ABSTRACT
A peptide ß2-m21-31, which is a fragment from residue 21 to residue 31 of ß2-microgloblin, is experimentally known to self-assemble and form amyloid fibrils. In order to understand the mechanism of amyloid fibril formations, we applied the replica-exchange molecular dynamics method to the system consisting of three fragments of ß2-m21-31. From the analyses on the temperature dependence, we found that there is a clear phase transition temperature in which the peptides aggregate with each other. Moreover, we found by the free energy analyses that there are two major stable states: One of them is like amyloid fibrils and the other is amorphous aggregates.
Subject(s)
Molecular Dynamics Simulation , Protein Multimerization , beta 2-Microglobulin/chemistry , Protein Structure, Quaternary , ThermodynamicsABSTRACT
We calculate the absolute binding free energies of tetra-methylated octa-acids host-guest systems as a part of the SAMPL6 blind challenge (receipt ID vq30p). We employed two different free energy simulation methods, i.e., the umbrella sampling (US) and double decoupling method (DDM). The US method was used with the weighted histogram analysis method (WHAM) (US-WHAM scheme). In the DDM scheme, Hamiltonian replica-exchange method (HREM) was combined with the Bennett acceptance ratio (BAR) (HREM-BAR scheme). We obtained initial binding poses via molecular docking using GalaxyDock-HG program, which is developed for the SAMPL challenge. The root mean square deviation (RMSD) and the mean absolute deviations (MAD) using US-WHAM scheme were 1.33 and 1.02 kcal/mol, respectively. The MAD was the top among all submissions, however the correlation with respect to experiment was unexceptional. While the RMSD and MAD via HREM-BAR scheme were greater than US-WHAM scheme, (i.e., 2.09 and 1.76 kcal/mol), their correlations were slightly better than US-WHAM. The correlation between the two methods was high. Further discussion on the DDM method can be found in a companion paper by Han et al. (receipt ID 3z83m) in the same issue.
Subject(s)
Carboxylic Acids/chemistry , Proteins/chemistry , Ligands , Molecular Conformation , Molecular Docking Simulation , Molecular Dynamics Simulation , Protein Binding , Quantum Theory , Solvents/chemistry , ThermodynamicsABSTRACT
This study reports the results of binding free energy calculations for CB[8] host-guest systems in the SAMPL6 blind challenge (receipt ID 3z83m). Force-field parameters were developed specific for each of host and guest molecules to improve configurational sampling. We used quantum mechanical (QM) implicit solvent calculations and QM force matching to determine non-bonded (partial atomic charges) and bonded terms, respectively. Free energy calculations were carried out using the double-decoupling method (DDM) combined with Hamiltonian replica exchange method (HREM) and Bennett acceptance ratio (BAR) method. The root mean square error (RMSE) of the predicted values using DDM with respect to the experimental results was 4.32 kcal/mol. The coefficient of determination (R2) and Kendall rank coefficient (τ) were 0.49 and 0.52, respectively, highest of all submissions. In addition, these were compared to the results obtained by umbrella sampling (US) and weighted histogram analysis method (WHAM). Overall, DDM achieved a higher prediction accuracy than the US method. Results are discussed in terms of parameterization and free energy simulations.
Subject(s)
Bridged-Ring Compounds/chemistry , Imidazoles/chemistry , Macrocyclic Compounds/chemistry , Proteins/chemistry , Ligands , Models, Theoretical , Molecular Conformation , Molecular Docking Simulation , Molecular Dynamics Simulation , Protein Binding , Quantum Theory , Solvents/chemistry , ThermodynamicsABSTRACT
Molecular simulations have been widely used in biomolecular systems such as proteins, DNA, etc. The search for stable conformations of proteins by molecular simulations is important to understand the function and stability of proteins. However, finding the stable state by conformational search is difficult, because the energy landscape of the system is characterized by many local minima separated by high energy barriers. In order to overcome this difficulty, various sampling and optimization methods for the conformation of proteins have been proposed. In this study, we propose a new conformational search method for proteins based on a genetic algorithm. We applied this method to an α-helical protein. We found that the conformations obtained from our simulations are in good agreement with the experimental results.
