scholarly journals Simple Physics-Based Analytical Formulas for the Potentials of Mean Force of the Interaction of Amino-Acid Side Chains in Water. VI. Oppositely Charged Side Chains

2011 ◽  
Vol 115 (19) ◽  
pp. 6130-6137 ◽  
Author(s):  
Mariusz Makowski ◽  
Adam Liwo ◽  
Harold A. Scheraga
Keyword(s):  
Amino Acid ◽  
Side Chains ◽  
Amino Acid Side Chains ◽  
Potentials Of Mean Force ◽  
Analytical Formulas ◽  
Oppositely Charged

scholarly journals Simple Physics-Based Analytical Formulas for the Potentials of Mean Force for the Interaction of Amino Acid Side Chains in Water. IV. Pairs of Different Hydrophobic Side Chains

2008 ◽  
Vol 112 (36) ◽  
pp. 11385-11395 ◽  
Author(s):  
Mariusz Makowski ◽  
Emil Sobolewski ◽  
Cezary Czaplewski ◽  
Stanisław Ołdziej ◽  
Adam Liwo ◽  
...  
Keyword(s):  
Amino Acid ◽  
Side Chains ◽  
Amino Acid Side Chains ◽  
Potentials Of Mean Force ◽  
Analytical Formulas

Predicting the Strength of Stacking Interactions Between Heterocycles and Aromatic Amino Acid Side Chains

2019 ◽  
Author(s):  
Andrea N. Bootsma ◽  
Analise C. Doney ◽  
Steven Wheeler
Keyword(s):  
Amino Acid ◽  
Binding Sites ◽  
Aromatic Amino Acid ◽  
Aromatic Amino Acids ◽  
Biologically Active ◽  
Side Chains ◽  
Stacking Interactions ◽  
Inhibitor Binding ◽  
Protein Binding Sites ◽  
Amino Acid Side Chains

<p>Despite the ubiquity of stacking interactions between heterocycles and aromatic amino acids in biological systems, our ability to predict their strength, even qualitatively, is limited. Based on rigorous <i>ab initio</i> data, we have devised a simple predictive model of the strength of stacking interactions between heterocycles commonly found in biologically active molecules and the amino acid side chains Phe, Tyr, and Trp. This model provides rapid predictions of the stacking ability of a given heterocycle based on readily-computed heterocycle descriptors. We show that the values of these descriptors, and therefore the strength of stacking interactions with aromatic amino acid side chains, follow simple predictable trends and can be modulated by changing the number and distribution of heteroatoms within the heterocycle. This provides a simple conceptual model for understanding stacking interactions in protein binding sites and optimizing inhibitor binding in drug design.</p>

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