We have employed two-to-one mapping scheme to develop three coarse-grained (CG) water models, namely, 1-, 2-, and 3-site CG models. Here, for the first time, particle swarm optimization (PSO) and gradient descent methods were coupled to optimize the force-field parameters of the CG models to reproduce the density, self-diffusion coefficient, and dielectric constant of real water at 300 K. The CG MD simulations of these new models conducted with various timesteps, for different system sizes, and at a range of different temperatures are able to predict the density, self-diffusion coefficient, dielectric constant, surface tension, heat of vaporization, hydration free energy, and isothermal compressibility of real water with excellent accuracy. The 1-site model is ∼3 and ∼4.5 times computationally more efficient than 2- and 3-site models, respectively. To utilize the speed of 1-site model and electrostatic interactions offered by 2- and 3-site models, CG MD simulations of 1:1 combination of 1- and 2-/3-site models were performed at 300 K. These mixture simulations could also predict the properties of real water with good accuracy. Two new CG models of benzene, consisting of beads with and without partial charges, were developed. All three water models showed good capacity to solvate these benzene models.

Summary: High-risk Human Papilloma Viruses (HPV) cause cervical, anal and oropharyngeal cancers, unlike the low-risk HPVs, which cause benign lesions. E6 oncoproteins from the high-risk strains are essential for cell proliferation and transformation in HPV induced cancers. We report that a cellular deubiquitinase, USP46 is selectively recruited by the E6 of high-risk, but not low-risk, HPV to deubiqutinate and stabilize Cdt2/DTL. Stabilization of Cdt2, a component of the CRL4Cdt2 E3 ubiquitin ligase, limits the level of Set8, an epigenetic writer, and promotes cell-proliferation. USP46 is essential for the proliferation of HPV-transformed cells, but not of cells without HPV. Cdt2 is elevated in human cervical cancers and knockdown of USP46 inhibits HPV-transformed tumor growth in xenografts. Recruitment of a cellular deubiquitinase to stabilize key cellular proteins is an important activity of oncogenic E6, and the importance of E6-USP46Cdt2-Set8 pathway in HPV-induced cancers makes USP46 a target for the therapy of such cancers.

Optimizing force-field (FF) parameters to perform molecular dynamics (MD) simulations is a challenging and time-consuming process. We present a novel FF optimization framework that integrates MD simulations with particle swarm optimization (PSO) algorithm and artificial neural network (ANN). This new ANN-assisted PSO framework was used to develop transferable coarse-grained (CG) models for DO and DMF as a proof of concept. The PSO algorithm was used to generate the set of input FF parameters for the MD simulations of the CG models of these solvents, which were optimized to reproduce their experimental properties. Herein, for the first time, a reverse approach was employed for on-the-fly training of the ANN model, where results (solvent properties) obtained from the MD simulations and their corresponding FF parameters were used as inputs and outputs, respectively. The ANN model was then required to predict a set of new FF parameters, which were tested for their ability to predict the desired experimental properties. This new framework can be extended to integrate any optimization algorithm with ANN and MD simulations to accelerate the FF development.

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