NetSurfP-3.0: accurate and fast prediction of protein structural features by protein language models and deep learning.
NetSurfP-3.0: accurate and fast prediction of protein structural features by protein language models and deep learning.
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DOI：
10.1093/nar/gkac439
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发表时间：
2022-07-05
影响因子：
14.9
通讯作者：
Marcatili, Paolo
中科院分区：
文献类型：
--
作者：
Hoie, Magnus Haraldson;Kiehl, Erik Nicolas;Petersen, Bent;Nielsen, Morten;Winther, Ole;Nielsen, Henrik;Hallgren, Jeppe;Marcatili, Paolo
机器学习和自然语言处理的最新进展使我们能够深刻地提高准确预测蛋白质结构及其功能的能力。虽然这些改进对生物学和生物技术领域产生了重大影响，但这些方法在计算能力和运行时间方面具有高要求的缺点，阻碍了它们对大型数据集的适用性。在这里，我们提出了NetSurfP-3.0，预测溶剂的可及性，二级结构，结构紊乱和骨干二面角的氨基酸序列的每个残基的工具。此NetSurfP更新利用了预训练蛋白质语言模型的最新进展，将其前身的运行时间大幅提高了两个数量级，同时显示出类似的预测性能。我们在几个独立的测试数据集上评估了NetSurfP-3.0的准确性，发现它能够为其每个输出功能持续生成最先进的预测，运行时间比执行相同任务的最常用方法快600倍。该工具作为一个Web服务器免费提供，具有用户友好的界面来导航结果，以及一个独立的可下载软件包。NetSurfP-3.0：通过蛋白质语言模型和深度学习准确快速地预测蛋白质结构特征。
Recent advances in machine learning and natural language processing have made it possible to profoundly advance our ability to accurately predict protein structures and their functions. While such improvements are significantly impacting the fields of biology and biotechnology at large, such methods have the downside of high demands in terms of computing power and runtime, hampering their applicability to large datasets. Here, we present NetSurfP-3.0, a tool for predicting solvent accessibility, secondary structure, structural disorder and backbone dihedral angles for each residue of an amino acid sequence. This NetSurfP update exploits recent advances in pre-trained protein language models to drastically improve the runtime of its predecessor by two orders of magnitude, while displaying similar prediction performance. We assessed the accuracy of NetSurfP-3.0 on several independent test datasets and found it to consistently produce state-of-the-art predictions for each of its output features, with a runtime that is up to to 600 times faster than the most commonly available methods performing the same tasks. The tool is freely available as a web server with a user-friendly interface to navigate the results, as well as a standalone downloadable package. NetSurfP-3.0: accurate and fast prediction of protein structural features by protein language models and deep learning.
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影响因子：
64.8
作者：
Jumper J;Evans R;Pritzel A;Green T;Figurnov M;Ronneberger O;Tunyasuvunakool K;Bates R;Žídek A;Potapenko A;Bridgland A;Meyer C;Kohl SAA;Ballard AJ;Cowie A;Romera-Paredes B;Nikolov S;Jain R;Adler J;Back T;Petersen S;Reiman D;Clancy E;Zielinski M;Steinegger M;Pacholska M;Berghammer T;Bodenstein S;Silver D;Vinyals O;Senior AW;Kavukcuoglu K;Kohli P;Hassabis D
通讯作者：
Hassabis D
影响因子：
5.8
作者：
Mirdita, Milot;Steinegger, Martin;Soeding, Johannes
通讯作者：
Soeding, Johannes
影响因子：
10.7
作者：
Katoh K;Standley DM
通讯作者：
Standley DM
影响因子：
4.6
作者：
通讯作者：
--
影响因子：
2.9
作者：
Pereira, Joana;Simpkin, Adam J.;Lupas, Andrei N.
通讯作者：
Lupas, Andrei N.