# End-to-End Learning on 3D Protein Structure for Interface Prediction

arXiv: https://arxiv.org/abs/1807.01297 · HTML: https://mlsanity.com/paper/1807.01297/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2018.

## Built on by (most cited first, 30 of 33)

- [Spherical Message Passing for 3D Graph Networks](https://mlsanity.com/paper/2102.05013/) (2021)
- [EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction](https://mlsanity.com/paper/2202.05146/) (2022)
- [Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking](https://mlsanity.com/paper/2111.07786/) (2021)
- [Learning Hierarchical Protein Representations via Complete 3D Graph Networks](https://mlsanity.com/paper/2207.12600/) (2022)
- [Multi-Scale Representation Learning on Proteins](https://mlsanity.com/paper/2204.02337/) (2022)
- [Structure-aware Protein Self-supervised Learning](https://mlsanity.com/paper/2204.04213/) (2022)
- [Contrastive Representation Learning for 3D Protein Structures](https://mlsanity.com/paper/2205.15675/) (2022)
- [A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications](https://mlsanity.com/paper/2403.00485/) (2024)
- [DiffDock-PP: Rigid Protein-Protein Docking with Diffusion Models](https://mlsanity.com/paper/2304.03889/) (2023)
- [Automated 3D Pre-Training for Molecular Property Prediction](https://mlsanity.com/paper/2306.07812/) (2023)
- [Geometric Transformers for Protein Interface Contact Prediction](https://mlsanity.com/paper/2110.02423/) (2021)
- [Learning to design protein-protein interactions with enhanced generalization](https://mlsanity.com/paper/2310.18515/) (2023)
- [Antibody-Antigen Docking and Design via Hierarchical Equivariant Refinement](https://mlsanity.com/paper/2207.06616/) (2022)
- [Integration of Pre-trained Protein Language Models into Geometric Deep Learning Networks](https://mlsanity.com/paper/2212.03447/) (2022)
- [A Survey of Graph Transformers: Architectures, Theories and Applications](https://mlsanity.com/paper/2502.16533/) (2025)
- [A Hierarchical Training Paradigm for Antibody Structure-sequence Co-design](https://mlsanity.com/paper/2311.16126/) (2023)
- [DIPS-Plus: The Enhanced Database of Interacting Protein Structures for Interface Prediction](https://mlsanity.com/paper/2106.04362/) (2021)
- [A Survey on Protein Representation Learning: Retrospect and Prospect](https://mlsanity.com/paper/2301.00813/) (2022)
- [Learning Harmonic Molecular Representations on Riemannian Manifold](https://mlsanity.com/paper/2303.15520/) (2023)
- [Knowledge-augmented Graph Machine Learning for Drug Discovery: A Survey](https://mlsanity.com/paper/2302.08261/) (2023)
- [Clustering for Protein Representation Learning](https://mlsanity.com/paper/2404.00254/) (2024)
- [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)
- [Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions](https://mlsanity.com/paper/2410.09543/) (2024)
- [SyNDock: N Rigid Protein Docking via Learnable Group Synchronization](https://mlsanity.com/paper/2305.15156/) (2023)
- [Sequence-Only Prediction of Binding Affinity Changes: A Robust and Interpretable Model for Antibody Engineering](https://mlsanity.com/paper/2505.20301/) (2025)
- [DockGame: Cooperative Games for Multimeric Rigid Protein Docking](https://mlsanity.com/paper/2310.06177/) (2023)
- [Learning Universal and Robust 3D Molecular Representations with Graph Convolutional Networks](https://mlsanity.com/paper/2307.12491/) (2023)
- [Exploiting Hierarchical Interactions for Protein Surface Learning](https://mlsanity.com/paper/2401.10144/) (2024)
- [PROflow: An iterative refinement model for PROTAC-induced structure prediction](https://mlsanity.com/paper/2405.06654/) (2024)

## Abstract (arXiv)

Despite an explosion in the number of experimentally determined, atomically detailed structures of biomolecules, many critical tasks in structural biology remain data-limited. Whether performance in such tasks can be improved by using large repositories of tangentially related structural data remains an open question. To address this question, we focused on a central problem in biology: predicting how proteins interact with one another---that is, which surfaces of one protein bind to those of another protein. We built a training dataset, the Database of Interacting Protein Structures (DIPS), that contains biases but is two orders of magnitude larger than those used previously. We found that these biases significantly degrade the performance of existing methods on gold-standard data. Hypothesizing that assumptions baked into the hand-crafted features on which these methods depend were the source of the problem, we developed the first end-to-end learning model for protein interface prediction, the Siamese Atomic Surfacelet Network (SASNet). Using only spatial coordinates and identities of atoms, SASNet outperforms state-of-the-art methods trained on gold-standard structural data, even when trained on only 3% of our new dataset. Code and data available at https://github.com/drorlab/DIPS.