Calculation of solvation force in molecular dynamics simulation by deep-learning method.

Electrostatic calculations are generally used in studying the thermodynamics and kinetics of biomolecules in solvent. Generally, this is performed by solving the Poisson-Boltzmann equation on a large grid system, a process known to be time consuming. In this study, we developed a deep neural network to predict the decomposed solvation free energies and forces of all atoms in a molecule. To train the network, the internal coordinates of the molecule were used as the input data, and the solvation free energies along with transformed atomic forces from the Poisson-Boltzmann equation were used as labels. Both the training and prediction tasks were accelerated on GPU. Formal tests demonstrated that our method can provide reasonable predictions for small molecules when the network is well-trained with its simulation data. This method is suitable for processing lots of snapshots of molecules in a long trajectory. Moreover, we applied this method in the molecular dynamics simulation with enhanced sampling. The calculated free energy landscape closely resembled that obtained from explicit solvent simulations.

SurfPB: A GPU-Accelerated Electrostatic Calculation and Visualization Tool for Biomolecules.

In this work, we present SurfPB as a useful tool for the study of biomolecules. It can do many typical calculations, including the molecular surface, electrostatic potential, solvation free energy, entropy, and binding free energy. Among all of the calculations, the entropy calculation is the most time-consuming one. In SurfPB, the calculation can be performed in a vacuum or implicit solvent and accelerated on GPU. The Poisson-Boltzmann equation solver is accelerated on GPU as well. Moreover, we developed a graphical user interface for SurfPB. It allows users to input the parameters and complete the whole calculation in a visual way. The calculated electrostatic potentials are shown on the molecular surface in a three-dimensional scene.

ZBTB7 evokes 5-fluorouracil resistance in colorectal cancer through the NF­κB signaling pathway.

Zinc finger and BTB domain containing 7A (ZBTB7), a POZ/BTB and Krüppel erythroid myeloid oncogenic factor, is critical for the tumorigenicity and progression of various cancer types. ZBTB7 has been reported to promote the cell proliferation of colorectal cancers (CRC). However, the function of ZBTB7 to 5-fluorouracil (5­FU) resistance has not yet been studied. In the current study, ZBTB7 expression and function in 5­FU resistance in CRC were investigated using with multidisciplinary approaches, including western blot analysis, Transwell assay, CCK8 and a tumor xenograft model. Overexpression of ZBTB7 was increased the level of proteins associated with cell invasion and epithelial-mesenchymal transition. ZBTB7 inhibition attenuated the invasion and enhanced the apoptosis of CRC cells. IC50 values and cell viability were significantly reduced in cells with short hairpin RNA (shRNA)-mediated ZBTB7 depletion compared with the control group. 5­FU administration decreased viability to a greater extent in the ZBTB7-shRNA group compared with the control, which was dose- and time-dependent. Analysis of gene expression omnibus data demonstrated that ZBTB7 mediated 5­FU resistance, potentially through nuclear factor (NF)-κB signaling. NF­κB inhibitor SN50 reversed ZBTB7-induced resistance in CRC. Collectively, the findings demonstrated that ZBTB7 mediated 5­FU resistance in CRC cells through NF­κB signaling. Thus, targeting ZBTB7 and NF­κB signaling may be an effective strategy to reverse 5­FU resistance in CRC.