Protein Simulation Methodology

We begin this chapter with an introduction to protein simulation methodology aimed at experimentalists and graduate students new to in silico investigations. Here, more emphasis is placed on the knowledge needed to select appropriate simulation protocols, leaving theoretical and mathematical depth for other texts to take care We explain some of the more practical considerations 

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Figure 3. Cartoon of an EPR approach to determine the orientation of the axial ligands in the heme pocket of low-spin ferric heme proteins. The methodology consists of measurement of the X-band HYSCORE spectra and simulation of the contribution stemming from the porphyrin nitrogens, which leads to determination of the orientation of the g tensor axes in the molecular frame. Subsequent analysis of the proton HYSCORE spectra and proton combination frequencies leads to a determination of the axial ligand orientation in the g axes frame, and therefore in the molecular frame. The last step of the procedure consists of a Ml simulation of the nitrogen HYSCORE spectra. The Ml methodology is explained in the text.

The simulation methodologies applied to proteins and nucleic acids are applicable to the biomembrane modeling at the atomic level. The jvidely distributed programs, CHARMM, AMBER, and GROMOS, have been applied for biomembrane studies. The common energy parameters of proteins, nucleic acids, and water are used with minor modifications for lipids. For instance, the AMBER energy function is written as follows ... 

The QM/MM and ab initio methodologies have just begun to be applied to challenging problems involving ion channels [73] and proton motion through them [74]. Reference [73] utilizes Hartree-Fock and DFT calculations on the KcsA channel to illustrate that classical force fields can fail to include polarization effects properly due to the interaction of ions with the protein, and protein residues with each other. Reference [74] employs a QM/MM technique developed in conjunction with Car-Parrinello ab initio simulations [75] to model proton and hydroxide ion motion in aquaporins. Due to the large system size, the time scale for these simulations was relatively short (lOps), but the influences of key residues and macrodipoles on the short time motions of the ions could be examined. 

These examples show that for difficult cases, and especially when a prediction is being made, a large number of simulations may be necessary. Today, the continuing increase in computer power has made such multiple simulations possible in a reasonable time frame. Several other recent studies illustrate the scope of molecular dynamics free energy for molecular recognition problems they include studies of nucleic acids [13], proteins [14-16], and methodological studies of convergence and precision [17, 18]. Several recent reviews provide additional examples [19, 20]. 

When addressing problems in computational chemistry, the choice of computational scheme depends on the applicability of the method (i.e. the types of atoms and/or molecules, and the type of property, that can be treated satisfactorily) and the size of the system to be investigated. In biochemical applications the method of choice - if we are interested in the dynamics and effects of temperature on an entire protein with, say, 10,000 atoms - will be to run a classical molecular dynamics (MD) simulation. The key problem then becomes that of choosing a relevant force field in which the different atomic interactions are described. If, on the other hand, we are interested in electronic and/or spectroscopic properties or explicit bond breaking and bond formation in an enzymatic active site, we must resort to a quantum chemical methodology in which electrons are treated explicitly. These phenomena are usually highly localized, and thus only involve a small number of chemical groups compared with the complete macromolecule. 

It should be noted that the above strategy, although first employed In the delineation of protein antigenic sites. Is applicable, with appropriate adaptations, to the precise delineation and chemical synthesis of other types of protein binding sites. The Introduction of the concept of surface-simulation synthesis ( 4, ) has provided a methodology by which In principle any type of protein binding site can be mimicked synthetically after careful chemical characterization. 

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