Screening and Analysis of Hypolipidemic Components from Shuangdan Capsule Based on Pancreatic Lipase

Enhanced triolein and ethyl ferulate interesterification performance by CRL-AuNPs

This study established a Candida rugosa lipase (CRL) system to catalyze triolein and ethyl ferulate interesterification. The products were identified, and the binding mode between the substrates and CRL was predicted through molecular docking. Three methods for preparing CRL-AuNPs were proposed and characterized. It was found that the addition of 40 mL of 15 nm gold nanoparticles increased the CRL activity from 3.05 U/mg to 4.75 U/mg, but the hybridization efficiency was only 32.7 %. By using 4 mL of 0.1 mg/mL chloroauric acid, the hybridization efficiency was improved to 50.7 %, but the enzyme activity was sharply decreased. However, when the molar ratio of Mb to HAuCl4 was 0.2, the hybridization efficiency increased to 71.8 %, and the CRL activity was also enhanced to 5.98 U/mg. Under optimal conditions, the enzyme activity of CRL-AuNPs③ was maintained at 95 % after 6 repetitions and 85.6 % after 30 days at room temperature.

TransDFL: Identification of Disordered Flexible Linkers in Proteins by Transfer Learning

Disordered flexible linkers (DFLs) are the functional disordered regions in proteins, which are the sub-regions of intrinsically disordered regions (IDRs) and play important roles in connecting domains and maintaining inter-domain interactions. Trained with the limited available DFLs, the existing DFL predictors based on the machine learning techniques tend to predict the ordered residues as DFLs, leading to a high falsepositive rate (FPR) and low prediction accuracy. Previous studies have shown that DFLs are extremely flexible disordered regions, which are usually predicted as disordered residues with high confidence [P(D) > 0.9] by an IDR predictor. Therefore, transferring an IDR predictor to an accurate DFL predictor is of great significance for understanding the functions of IDRs. In this study, we proposed a new predictor called TransDFL for identifying DFLs by transferring the RFPR-IDP predictor for IDR identification to the DFL prediction. The RFPR-IDP was pre-trained with IDR sequences to learn the general features between IDRs and DFLs, which is helpful to reduce the false positives in the ordered regions. RFPR-IDP was fine-tuned with the DFL sequences to capture the specific features of DFLs so as to be transferred into the TransDFL. Experimental results of two application scenarios (prediction of DFLs only in IDRs or prediction of DFLs in entire proteins) showed that TransDFL consistently outperformed other existing DFL predictors with higher accuracy. The corresponding web server of TransDFL can be freely accessed at http://bliulab.net/TransDFL/.