# Pre-training of Graph Neural Network for Modeling Effects of Mutations on Protein-Protein Binding Affinity

arXiv: https://arxiv.org/abs/2008.12473 · HTML: https://mlsanity.com/paper/2008.12473/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2020.

## Built on by (most cited first, 18 of 18)

- [PEER: A Comprehensive and Multi-Task Benchmark for Protein Sequence Understanding](https://mlsanity.com/paper/2206.02096/) (2022)
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- [AntBO: Towards Real-World Automated Antibody Design with Combinatorial Bayesian Optimisation](https://mlsanity.com/paper/2201.12570/) (2022)
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- [Semi-Supervised Dual-Stream Self-Attentive Adversarial Graph Contrastive Learning for Cross-Subject EEG-based Emotion Recognition](https://mlsanity.com/paper/2308.11635/) (2023)
- [Predicting mutational effects on protein-protein binding via a side-chain diffusion probabilistic model](https://mlsanity.com/paper/2310.19849/) (2023)
- [Revealing data leakage in protein interaction benchmarks](https://mlsanity.com/paper/2404.10457/) (2024)
- [Multi-level Interaction Modeling for Protein Mutational Effect Prediction](https://mlsanity.com/paper/2405.17802/) (2024)
- [Multimodal Pre-Training Model for Sequence-based Prediction of Protein-Protein Interaction](https://mlsanity.com/paper/2112.04814/) (2021)
- [Sequence-Only Prediction of Binding Affinity Changes: A Robust and Interpretable Model for Antibody Engineering](https://mlsanity.com/paper/2505.20301/) (2025)
- [Online Multi-level Contrastive Representation Distillation for Cross-Subject fNIRS Emotion Recognition](https://mlsanity.com/paper/2409.16081/) (2024)
- [Bridging the Gap between Learning and Inference for Diffusion-Based Molecule Generation](https://mlsanity.com/paper/2411.05472/) (2024)
- [AffinityFlow: Guided Flows for Antibody Affinity Maturation](https://mlsanity.com/paper/2502.10365/) (2025)
- [Protein binding affinity prediction under multiple substitutions applying eGNNs on Residue and Atomic graphs combined with Language model information: eGRAL](https://mlsanity.com/paper/2405.02374/) (2024)
- [Investigating Knowledge Distillation Through Neural Networks for Protein Binding Affinity Prediction](https://mlsanity.com/paper/2601.03704/) (2026)

## Abstract (arXiv)

Modeling the effects of mutations on the binding affinity plays a crucial role in protein engineering and drug design. In this study, we develop a novel deep learning based framework, named GraphPPI, to predict the binding affinity changes upon mutations based on the features provided by a graph neural network (GNN). In particular, GraphPPI first employs a well-designed pre-training scheme to enforce the GNN to capture the features that are predictive of the effects of mutations on binding affinity in an unsupervised manner and then integrates these graphical features with gradient-boosting trees to perform the prediction. Experiments showed that, without any annotated signals, GraphPPI can capture meaningful patterns of the protein structures. Also, GraphPPI achieved new state-of-the-art performance in predicting the binding affinity changes upon both single- and multi-point mutations on five benchmark datasets. In-depth analyses also showed GraphPPI can accurately estimate the effects of mutations on the binding affinity between SARS-CoV-2 and its neutralizing antibodies. These results have established GraphPPI as a powerful and useful computational tool in the studies of protein design.