Recent Advances in Machine Learning Variant Effect Prediction Tools for Protein Engineering.
Recent Advances in Machine Learning Variant Effect Prediction Tools for Protein Engineering.
复制标题
机器学习变体效果预测工具的最新进展。
DOI：
10.1021/acs.iecr.1c04943
复制
发表时间：
2022-05-18
影响因子：
4.2
通讯作者：
Shukla, Diwakar
中科院分区：
文献类型：
--
作者：
Horne, Jesse;Shukla, Diwakar
蛋白质是大自然的分子机器，具有多种作用，同时由化学相似的结构单元组成。近年来，蛋白质工程和设计已成为重要的研究领域，在制药、能源和生物催化等领域有许多应用，其目的是最终创造出具有所需结构和功能特性的蛋白质。对蛋白质序列、折叠结构和生物功能之间的关系进行建模对于协助此类蛋白质工程研究通常至关重要。然而，在将氨基酸序列具体映射到特定蛋白质特性和生物活性方面仍然存在重大挑战。突变可能增强或减弱分子蛋白质功能，突变之间的上位相互作用导致遗传修饰和蛋白质功能之间固有的复杂映射。因此，估计突变对蛋白质功能的定量影响仍然是生物学、生物信息学和许多相关领域的巨大挑战，一旦成功，将迅速加速蛋白质工程任务。这种估计通常称为变异效应预测（VEP）。然而，近年来，随着机器学习 (ML) 方法在突变与蛋白质功能之间关系建模方面的发展，已经取得了进展。在这篇综述中，讨论了变异效应预测 (VEP) 作为蛋白质工程工具的最新进展，重点关注融合了更广泛的 ML 社区的成果的技术以及估计生物分子功能差异方面的挑战。强调的主要发展包括卷积神经网络、图神经网络和蛋白质序列的自然语言嵌入。
Proteins are Nature’s molecular machinery and comprise diverse roles while consisting of chemically similar building blocks. In recent years, protein engineering and design have become important research areas, with many applications in the pharmaceutical, energy, and biocatalysis fields, among others—where the aim is to ultimately create a protein given desired structural and functional properties. It is often critical to model the relationship between a protein’s sequence, folded structure, and biological function to assist in such protein engineering pursuits. However, significant challenges remain in concretely mapping an amino acid sequence to specific protein properties and biological activities. Mutations may enhance or diminish molecular protein function, and the epistatic interactions between mutations result in an inherently complex mapping between genetic modifications and protein function. Therefore, estimating the quantitative effects of mutations on protein function(s) remains a grand challenge of biology, bioinformatics, and many related fields and would rapidly accelerate protein engineering tasks when successful. Such estimation is often known as variant effect prediction (VEP). However, progress has been demonstrated in recent years with the development of machine learning (ML) methods in modeling the relationship between mutations and protein function. In this Review, recent advances in variant effect prediction (VEP) are discussed as tools for protein engineering, focusing on techniques incorporating gains from the broader ML community and challenges in estimating biomolecular functional differences. Primary developments highlighted include convolutional neural networks, graph neural networks, and natural language embeddings for protein sequences.
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影响因子：
48
作者：
Fowler, Douglas M.;Fields, Stanley
通讯作者：
Fields, Stanley
DOI：
10.1038/s41576-021-00441-w
发表时间：
2022-06
期刊：
Nature reviews. Genetics
影响因子：
--
作者：
Braberg H;Echeverria I;Kaake RM;Sali A;Krogan NJ
通讯作者：
Krogan NJ
影响因子：
9.3
作者：
Gray, Vanessa E.;Hause, Ronald J.;Fowler, Douglas M.
通讯作者：
Fowler, Douglas M.
DOI：
10.1109/tpami.2021.3095381
发表时间：
2022-10-01
影响因子：
23.6
作者：
Elnaggar, Ahmed;Heinzinger, Michael;Rost, Burkhard
通讯作者：
Rost, Burkhard
影响因子：
48
作者：
Alley, Ethan C.;Khimulya, Grigory;Church, George M.
通讯作者：
Church, George M.