Molecular Dynamics Simulations of Proteins and Protein-Protein Complexes

1990 ◽  
pp. 68-84 ◽  
Author(s):  
J. E. Wampler ◽  
D. E. Stewart ◽  
S. L. Gallion
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Protein Complexes ◽  
Dynamics Simulations

scholarly journals Elucidation of SARS-Cov-2 Budding Mechanisms through Molecular Dynamics Simulations of M and E Protein Complexes

2021 ◽  
pp. 12249-12255
Author(s):  
Logan Thrasher Collins ◽  
Tamer Elkholy ◽  
Shafat Mubin ◽  
David Hill ◽  
Ricky Williams ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Protein Complexes ◽  
E Protein ◽  
Dynamics Simulations

scholarly journals Towards Molecular Dynamics Simulations of Large Protein Complexes

2010 ◽  
Vol 98 (3) ◽  
pp. 57a
Author(s):  
Djurre de Jong ◽  
Xavier Periole ◽  
Siewert Jan Marrink
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Protein Complexes ◽  
Large Protein ◽  
Dynamics Simulations

Binding affinity prediction of nanobody–protein complexes by scoring of molecular dynamics trajectories

2018 ◽  
Vol 20 (5) ◽  
pp. 3438-3444 ◽  
Author(s):  
Miguel A. Soler ◽  
Sara Fortuna ◽  
Ario de Marco ◽  
Alessandro Laio
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Binding Affinity ◽  
Protein Complexes ◽  
Binding Affinity Prediction ◽  
Affinity Prediction ◽  
Dynamics Simulations

Accurate binding affinity prediction of modelled nanobody–protein complexes by using the assistance of molecular dynamics simulations for achieving stable conformations.

scholarly journals Predicting the Specificity- Determining Positions of Receptor Tyrosine Kinase Axl

2021 ◽  
Vol 8 ◽  
Author(s):  
Tülay Karakulak ◽  
Ahmet Sureyya Rifaioglu ◽  
João P. G. L. M. Rodrigues ◽  
Ezgi Karaca
Keyword(s):  
Molecular Dynamics ◽  
Tyrosine Kinase ◽  
Molecular Dynamics Simulations ◽  
Cancer Progression ◽  
Receptor Tyrosine Kinase ◽  
Protein Complexes ◽  
Immune System Diseases ◽  
Specificity Determining Positions ◽  
Therapeutic Molecules ◽  
Dynamics Simulations

Owing to its clinical significance, modulation of functionally relevant amino acids in protein-protein complexes has attracted a great deal of attention. To this end, many approaches have been proposed to predict the partner-selecting amino acid positions in evolutionarily close complexes. These approaches can be grouped into sequence-based machine learning and structure-based energy-driven methods. In this work, we assessed these methods’ ability to map the specificity-determining positions of Axl, a receptor tyrosine kinase involved in cancer progression and immune system diseases. For sequence-based predictions, we used SDPpred, Multi-RELIEF, and Sequence Harmony. For structure-based predictions, we utilized HADDOCK refinement and molecular dynamics simulations. As a result, we observed that (i) sequence-based methods overpredict partner-selecting residues of Axl and that (ii) combining Multi-RELIEF with HADDOCK-based predictions provides the key Axl residues, covered by the extensive molecular dynamics simulations. Expanding on these results, we propose that a sequence-structure-based approach is necessary to determine specificity-determining positions of Axl, which can guide the development of therapeutic molecules to combat Axl misregulation.

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