Docking with SwissDock.

Protein-ligand docking simulation is central in drug design and development. Therefore, the development of web servers intended to docking simulations is of pivotal importance. SwissDock is a web server dedicated to carrying out protein-ligand docking simulation intuitively and elegantly. SwissDock is based on the protein-ligand docking program EADock DSS and has a simple and integrated interface. The SwissDock allows the user to upload structure files for a protein and a ligand, and returns the results by e-mail. To facilitate the upload of the protein and ligand files, we can prepare these input files using the program UCSF Chimera. In this chapter, we describe how to use UCSF Chimera and SwissDock to perform protein-ligand docking simulations. To illustrate the process, we describe the molecular docking of the competitive inhibitor roscovitine against the structure of human cyclin-dependent kinase 2.

[1]  Arlindo L. Oliveira,et al.  Computational approach to the discovery of phytochemical molecules with therapeutic potential targets to the PKCZ protein , 2018 .

[2]  G. Zhai,et al.  Advances in non-peptidomimetic HIV protease inhibitors. , 2014, Current medicinal chemistry.

[3]  Val Oliveira Pintro,et al.  Supervised machine learning techniques to predict binding affinity. A study for cyclin-dependent kinase 2. , 2017, Biochemical and biophysical research communications.

[4]  Amedeo Caflisch,et al.  Protein structure-based drug design: from docking to molecular dynamics. , 2018, Current opinion in structural biology.

[5]  Peng Zhan,et al.  Conformational restriction: an effective tactic in 'follow-on'-based drug discovery. , 2014, Future medicinal chemistry.

[6]  Arthur J. Olson,et al.  AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading , 2009, J. Comput. Chem..

[7]  David S. Goodsell,et al.  AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility , 2009, J. Comput. Chem..

[8]  W. F. Azevedo MolDock applied to structure-based virtual screening. , 2010 .

[9]  Val Oliveira Pintro,et al.  Development of CDK-targeted scoring functions for prediction of binding affinity. , 2018, Biophysical chemistry.

[10]  Jordi Mestres,et al.  Guided docking approaches to structure-based design and screening. , 2004, Current topics in medicinal chemistry.

[11]  Sony Malhotra,et al.  Structural Biology and the Design of New Therapeutics: From HIV and Cancer to Mycobacterial Infections: A Paper Dedicated to John Kendrew. , 2017, Journal of molecular biology.

[12]  Gabriela Bitencourt-Ferreira,et al.  Development of a machine-learning model to predict Gibbs free energy of binding for protein-ligand complexes. , 2018, Biophysical chemistry.

[13]  S. W. Park,et al.  Journey describing the discoveries of anti-HIV triterpene acid families targeting HIV-entry/fusion, protease functioning and maturation stages. , 2014, Current topics in medicinal chemistry.

[14]  Ion Petre,et al.  Tailored Approaches in Drug Development and Diagnostics: From Molecular Design to Biological Model Systems , 2017, Advanced healthcare materials.

[15]  Gabriela Sehnem Heck,et al.  Supervised Machine Learning Methods Applied to Predict Ligand- Binding Affinity. , 2017, Current medicinal chemistry.

[16]  S. White,et al.  Recent advances in computer-aided drug design as applied to anti-influenza drug discovery. , 2014, Current topics in medicinal chemistry.

[17]  A. Murray,et al.  Cyclin-dependent kinases: regulators of the cell cycle and more. , 1994, Chemistry & biology.

[18]  Val Oliveira Pintro,et al.  SAnDReS a Computational Tool for Statistical Analysis of Docking Results and Development of Scoring Functions. , 2016, Combinatorial chemistry & high throughput screening.

[19]  M. Soliman,et al.  Therapeutic, Molecular and Computational Aspects of Novel Monoamine Oxidase (MAO) Inhibitors. , 2017, Combinatorial chemistry & high throughput screening.

[20]  Dan Li,et al.  The application of in silico drug-likeness predictions in pharmaceutical research. , 2015, Advanced drug delivery reviews.

[21]  Peng Zhan,et al.  Anti-HIV Drug Discovery and Development: Current Innovations and Future Trends. , 2016, Journal of medicinal chemistry.

[22]  Mohammed H Bohari,et al.  Modeling anti-HIV compounds: the role of analogue-based approaches. , 2012, Current computer-aided drug design.

[23]  Florbela Pereira,et al.  Computational Methodologies in the Exploration of Marine Natural Product Leads , 2018, Marine drugs.

[24]  K. Ahmad,et al.  Computer Aided Drug Design and its Application to the Development of Potential Drugs for Neurodegenerative Disorders , 2017, Current neuropharmacology.

[25]  Joungmok Kim,et al.  Targeting of AMP-activated protein kinase: prospects for computer-aided drug design , 2017, Expert opinion on drug discovery.

[26]  S H Kim,et al.  Inhibition of cyclin-dependent kinases by purine analogues: crystal structure of human cdk2 complexed with roscovitine. , 1997, European journal of biochemistry.

