Neural network-derived Potts models for structure-based protein design using backbone atomic coordinates and tertiary motifs.
Neural network-derived Potts models for structure-based protein design using backbone atomic coordinates and tertiary motifs.
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DOI：
10.1002/pro.4554
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发表时间：
2023-03
期刊：
影响因子：
8
通讯作者：
Keating, Amy E.
中科院分区：
文献类型：
--
作者：
Li, Alex J.;Lu, Mindren;Desta, Israel;Sundar, Vikram;Grigoryan, Gevorg;Keating, Amy E.
关键词：
设计新的蛋白质来执行所需的功能，如结合或催化，是合成生物学的一个主要目标。各种计算方法可以帮助完成这项任务。植根于三级基序（TERM）的序列结构统计的基于能量的框架可用于预定义主链上的序列设计。使用骨架坐标衍生特征的神经网络模型提供了另一种设计新蛋白质的方法。在这项工作中，我们将这两种方法联合收割机结合起来，使基于神经结构的模型更适合蛋白质设计。具体来说，我们用TERM导出的数据作为输入来补充骨架坐标特征，并生成能量函数作为输出。我们提出了两种在序列空间上生成Potts模型的架构：TERMinator，它使用基于TERM和基于坐标的信息，以及COordinator，它只使用基于坐标的信息。使用这两个模型，我们证明了TERMs可以用来提高天然序列恢复性能的神经模型。此外，我们证明了由TERMinator设计的序列被预测为通过AlphaFold折叠到其目标结构。最后，我们证明了TERMinator和COordinator都学习了能量学的概念，并且这些方法可以根据实验数据进行微调以改善预测。我们的研究结果表明，同时使用基于TERM和基于坐标的特征可能有利于蛋白质设计，并且基于结构的神经模型可以产生Potts能量表，可用于蛋白质科学中的灵活应用。
Designing novel proteins to perform desired functions, such as binding or catalysis, is a major goal in synthetic biology. A variety of computational approaches can aid in this task. An energy‐based framework rooted in the sequence‐structure statistics of tertiary motifs (TERMs) can be used for sequence design on predefined backbones. Neural network models that use backbone coordinate‐derived features provide another way to design new proteins. In this work, we combine the two methods to make neural structure‐based models more suitable for protein design. Specifically, we supplement backbone‐coordinate features with TERM‐derived data, as inputs, and we generate energy functions as outputs. We present two architectures that generate Potts models over the sequence space: TERMinator, which uses both TERM‐based and coordinate‐based information, and COORDinator, which uses only coordinate‐based information. Using these two models, we demonstrate that TERMs can be utilized to improve native sequence recovery performance of neural models. Furthermore, we demonstrate that sequences designed by TERMinator are predicted to fold to their target structures by AlphaFold. Finally, we show that both TERMinator and COORDinator learn notions of energetics, and these methods can be fine‐tuned on experimental data to improve predictions. Our results suggest that using TERM‐based and coordinate‐based features together may be beneficial for protein design and that structure‐based neural models that produce Potts energy tables have utility for flexible applications in protein science.
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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
DOI：
10.1126/science.add2187
发表时间：
2022-10-07
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
通讯作者：
--
影响因子：
64.8
作者：
Grigoryan, Gevorg;Reinke, Aaron W.;Keating, Amy E.
通讯作者：
Keating, Amy E.
影响因子：
16.6
作者：
Anand N;Eguchi R;Mathews II;Perez CP;Derry A;Altman RB;Huang PS
通讯作者：
Huang PS
DOI：
10.1073/pnas.1607178113
发表时间：
2016-11-22
影响因子：
11.1
作者：
Mackenzie, Craig O.;Zhou, Jianfu;Grigoryan, Gevorg
通讯作者：
Grigoryan, Gevorg