Analysis of vibrational motions and thermal fluctuational dynamics is a widely used approach for studying structural, dynamic and functional properties of proteins and nucleic acids. Development of a freely accessible web server for computation of vibrational and thermal fluctuational dynamics of biomolecules is thus useful for facilitating the relevant studies. We have developed a computer program for computing vibrational normal modes and thermal fluctuational properties of proteins and nucleic acids and applied it in several studies. In our program, vibrational normal modes are computed by using modified AMBER molecular mechanics force fields, and thermal fluctuational properties are computed by means of a selfconsistent harmonic approximation method. A web version of our program, MoViES (Molecular Vibrations Evaluation Server), was set up to facilitate the use of our program to study vibrational dynamics of proteins and nucleic acids. This software was tested on selected proteins, which show that the computed normal modes and thermal fluctuational bond disruption probabilities are consistent with experimental findings and other normal mode computations. MoViES can be accessed at http://ang.cz3.nus. edu.sg/cgi-bin/prog/norm.pl.

The chemical fixation of nitric oxide (NO) reacting with alkynyllithium to produce 5-methyl-3-oxide-1,2,3-oxadiazole has been investigated by using ab initio (U)MP2 and DFT/(U)B3LYP methods. The solvent effect was assessed using the combination of microsolvation model with explicit THF ligands on lithium and continuum solvent model based on the SCRF/CPCM method at the (U)-B3LYP/6-31G* level. Our results reveal that the overall reaction is stepwise and considered to include two processes. In process 1, the nitrogen atom in nitric oxide at first attacks the C1 atom in alkynyllithium to afford the intermediate 5. In process 2, after another nitric oxide reacted with the intermediate 5 to produce 8a, we found that two pathways are involved. For path 1, the O2 atom at first attacks the C2 atom to form a five-membered ring geometry, and then lithium can rotate around the N1-O1 bond, leading to the product 5-methyl-3-oxide-1,2,3-oxadiazole followed addition of water. However, for path 2, lithium atom rotates first around the N1-O1 bond, and then the product 5-methyl-3-oxide-1,2,3-oxadiazole is also generated by addition of water. Our calculations indicate that path 1 is more favorable than path 2 in the gas phase, while both of them exist possibly in THF solvent. The overall reaction is exothermic.

Statistical-learning methods have been developed for facilitating the prediction of pharmacokinetic and toxicological properties of chemical agents. These methods employ a variety of molecular descriptors to characterize structural and physicochemical properties of molecules. Some of these descriptors are specifically designed for the study of a particular type of properties or agents, and their use for other properties or agents might generate noise and affect the prediction accuracy of a statistical learning system. This work examines to what extent the reduction of this noise can improve the prediction accuracy of a statistical learning system. A feature selection method, recursive feature elimination (RFE), is used to automatically select molecular descriptors for support vector machines (SVM) prediction of P-glycoprotein substrates (P-gp), human intestinal absorption of molecules (HIA), and agents that cause torsades de pointes (TdP), a rare but serious side effect. RFE significantly reduces the number of descriptors for each of these properties thereby increasing the computational speed for their classification. The SVM prediction accuracies of P-gp and HIA are substantially increased and that of TdP remains unchanged by RFE. These prediction accuracies are comparable to those of earlier studies derived from a selective set of descriptors. Our study suggests that molecular feature selection is useful for improving the speed and, in some cases, the accuracy of statistical learning methods for the prediction of pharmacokinetic and toxicological properties of chemical agents.

*: PM3), and ONIOM (MP2/6-31G*: HF/3-21G)-were applied to investigate thermal decomposition mechanisms of four 2-phenoxycarboxylic acids (2-phenoxyacetic acid, 2-phenoxypropionic acid, 2-phenoxybutyric acid, and 2-phenoxyisobutyric acid) in the gas phase. All the transition states and intermediates of the reaction paths were optimized. The reaction pathway of four reactants yielding the phenol, CO, and the corresponding carbonyl compound was characterized on the potential energy surface and found to proceed stepwise. The first step corresponds to the elimination of phenol and the formation of α-lactone intermediate through a five-membered ring transition state, and the second step is the cycloreversion process of α-lactone intermediate to form CO and the corresponding carbonyl compound. The reaction pathway of latter three compounds to produce the carboxylic acid and phenol via a four-membered cyclic transition structure was also examined theoretically. Comparison with experiment indicates that the activation parameters for the fist reaction channel are accurately predicted at the ONIOM (MP2/6-31G*: HF/3-21G) level of theory.

Density functional theory (DFT) and Monte Carlo (MC) simulation with free energy perturbation (FEP) techniques have been used to study the tautomeric proton transfer reaction of 2-amino-2-oxazoline, 2-amino-2-thiazoline, and 2-amino-2-imidazoline in the gas phase and in water. Two reaction pathways were considered: the direct and water-assisted transfers. The optimized structures and thermodynamic properties of stationary points for the title reaction system in the gas phase were calculated at the B3LYP/6-311 G(d, p) level of theory. The potential energy profiles along the minimum energy path in the gas phase and in water were obtained. The study of the solvent effect of water on the proton transfer of 2-amino-2-oxozoline, 2-amino-2-thiazoline, and 2-amino-2-imidazoline indicates that water as a solvent is favorable for the water-assisted process and slows down the rate of the direct transfer pathway.