“可折叠性”在酶智能设计改造中的应用研究——以AlphaFold2为例

孟巧珍; 郭菲

doi:10.12211/2096-8280.2023-011

您当前的位置：

首页 >

文章列表页 >

“可折叠性”在酶智能设计改造中的应用研究——以AlphaFold2为例

特约评述 | 更新时间：2025-03-20

- “可折叠性”在酶智能设计改造中的应用研究——以AlphaFold2为例
- Applications of foldability in intelligent enzyme engineering and design: take AlphaFold2 for example
- 合成生物学 2023年4卷第3期页码：571-589
- 作者机构：
  
  1.天津大学智能与计算学部计算机学院，天津 300350
  2.中南大学计算机学院，湖南长沙 410000
- 作者简介：
  
  [ "孟巧珍（1993—），女，博士研究生。研究方向为蛋白质结构预测，蛋白质序列设计，生物信息学等。 E-mail：2015210125@tju.edu.cn" ]
  [ "郭菲（1984—），女，教授，博士生导师。研究方向为机器学习、深度学习、数据挖掘、生物信息学、医学图像分析等。 E-mail：guofei@csu.edu.cn" ]
- 基金信息：
  
  国家自然科学基金(62172296);国家科技计划(2020YFA0908400)
- DOI：10.12211/2096-8280.2023-011
  中图分类号： Q816
- 收稿：2023-02-06，
  
  修回：2023-03-28，
  
  纸质出版：2023-06-30
- 稿件说明：
移动端阅览
孟巧珍，郭菲. “可折叠性”在酶智能设计改造中的应用研究——以AlphaFold2为例［J］. 合成生物学， 2023， 4（3）： 571-589

MENG Qiaozhen， GUO Fei. Applications of foldability in intelligent enzyme engineering and design： take AlphaFold2 for example［J］. Synthetic Biology Journal， 2023， 4（3）： 571-589
孟巧珍，郭菲. “可折叠性”在酶智能设计改造中的应用研究——以AlphaFold2为例［J］. 合成生物学， 2023， 4（3）： 571-589 DOI： 10.12211/2096-8280.2023-011.

MENG Qiaozhen， GUO Fei. Applications of foldability in intelligent enzyme engineering and design： take AlphaFold2 for example［J］. Synthetic Biology Journal， 2023， 4（3）： 571-589 DOI： 10.12211/2096-8280.2023-011.

摘要

天然酶具有绿色环保、高效催化的优点，但由于工业环境的酸碱性、温度等条件不够适宜，天然酶在实际工业生产中往往存在错误折叠、功能受限等问题。使用人工智能技术辅助酶的改造设计，相比传统方法具有高效、快速、低成本的优势，但在这个过程中大部分工作没有考虑设计改造酶的“可折叠性”问题。同时，最近几年来，以AlphaFold2为代表的蛋白质结构预测工具借助人工智能技术取得了突破性的进展，已经具有原子级别的结构预测精度。这一工具的日益成熟，不仅有助于对蛋白结构功能机制的了解，同时可以丰富现有酶结构数据，用于后续的研究。因此，基于现有酶改造以及从头设计新酶过程中出现的错误折叠导致成功率不高、实验验证成本高的问题，我们认为结合蛋白质结构预测工具辅助酶的改造设计任务，可以增加设计可靠酶的数量，同时降低实验成本。本文首先梳理回顾人工智能技术在酶设计改造中的应用，主要从序列和结构两个角度展开。然后将现有蛋白质结构预测工具归纳成四种类型分别介绍其设计原理和预测能力。接着以AlphaFold2为代表性工作，归纳了三种在现有技术基础上利用结构预测工具进一步提高酶改造的合理性以及酶设计的“可折叠性”的方式：①结构“分析器”；②突变“筛选器”；③折叠“监督器”。最后在讨论部分总结并提出了一些现有算法的不足和缺陷。随着人工智能技术的逐渐发展以及人类对蛋白质作用机理的研究，酶的改造设计精度一定会有所提高，这将助力合成生物学的快速发展。

Abstract

Natural enzymes often have advantages of environmental friendliness

high catalytic efficiency and so on. However

due to inappropriate pH

temperature and other conditions in industrial environment

the application of natural enzymes in industrial production is unsatisfactory owing to challenges such as misfolding of proteins and limited functions. Compared with traditional methods

enzyme design and engineering with the help of artificial intelligence (AI) have advantages of high efficiency

high speed and low cost

but most work does not consider the 'foldability' in the process of enzyme engineering. A designed enzyme may fold to another state for minimum energy

so called misfolding. As we all know

protein design is regarded as an inverse folding process. Can we utilize protein folding tools to constrain the foldability of the designed enzyme? In recent years

protein structure prediction tools represented by AlphaFold2 have made breakthroughs with the help of AI for accuracy at atomic levels

which enriches existing enzyme structure data for subsequent studies to address the above question. Therefore

we discuss applying protein structural tools to fulfill the task of enzyme design and engineering

increase the proportion of reliable enzymes designed and reduce the cost of experiments. Firstly

we review the application of artificial intelligence technology in enzyme design and engineering from the perspective of sequence and structure. Then

we summarize existing protein structure prediction tools into four types and introduce their methods and prediction ability respectively. Furthermore

taking AlphaFold2 as an example

we group the applications which improve the rationality of enzyme modification and the "foldability" of design into three categories: 1) Structure 'Analyzer'

