Subtle differences in chemical pattern between human toll-like receptor 8 agonists and antagonists: Emerging chemical patterns analysis

Due to the potencies in the treatments of cancer, infectious diseases, and autoimmune diseases, the developments of human TLR8 (hTLR8) agonists and antagonists have attracted widespread attentions. The hTLR8 agonists and antagonists have similar structures but with completely opposite biological effects. Up to date, the subtle differences in the structures between the hTLR8 agonists and antagonists are still unknown. In this work, emerging chemical pattern (ECP) was successfully used to extract the key chemical patterns of the hTLR8 agonists and antagonists. By using CAEP classifier, an optimal ECP model with only 3 descriptors was established with the overall prediction accuracy larger than 90%. Further hierarchical cluster analysis and molecular docking showed that the H-bond and hydrophobic properties are the key features distinguishing the hTLR8 agonists from antagonists. Comparing with the antagonists, the agonists show stronger specific H-bond properties, while antagonists have stronger non-specific hydrophobic properties. The significant differences in the structural properties may be closely related to the activation/inhibition mechanism of hTLR8.

The emerging chemical patterns applied in predicting human toll-like receptor 8 agonists

Toll-like receptors (TLRs) are important pattern recognition receptors to human innate immunity, which can recognize pathogen-associated molecular patterns and initiate innate immune responses. As the receptor of single stranded RNA (ssRNA), toll-like receptor 8 (TLR8) has potential in the treatment of tumors, microbial infection, and inflammatory diseases. Herein, an emerging chemical pattern (ECP) method was utilized to predict the key chemical patterns of TLR8 agonists. Based on the ECPs discovered, a robust and predictive ECP model was derived with prediction accuracies of 83.3%, 81.0%, and 80.0% for 132 training samples, 79 validation samples, and 75 test samples, respectively. When the ECP model was applied with a molecular docking method, the hit rate of TLR8 agonists was greatly enhanced. The results of ECP-based hierarchical cluster analysis and Connolly surface analysis of the TLR8 receptor showed that the H-bonding, hydrophilic and hydrophobic potentials as well as the unbalanced degree of property distributions are very important for distinguishing the TLR8 agonists from non-agonists.