Crystal structure of the PEG-bound SH3 domain of myosin IB fromEntamoeba histolyticareveals its mode of ligand recognition

2017 ◽  
Vol 73 (8) ◽  
pp. 672-682 ◽  
Author(s):  
Gunjan Gautam ◽  
Syed Arif Abdul Rehman ◽  
Preeti Pandey ◽  
Samudrala Gourinath
Keyword(s):  
Crystal Structure ◽  
Bound States ◽  
Structural Similarity ◽  
Sh3 Domain ◽  
Docking Studies ◽  
Structural Knowledge ◽  
Cellular Functions ◽  
Ligand Interaction ◽  
Protein Protein Interaction ◽  
Domain Structures

The versatility in the recognition of various interacting proteins by the SH3 domain drives a variety of cellular functions. Here, the crystal structure of the C-terminal SH3 domain of myosin IB fromEntamoeba histolytica(EhMySH3) is reported at a resolution of 1.7 Å in native and PEG-bound states. Comparisons with other structures indicated that the PEG molecules occupy protein–protein interaction pockets similar to those occupied by the peptides in other peptide-bound SH3-domain structures. Also, analysis of the PEG-boundEhMySH3 structure led to the recognition of two additional pockets, apart from the conventional polyproline and specificity pockets, that are important for ligand interaction. Molecular-docking studies combined with various comparisons revealed structural similarity betweenEhMySH3 and the SH3 domain of β-Pix, and this similarity led to the prediction thatEhMySH3 preferentially binds targets containing type II-like PXXP motifs. These studies expand the understanding of theEhMySH3 domain and provide extensive structural knowledge, which is expected to help in predicting the interacting partners which function together with myosin IB during phagocytosis inE. histolyticainfections.

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.

Analysis of Protein-Protein Interaction Networks through Computational Approaches

2020 ◽  
Vol 27 (4) ◽  
pp. 265-278 ◽  
Author(s):  
Ying Han ◽  
Liang Cheng ◽  
Weiju Sun
Keyword(s):  
Protein Interaction ◽  
Biological Networks ◽  
Interaction Networks ◽  
Computational Techniques ◽  
Cellular Functions ◽  
Protein Protein Interaction ◽  
Comprehensive Information ◽  
Protein Interaction Prediction ◽  
Or Gene ◽  
Experimental Findings

The interactions among proteins and genes are extremely important for cellular functions. Molecular interactions at protein or gene levels can be used to construct interaction networks in which the interacting species are categorized based on direct interactions or functional similarities. Compared with the limited experimental techniques, various computational tools make it possible to analyze, filter, and combine the interaction data to get comprehensive information about the biological pathways. By the efficient way of integrating experimental findings in discovering PPIs and computational techniques for prediction, the researchers have been able to gain many valuable data on PPIs, including some advanced databases. Moreover, many useful tools and visualization programs enable the researchers to establish, annotate, and analyze biological networks. We here review and list the computational methods, databases, and tools for protein−protein interaction prediction.

In silico Discovery of Resveratrol Analogues as Potential Agents in Treatment of Metabolic Disorders

2019 ◽  
Vol 25 (35) ◽  
pp. 3776-3783
Author(s):  
Nebojša Pavlović ◽  
Maja Đanić ◽  
Bojan Stanimirov ◽  
Svetlana Goločorbin-Kon ◽  
Karmen Stankov ◽  
...  
Keyword(s):  
In Silico ◽  
Metabolic Disorders ◽  
Partial Agonist ◽  
Aqueous Solubility ◽  
Structural Similarity ◽  
Data Bank ◽  
Docking Studies ◽  
Binding Energies ◽  
Molegro Virtual Docker ◽  
Ppar Γ

