Machine learning to navigate fitness landscapes for protein engineering.
Machine learning to navigate fitness landscapes for protein engineering.
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DOI：
10.1016/j.copbio.2022.102713
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发表时间：
2022-06
影响因子：
7.7
通讯作者：
中科院分区：
文献类型：
--
作者：
机器学习 (ML) 正在彻底改变我们理解和预测蛋白质序列、结构和功能之间复杂关系的能力。预测序列功能模型使蛋白质工程师能够有效地搜索序列空间，寻找在生物技术中具有广泛应用的有用蛋白质。在这篇综述中，我们重点介绍了将机器学习应用于蛋白质工程的最新进展。我们讨论从实验数据推断序列函数映射的监督学习方法以及用于数据高效建模的新序列表示策略。然后，我们描述了将机器学习纳入蛋白质工程工作流程的各种方式，包括纯粹的计算机搜索、机器学习辅助的定向进化以及学习序列空间中蛋白质功能的基本分布的生成模型。随着高通量数据生成、数据科学和深度学习的不断进步，机器学习驱动的蛋白质工程将变得越来越强大。
Machine learning (ML) is revolutionizing our ability to understand and predict the complex relationships between protein sequence, structure, and function. Predictive sequence-function models are enabling protein engineers to efficiently search sequence space for useful proteins with broad applications in biotechnology. In this review, we highlight recent advances applying machine learning to protein engineering. We discuss supervised learning methods that infer the sequence-function mapping from experimental data and new sequence representation strategies for data-efficient modeling. We then describe the various ways ML can be incorporated into protein engineering workflows, including purely in silico searches, machine learning-assisted directed evolution, and generative models that learn the underlying distribution of protein function in sequence space. ML-driven protein engineering will become increasingly powerful with continued advances in high-throughput data generation, data science, and deep learning.
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影响因子：
3.7
作者：
Carlin DA;Caster RW;Wang X;Betzenderfer SA;Chen CX;Duong VM;Ryklansky CV;Alpekin A;Beaumont N;Kapoor H;Kim N;Mohabbot H;Pang B;Teel R;Whithaus L;Tagkopoulos I;Siegel JB
通讯作者：
Siegel JB
影响因子：
4.3
作者：
Bedbrook CN;Yang KK;Rice AJ;Gradinaru V;Arnold FH
通讯作者：
Arnold FH
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：
64.8
作者：
Chevalier A;Silva DA;Rocklin GJ;Hicks DR;Vergara R;Murapa P;Bernard SM;Zhang L;Lam KH;Yao G;Bahl CD;Miyashita SI;Goreshnik I;Fuller JT;Koday MT;Jenkins CM;Colvin T;Carter L;Bohn A;Bryan CM;Fernández-Velasco DA;Stewart L;Dong M;Huang X;Jin R;Wilson IA;Fuller DH;Baker D
通讯作者：
Baker D
影响因子：
16.6
作者：
Greenhalgh JC;Fahlberg SA;Pfleger BF;Romero PA
通讯作者：
Romero PA