Protein flexibility from discrete molecular dynamics simulations using quasi-physical potentials

2009 ◽  
Vol 78 (1) ◽  
pp. 83-94 ◽  
Author(s):  
Agustí Emperador ◽  
Tim Meyer ◽  
Modesto Orozco
2005 ◽  
Vol 2 (4) ◽  
pp. S137-S147 ◽  
Author(s):  
Tatyana Mamonova ◽  
Brandon Hespenheide ◽  
Rachel Straub ◽  
M F Thorpe ◽  
Maria Kurnikova

2017 ◽  
Vol 3 (7) ◽  
pp. e1700479 ◽  
Author(s):  
Nicholas I. Brodie ◽  
Konstantin I. Popov ◽  
Evgeniy V. Petrotchenko ◽  
Nikolay V. Dokholyan ◽  
Christoph H. Borchers

PROTEOMICS ◽  
2021 ◽  
pp. 2000298
Author(s):  
Jason J. Serpa ◽  
Konstantin I. Popov ◽  
Evgeniy V. Petrotchenko ◽  
Nikolay V. Dokholyan ◽  
Christoph H. Borchers

2006 ◽  
Vol 119 (2) ◽  
pp. 146-157 ◽  
Author(s):  
F.S. Legge ◽  
A. Budi ◽  
H. Treutlein ◽  
I. Yarovsky

2012 ◽  
Vol 8 (11) ◽  
pp. 4707-4718 ◽  
Author(s):  
Pedro Sfriso ◽  
Agusti Emperador ◽  
Laura Orellana ◽  
Adam Hospital ◽  
Josep Lluis Gelpí ◽  
...  

2021 ◽  
Vol 8 ◽  
Author(s):  
Paulo C. T. Souza ◽  
Vittorio Limongelli ◽  
Sangwook Wu ◽  
Siewert J. Marrink ◽  
Luca Monticelli

Molecular docking is central to rational drug design. Current docking techniques suffer, however, from limitations in protein flexibility and solvation models and by the use of simplified scoring functions. All-atom molecular dynamics simulations, on the other hand, feature a realistic representation of protein flexibility and solvent, but require knowledge of the binding site. Recently we showed that coarse-grained molecular dynamics simulations, based on the most recent version of the Martini force field, can be used to predict protein/ligand binding sites and pathways, without requiring any a priori information, and offer a level of accuracy approaching all-atom simulations. Given the excellent computational efficiency of Martini, this opens the way to high-throughput drug screening based on dynamic docking pipelines. In this opinion article, we sketch the roadmap to achieve this goal.


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