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Multi-target Parallel Processing Approach for Gene-to-structure Determination of the Influenza Polymerase PB2 Subunit

Multi-target Parallel Processing Approach for Gene-to-structure Determination of the Influenza Polymerase PB2 Subunit

Published on: June 28, 2013

AlphaFast: High-throughput AlphaFold 3 via GPU-accelerated MSA construction.

Benjamin C Perry¹, Jeonghyeon Kim¹, Philip A Romero¹

¹Department of Biomedical Engineering, Duke University, 101 Science Drive, Durham, 27708, NC, USA.

Biorxiv : the Preprint Server for Biology

|February 27, 2026

Summary

This summary is machine-generated.

AlphaFast significantly accelerates biomolecular modeling by optimizing multiple sequence alignment (MSA) generation using GPU-accelerated search. This framework dramatically reduces runtime and cost for accurate protein structure prediction.

Keywords:

AlphaFold 3 High-Performance Computing MMseqs2-GPU Structural Bioinformatics

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Area of Science:

Computational biology
Structural biology
Bioinformatics

Background:

Accurate biomolecular modeling is crucial for biological research.
AlphaFold 3 (AF3) provides high-quality predictions but is hindered by slow multiple sequence alignment (MSA) generation.
The CPU-bound nature of MSA limits the speed and scalability of AF3.

Purpose of the Study:

To develop a faster method for MSA generation to overcome the limitations of AlphaFold 3.
To reduce the computational bottleneck in AlphaFold 3's workflow.
To enable rapid and cost-effective protein structure prediction.

Main Methods:

Integration of GPU-accelerated MMseqs2 sequence search into a drop-in framework named AlphaFast.
Utilizing a single GPU for MSA construction and end-to-end runtime analysis.
Deployment on multiple GPUs for high-throughput structure prediction.

Main Results:

Achieved a 68.5x speedup in MSA construction.
Reduced end-to-end runtime by 22.8x on a single GPU.
Enabled structure prediction in 8 seconds per input using four GPUs with maintained accuracy.
Demonstrated cost-effective serverless deployment at $0.035 per input.

Conclusions:

AlphaFast effectively removes the MSA bottleneck in AlphaFold 3.
The framework offers substantial speedups and cost reductions for biomolecular modeling.
AlphaFast makes accurate protein structure prediction more accessible and efficient.