations.

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

2019 ◽  
Vol 122 ◽  
pp. 289-297 ◽  
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

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

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

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 . <br>

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

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

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 . <br>

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

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.

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

2007 ◽  
Vol 5 (4) ◽  
pp. 1064-1072 ◽  
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.

scholarly journals Protein Ligand Interaction Fingerprints

2017 ◽  
pp. 1072-1091
Author(s):  
Ali HajiEbrahimi ◽  
Hamidreza Ghafouri ◽  
Mohsen Ranjbar ◽  
Amirhossein Sakhteman
Keyword(s):  
Virtual Screening ◽  
Docking Studies ◽  
Scoring Functions ◽  
Ligand Interaction ◽  
Advantages And Disadvantages ◽  
Protein Affinity ◽  
Binding Cavity ◽  
Protein Ligand Interaction ◽  
Interaction Fingerprints ◽  
Activity Information

A most challenging part in docking-based virtual screening is the scoring functions implemented in various docking programs in order to evaluate different poses of the ligands inside the binding cavity of the receptor. Precise and trustable measurement of ligand-protein affinity for Structure-Based Virtual Screening (SB-VS) is therefore, an outstanding problem in docking studies. Empirical post-docking filters can be helpful as a way to provide various types of structure-activity information. Different types of interaction have been presented between the ligands and the receptor so far. Based on the diversity and importance of PLIF methods, this chapter will focus on the comparison of different protocols. The advantages and disadvantages of all methods will be discussed explicitly in this chapter as well as future sights for further progress in this field. Different classifications approaches for the protein-ligand interaction fingerprints were also discussed in this chapter.

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

2016 ◽  
Vol 2 (2) ◽  
pp. 120-124
Author(s):  
Fitri Kusvila Aziz ◽  
Cantika Nukitasari ◽  
Fauziyah Ardli Oktavianingrum ◽  
Lita Windy Aryati ◽  
Broto Santoso
Keyword(s):  
Binding Energy ◽  
Human Liver ◽  
Drug Targets ◽  
Glycogen Phosphorylase ◽  
Liver Glycogen ◽  
Docking Studies ◽  
Ligand Interaction ◽  
Drug Compounds ◽  
Protein Ligand Interaction ◽  
And Control

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

scholarly journals Molecular docking studies of deacetylbisacodyl with intestinal sucrase-maltase enzyme

2017 ◽  
Vol 2 (12) ◽  
pp. 191 ◽  
Author(s):  
Ramchander Merugu ◽  
Uttam Kumar Neerudu ◽  
Karunakar Dasa ◽  
Kalpana V. Singh
Keyword(s):  
Hydrogen Bond ◽  
Molecular Docking ◽  
Electrostatic Interactions ◽  
Docking Studies ◽  
Amino Acid Residues ◽  
Interaction Study ◽  
Ligand Interaction ◽  
Docking Analysis ◽  
Protein Ligand Interaction ◽  
Intestinal Sucrase

Molecular docking of sucrase-isomaltase with ligand deacetylbisacodyl when subjected to docking analysis using docking server, predicted in-silico result with a free energy of -3.36 Kcal/mol which was agreed well with physiological range for protein-ligand interaction, making bisacodyl probable potent anti-isomaltase molecule. According to docking server Inhibition constant is 5.98Mm. which predicts that the ligand is going to inhibits enzyme and result in a clinically relevant drug interaction with a substrate for the enzyme. Hydrogen bond with bond length 3.45is formed between Pro 64 (A) of target and of ligand, which is again indicative of the docking between target and ligand. Excellent electrostatic interactions of polar, hydrophobic, pi-pi and Van der walls are observed. The proteinligand interaction study showed 6 amino acid residues interaction with the ligand.

Protein Ligand Interaction Fingerprints

2016 ◽  
pp. 128-147
Author(s):  
Ali HajiEbrahimi ◽  
Hamidreza Ghafouri ◽  
Mohsen Ranjbar ◽  
Amirhossein Sakhteman
Keyword(s):  
Virtual Screening ◽  
Docking Studies ◽  
Scoring Functions ◽  
Ligand Interaction ◽  
Advantages And Disadvantages ◽  
Protein Affinity ◽  
Binding Cavity ◽  
Protein Ligand Interaction ◽  
Interaction Fingerprints ◽  
Activity Information

A most challenging part in docking-based virtual screening is the scoring functions implemented in various docking programs in order to evaluate different poses of the ligands inside the binding cavity of the receptor. Precise and trustable measurement of ligand-protein affinity for Structure-Based Virtual Screening (SB-VS) is therefore, an outstanding problem in docking studies. Empirical post-docking filters can be helpful as a way to provide various types of structure-activity information. Different types of interaction have been presented between the ligands and the receptor so far. Based on the diversity and importance of PLIF methods, this chapter will focus on the comparison of different protocols. The advantages and disadvantages of all methods will be discussed explicitly in this chapter as well as future sights for further progress in this field. Different classifications approaches for the protein-ligand interaction fingerprints were also discussed in this chapter.

Homology Modeling of Mus Musculus CDK5 and Molecular Docking Studies with Flavonoids

2017 ◽  
Vol 9 (6) ◽  
Author(s):  
Sowmya Suri ◽  
Rumana Waseem ◽  
Seshagiri Bandi ◽  
Sania Shaik
Keyword(s):  
Molecular Docking ◽  
3D Model ◽  
Structural Features ◽  
Docking Studies ◽  
Amino Acid Residues ◽  
Cyclin Dependent Kinase ◽  
Ligand Complex ◽  
Ligand Interaction ◽  
Molecular Docking Studies ◽  
Cyclin Dependent Kinase 5

A 3D model of Cyclin-dependent kinase 5 (CDK5) (Accession Number: Q543f6) is generated based on crystal structure of P. falciparum PFPK5-indirubin-5-sulphonate ligand complex (PDB ID: 1V0O) at 2.30 Å resolution was used as template. Protein-ligand interaction studies were performed with flavonoids to explore structural features and binding mechanism of flavonoids as CDK5 (Cyclin-dependent kinase 5) inhibitors. The modelled structure was selected on the basis of least modeler objective function. The model was validated by PROCHECK. The predicted 3D model is reliable with 93.0% of amino acid residues in core region of the Ramachandran plot. Molecular docking studies with flavonoids viz., Diosmetin, Eriodictyol, Fortuneletin, Apigenin, Ayanin, Baicalein, Chrysoeriol and Chrysosplenol-D with modelled protein indicate that Diosmetin is the best inhibitor containing docking score of -8.23 kcal/mol. Cys83, Lys89, Asp84. The compound Diosmetin shows interactions with Cys83, Lys89, and Asp84.

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