[27]  Walter Filgueira de Azevedo,et al.  Optimized Virtual Screening Workflow: Towards Target-Based Polynomial Scoring Functions for HIV-1 Protease. , 2017, Combinatorial chemistry & high throughput screening.

[28]  Sheikh Arslan Sehgal,et al.  Current Therapeutic Molecules and Targets in Neurodegenerative Diseases Based on in silico Drug Design , 2018, Current neuropharmacology.

[29]  Arun K. Ghosh,et al.  Recent Progress in the Development of HIV-1 Protease Inhibitors for the Treatment of HIV/AIDS. , 2016, Journal of medicinal chemistry.

[30]  W. F. Azevedo,et al.  Development of machine learning models to predict inhibition of 3‐dehydroquinate dehydratase , 2018 .

[31]  Surovi Saikia,et al.  Molecular Docking: Challenges, Advances and its Use in Drug Discovery Perspective. , 2019, Current drug targets.

[32]  Inho Choi,et al.  Computer Aided Drug Design: Success and Limitations. , 2016, Current pharmaceutical design.

[33]  A. Olson,et al.  Computational challenges of structure-based approaches applied to HIV. , 2015, Current topics in microbiology and immunology.

[34]  Yoshifumi Fukunishi,et al.  Miscellaneous Topics in Computer-Aided Drug Design: Synthetic Accessibility and GPU Computing, and Other Topics , 2016, Current pharmaceutical design.

[35]  David S. Goodsell,et al.  Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function , 1998 .

[36]  S H Kim,et al.  Structural basis for specificity and potency of a flavonoid inhibitor of human CDK2, a cell cycle kinase. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[37]  Stephani Joy Y Macalino,et al.  Role of computer-aided drug design in modern drug discovery , 2015, Archives of Pharmacal Research.

[38]  D. Goodsell,et al.  Automated docking of substrates to proteins by simulated annealing , 1990, Proteins.

[39]  Val Oliveira Pintro,et al.  Understanding the Structural Basis for Inhibition of Cyclin-Dependent Kinases. New Pieces in the Molecular Puzzle. , 2017, Current drug targets.

[40]  René Thomsen,et al.  MolDock: a new technique for high-accuracy molecular docking. , 2006, Journal of medicinal chemistry.

[41]  S. Kim,et al.  Structural basis for chemical inhibition of CDK2. , 1996, Progress in cell cycle research.

[42]  M. Scotti,et al.  Computer-aided Drug Design Applied to Parkinson Targets , 2017, Current neuropharmacology.

[43]  Conrad C. Huang,et al.  UCSF Chimera—A visualization system for exploratory research and analysis , 2004, J. Comput. Chem..

[44]  W. F. de Azevedo,et al.  Bio-inspired algorithms applied to molecular docking simulations. , 2011, Current medicinal chemistry.

[45]  Rita C. Guedes,et al.  Computational Approaches for the Discovery of Human Proteasome Inhibitors: An Overview , 2016, Molecules.

[46]  Mire Zloh,et al.  The benefits of in silico modeling to identify possible small-molecule drugs and their off-target interactions. , 2018, Future medicinal chemistry.

[47]  Aurélien Grosdidier,et al.  SwissDock, a protein-small molecule docking web service based on EADock DSS , 2011, Nucleic Acids Res..

[48]  Gabriela Bitencourt-Ferreira,et al.  Cyclin-Dependent Kinase 2 in Cellular Senescence and Cancer. A Structural and Functional Review. , 2019, Current drug targets.

[49]  Arun K. Ghosh,et al.  Organic Carbamates in Drug Design and Medicinal Chemistry , 2015, Journal of medicinal chemistry.

[50]  David O. Morgan,et al.  Principles of CDK regulation , 1995, Nature.

[51]  M. Campos,et al.  Pre-clinical effects of metformin and aspirin on the cell lines of different breast cancer subtypes , 2018, Investigational new drugs.

[52]  Michael M. Mysinger,et al.  Automated Docking Screens: A Feasibility Study , 2009, Journal of medicinal chemistry.

[53]  M. Scotti,et al.  Computer Aided Drug Design Studies in the Discovery of Secondary Metabolites Targeted Against Age-Related Neurodegenerative Diseases. , 2015, Current topics in medicinal chemistry.

[54]  Bianca Villavicencio,et al.  Recent Progress of Molecular Docking Simulations Applied to Development of Drugs , 2012 .

[55]  Aurélien Grosdidier,et al.  Fast docking using the CHARMM force field with EADock DSS , 2011, J. Comput. Chem..

[56]  P. Fischer,et al.  4-arylazo-3,5-diamino-1H-pyrazole CDK inhibitors: SAR study, crystal structure in complex with CDK2, selectivity, and cellular effects. , 2006, Journal of medicinal chemistry.

[57]  David S. Goodsell,et al.  Distributed automated docking of flexible ligands to proteins: Parallel applications of AutoDock 2.4 , 1996, J. Comput. Aided Mol. Des..