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The paper introduces AF_Cache, a high-throughput Nextflow pipeline designed to enhance the efficiency of protein-protein interaction predictions using AlphaFold2 and AlphaFold3. By integrating GPU-accelerated multiple sequence alignment (MSA) generation, feature caching, and sequence length bucketing, AF_Cache achieves significant reductions in computational time while maintaining predictive accuracy. Benchmark results indicate a nearly 2-fold decrease in inference time for AlphaFold2 and up to a 13-fold acceleration in MSA generation, facilitating large-scale interaction screening in biological research.
Achieving up to a 13-fold speedup in MSA generation could revolutionize high-throughput protein interaction studies.
Motivation: Accurate prediction of protein-protein interactions is essential for understanding biological processes, and recent advances such as AlphaFold2 and AlphaFold3 have enabled structure-based interaction prediction at unprecedented accuracy. However, the high computational cost of these methods, driven primarily by CPU-based repeated multiple sequence alignment (MSA) generation and, for AlphaFold2, repeated model recompilations, limits their applicability in large-scale, high-throughput settings. This creates a need for efficient pipelines that retain predictive performance while substantially reducing runtime. Results: We present AF_Cache, a high-throughput Nextflow pipeline for accelerating protein-protein interaction prediction using AlphaFold2 and AlphaFold3. AF_Cache combines GPU-accelerated MSA generation with MMseqs2, feature caching to eliminate redundant alignment computations, and sequence length bucketing to minimise repeated JAX compilations. Benchmarking on a dataset of 5,050 human mitochondrial protein pairs demonstrates a $\sim$2-fold reduction in inference time for AlphaFold2 and up to a 13-fold speedup of the MSA generation. AF\_Cache enables efficient large-scale interaction screening and provides a practical framework for deploying AlphaFold-based methods in high-throughput applications. Availability and implementation: The code and Nextflow pipeline are available on GitHub here: https://github.com/clami66/AF_cache. The code for reproducing the results of the paper, the MSAs, and the predicted models can be found at Zenodo: https://zenodo.org/records/20478892