Machine learning for reparameterization of four-site water models: TIP4P-BG and TIP4P-BGT.

Parameterizing an effective water model is a challenging issue because of the difficulty in maintaining a comprehensive balance among the diverse physical properties of water with a limited number of parameters. The advancement in machine learning provides a promising path to search for a reliable set of parameters. Based on the TIP4P water model, hence, about 6000 molecular dynamics (MD) simulations for pure water at 1 atm and in the range of 273-373 K are conducted here as the training data. The back-propagation (BP) neural network is then utilized to construct an efficient mapping between the model parameters and four crucial physical properties of water, including the density, vaporization enthalpy, self-diffusion coefficient and viscosity. Without additional time-consuming MD simulations, this mapping operation could result in sufficient and accurate data for high-population genetic algorithm (GA) to optimize the model parameters as much as possible. Based on the proposed parameterizing strategy, TIP4P-BG (a conventional four-site water model) and TIP4P-BGT (an advanced model with temperature-dependent parameters) are established. Both the water models exhibit excellent performance with a reasonable balance among the four crucial physical properties. The relevant mean absolute percentage errors are 3.53% and 3.08%, respectively. Further calculations on the temperature of maximum density, isothermal compressibility, thermal expansion coefficient, radial distribution function and surface tension are also performed and the resulting values are in good agreement with the experimental values. Through this water modeling example, the potential of the proposed data-driven machine learning procedure has been demonstrated for parameterizing a MD-based material model.

Identification of proteins that interact with alpha A-crystallin using a human proteome microarray.

PURPOSE: To identify proteins interacting with alpha A-crystallin (CRYAA) and to investigate the potential role that these protein interactions play in the function of CRYAA using a human proteome (HuProt) microarray. METHODS: The active full-length CRYAA protein corresponding to amino acids 1-173 of CRYAA was recombined. A HuProt microarray composed of 17,225 human full-length proteins with N-terminal glutathione S-transferase (GST) tags was used to identify protein-protein interactions. The probes were considered detectable when the signal to noise ratio (SNR) was over 1.2. The identified proteins were subjected to subsequent bioinformatics analysis using the DAVID database. RESULTS: The HuProt microarray results showed that the signals of 343 proteins were higher in the recombinant CRYAA group than in the control group. The SNR of 127 proteins was ≥ 1.2. The SNR of the following eight proteins was > 3.0: hematopoietic cell-specific Lyn substrate 1 (HCLS1), Kelch domain-containing 6 (KLHDC6), sarcoglycan delta (SGCD), KIAA1706 protein (KIAA1706), RNA guanylyltransferase and 5'-phosphatase (RNGTT), chromosome 10 open reading frame 57 (C10orf57), chromosome 9 open reading frame 52 (C9orf52), and plasminogen activator, urokinase receptor (PLAUR). The bioinformatics analysis revealed 127 proteins associated with phosphoproteins, alternative splicing, acetylation, DNA binding, the nuclear lumen, ribonucleotide binding, the cell cycle, WD40 repeats, protein transport, transcription factor activity, GTP binding, and cellular response to stress. Functional annotation clustering showed that they belong to cell cycle, organelle or nuclear lumen, protein transport, and DNA binding and repair clusters. CRYAA interacted with these proteins to maintain their solubility and decrease the accumulation of denatured target proteins. The protein-protein interactions may help CRYAA carry out multifaceted functions. CONCLUSIONS: One-hundred and twenty-seven of 17,225 human full-length proteins were identified that interact with CRYAA. The advent of microarray analysis enables a better understanding of the functions of CRYAA as a molecular chaperone.

αA crystallin may protect against geographic atrophy-meta-analysis of cataract vs. cataract surgery for geographic atrophy and experimental studies.

BACKGROUND: Cataract and geographic atrophy (GA, also called advanced "dry" age-related macular degeneration) are the two major causes of visual impairment in the developed world. The association between cataract surgery and the development of GA was controversial in previous studies. METHODS/PRINCIPAL FINDINGS: We performed a meta-analysis by pooling the current evidence in literature and found that cataract is associated with an increased risk of geographic atrophy with a summary odds ratio (OR) of 3.75 (95% CI: 95% CI: 1.84-7.62). However, cataract surgery is not associated with the risk of geographic atrophy (polled OR=3.23, 95% CI: 0.63-16.47). Further experiments were performed to analyze how the αA-crystallin, the major component of the lens, influences the development of GA in a mouse model. We found that theαA-crystallin mRNA and protein expression increased after oxidative stress induced by NaIO(3) in immunohistochemistry of retinal section and western blot of posterior eyecups. Both functional and histopathological evidence confirmed that GA is more severe in αA-crystallin knockout mice compared to wild-type mice. CONCLUSIONS: Therefore, αA-crystallin may protect against geographic atrophy. This study provides a better understanding of the relationship between cataract, cataract surgery, and GA.

[The progress of proteomics approaches in cataract research].

Researches on lens proteomics are becoming prevalent in recent years. Characterized by their high resolution and efficiency, proteomics approaches provide reliable methodological support for lenses proteomic research, which demonstrates a new orientation for revealing the mechanism of cataractogenesis. This review outlines the separation, identification and some novel approaches of proteomic techniques applied in recent lenses proteomic research.

Water diffusion inside carbon nanotubes: mutual effects of surface and confinement.

The mutual effects of two crucial features of carbon nanotubes (CNTs) (surface and confinement) on the temperature-dependent water diffusion are studied through molecular dynamics simulations. A two-stage diffusion mechanism is detected in the CNTs of diameter smaller than 12.2 Å, which becomes obscure as the temperature increases. This peculiar phenomenon can be ascribed to the cooperation of the small confinement and the periodic surface. The diffusion coefficient of the confined water exhibits a nonmonotonic dependence on the confinement size and an unexpected increase inside the large CNTs (compared to that of bulk water). These anomalous behaviors can be attributed to the competition of the smooth surface and the small confinement. Considering the mutual effects, an empirical formula is proposed on the basis of two groups of numerical examples, whose results indicate that the confinement effect will dominate over the surface effect until the CNT diameter increases up to ~16 Å, whereas thereafter the surface effect becomes dominant and finally both of them vanish gradually.