2) Mutation 'Filter' and 3) Folding 'Monitor'. Finally

we highlight drawbacks with existing algorithms for further improvements. With the rapid development of AI and understanding on protein function mechanism

the precision of enzyme modifications and designs will be increased.

关键词

Keywords

references

FERRER S , RUIZ-PERNÍA J , MARTÍ S , et al . Hybrid schemes based on quantum mechanics/molecular mechanics simulations goals to success, problems, and perspectives [J ] . Advances in Protein Chemistry and Structural Biology , 2011 , 85 : 81 - 142 .

MAZURENKO S , PROKOP Z , DAMBORSKY J . Machine learning in enzyme engineering [J ] . ACS Catalysis , 2020 , 10 ( 2 ): 1210 - 1223 .

DINMUKHAMED T , HUANG Z Y , LIU Y F , et al . Current advances in design and engineering strategies of industrial enzymes [J ] . Systems Microbiology and Biomanufacturing , 2021 , 1 ( 1 ): 15 - 23 .

YANG H Q , LI J H , SHIN H D , et al . Molecular engineering of industrial enzymes: recent advances and future prospects [J ] . Applied Microbiology and Biotechnology , 2014 , 98 ( 1 ): 23 - 29 .

SHELDON R A , PEREIRA P C . Biocatalysis engineering: the big picture [J ] . Chemical Society Reviews , 2017 , 46 ( 10 ): 2678 - 2691 .

LI G Y , DONG Y J , REETZ M T . Can machine learning revolutionize directed evolution of selective enzymes? [J ] . Advanced Synthesis & Catalysis , 2019 , 361 ( 11 ): 2377 - 2386 .

JIANG L , ALTHOFF E A , CLEMENTE F R , et al . De novo computational design of retro-aldol enzymes [J ] . Science , 2008 , 319 ( 5868 ): 1387 - 1391 .

RÖTHLISBERGER D , KHERSONSKY O , WOLLACOTT A M , et al . Kemp elimination catalysts by computational enzyme design [J ] . Nature , 2008 , 453 ( 7192 ): 190 - 195 .

SIEGEL J B , ZANGHELLINI A , LOVICK H M , et al . Computational design of an enzyme catalyst for a stereoselective bimolecular Diels-Alder reaction [J ] . Science , 2010 , 329 ( 5989 ): 309 - 313 .

YANG K K , WU Z , ARNOLD F H . Machine-learning-guided directed evolution for protein engineering [J ] . Nature Methods , 2019 , 16 ( 8 ): 687 - 694 .

SUN J Y , CUI Y L , WU B . GRAPE, a greedy accumulated strategy for computational protein engineering [J ] . Methods in Enzymology , 2021 , 648 : 207 - 230 .

PEARCE R , HUANG X , OMENN G S , et al . De novo protein fold design through sequence-independent fragment assembly simulations [J ] . Proceedings of the National Academy of Sciences of the United States of America , 2023 , 120 ( 4 ): e2208275120 .

LISTOV D , LIPSH-SOKOLIK R , ROSSET S , et al . Assessing and enhancing foldability in designed proteins [J ] . Protein Science , 2022 , 31 ( 9 ): e4400 .

TUNYASUVUNAKOOL K , ADLER J , WU Z , et al . Highly accurate protein structure prediction for the human proteome [J ] . Nature , 2021 , 596 ( 7873 ): 590 - 596 .

SENIOR A W , EVANS R , JUMPER J , et al . Improved protein structure prediction using potentials from deep learning [J ] . Nature , 2020 , 577 ( 7792 ): 706 - 710 .

YANG J Y , ANISHCHENKO I , PARK H , et al . Improved protein structure prediction using predicted interresidue orientations [J ] . Proceedings of the National Academy of Sciences of the United States of America , 2020 , 117 ( 3 ): 1496 - 1503 .

KAWASHIMA S , KANEHISA M . AAindex: amino acid index database [J ] . Nucleic Acids Research , 2000 , 28 ( 1 ): 374 .

