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https://www.selleckchem.com/products/icg-001.html Deamidation of asparagine (Asn) residues is a nonenzymatic post-translational modification of proteins. Asn deamidation is associated with pathogenesis of age-related diseases and hypofunction of monoclonal antibodies. Deamidation rate is known to be affected by the residue following Asn on the carboxyl side and by secondary structure. Information about main-chain conformation of Asn residues is necessary to accurately predict deamidation rate. In this study, the effect of main-chain conformation of Asn residues on deamidation rate was computationally investigated using molecular dynamics (MD) simulations and quantum chemical calculations. The results of MD simulations for γS-crystallin suggested that frequently deamidated Asn residues have common main-chain conformations on the N-terminal side. Based on the simulated structure, initial structures for the quantum chemical calculations were constructed and optimized geometries were obtained using the B3LYP density functional method. Structures that were frequently deamidated had a lower activation energy barrier than that of the little deamidated structure. We also showed that dihydrogen phosphate and bicarbonate ions are important catalysts for deamidation of Asn residues.Recently, artificial intelligence (AI) technologies have been employed to predict construction and demolition (C&D) waste generation. However, most studies have used machine learning models with continuous data input variables, applying algorithms, such as artificial neural networks, adaptive neuro-fuzzy inference systems, support vector machines, linear regression analysis, decision trees, and genetic algorithms. Therefore, machine learning algorithms may not perform as well when applied to categorical data. This article uses machine learning algorithms to predict C&D waste generation from a dataset, as a way to improve the accuracy of waste management in C&D facilities. These datasets include cate

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