• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Nature >Highly accurate protein structure prediction with AlphaFold
【24h】

Highly accurate protein structure prediction with AlphaFold

机译：高精度蛋白质结构与alphafold预测

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort(1-4), the structures of around 100,000 unique proteins have been determined(5), but this represents a small fraction of the billions of known protein sequences(6,7). 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 three-dimensional 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 years(9). Despite recent progress(10-14), 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 in cases in which 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 experimental structures 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.
机译：蛋白质对生命至关重要，并且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（1-4），已经确定了大约100,000个独特蛋白质的结构（5），但这代表了数十亿已知蛋白质序列的一小部分（6,7）。结构覆盖率是以确定单一蛋白质结构所需的艰苦努力的瓶颈瓶颈。需要准确的计算方法来解决这种差距，并实现大规模的结构生物信息学。预测蛋白质将仅基于其氨基酸序列采用的三维结构 - “蛋白质折叠问题”（8）-Has的结构预测组分是50多年（9）的重要开放研究问题。尽管最近进展（10-14），现有方法远远缩短原子准确性，特别是当没有使用同源结构时。在这里，我们提供了第一计算方法，即使在没有相似结构的情况下，也可以定期预测具有原子精度的蛋白质结构。我们验证了一个完全重新设计的基于神经网络的模型，alphafold，在挑战的第14次临界评估蛋白质结构预测（Casp14）（15），证明了大多数情况下的实验结构竞争力，并且大大优于其他方法。支持最新版本的alphafold是一种新型机器学习方法，它包括蛋白质结构的物理和生物学知识，利用多序列对准，进入深度学习算法的设计。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号