Candida antarctica lipase B (CALB) belongs to psychrophilic lipases which hydrolyze carboxyl ester bonds at low temperatures. There have been some features reported about cold-activity of the enzyme through experimental methods, whereas there is no detailed information on its mechanism of action at molecular level. Herein, a comparative molecular dynamics simulation and essential dynamics analysis have been carried out at three temperatures (5, 35 and 50°C) to trace the dominant factors in the psychrophilic properties of CALB under cold condition. The results clearly describe the effect of temperature on CALB with meaningful differences in the flexibility of the lid region (α5 helix), covering residues 141–147. Open- closed conformations have been obtained from different sets of long-term simulations (60 ns) at 5°C gave two reproducible distinct forms of CALB. The starting open conformation became closed immediately at 35 and 50°C during 60 ns of simulation, while a sequential open-closed form was observed at 5°C. These structural alterations were resulted from α5 helical movements, where the closed conformation of active site cleft was formed by displacement of both helix and its side chains. Analysis of normal mode showed concerted motions that are involved in the movement of both α5 and α10 helices. It is suggested that the functional motions needed for lypolytic activity of CALB is constructed from short-range movement of α5, accompanied by long-range movement of the domains connected to the lid region.

Aspect-based sentiment analysis (ABSA) is a more detailed task in sentiment analysis, by identifying opinion polarity toward a certain aspect in a text. This method is attracting more attention from the community, due to the fact that it provides more thorough and useful information. However, there are few language-specific researches on Persian language. The present research aims to improve the ABSA on the Persian Pars-ABSA dataset. This research shows the potential of using pre-trained BERT model and taking advantage of using sentence-pair input on an ABSA task. The results indicate that employing Pars-BERT pre-trained model along with natural language inference auxiliary sentence (NLI-M) could boost the ABSA task accuracy up to 91% which is 5.5% (absolute) higher than state-of-the-art studies on Pars-ABSA dataset.

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