小柯机器人

英国DeepMind公司Demis Hassabis、John Jumper等研究人员合作使用AlphaFold实现高度准确的蛋白质结构预测。2021年7月15日，《自然》杂志在线发表了这一最新研究成果。

研究人员提供了第一个可以定期预测蛋白质结构的计算方法，即使在没有类似结构的情况下也能达到原子级精度。研究人员在具有挑战性的第14届蛋白质结构预测关键评估（CASP14）中验证了这个完全重新设计的基于神经网络的模型：AlphaFold，其在大多数情况下显示出与实验相竞争的准确性，并大大超过了其他方法。最新版本的AlphaFold的基础是一种新的机器学习方法，通过利用多序列排列的方式，它将有关蛋白质结构的物理和生物知识纳入深度学习算法的设计中。

据介绍，蛋白质对生命至关重要，了解其结构可以促进对其功能的机制理解。通过巨大的实验努力，大约10万个独特的蛋白质的结构已被确定，但这只是数十亿已知蛋白质序列中的一小部分。由于确定一个蛋白质结构需要数月至数年的艰苦努力，结构覆盖率遇到了瓶颈。目前需要精确的计算方法来解决这一问题，并实现大规模的结构生物信息学。预测一个蛋白质将采用的三维结构，仅仅基于其氨基酸序列，即 "蛋白质折叠问题 "的结构预测部分，50多年来一直是一个重要的开放式研究问题。尽管最近取得了一些进展，但现有的方法远远达不到原子的准确性，特别是在没有同源结构的时候。

附：英文原文

Title: Highly accurate protein structure prediction with AlphaFold

Author: John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin dek, Anna Potapenko, Alex Bridgland, Clemens Meyer, Simon A. A. Kohl, Andrew J. Ballard, Andrew Cowie, Bernardino Romera-Paredes, Stanislav Nikolov, Rishub Jain, Jonas Adler, Trevor Back, Stig Petersen, David Reiman, Ellen Clancy, Michal Zielinski, Martin Steinegger, Michalina Pacholska, Tamas Berghammer, Sebastian Bodenstein, David Silver, Oriol Vinyals, Andrew W. Senior, Koray Kavukcuoglu, Pushmeet Kohli, Demis Hassabis

Issue&Volume: 2021-07-15

Abstract: Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort1–4, the structures of around 100,000 unique proteins have been determined5, but this represents a small fraction of the billions of known protein sequences6,7. Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the 3-D structure that a protein will adopt based solely on its amino acid sequence, the structure prediction component of the ‘protein folding problem’8, has been an important open research problem for more than 50 years9. Despite recent progress10–14, existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even where no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14)15, demonstrating accuracy competitive with experiment in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm.

DOI: 10.1038/s41586-021-03819-2

Source: https://www.nature.com/articles/s41586-021-03819-2

Nature：《自然》，创刊于1869年。隶属于施普林格·自然出版集团，最新IF：69.504
官方网址：http://www.nature.com/
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