Computational methods structural kinetic modeling

Kinetic models of refining processes have evolved historically attempting to predict the vast product distribution or at least part of it. Up to now, most kinetic models are based on the lumping approach for practical reasons. Lump-based kinetics can be easily derived from conventional analytical information. However, the advances in analytical methods, which currently allow obtaining relevant information on the molecular structure of complex hydrocarbons, and the increase in computational capacity have enabled the development of detailed molecule-based models [41]. 

The ability to accurately compute kinetic isotope effects (KIEs) for chemical reactions in solution and in enzymes is important because the measured KIEs provide the most direct probe to the nature of the transition state and the computational results can help rationalize experimental findings. This is illustrated by the work of Schramm and co-workers, who have used the experimental KIEs to develop transition state models for the enzymatic process catalyzed by purine nucleoside phosphorylase (PNP), which in turn were used to design picomolar inhibitors. In principle, Schramm s approach can be applied to other enzymes however, in order to establish a useful transition state model for enzymatic reactions, it is often necessary to use sophisticated computational methods to model the structure of the transition state and to match the computed KIEs with experiments. The challenge to theory is the difficulty in accurately determining the small difference in free energy of activation due to isotope replacements, especially for secondary and heavy isotope effects. Furthermore, unlike studies of reactions in the gas phase, one has to consider... 

Independent of the exact features of the model or criterion defining the protein s folded state, the computational demands of evaluating thermodynamic and kinetic properties of these models can be formidable. At the present time, the best methods combined with the most powerful computational engines are inadequate to fold an all-atom model of a protein in computo. As such, a careful choice of the computational method is essential. The development of new computational methods is infinite in its possibilities. The field of development of conformational optimization algorithms for proteins has shown rapid progress in recent years. This rapid development of new algorithms promises to continue. This article provides a snapshot of the field of protein structure prediction as a problem of conformational optimization. There is an emphasis on the most general and fundamental methods where further development appears to be most likely. The discussion is not intended to be a comprehensive review or even a survey of the most effective methods. The reader is referred to the references for a more comprehensive discussion. 

Figure 2 illustrates major modeling methods, i.e., ab initio molecular dynamic (AIMD), molecular dynamic (MD), kinetic Monte Carlo (KMC), and continuum methods in terms of their spatial and temporal scales. Models for microscopic and macroscopic components of a PEFC are placed in the figure in terms of their characteristic dimensions for comparison. While continuum models are successful in rationalizing the macroscopic behaviors based on a few assumptions and the average properties of the materials, molecular or atomistic modeling can evaluate the nanostructures or molecular structures and microscopic properties. In computational... 

---