No abstract

Better adjuvants are needed for vaccines against seasonal influenza. TLR7 agonists are potent activators of innate immune responses and thereby may be promising adjuvants. Among the imidazoquinoline compounds, 1-benzyl-2-butyl-1H-imidazo[4,5-c]quinolin-4-amine (BBIQ) was reported to be a highly active TLR7 agonist but has remained relatively unexplored because of its commercial unavailability. Indeed, in silico molecular modeling studies predicted that BBIQ had a higher TLR7 docking score and binding free energy than imiquimod, the gold standard TLR7 agonist. To circumvent the availability issue, we developed an improved and higher yield method to synthesize BBIQ. Testing BBIQ on human and mouse TLR7 reporter cell lines confirmed it to be TLR7 specific with significantly higher potency than imiquimod. To test its adjuvant potential, BBIQ or imiquimod were admixed with recombinant influenza hemagglutinin protein and administered to mice as two intramuscular immunizations 2 weeks apart. Serum anti-influenza IgG responses assessed by ELISA 2 weeks after the second immunization confirmed that the mice that received vaccine admixed with BBIQ had significantly higher anti-influenza IgG1 and IgG2c responses than mice immunized with antigen alone or admixed with imiquimod. This confirmed BBIQ to be a TLR7-specific adjuvant able to enhance humoral immune responses.

BackgroundToll-like receptor 9 is a key innate immune receptor involved in detecting infectious diseases and cancer. TLR9 activates the innate immune system following the recognition of single-stranded DNA oligonucleotides (ODN) containing unmethylated cytosine-guanine (CpG) motifs. Due to the considerable number of rotatable bonds in ODNs, high-throughput in silico screening for potential TLR9 activity via traditional structure-based virtual screening approaches of CpG ODNs is challenging. In the current study, we present a machine learning based method for predicting novel mouse TLR9 (mTLR9) agonists based on features including count and position of motifs, the distance between the motifs and graphically derived features such as the radius of gyration and moment of Inertia. We employed an in-house experimentally validated dataset of 396 single-stranded synthetic ODNs, to compare the results of five machine learning algorithms. Since the dataset was highly imbalanced, we used an ensemble learning approach based on repeated random down-sampling.ResultsUsing in-house experimental TLR9 activity data we found that random forest algorithm outperformed other algorithms for our dataset for TLR9 activity prediction. Therefore, we developed a cross-validated ensemble classifier of 20 random forest models. The average Matthews correlation coefficient and balanced accuracy of our ensemble classifier in test samples was 0.61 and 80.0%, respectively, with the maximum balanced accuracy and Matthews correlation coefficient of 87.0% and 0.75, respectively. We confirmed common sequence motifs including ‘CC’, ‘GG’,‘AG’, ‘CCCG’ and ‘CGGC’ were overrepresented in mTLR9 agonists. Predictions on 6000 randomly generated ODNs were ranked and the top 100 ODNs were synthesized and experimentally tested for activity in a mTLR9 reporter cell assay, with 91 of the 100 selected ODNs showing high activity, confirming the accuracy of the model in predicting mTLR9 activity.ConclusionWe combined repeated random down-sampling with random forest to overcome the class imbalance problem and achieved promising results. Overall, we showed that the random forest algorithm outperformed other machine learning algorithms including support vector machines, shrinkage discriminant analysis, gradient boosting machine and neural networks. Due to its predictive performance and simplicity, the random forest technique is a useful method for prediction of mTLR9 ODN agonists.

Lipopeptides including diacylated Pam2CSK4 as well as triacylated Pam3CSK4 act as ligands of Toll-like receptor (TLR)-2, a promising target for the development of vaccine adjuvants. The highly investigated Pam2CSK4 and...