An analysis of the rate of isomerization in molecules with an asymmetric double well potential

The classical theory of the rate of unimolecular isomerization developed by Gray and Rice as extended by Zhao and Rice is applied to the calculation of the rate of isomerization in model systems which have linear asymmetric double well potentials. We are interested in this system for two reasons. First, we are interested in the detailed dynamical processes for the mentioned system because it is widely related to practical chemical reactions. Second, the present model system has an asymmetric double well potential, which provides a different test of the accuracy of the approximations used in the Gray-Zhao-Rice theory than posed by previous applications. We have calculated relaxation rates and relaxation times for the model systems on different time scales. We find that for the systems under studies the Gray-Zhao-Rice version of the classical theory of isomerization rate yields values in good agreement with those generated from trajectory calculations and from the Reactive Island theory of De Leon et al.

Aerolysin Nanopore Identification of Single Nucleotides Using the AdaBoost Model

Nanopores employ the ionic current from the single molecule blockage to identify the structure,conformation,chemical groups and charges of a single molecule.Despite the tremendous development in designing sensitive pore-forming materials,at some extent,the analyte with the single group difference still exhibits similar residual current or duration time.The serious overlap in the statistical results of residual current and duration time brings the difficulties in the nanopore discrimination of each single molecules from the mixture.In this paper,we present the AdaBoost-based machine learning model to identify the multiple analyte with single group difference in the mixed blockages.A set of feature vectors which is obtained from Hidden Markov Model(HMM)is used to train the AdaBoost model.By employing the aerolysin sensing of 5ʹ-AAAA-3ʹ(AA3)and 5ʹ-GAAA-3ʹ(GA3)as the model system,our results show that AdaBoost model increases the identification accu-racy from~0.293 to above 0.991.Furthermore,five sets of mixed blockages of AA3 and GA3 further validate the average accuracy of training and validation,which are 0.997 and 0.989,respectively.The proposed methods improve the capacity of wild-type biological nanopore in efficiently identify the single nucleotide difference without designing of protein and optimizing of the experimental condition.Therefore,the AdaBoost-based machine learning approach could promote the nanopore practical application such as genetic and epigenetic detection.

Theoretical Study on N-H…O Blue-shifted H-Bond for HNO…H2O2 Complex

A theoretical study on the blue-shifted H-bond N-H…O and red-shifted H-bond O-H…O in the complex HNO…H2O2 was conducted by employment of both standard and counterpoise-corrected methods to calculate the geometric structures and vibrational frequencies at the MP2/6-31G（d）, MP2/6-31 ＋ G（d,p）, MP2/6-311 ＋ ＋ G（d,p）, B3LYP/6-31G（d）, B3LYP/6-31 ＋G（d,p） and B3LYP/6-311 ＋ ＋G（d,p） levels. In the H-bond N-H…O, the calculated blue shift of N-H stretching frequency is in the vicinity of 120 cm^-1 and this is indeed the largest theoretical estimate of a blue shift in the X-H…Y H-bond ever reported in the literature. From the natural bond orbital analysis, the red-shifted H-bond O-H…O can be explained on the basis of the dominant role of the hyperconjugation. For the blue-shifted H-bond N-H…O, the hyperconjugation was inhibited due to the existence of significant electron density redistribution effect, and the large blue shift of the N-H stretching frequency was prominently due to the rehybridization of spn N-H hybrid orbital.