Toll-Like Receptors in Mycoplasma Infections: Immunopathogenesis, Cheminformatics, and Machine Learning-Based Insights into TLR-Targeted Therapeutics

Abstract

Mycoplasma species are the minimal-genome pathogens that elicit complex host immune responses involving both protective and pathogenic inflammation. Toll-like receptors (TLRs) are pivotal components of the immune system, recognizing pathogen-associated molecular patterns and damage-associated molecular patterns to initiate immune responses. TLRs recognize Mycoplasma-derived lipoproteins, glycolipids, and CpG-rich DNA motifs, initiating NF-κB and MAPK signaling cascades. While these pathways are indispensable for microbial clearance, their dysregulation contributes to chronic inflammation, autoimmunity, and tissue damage. Consequently, selective modulation of TLR signaling has emerged as a promising therapeutic strategy for restoring immune homeostasis. In this study, we summarized the recent advances in delineating the molecular crosstalk between TLRs and Mycoplasma, emphasizing the distinct roles of TLRs in pathogen recognition and immunopathology. In parallel, it integrates a cheminformatics-based analysis of TLR inhibitory ligands retrieved from the ChEMBL database, encompassing over 230 compounds across TLR1–5. Multivariate visualization techniques such as principal component analysis (PCA), uniform manifold approximation and projection (UMAP), and t-distributed stochastic neighbor embedding (t-SNE) were employed to map the chemical space and reveal receptor-specific clustering patterns. The analysis identified TLR2 and TLR4 inhibitors as occupying distinct but overlapping chemical regions, characterized by higher molecular weight, lipophilicity, and structural complexity. Correlation and similarity mapping further highlighted scaffold conservation among active ligands, providing a quantitative foundation for rational drug design and structure–activity modeling. Furthermore, we explore how machine learning (ML) and computational modeling are reshaping TLR-targeted drug discovery. ML-driven frameworks facilitate ligand prediction, virtual screening, and structure–activity optimization through deep docking and transformer-based scoring models. Integrative ML approaches also extend beyond ligand discovery to encompass multiomics data integration, biomarker identification, and predictive modeling of host–pathogen interactions and immune responses. By bridging immunological insight with cheminformatics and ML-based analytics, this article outlines a multidisciplinary roadmap for precision immunomodulation in Mycoplasma-associated diseases. The convergence of experimental pharmacology and computational intelligence holds the potential to accelerate the discovery of next-generation TLR-targeted therapeutics, advancing personalized strategies for immune regulation and infection control.