# Sequence-Only Prediction of Binding Affinity Changes: A Robust and Interpretable Model for Antibody Engineering

arXiv: https://arxiv.org/abs/2505.20301 · HTML: https://mlsanity.com/paper/2505.20301/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 48 references that are in the map.
- First posted on arXiv 14 May 2025.

## Builds on (most cited first, 10 of 10)

- [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://mlsanity.com/paper/2104.09864/) (2021)
- [Are Transformers More Robust Than CNNs?](https://mlsanity.com/paper/2111.05464/) (2021)
- [ProtTrans: Towards Cracking the Language of Life's Code Through Self-Supervised Deep Learning and High Performance Computing](https://mlsanity.com/paper/2007.06225/) (2020)
- [Ankh: Optimized Protein Language Model Unlocks General-Purpose Modelling](https://mlsanity.com/paper/2301.06568/) (2023)
- [End-to-End Learning on 3D Protein Structure for Interface Prediction](https://mlsanity.com/paper/1807.01297/) (2018)
- [ProteinGPT: Multimodal LLM for Protein Property Prediction and Structure Understanding](https://mlsanity.com/paper/2408.11363/) (2024)
- [Pre-training of Graph Neural Network for Modeling Effects of Mutations on Protein-Protein Binding Affinity](https://mlsanity.com/paper/2008.12473/) (2020)
- [Retrieval-Enhanced Mutation Mastery: Augmenting Zero-Shot Prediction of Protein Language Model](https://mlsanity.com/paper/2410.21127/) (2024)
- [Simple, Efficient and Scalable Structure-aware Adapter Boosts Protein Language Models](https://mlsanity.com/paper/2404.14850/) (2024)
- [Immunogenicity Prediction with Dual Attention Enables Vaccine Target Selection](https://mlsanity.com/paper/2410.02647/) (2024)

## Built on by (most cited first, 3 of 3)

- [Quantifying Cross-Attention Interaction in Transformers for Interpreting TCR-pMHC Binding](https://mlsanity.com/paper/2507.03197/) (2025)
- [DuaDeep-SeqAffinity: Dual-Branch Deep Learning for Tri-Stream Sequence-Based Antibody--Antigen Affinity Prediction](https://mlsanity.com/paper/2512.22007/) (2025)
- [AbICL: In-Context Learning for Antigen-Specific Antibody Affinity Ranking](https://mlsanity.com/paper/2607.05846/) (2026)

## Abstract (arXiv)

A pivotal area of research in antibody engineering is to find effective modifications that enhance antibody-antigen binding affinity. Traditional wet-lab experiments assess mutants in a costly and time-consuming manner. Emerging deep learning solutions offer an alternative by modeling antibody structures to predict binding affinity changes. However, they heavily depend on high-quality complex structures, which are frequently unavailable in practice. Therefore, we propose ProtAttBA, a deep learning model that predicts binding affinity changes based solely on the sequence information of antibody-antigen complexes. ProtAttBA employs a pre-training phase to learn protein sequence patterns, following a supervised training phase using labeled antibody-antigen complex data to train a cross-attention-based regressor for predicting binding affinity changes. We evaluated ProtAttBA on three open benchmarks under different conditions. Compared to both sequence- and structure-based prediction methods, our approach achieves competitive performance, demonstrating notable robustness, especially with uncertain complex structures. Notably, our method possesses interpretability from the attention mechanism. We show that the learned attention scores can identify critical residues with impacts on binding affinity. This work introduces a rapid and cost-effective computational tool for antibody engineering, with the potential to accelerate the development of novel therapeutic antibodies.