Meet DeepAIR: A Deep Learning Framework Integrating Sequence and 3D St …

Studying how our immune system identifies and fights off infections and diseases has always been challenging for scientists. One fundamental process in this intricate system involves the interaction between adaptive immune receptors (AIRs) like T cell receptors (TCRs) and B cell receptors (BCRs) with their matching antigens. However, predicting how these receptors bind to antigens has been difficult, as current methods primarily rely on genetic sequence information, ignoring crucial structural details that determine binding strength.

Several methods have been developed to predict how AIRs bind to antigens. These methods mostly focus on analyzing the genetic sequence of AIRs. They use statistical approaches or advanced deep learning technologies to predict whether an AIR binds to a specific antigen (binding reactivity) or how strong the binding is (binding affinity). However, these methods have limitations, especially in accurately predicting binding affinity, which remains a significant challenge in understanding immune responses.

In light of these challenges, a new solution called DeepAIR has emerged. DeepAIR is a deep learning framework that revolutionizes the analysis of AIR-antigen binding by integrating both the sequence and structural features of AIRs. Unlike previous methods, DeepAIR uses predicted structural data of AIRs generated by AlphaFold2, a highly accurate protein structure predictor. By combining sequence and structural information, DeepAIR aims to improve the accuracy of predicting how AIRs bind to antigens.

DeepAIR’s performance metrics showcase its remarkable capabilities. It achieves a high Pearson’s correlation of 0.813 in predicting TCR binding affinity and impressive median area under the receiver-operating characteristic curve (AUC) values of 0.904 and 0.942 for predicting TCR and BCR binding reactivity, respectively. Moreover, DeepAIR’s analysis using TCR and BCR repertoires accurately identifies patients with specific diseases like nasopharyngeal carcinoma and inflammatory bowel disease, showcasing its potential in disease identification.

In conclusion, DeepAIR emerges as a breakthrough in understanding how our immune system recognizes and fights off infections. By integrating both sequence and structural information, DeepAIR outperforms existing methods in predicting AIR-antigen binding. Its remarkable performance metrics and potential for disease identification within immune repertoires make it a promising tool for advancing personalized immunotherapy and better understanding the complexities of our immune system. DeepAIR paves the way for a deeper understanding of adaptive immunity, promising better-designed therapies and vaccines tailored to individual immune responses.

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