Beating the Best: Improving on AlphaFold2 at Protein Structure Prediction
Beating the Best: Improving on AlphaFold2 at Protein Structure Prediction
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DOI：
10.48550/arxiv.2301.07568
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发表时间：
2023-01
期刊：
影响因子：
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通讯作者：
A. Abdel-Rehim;Oghenejokpeme I. Orhobor;Hang Lou;Hao Ni;R. King
中科院分区：
文献类型：
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作者：
A. Abdel-Rehim;Oghenejokpeme I. Orhobor;Hang Lou;Hao Ni;R. King
蛋白质结构预测（PSP）问题的目标是根据蛋白质的氨基酸序列预测蛋白质的三维结构（确认）。自从Anfinsen获得诺贝尔奖的工作证明蛋白质构象是由序列决定的以来，这个问题一直是科学的“圣杯”。朝着这一目标迈出的重要一步是AlphaFold2的开发，这是目前最好的PSP方法。AlphaFold2可能是人工智能在科学领域最引人注目的应用。AlphaFold2和RoseTTAFold（另一种令人印象深刻的PSP方法）都已发布并置于公共领域（代码和模型）。堆叠是集成机器学习ML的一种形式，其中首先学习多个基线模型，然后使用基线水平模型的输出来学习元模型，以形成优于基本模型的模型。堆叠在许多应用中是成功的。我们通过堆叠AlphaFold2和RoseTTAFold开发了ARStack PSP方法。ARStack的性能明显优于AlphaFold2。我们使用两组非同源蛋白质以及在AlphaFold2和RoseTTAFold之后发表的一组蛋白质结构测试来严格证明这一点。随着越来越多的高质量预测方法的发表，集成方法很可能会越来越优于任何单一的方法。
The goal of Protein Structure Prediction (PSP) problem is to predict a protein's 3D structure (confirmation) from its amino acid sequence. The problem has been a ‘holy grail’ of science since the Noble prize-winning work of Anfinsen demonstrated that protein conformation was determined by sequence. A recent and important step towards this goal was the development of AlphaFold2, currently the best PSP method. AlphaFold2 is probably the highest profile application of AI to science. Both AlphaFold2 and RoseTTAFold (another impressive PSP method) have been published and placed in the public domain (code & models). Stacking is a form of ensemble machine learning ML in which multiple baseline models are first learnt, then a meta-model is learnt using the outputs of the baseline level model to form a model that outperforms the base models. Stacking has been successful in many applications. We developed the ARStack PSP method by stacking AlphaFold2 and RoseTTAFold. ARStack significantly outperforms AlphaFold2. We rigorously demonstrate this using two sets of non-homologous proteins, and a test set of protein structures published after that of AlphaFold2 and RoseTTAFold. As more high quality prediction methods are published it is likely that ensemble methods will increasingly outperform any single method.