Hydrogen abstraction from toluene by OH, H, O, CH3, and HO2 radicals are important reactions in oxidation process of toluene. Geometries and corresponding harmonic frequencies of the reactants, transition states as well as products involved in these reactions are determined at the B3LYP/6-31G(2df,p) level. To achieve highly accurate thermochemical data for these stationary points on the potential energy surfaces, the Gaussian-4(G4) composite method was employed. Torsional motions are treated either as free rotors or hindered rotors in calculating partion functions to determine thermodynamic properties. The obtained standard enthalpies of formation for reactants and some prodcuts are shown to be in excellent agreement with experimental data with the largest error of 0.5 kcal mol(-1). The conventional transition state theory (TST) with tunneling effects was adopted to determine rate constants of these hydrogen abstraction reactions based on results from quantum chemistry calculations. To faciliate its application in kinetic modeling, the obtained rate constants are given in Arrhenius expression: k(T) = AT(n) exp(-EaR/T). The obtained reaction rate constants also agree reasonably well with available expermiental data and previous theoretical values. Branching ratios of these reactions have been determined. The present reaction rates for these reactions have been used in a toluene combustion mechanism, and their effects on some combustion properties are demonstrated.

Discovering the mechanism of protein folding, in molecular biology, is a great challenge. A key step to this end is to find factors that correlate with protein folding rates. Over the past few years, many empirical parameters, such as contact order, long-range order, total contact distance, secondary structure contents, have been developed to reflect the correlation between folding rates and protein tertiary or secondary structures. However, the correlation between proteins' folding rates and their amino acid compositions has not been explored. In the present work, we examined systematically the correlation between proteins' folding rates and their amino acid compositions for two-state and multistate folders and found that different amino acids contributed differently to the folding progress. The relation between the amino acids' molecular weight and degeneracy and the folding rates was examined, and the role of hydrophobicity in the protein folding process was also inspected. As a consequence, a new indicator called composition index was derived, which takes no structure factors into account and is merely determined by the amino acid composition of a protein. Such an indicator is found to be highly correlated with the protein's folding rate (r> 0.7). From the results of this work, three points of concluding remarks are evident. (1) Two-state folders and multistate folders have different rate-determining amino acids. (2) The main determining information of a protein's folding rate is largely reflected in its amino acid composition. (3) Composition index may be the best predictor for an ab initio protein folding rate prediction directly from protein sequence from the standpoint of practical application.

The chemical structure stabilities of a set of SixFy (x ≤ 6, y ≤ 12) compounds were explored using theoretical and experimental methods.

One of the most important challenges in computational and molecular biology is to understand the relationship between amino acid sequences and the folding rates of proteins. Recent works suggest that topological parameters, amino acid properties, chain length and the composition index relate well with protein folding rates, however, sequence order information has seldom been considered as a property for predicting protein folding rates. In this study, amino acid sequence order was used to derive an effective method, based on an extended version of the pseudo-amino acid composition, for predicting protein folding rates without any explicit structural information. Using the jackknife cross validation test, the method was demonstrated on the largest dataset (99 proteins) reported. The method was found to provide a good correlation between the predicted and experimental folding rates. The correlation coefficient is 0.81 (with a highly significant level) and the standard error is 2.46. The reported algorithm was found to perform better than several representative sequence-based approaches using the same dataset. The results indicate that sequence order information is an important determinant of protein folding rates.