Background: Resveratrol was demonstrated to act as partial agonist of PPAR-γ receptor, which opens up the possibility for its use in the treatment of metabolic disorders. Considering the poor bioavailability of resveratrol, particularly due to its low aqueous solubility, we aimed to identify analogues of resveratrol with improved pharmacokinetic properties and higher binding affinities towards PPAR-γ. Methods: 3D structures of resveratrol and its analogues were retrieved from ZINC database, while PPAR-γ structure was obtained from Protein Data Bank. Docking studies were performed using Molegro Virtual Docker software. Molecular descriptors relevant to pharmacokinetics were calculated from ligand structures using VolSurf+ software. Results: Using structural similarity search method, 56 analogues of resveratrol were identified and subjected to docking analyses. Binding energies were ranged from -136.69 to -90.89 kcal/mol, with 16 analogues having higher affinities towards PPAR-γ in comparison to resveratrol. From the calculated values of SOLY descriptor, 23 studied compounds were shown to be more soluble in water than resveratrol. However, only two tetrahydroxy stilbene derivatives, piceatannol and oxyresveratrol, had both better solubility and affinity towards PPAR-γ. These compounds also had more favorable ADME profile, since they were shown to be more metabolically stable and wider distributed in body than resveratrol. Conclusion: Piceatannol and oxyresveratrol should be considered as potential lead compounds for further drug development. Although experimental validation of obtained in silico results is required, this work can be considered as a step toward the discovery of new natural and safe drugs in treatment of metabolic disorders.

Towards further Understanding the Structural Requirements of Combretastatin- like Chalcones as Inhibitors of Microtubule Polymerization

2020 ◽  
Vol 16 (2) ◽  
pp. 155-166
Author(s):  
Naveen Dhingra ◽  
Anand Kar ◽  
Rajesh Sharma
Keyword(s):  
Three Dimensional ◽  
3D Qsar ◽  
Docking Study ◽  
Structure Activity Relationship ◽  
Docking Studies ◽  
Activity Relationship ◽  
Structural Requirement ◽  
Ligand Interaction ◽  
Microtubule Polymerization ◽  
Structure Activity

Background: Microtubules are dynamic filamentous cytoskeletal structures which play several key roles in cell proliferation and trafficking. They are supposed to contribute in the development of important therapeutic targeting tumor cells. Chalcones are important group of natural compounds abundantly found in fruits & vegetables that are known to possess anticancer activity. We have used QSAR and docking studies to understand the structural requirement of chalcones for understanding the mechanism of microtubule polymerization inhibition. Methods: Three dimensional (3D) QSAR (CoMFA and CoMSIA), pharmacophore mapping and molecular docking studies were performed for the generation of structure activity relationship of combretastatin-like chalcones through statistical models and contour maps. Results: Structure activity relationship revealed that substitution of electrostatic, steric and donor groups may enhance the biological activity of compounds as inhibitors of microtubule polymerization. From the docking study, it was clear that compounds bind at the active site of tubulin protein. Conclusion: The given strategies of modelling could be an encouraging way for designing more potent compounds as well as for the elucidation of protein-ligand interaction.

Square Planar trans‐ N 2 O 2 Cu(II) Complex: Synthesis, Crystal Structure, Hirshfeld Surface, DFT, Antimicrobial and Docking Studies

2021 ◽  
Vol 6 (24) ◽  
pp. 6240-6255
Author(s):  
Allampura H. Udaya Kumar ◽  
Karthik Kumara ◽  
Nanishankar V. Harohally ◽  
Kudigana J. Pampa ◽  
Neratur K. Lokanath
Keyword(s):  
Crystal Structure ◽  
Docking Studies ◽  
Hirshfeld Surface ◽  
Square Planar

scholarly journals Deep learning for visualization and novelty detection in large X-ray diffraction datasets

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Lars Banko ◽  
Phillip M. Maffettone ◽  
Dennis Naujoks ◽  
Daniel Olds ◽  
Alfred Ludwig
Keyword(s):  
Thin Film ◽  
Crystal Structure ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
Novelty Detection ◽  
Structural Similarity ◽  
X Ray Diffraction ◽  
X Ray ◽  
Diffraction Patterns ◽  
Post Hoc

AbstractWe apply variational autoencoders (VAE) to X-ray diffraction (XRD) data analysis on both simulated and experimental thin-film data. We show that crystal structure representations learned by a VAE reveal latent information, such as the structural similarity of textured diffraction patterns. While other artificial intelligence (AI) agents are effective at classifying XRD data into known phases, a similarly conditioned VAE is uniquely effective at knowing what it doesn’t know: it can rapidly identify data outside the distribution it was trained on, such as novel phases and mixtures. These capabilities demonstrate that a VAE is a valuable AI agent for aiding materials discovery and understanding XRD measurements both ‘on-the-fly’ and during post hoc analysis.

Sign in / Sign up

Export Citation Format

Share Document