Pressor mechanism evaluation for phytochemical compounds using in silico compound–protein interaction prediction

Multi-Target Screening and Experimental Validation of Natural Products from Selaginella Plants against Alzheimer's Disease

Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disorder which is considered to be the most common cause of dementia. It has a greater impact not only on the learning and memory disturbances but also on social and economy. Currently, there are mainly single-target drugs for AD treatment but the complexity and multiple etiologies of AD make them difficult to obtain desirable therapeutic effects. Therefore, the choice of multi-target drugs will be a potential effective strategy inAD treatment. To find multi-target active ingredients for AD treatment from Selaginella plants, we firstly explored the behaviors effects on AD mice of total extracts (TE) from Selaginella doederleinii on by Morris water maze test and found that TE has a remarkable improvement on learning and memory function for AD mice. And then, multi-target SAR models associated with AD-related proteins were built based on Random Forest (RF) and different descriptors to preliminarily screen potential active ingredients from Selaginella. Considering the prediction outputs and the quantity of existing compounds in our laboratory, 13 compounds were chosen to carry out the in vitro enzyme inhibitory experiments and 4 compounds with BACE1/MAO-B dual inhibitory activity were determined. Finally, the molecular docking was applied to verify the prediction results and enzyme inhibitory experiments. Based on these study and validation processes, we explored a new strategy to improve the efficiency of active ingredients screening based on trace amount of natural product and numbers of targets and found some multi-target compounds with biological activity for the development of novel drugs for AD treatment.

Identification of compound-protein interactions through the analysis of gene ontology, KEGG enrichment for proteins and molecular fragments of compounds

Compound-protein interactions play important roles in every cell via the recognition and regulation of specific functional proteins. The correct identification of compound-protein interactions can lead to a good comprehension of this complicated system and provide useful input for the investigation of various attributes of compounds and proteins. In this study, we attempted to understand this system by extracting properties from both proteins and compounds, in which proteins were represented by gene ontology and KEGG pathway enrichment scores and compounds were represented by molecular fragments. Advanced feature selection methods, including minimum redundancy maximum relevance, incremental feature selection, and the basic machine learning algorithm random forest, were used to analyze these properties and extract core factors for the determination of actual compound-protein interactions. Compound-protein interactions reported in The Binding Databases were used as positive samples. To improve the reliability of the results, the analytic procedure was executed five times using different negative samples. Simultaneously, five optimal prediction methods based on a random forest and yielding maximum MCCs of approximately 77.55 % were constructed and may be useful tools for the prediction of compound-protein interactions. This work provides new clues to understanding the system of compound-protein interactions by analyzing extracted core features. Our results indicate that compound-protein interactions are related to biological processes involving immune, developmental and hormone-associated pathways.