AlphaFold at CASP13
AlphaFold at CASP13
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DOI：
10.1093/bioinformatics/btz422
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发表时间：
2019-11-15
期刊：
影响因子：
5.8
通讯作者：
AlQuraishi, Mohammed
中科院分区：
文献类型：
--
作者：
AlQuraishi, Mohammed
摘要：从序列计算预测蛋白质结构被广泛视为生物化学的基本问题，也是生物信息学中最困难的挑战之一。每两年举行一次蛋白质结构预测批判性评估 (CASP) 实验，通过提供结构已被解析但尚未公开的蛋白质序列的预测组，以盲目方式评估该领域的最新技术。第一届 CASP 于 1994 年举办，最近一次 CASP13 于去年 12 月举行，当时工业实验室 DeepMind 首次参加比赛。 DeepMind 的参赛作品 AlphaFold 在自由建模 (FM) 类别中排名第一，该类别评估了预测新型蛋白质折叠的方法的能力（Zhang 团队在基于模板的建模 (TBM) 类别中排名第一，该类别评估了预测与蛋白质数据库中已有折叠相关的蛋白质的方法。）DeepMind 的成功引起了公众的极大兴趣。他们的方法建立在学术界在过去十年中发展的两个想法的基础上：（i）使用共同进化分析将蛋白质序列中的残基共变映射到蛋白质结构中的物理接触，以及（ii）应用深度神经网络来稳健地识别蛋白质序列和共同进化耦合中的模式并将其转换为接触图。在这封信中，我们将 DeepMind 进入 CASP 更广泛历史的意义置于背景中，将 AlphaFold 的方法论进展与之前的工作联系起来，并推测这个重要问题的未来。
A Summary: Computational prediction of protein structure from sequence is broadly viewed as a foundational problem of biochemistry and one of the most difficult challenges in bioinformatics. Once every two years the Critical Assessment of protein Structure Prediction (CASP) experiments are held to assess the state of the art in the field in a blind fashion, by presenting predictor groups with protein sequences whose structures have been solved but have not yet been made publicly available. The first CASP was organized in 1994, and the latest, CASP13, took place last December, when for the first time the industrial laboratory DeepMind entered the competition. DeepMind's entry, AlphaFold, placed first in the Free Modeling (FM) category, which assesses methods on their ability to predict novel protein folds (the Zhang group placed first in the Template-Based Modeling (TBM) category, which assess methods on predicting proteins whose folds are related to ones already in the Protein Data Bank.) DeepMind's success generated significant public interest. Their approach builds on two ideas developed in the academic community during the preceding decade: (i) the use of co-evolutionary analysis to map residue co-variation in protein sequence to physical contact in protein structure, and (ii) the application of deep neural networks to robustly identify patterns in protein sequence and co-evolutionary couplings and convert them into contact maps. In this Letter, we contextualize the significance of DeepMind's entry within the broader history of CASP, relate AlphaFold's methodological advances to prior work, and speculate on the future of this important problem.