Accelerating AlphaFold3 for High-Throughput Protein Design
PI: Soundar Kumara (IndustrialEngineering)
Plan for funding tuition for graduate students, or the remainder of the researcher’s salary for postdoc and research faculty: Through funding from the Cornell project, which has gained national attention, it will be possible to raise tuition money.
Overview
AlphaFold3 (AF3) delivers state-of-the-art protein structure predictions but at a high computational cost, especially when evaluating large numbers of designed sequences. The most time-consuming step is often multiple sequence alignment (MSA) generation – over 75% of runtime in local AF3 inference is spent on CPU-intensive sequence searches. Even with optimizations like ColabFold (which replaces AF3’s default JackHmmer search with MMseqs2), predicting ~1,000 structures still require several GPU hours. This limited throughput bottlenecks protein design workflows that may generate tens of thousands of candidate sequences. Singlesequence predictors such as ESMFold offer ~10× faster predictions by forgoing MSAs but they sacrifice accuracy. The proposed project targets major inference speed-ups for diffusion-based protein prediction models like Alphafold 3, Boltz and Chai -1 without significant loss of accuracy, enabling the evaluation of thousands of protein designs in silico.
Objectives
• Accelerate AlphaFold3 Inference: Develop methods to achieve an order-of-magnitude speedup in AF3 structure prediction, reducing per-sequence latency to seconds. This will allow highthroughput folding of designed sequences (scaling to 10^3–10^4 predictions).
• Maintain Prediction Quality & Benchmarking: Ensure that accelerated pipelines preserve high accuracy (predicted LDDT, TM‑score, fold correctness) comparable to vanilla AF3, and quantify any speed–accuracy trade‑offs. In parallel, we will benchmark open‑source PyTorch variants (e.g., AF3‑Protenix) on both Roar A100 nodes and Apple‑silicon (M‑series) laptops to verify whether state‑of‑the‑art performance is attainable on commodity hardware.
• Integrate into Design Pipeline: Create a workflow combining generative protein design tools with fast AF3 refinement, so candidate sequences can be rapidly screened and filtered by structure confidence.
Methodology
Structure-Guided Initialization with Generative Models
We propose to pre-condition AlphaFold3 with an initial backbone structure generated by a diffusion-based protein design model (e.g. RFdiffusion). Generative models like RFdiffusion can rapidly produce plausible protein folds for a target sequence or design objective, avoiding a blind search from scratch. We will feed these initial coordinates into AF3 as a structural prior (by initializing AF3’s internal pair representation) to guide its prediction. Recent studies show that this “initial guess” approach improves AlphaFold’s convergence: for example, seeding AlphaFold2 with an RFDiffusion-designed binder structure significantly boosted success rates in predicting protein–protein complexes. We will implement this by modifying the existing open-source AF3 pipeline to accept an initial 3D structure hypothesis. Experiments will compare runtime and accuracy with and without guided initialization, measuring if fewer recycles or lower sampling iterations can achieve similar confidence.
Knowledge Distillation for Faster Inference
We will apply model distillation techniques to compress AF3’s neural network, aiming to preserve its accuracy while making inference faster. AlphaFold3’s architecture is complex – it processes large MSAs and uses iterative refinement (recycling or diffusion steps) for structure prediction. These features complicate distillation compared to standard vision models. Our approach will explore two levels of distillation: (1) Structure Module Distillation – train a lightweight “student” network to replicate the function of AF3’s structure module (which in AF3 is a diffusion-based transformer) in fewer steps. The student would learn to predict final atomic coordinates directly from the trunk’s pair representation, effectively collapsing multiple diffusion/recycling iterations into one. (2) Pipeline Simplification – create a distilled model that requires less input overhead (for example, using a compact single-sequence representation or a precomputed MSA embedding instead of a full MSA stack). We will utilize Protenix (trainable PyTorch AF3) to implement these variants, using AlphaFold3 as the teacher. The training will use knowledge distillation loss where the student model’s predictions (coordinates or distance maps) are penalized against the teacher’s outputs. Building on this idea, our project will distill AF3’s predictive capability into a faster surrogate that still outputs full 3D structures. These will further be evaluated for performance and accuracy.
Benchmarking and Evaluation
Open-source implementations of AF3 have not yet been benchmarked for numerical stability or performance, limiting confidence in their use. To close this gap, we will benchmark the Protenix PyTorch version of AF3 against the official JAX release on Roar’s A100 GPUs, recording wall-clock inference time, peak memory usage, and coordinate-level RMSD to assess numerical stability. The identical tests will be repeated on Apple-silicon (M-series) laptops, allowing us to quantify any throughput loss when moving from HPC nodes to locally available Metal-accelerated GPUs. This benchmark will give the academic community evidence of the accuracy of such an implementation, helping them decide whether it is reliable enough to adopt for their use case.
Computational Resources and Workflow
This project will utilize Penn State’s Roar supercomputing infrastructure for both development and large-scale experiments. GPU-accelerated computing is required for neural network training and inference: we anticipate needing multiple NVIDIA A100 GPUs for training the distilled AF3 models. In addition, AF3’s data pipeline (MSA search) requires substantial CPU resources and fast storage. The project will make use of JAX (for running official AF3 or parts of it for baseline comparisons) and PyTorch (for Boltz, Chai 1 and AF3-Protenix).
Alignment with ICDS Mission
This interdisciplinary project aligns with the ICDS’s mission of applying innovative computational methods to solve cutting-edge scientific problems. By using techniques from AI/deep learning, high-performance computing, and structural biology, the research bridges computer science and the life sciences.