Identification of Pharmacophoric Fragments of DYRK1A Inhibitors Using Machine Learning Classification Models

Dual-specific tyrosine phosphorylation regulated kinase 1 (DYRK1A) has been regarded as a potential therapeutic target of neurodegenerative diseases, and considerable progress has been made in the discovery of DYRK1A inhibitors. Identification of pharmacophoric fragments provides valuable information for structure- and fragment-based design of potent and selective DYRK1A inhibitors. In this study, seven machine learning methods along with five molecular fingerprints were employed to develop qualitative classification models of DYRK1A inhibitors, which were evaluated by cross-validation, test set, and external validation set with four performance indicators of predictive classification accuracy (CA), the area under receiver operating characteristic (AUC), Matthews correlation coefficient (MCC), and balanced accuracy (BA). The PubChem fingerprint-support vector machine model (CA = 0.909, AUC = 0.933, MCC = 0.717, BA = 0.855) and PubChem fingerprint along with the artificial neural model (CA = 0.862, AUC = 0.911, MCC = 0.705, BA = 0.870) were considered as the optimal modes for training set and test set, respectively. A hybrid data balancing method SMOTETL, a combination of synthetic minority over-sampling technique (SMOTE) and Tomek link (TL) algorithms, was applied to explore the impact of balanced learning on the performance of models. Based on the frequency analysis and information gain, pharmacophoric fragments related to DYRK1A inhibition were also identified. All the results will provide theoretical supports and clues for the screening and design of novel DYRK1A inhibitors

Machine Learning Models for the Classification of CK2 Natural Products Inhibitors with Molecular Fingerprint Descriptors

Casein kinase 2 (CK2) is considered an important target for anti-cancer drugs. Given the structural diversity and broad spectrum of pharmaceutical activities of natural products, numerous studies have been performed to prove them as valuable sources of drugs. However, there has been little study relevant to identifying structural factors responsible for their inhibitory activity against CK2 with machine learning methods. In this study, classification studies were conducted on 115 natural products as CK2 inhibitors. Seven machine learning methods along with six molecular fingerprints were employed to develop qualitative classification models. The performances of all models were evaluated by cross-validation and test set. By taking predictive accuracy(CA), the area under receiver operating characteristic (AUC), and (MCC)as three performance indicators, the optimal models with high reliability and predictive ability were obtained, including the Extended Fingerprint-Logistic Regression model (CA = 0.859, AUC = 0.826, MCC = 0.520) for training test andPubChem fingerprint along with the artificial neural model (CA = 0.826, AUC = 0.933, MCC = 0.628) for test set. Meanwhile, the privileged substructures responsible for their inhibitory activity against CK2 were also identified through a combination of frequency analysis and information gain. The results are expected to provide useful information for the further utilization of natural products and the discovery of novel CK2 inhibitors

Irisin Improves Autophagy of Aged Hepatocytes via Increasing Telomerase Activity in Liver Injury

An aged liver has decreased reparative capacity during ischemia-reperfusion (IR) injury. A recent study showed that plasma irisin levels predict telomere length in healthy adults. The aim of the present study is to clarify the role of irisin, telomerase activity, and autophagy during hepatic IR in the elderly. To study this, hepatic IR was established in 22-month- and 3-month-old rats and primary hepatocytes were isolated. The results showed that the old rats exhibited more serious liver injury and lower levels of irisin expression, telomerase activity, autophagy ability, and mitochondrial function than young rats during hepatic IR. Irisin activated autophagy and improved mitochondrial function via increasing telomerase activity in aged hepatocytes. Inhibition of telomerase activity by BIBP1532 abolished the protective role of irisin in hepatocytes during hypoxia and reoxygenation. Additionally, this study proved irisin increased the telomerase activity via inhibition of the phosphorylation of JNK during hepatic IR. Administration of exogenous irisin significantly mitigated the inflammation, oxidative stress, apoptosis, and liver injury in an old rat model of hepatic IR. In conclusion, irisin improves autophagy of aged hepatocytes via increasing telomerase activity in hepatic IR. Irisin exhibits conspicuous benefits in increasing reparative capacity of an aged liver during hepatic IR

Targeting extracellular CIRP with an X-aptamer shows therapeutic potential in acute pancreatitis

Summary: Severe acute pancreatitis (AP) is associated with a high mortality rate. Cold-inducible RNA binding protein (CIRP) can be released from cells in inflammatory conditions and extracellular CIRP acts as a damage-associated molecular pattern. This study aims to explore the role of CIRP in the pathogenesis of AP and evaluate the therapeutic potential of targeting extracellular CIRP with X-aptamers. Our results showed that serum CIRP concentrations were significantly increased in AP mice. Recombinant CIRP triggered mitochondrial injury and ER stress in pancreatic acinar cells. CIRP−/− mice suffered less severe pancreatic injury and inflammatory responses. Using a bead-based X-aptamer library, we identified an X-aptamer that specifically binds to CIRP (XA-CIRP). Structurally, XA-CIRP blocked the interaction between CIRP and TLR4. Functionally, it reduced CIRP-induced pancreatic acinar cell injury in vitro and L-arginine-induced pancreatic injury and inflammation in vivo. Thus, targeting extracellular CIRP with X-aptamers may be a promising strategy to treat AP