By Oren M. Becker, Alexander D. MacKerell Jr., Benoit Roux, Masakatsu Watanabe
Protecting theoretical equipment and computational strategies in biomolecular study, this ebook makes a speciality of ways for the remedy of macromolecules, together with proteins, nucleic acids, and bilayer membranes. It makes use of ideas in unfastened power calculations, conformational research, response premiums, and transition pathways to calculate and interpret biomolecular houses gleaned from computer-generated membrane simulations. It additionally demonstrates comparative protein constitution modeling, outlines computer-aided drug layout, discusses Bayesian records in molecular and structural biology, and examines the RISM-SCF/MCSCF method of chemical techniques in resolution.
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Additional info for Computational Biochemistry and Biophysics
09, with the carbon charge adjusted to yield a unit charge. 0. 09 based on the electrostatic contribution to the butane torsional surface , is justified by both the free energy of solvation of alkanes (S Fischer, M Karplus, personal communication) and the pure solvent properties of ethane being insensitive to the charge distribution. 115, based on the condensed phase properties of benzene , are used on all aromatic functional groups. , pyridine), where the carbon–hydrogen pairs adjacent to the heteroatom may have different charges; these charges would be determined via interactions with water as presented above.
No correction for basis-set superposition error (BSSE) is required in the present approach. At this stage the QM interaction distances and energies are offset and scaled as follows to produce the final target data that will allow for the optimization of partial atomic charges that yield reasonable condensed phase properties. The offsets and scalings are performed as follows. 2 A charged species, and not offset for interactions between water and nonpolar sites. 16 is performed for all interactions involving polar–neutral compounds and no scaling is performed for the charged compounds.
This again emphasizes the necessity of iterating over all the individual optimization steps in order to obtain a final, consistent set of parameters. Following initial optimization of all the bond, angle, and dihedral parameters, it is important to reemphasize that all the target data must be rechecked for convergence (see below) via an iterative approach (see loop II of Fig. 2). This is due to the parameter correlation problem. Even though excellent agreement may be achieved with the geometric target data initially, adjustment of the force constants often alters the minimized geometry such that the bond and angle equilibrium parameters must be readjusted; typically the dihedral multiplicity and phase are not significantly affected.