Simulations and the thermodynamic cycle

Simulations and the Thermodynamic Cycle. Given a known structure of a drug-receptor complex with a measured affinity of the ligand, the thermodynamic cycle paradigm al-... 

Components of bonding affinity (n) Simulations and the thermodynamic cycle (/n) Ligand-receptor recognition. 

When thermodynamic integration simulations and the thermodynamic cycle approach are used to evaluate free energy differences, the contribution of the kinetic energy usually cancels and therefore does not need to be calculated. Since Monte Carlo simulations generate ensembles of configurations stochastically, momenta are not available, and the contribution cannot be evaluated. 

An alternative approach to calculating the free energy of solvation is to carry out simulations corresponding to the two vertical arrows in the thermodynamic cycle in Fig. 2.6. The transformation to nothing should not be taken literally -this means that the perturbed Hamiltonian contains not only terms responsible for solute-solvent interactions - viz. for the right vertical arrow - but also all the terms that involve intramolecular interactions in the solute. If they vanish, the solvent is reduced to a collection of noninteracting atoms. In this sense, it disappears or is annihilated from both the solution and the gas phase. For this reason, the corresponding computational scheme is called double annihilation. Calculations of... 

The free energy perturbation calculations on mutation of the central statine residue of pepstatin to its dehydroxy and other derivatives were carried out using the window method. The crystal structure reported by Suguna et al.l4 l5was used for these calculations. In most simulations, the mutations were achieved either in 101 or 51 windows with 0.4 ps of equilibration and 0.4 ps of data collection at each window. The calculation for each mutation was repeated in water to determine the difference in the free energies of solvation and to complete the thermodynamic cycle. 

In the present chapter we have reviewed a numerically efficient and accurate equation of library state for high pressure fluids and solids. Thermodynamic cycle theories allow us to apply this model profitably to the reactions of energetic materials. The equation of state is based on HMSA integral equation theory, with a correction based on extensive Monte Carlo simulations. We have also shown that our equation of state can be used to accurately model the properties of molecular fluids and detonation products. The accuracy of the equation of state of polar fluids is significantly enhanced by using a multi-species or cluster representation of the fluid. 

Figure 3 A thermodynamic cycle for the binding of two ligands, LI and LI, to a protein P. In the experiment the ligands are transferred from the solvent to the active site, and the difference AAF = AFj - is measured. In simulations the nonphysical transformation of LI - L2 is carried out in the protein and in solution, and the corresponding free energies AFp and AF are calculated. The thermodynamic cycle leads to the desired AAF in terms of the latter free energy differences, AAF = AFp - AF. ...

One approach is the thermodynamic cycle-perturbation method that allows one to eompare the relative binding affinity of a number of similar ligands. This approaeh reeognizes the difficulty in simulating the absolute binding affinity AG between a ligand and its receptor ... 

Although it is not strictly a molecular simulation method, we mention the GSE here since it can be derived from the thermodynamic cycle of crystal to supercooled liquid to solution provided that some assumptions are made about the entropy of melting, AS. The GSE provides useful estimates of solubihty when experimental melting point and AS data are available [34], However, the GSE is not usually applicable to unsynthesized molecules as the best empirical methods for predicting melting point give predictive errors of 40-50°C [35, 36], The GSE has provided the basis of empirical methods to predict solubility such as the Solubihty Forecast Index [37]. 

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