PPAP
A structure-aware deep learning model for high-accuracy protein-protein binding affinity prediction (Kd).
Overview
A deep learning framework that captures protein-protein binding affinity by fusing structural insights with sequence representations. By utilizing an interfacial contact-aware attention mechanism, the model focuses on critical interface residues, leveraging both ESM-based sequence features and AlphaFold-derived structural data to outperform traditional graph-based and sequence-only benchmarks.
Run PPAP on Neurosnap
The PPAP online webserver allows anybody with a Neurosnap account to run and access PPAP, no downloads required. Information submitted through this webserver is kept confidential and never sold to third parties as detailed by our strong Terms of Use and Privacy Policy.
Features
- Integrates AlphaFold structural insights with ESM sequence representations.
- Employs an interfacial contact-aware attention mechanism to prioritize interaction residues.
- Achieves high correlation (R = 0.63) on external benchmarks, outperforming sequence-based LLMs.
- Proven to enhance protein binder design enrichment by up to 10-fold compared to AlphaFold-Multimer metrics.
Statistics
Neurosnap periodically calculates runtime statistics based on job execution data. These estimates provide a general guideline for how long your job may take, but actual runtimes can vary significantly depending on factors like input size or settings used.
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API Request
Access PPAP using the Neurosnap API by sending a request using any programming language with HTTP support. To safely generate an API key, visit the API tab of your overview page.
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Citations
Please cite the original work when using PPAP in publications or research outputs.
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Qian J, Yang L, Duan Z, Wang R, Qi Y. PPAP: A Protein-protein Affinity Predictor Incorporating Interfacial Contact-Aware Attention. J Chem Inf Model. 2025 Oct 13;65(19):9987-9998. doi: 10.1021/acs.jcim.5c01390. Epub 2025 Sep 19. PMID: 40970903. |
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Neurosnap Inc. (2022). Neurosnap: An online platform for computational biology and chemistry. Available at: https://neurosnap.ai/ |