Literature DB >> 28240180

Structure-based Virtual Screening Approaches in Kinase-directed Drug Discovery.

David Bajusz1, Gyorgy G Ferenczy1, Gyorgy M Keseru1.   

Abstract

Protein kinases are one of the most targeted protein families in current drug discovery pipelines. They are implicated in many oncological, inflammatory, CNS-related and other clinical indications. Virtual screening is a computational technique with a diverse set of available tools that has been shown many times to provide novel starting points for kinase-directed drug discovery. This review starts with a concise overview of the function, structural features and inhibitory mechanisms of protein kinases. In addition to briefly reviewing the practical aspects of structure-based virtual screenings, we discuss several case studies to illustrate the state of the art in the virtual screening for type I, type II, allosteric (type III-V) and covalent (type VI) kinase inhibitors. With this review, we strive to provide a summary of the latest advances in the structure-based discovery of novel kinase inhibitors, as well as a practical tool to anyone who wishes to embark on such an endeavor. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Keywords:  Activation segment; Covalent docking; DFG motif; Docking; Drug discovery; Inhibitor; Kinase; Structure-based virtual screening; hinge

Mesh:

Substances:

Year:  2017        PMID: 28240180     DOI: 10.2174/1568026617666170224121313

Source DB:  PubMed          Journal:  Curr Top Med Chem        ISSN: 1568-0266            Impact factor:   3.295


  16 in total

Review 1.  Kinase Atlas: Druggability Analysis of Potential Allosteric Sites in Kinases.

Authors:  Christine Yueh; Justin Rettenmaier; Bing Xia; David R Hall; Andrey Alekseenko; Kathryn A Porter; Krister Barkovich; Gyorgy Keseru; Adrian Whitty; James A Wells; Sandor Vajda; Dima Kozakov
Journal:  J Med Chem       Date:  2019-07-05       Impact factor: 7.446

2.  Disease-Ligand Identification Based on Flexible Neural Tree.

Authors:  Bin Yang; Wenzheng Bao; Baitong Chen
Journal:  Front Microbiol       Date:  2022-06-06       Impact factor: 6.064

3.  Supervised learning with word embeddings derived from PubMed captures latent knowledge about protein kinases and cancer.

Authors:  Vida Ravanmehr; Hannah Blau; Luca Cappelletti; Tommaso Fontana; Leigh Carmody; Ben Coleman; Joshy George; Justin Reese; Marcin Joachimiak; Giovanni Bocci; Peter Hansen; Carol Bult; Jens Rueter; Elena Casiraghi; Giorgio Valentini; Christopher Mungall; Tudor I Oprea; Peter N Robinson
Journal:  NAR Genom Bioinform       Date:  2021-12-08

4.  Three-Dimensional Convolutional Neural Networks and a Cross-Docked Data Set for Structure-Based Drug Design.

Authors:  Paul G Francoeur; Tomohide Masuda; Jocelyn Sunseri; Andrew Jia; Richard B Iovanisci; Ian Snyder; David R Koes
Journal:  J Chem Inf Model       Date:  2020-09-10       Impact factor: 4.956

5.  A novel ligand of the translationally controlled tumor protein (TCTP) identified by virtual drug screening for cancer differentiation therapy.

Authors:  Nicolas Fischer; Ean-Jeong Seo; Sara Abdelfatah; Edmond Fleischer; Anette Klinger; Thomas Efferth
Journal:  Invest New Drugs       Date:  2021-01-25       Impact factor: 3.850

6.  Consensus Virtual Screening Identified [1,2,4]Triazolo[1,5-b]isoquinolines As MELK Inhibitor Chemotypes.

Authors:  Anita Rácz; Roberta Palkó; Dorottya Csányi; Zsuzsanna Riedl; Dávid Bajusz; György M Keserű
Journal:  ChemMedChem       Date:  2021-10-19       Impact factor: 3.540

7.  Extended many-item similarity indices for sets of nucleotide and protein sequences.

Authors:  Dávid Bajusz; Ramón Alain Miranda-Quintana; Anita Rácz; Károly Héberger
Journal:  Comput Struct Biotechnol J       Date:  2021-06-16       Impact factor: 7.271

Review 8.  Targeting the C-Terminal Domain Small Phosphatase 1.

Authors:  Harikrishna Reddy Rallabandi; Palanivel Ganesan; Young Jun Kim
Journal:  Life (Basel)       Date:  2020-05-08

9.  Development and evaluation of a deep learning model for protein-ligand binding affinity prediction.

Authors:  Marta M Stepniewska-Dziubinska; Piotr Zielenkiewicz; Pawel Siedlecki
Journal:  Bioinformatics       Date:  2018-11-01       Impact factor: 6.937

10.  Exploring protein hotspots by optimized fragment pharmacophores.

Authors:  Dávid Bajusz; Warren S Wade; Grzegorz Satała; Andrzej J Bojarski; Janez Ilaš; Jessica Ebner; Florian Grebien; Henrietta Papp; Ferenc Jakab; Alice Douangamath; Daren Fearon; Frank von Delft; Marion Schuller; Ivan Ahel; Amanda Wakefield; Sándor Vajda; János Gerencsér; Péter Pallai; György M Keserű
Journal:  Nat Commun       Date:  2021-05-27       Impact factor: 14.919

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