AttABseq: an attention-based deep learning prediction method for antigen-antibody binding affinity changes based on protein sequences

Insights into next-generation immunotherapy designs and tools: molecular mechanisms and therapeutic prospects

Immunotherapy has revolutionized the oncology treatment paradigm, and CAR-T cell therapy in particular represents a significant milestone in treating hematological malignancies. Nevertheless, tumor resistance due to target heterogeneity or mutation remains a Gordian knot for immunotherapy. This review elucidates molecular mechanisms and therapeutic potential of next-generation immunotherapeutic tools spanning genetically engineered immune cells, multi-specific antibodies, and cell engagers, emphasizing multi-targeting strategies to enhance personalized immunotherapy efficacy. Development of logic gate modulation-based circuits, adapter-mediated CARs, multi-specific antibodies, and cell engagers could minimize adverse effects while recognizing tumor signals. Ultimately, we highlight gene delivery, gene editing, and other technologies facilitating tailored immunotherapy, and discuss the promising prospects of artificial intelligence in gene-edited immune cells.

Recent advances in antibody optimization based on deep learning methods

Antibodies currently comprise the predominant treatment modality for a variety of diseases; therefore, optimizing their properties rapidly and efficiently is an indispensable step in antibody-based drug development. Inspired by the great success of artificial intelligence-based algorithms, especially deep learning-based methods in the field of biology, various computational methods have been introduced into antibody optimization to reduce costs and increase the success rate of lead candidate generation and optimization. Herein, we briefly review recent progress in deep learning-based antibody optimization, focusing on the available datasets and algorithm input data types that are crucial for constructing appropriate deep learning models. Furthermore, we discuss the current challenges and potential solutions for the future development of general-purpose deep learning algorithms in antibody optimization.

TransABseq: A Two-Stage Approach for Predicting Antigen-Antibody Binding Affinity Changes upon Mutation Based on Protein Sequences

The antigen-antibody interaction represents a critical mechanism in host defense, contributing to pathogen neutralization, tumor surveillance, immunotherapy, and in vitro disease detection. Owing to their exceptional specificity, affinity, and selectivity, antibodies have been extensively utilized in the development of clinical diagnostic, therapeutic, and prophylactic strategies. In this study, we propose TransABseq, a novel computational framework specifically designed to predict the effects of missense mutations on antigen-antibody interactions. The model's innovative two-stage architecture enables comprehensive feature analysis: in the first stage, multiple embeddings of protein language models are processed through a Transformer encoder module and a multiscale convolutional module; in the second stage, the XGBOOST model is used to perform quantitative output based on the deeply fused features. A critical advancement contributing to the effectiveness of TransABseq is the deep feature fusion strategy, which reveals the biochemical properties of proteins. By leveraging the multilayer self-attention mechanism of the Transformer to capture complex global dependencies within sequences and mining features at different hierarchical levels through multiscale convolution, the feature abstraction capability of TransABseq is significantly enhanced. We evaluated TransABseq through three distinct cross-validation strategies on two established benchmarks and a newly reconstructed data set. As a result, TransABseq achieved average PCC values of 0.607, 0.843, and 0.794 and average RMSE values of 1.166, 1.314, and 1.337 kcal/mol in 10-fold cross-validation. Furthermore, its robustness and predictive accuracy were validated on blind test data sets, where TransABseq outperformed existing methods, enabling it to attain a PCC of 0.721 and an RMSE of 0.925 kcal/mol. The relevant data and code have been made publicly available for academic research at: https://github.com/cuifengLI/TransABseq.

AntiBinder: utilizing bidirectional attention and hybrid encoding for precise antibody-antigen interaction prediction

Antibodies play a key role in medical diagnostics and therapeutics. Accurately predicting antibody-antigen binding is essential for developing effective treatments. Traditional protein-protein interaction prediction methods often fall short because they do not account for the unique structural and dynamic properties of antibodies and antigens. In this study, we present AntiBinder, a novel predictive model specifically designed to address these challenges. AntiBinder integrates the unique structural and sequence characteristics of antibodies and antigens into its framework and employs a bidirectional cross-attention mechanism to automatically learn the intrinsic mechanisms of antigen-antibody binding, eliminating the need for manual feature engineering. Our comprehensive experiments, which include predicting interactions between known antigens and new antibodies, predicting the binding of previously unseen antigens, and predicting cross-species antigen-antibody interactions, demonstrate that AntiBinder outperforms existing state-of-the-art methods. Notably, AntiBinder excels in predicting interactions with unseen antigens and maintains a reasonable level of predictive capability in challenging cross-species prediction tasks. AntiBinder's ability to model complex antigen-antibody interactions highlights its potential applications in biomedical research and therapeutic development, including the design of vaccines and antibody therapies for rapidly emerging infectious diseases.