Comparison of a GB Solvation Model with Explicit Solvent Simulations:  Potentials of Mean Force and Conformational Preferences of Alanine Dipeptide and 1,2-Dichloroethane

1998 ◽  
Vol 102 (18) ◽  
pp. 3637-3641 ◽  
Author(s):  
Marco Scarsi ◽  
Joannis Apostolakis ◽  
Amedeo Caflisch
Keyword(s):  
Solvation Model ◽  
Explicit Solvent ◽  
Conformational Preferences ◽  
Potentials Of Mean Force ◽  
Alanine Dipeptide

Comparison of Continuum and Explicit Models of Solvation:  Potentials of Mean Force for Alanine Dipeptide

1996 ◽  
Vol 100 (5) ◽  
pp. 1439-1441 ◽  
Author(s):  
Tami J. Marrone ◽  
Michael K. Gilson ◽  
J. Andrew McCammon
Keyword(s):  
Potentials Of Mean Force ◽  
Alanine Dipeptide

Role of Solvent in Determining Conformational Preferences of Alanine Dipeptide in Water

2004 ◽  
Vol 126 (8) ◽  
pp. 2574-2581 ◽  
Author(s):  
Alexander N. Drozdov ◽  
Alan Grossfield ◽  
Rohit V. Pappu
Keyword(s):  
Conformational Preferences ◽  
Alanine Dipeptide ◽  
Role Of Solvent

scholarly journals Computational studies of anti-cancer Aurein peptides

2015 ◽  
Author(s):  
◽  
Neha Manhas
Keyword(s):  
Three Dimensional ◽  
Underlying Mechanism ◽  
Computational Method ◽  
Amphibian Skin ◽  
Comprehensive Information ◽  
Explicit Solvent ◽  
Conformational Preferences ◽  
Anti Cancer ◽  
Implicit And Explicit ◽  
External Stimuli

Peptide folding is a very complicated and dynamic process taking place in all living systems. The understanding of a bioactive conformation of the peptides is very important to understand their biological functions and underlying mechanism of action. However, the high flexible nature of peptides makes this process difficult as they can adopt thousands of conformations within the fraction of a second. The usage of experimental techniques in the characterization process is also limited due to several associated complications including synthesis, isolation and crystallization of peptides. The present computational methodologies, on the other hand, are solid enough to provide detailed complementary information about the intrinsic conformational features of peptides by mimicking their physiological conditions. In the present work, molecular dynamics (MD) computational method was used to explore the configurational space of three Aurein peptides, namely Aurein 2.3, Aurein 2.4 and Aurein 2.5. These peptides are secreted by the amphibian skin when they are exposed to external stimuli. These peptides have been reported to possess anti-cancer and anti-bacterial activity with minimum resistance compared to the available drugs. However, despite their medicinal significance, the precise three dimensional structures of Aurein 2.4 and Aurein 2.5 are not as yet known. First, a validation study was performed on Aurein 2.3 to check the efficiency of the computational protocol. The results obtained revealed the presence of -helicity in all residues of the Aurein 2.3, in accordance with its experimental structure. A similar protocol was further used to explore the conformational profiles of the remaining two peptides (Aurein 2.4 and Aurein 2.5) under implicit and explicit solvent conditions. The results obtained revealed that both these peptides exhibit -helical character in all residues although in varying percentages. The -helical region in the case of Aurein 2.4 was localized predominantly in the central residues extending towards its N-terminal residues, whereas it was flanked by N-terminal and the central residues in Aurein 2.5. However, -helicity was completely absent in the explicit solvents, and the peptides preferred to stay either in -turns or extended forms. Hence, the present work provides comprehensive information about the conformational preferences of Aurein peptides which could lead to a better understanding of their native conformations for future investigations and point the way towards developing their new agonists.

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