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Lesion-induced thermodynamic destabilization is believed to facilitate β-hairpin intrusion by the human XPC/hHR23B nucleotide excision repair (NER) recognition factor, accompanied by partner-base flipping, as suggested by the crystal structure of the yeast orthologue (Min, J. H., and Pavletich, N. P. (2007) Nature 449, 570–575). To investigate this proposed mechanism, we employed umbrella sampling to compute partner base flipping free energies for the repair susceptible 14R (+)-trans-anti-DB[a,l]P-N2-dG modified duplex 11-mer, derived from the fjord region polycyclic aromatic hydrocarbon dibenzo[a,l]pyrene, and for the undamaged duplex. Our flipping free energy profiles show that the adduct has a lower flipping barrier by ~7.7 kcal/mol, consistent with its thermally destabilizing impact on the damaged DNA duplex and its susceptibility to NER.

Born-Oppenheimer ab initio QM/MM molecular dynamics simulation with umbrella sampling is a state-of-the-art approach to calculate free energy profiles of chemical reactions in complex systems. To further improve its computational efficiency, a mass-scaling method with the increased time step in MD simulations has been explored and tested. It is found that by increasing the hydrogen mass to 10 amu, a time step of 3 fs can be employed in ab initio QM/MM MD simulations. In all our three test cases, including two solution reactions and one enzyme reaction, the resulted reaction free energy profiles with 3 fs time step and mass scaling are found to be in excellent agreement with the corresponding simulation results using 1 fs time step and the normal mass. These results indicate that for Born-Oppenheimer ab initio QM/MM molecular dynamics simulations with umbrella sampling, the mass-scaling method can significantly reduce its computational cost while has little effect on the calculated free energy profiles.

The crystal structure of Rad4/Rad23, the yeast homolog of the human nucleotide excision repair (NER) lesion recognition factor XPC-RAD23B (Min and Pavletich, (2007) Nature 449:570–575) reveals that the lesion-partner base is flipped out of the helix and binds the protein. This suggests the hypothesis that flipping of this partner base must overcome a free energy barrier, which constitutes one element contributing to changes in the thermodynamic properties induced by the DNA damage and sensed by the recognition protein. We explored this hypothesis by computing complete flipping free energy profiles for two lesions derived from the procarcinogenic polycyclic aromatic hydrocarbons (PAHs), dibenzo[a,l]pyrene (DB[a,l]P) and benzo[a]pyrene (B[a]P), R-trans-anti-DB[a,l]P-N6-dA (R-DB[a,l]P-dA) and R-trans-anti-B[a]P-N6-dA (R-B[a]P-dA), and the corresponding unmodified duplex. The DB[a,l]P and B[a]P adducts differ in number and organization of their aromatic rings. We integrate these results with prior profiles for the R-trans-anti-DB[a,l]P-dG adduct (Zheng et al., (2010) Chem. Res. Toxicol. 23:1868–1870). All adopt conformational themes involving intercalation of the PAH aromatic ring system into the DNA duplex; however, R-DB[a,l]P-dA and R-B[a]P-dA intercalate from the major groove, while R-DB[a,l]P-dG intercalates from the minor groove. These structural differences produce different computed van der Waals stacking interaction energies between the flipping partner base with the lesion aromatic ring system and adjacent bases; we find that the better the stacking, the higher the relative flipping free energy barrier and hence lower flipping probability. The better relative NER susceptibilities correlate with greater ease of flipping in these three differently intercalated lesions. In addition to partner base-flipping, the Rad4/Rad23 crystal structure shows that a protein-β-hairpin, BHD3, intrudes from the major groove side between the DNA strands at the lesion site. We present a molecular modeling study for the R-DB[a,l]P-dG lesion in Rad4/Rad23 showing BHD3 β-hairpin intrusion with lesion eviction, and we hypothesize that lesion steric effects play a role in the recognition of intercalated adducts.

We propose a new adaptive sampling approach to determine free energy profiles with molecular dynamics simulations, which is called as "repository based adaptive umbrella sampling" (RBAUS). Its main idea is that a sampling repository is continuously updated based on the latest simulation data, and the accumulated knowledge and sampling history are then employed to determine whether and how to update the biasing umbrella potential for subsequent simulations. In comparison with other adaptive methods, a unique and attractive feature of the RBAUS approach is that the frequency for updating the biasing potential depends on the sampling history and is adaptively determined on the fly, which makes it possible to smoothly bridge nonequilibrium and quasiequilibrium simulations. The RBAUS method is first tested by simulations on two simple systems: a double well model system with a variety of barriers and the dissociation of a NaCl molecule in water. Its efficiency and applicability are further illustrated in ab initio quantum mechanics/molecular mechanics molecular dynamics simulations of a methyl-transfer reaction in aqueous solution.

Fatigued driving is one of the leading causes of traffic accidents, and detecting fatigued driving effectively is critical to improving driving safety. Given the variety and individual variability of the driving surroundings, the drivers’ states of weariness, and the uncertainty of the key characteristic factors, in this paper, we propose a deep-learning-based study of the MAX-MIN driver fatigue detection algorithm. First, the ShuffleNet V2K16 neural network is used for driver face recognition, which eliminates the influence of poor environmental adaptability in fatigue detection; second, ShuffleNet V2K16 is combined with Dlib to obtain the coordinates of driver face feature points; and finally, the values of EAR and MAR are obtained by comparing the first 100 frames of images to EAR-MAX and MAR-MIN. Our proposed method achieves 98.8% precision, 90.2% recall, and 94.3% F-Score in the actual driving scenario application.

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