Sheng-Li Yang

Classification of Substrates and Inhibitors of P-Glycoprotein Using Unsupervised Machine Learning Approach

P-glycoprotein (P-gp), a drug efflux pump, affects the bioavailability of therapeutic drugs and plays a potentially important role in clinical drug-drug interactions. Classification of candidate drugs as substrates or inhibitors of the carrier protein is of crucial importance in drug development. Accurate classification is difficult to achieve due to two major factors: i. The extreme diversity of substrates and the presence of multiple binding sites complicate the understanding of the mechanisms behind and hinder the development of a true, conclusive quantitative structure-activity relationship (QSAR) for P-gp substrates. ii. Both inhibitors and substrates interact with the same binding site of P-gp, as a result, it is not surprising that both share many common structural features. In this work, an unsupervised machine learning approach based on the Kohonen self-organizing maps (SOM) was explored, which incorporated a predefined set of physicochemical descriptors encoding the key molecular properties capable of discerning a substrate from an inhibitor. The SOM model can discriminate between substrates and inhibitors with an average accuracy of 82.3%. The current results show that the SOM-based method provides a potential in silico model for virtual screening.

A System-Level Investigation into the Mechanisms of Chinese Traditional Medicine: Compound Danshen Formula for Cardiovascular Disease Treatment

Compound Danshen Formula (CDF) is a widely used Traditional Chinese Medicine (TCM) which has been extensively applied in clinical treatment of cardiovascular diseases (CVDs). However, the underlying mechanism of clinical administrating CDF on CVDs is not clear. In this study, the pharmacological effect of CDF on CVDs was analyzed at a systemic point of view. A systems-pharmacological model based on chemical, chemogenomics and pharmacological data is developed via network reconstruction approach. By using this model, we performed a high-throughput in silico screen and obtained a group of compounds from CDF which possess desirable pharmacodynamical and pharmacological characteristics. These compounds and the corresponding protein targets are further used to search against biological databases, such as the compound-target associations, compound-pathway connections and disease-target interactions for reconstructing the biologically meaningful networks for a TCM formula. This study not only made a contribution to a better understanding of the mechanisms of CDF, but also proposed a strategy to develop novel TCM candidates at a network pharmacology level.

Haplotype frequency distribution and linkage disequilibrium analysis of single nucleotide polymorphisms at the human FMO3 gene locus

We analyzed flavin-containing monooxygenase 3 (FMO3) polymorphisms, haplotype structure, and linkage disequilibrium (LD) in 256 Han Chinese and 50 African-American individuals to compare their haplotype frequencies and LD with other world populations. For the Han Chinese, genotyping of three haplotype tag single nucleotide polymorphisms (E158K, V257M, and E308G) was performed by polymerase chain reaction (PCR)-restriction fragment length polymorphism. For the African-Americans, genotyping of all coding exons was performed by modified PCR-single strand conformational polymorphism. Haplotype frequencies, LD, and evolutionary rates were inferred and estimated computationally. There were significant differences in haplotype frequency distribution and LD pattern among Han Chinese, African-Americans, and other world populations. Four major haplotypes of Han Chinese were EVE, KVE, EME, and EVG. Two major haplotypes of African-Americans were EVE and KVE. We found that sites 158 and 257 are in significant LD in both populations. This is the first report comparing FMO haplotypes and LD of Han Chinese with African-Americans. The data presented here justify further pharmacogenetic studies for potentially optimizing recommended drug dosages and evaluating relationships with disease processes.