SANDBERG M , ERIKSSON L , JONSSON J , et al . New chemical descriptors relevant for the design of biologically active peptides. A multivariate characterization of 87 amino acids [J ] . Journal of Medicinal Chemistry , 1998 , 41 ( 14 ): 2481 - 2491 .

KULIKOVA A V , DIAZ D J , LOY J M , et al . Learning the local landscape of protein structures with convolutional neural networks [J ] . Journal of Biological Physics , 2021 , 47 ( 4 ): 435 - 454 .

ASGARI E , MOFRAD M R . Continuous distributed representation of biological sequences for deep proteomics and genomics [J ] . PLoS One , 2015 , 10 ( 11 ): e0141287 .

MEIER J , RAO R S , VERKUIL R , et al . Language models enable zero-shot prediction of the effects of mutations on protein function [C/OL ] // Advances in Neural Information Processing Systems 34 (NeurIPS 2021 ), 2021 . 34 : 29287 - 29303 [2023-02-01] . https://proceedings.neurips.cc/paper_files/paper/2021/hash/f51338d736f95dd42427296047067694-Abstract.html https://proceedings.neurips.cc/paper_files/paper/2021/hash/f51338d736f95dd42427296047067694-Abstract.html .

RAO R , BHATTACHARYA N , THOMAS N , et al . Evaluating protein transfer learning with TAPE [J ] . Advances in Neural Information Processing Systems , 2019 , 32 : 9689 - 9701 .

SVERRISSON F , FEYDY J , CORREIA B E , et al . Fast end-to-end learning on protein surfaces [C ] // 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . June 20-25, 2021 , Nashville, Tennessee, USA. IEEE , 2021 : 15267 - 15276 .

JIANG Y , RAN X , YANG Z J . Data-driven enzyme engineering to identify function-enhancing enzymes [J ] . Protein Engineering , Design & Selection, 2023 , 36 : gzac009 .

WU Z , KAN S B J , LEWIS R D , et al . Machine learning-assisted directed protein evolution with combinatorial libraries [J ] . Proceedings of the National Academy of Sciences of the United States of America , 2019 , 116 ( 18 ): 8852 - 8858 .

BISWAS S , KHIMULYA G , ALLEY E C , et al . Low-N protein engineering with data-efficient deep learning [J ] . Nature Methods , 2021 , 18 ( 4 ): 389 - 396 .

SHASHKOVA T I , UMERENKOV D , SALNIKOV M , et al . SEMA: antigen B-cell conformational epitope prediction using deep transfer learning [J ] . Frontiers in Immunology , 2022 , 13 : 960985 .

LU H Y , DIAZ D J , CZARNECKI N J , et al . Machine learning-aided engineering of hydrolases for PET depolymerization [J ] . Nature , 2022 , 604 ( 7907 ): 662 - 667 .

SHROFF R , COLE A W , DIAZ D J , et al . Discovery of novel gain-of-function mutations guided by structure-based deep learning [J ] . ACS Synthetic Biology , 2020 , 9 ( 11 ): 2927 - 2935 .

RIVES A , MEIER J , SERCU T , et al . Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences [J ] . Proceedings of the National Academy of Sciences of the United States of America , 2021 , 118 ( 15 ): e2016239118 .

PERTUSI D A , MOURA M E , JEFFRYES J G , et al . Predicting novel substrates for enzymes with minimal experimental effort with active learning [J ] . Metabolic Engineering , 2017 , 44 : 171 - 181 .

HUANG B , XU Y , HU X H , et al . A backbone-centred energy function of neural networks for protein design [J ] . Nature , 2022 , 602 ( 7897 ): 523 - 528 .

ANAND N , HUANG P S . Generative modeling for protein structures [EB/OL ] . Advances in Neural Information Processing Systems 31 (NeurIPS 2018 ), 2018 , 31 [ 2023-02-01 ] . https://proceedings.neurips.cc/paper_files/paper/2018/hash/afa299a4d1d8c52e75dd8a24c3ce534f-Abstract.html https://proceedings.neurips.cc/paper_files/paper/2018/hash/afa299a4d1d8c52e75dd8a24c3ce534f-Abstract.html .

ANAND N , EGUCHI R R , HUANG P S . Fully differentiable full-atom protein backbone generation [C/OL ] //Deep Generative Models for Highl y Structured Data, New Orleans , Louisiana, USA , May 6 - 9 , 2019, ICLR 2019 Workshop , 2019[2023-02-01] . https://openreview.net/forum?id=SJxnVL8YOV https://openreview.net/forum?id=SJxnVL8YOV .

WANG C , GARLICK S , ZLOH M . Deep learning for novel antimicrobial peptide design [J ] . Biomolecules , 2021 , 11 ( 3 ): 471 .

MÜLLER A T , HISS J A , SCHNEIDER G . Recurrent neural network model for constructive peptide design [J ] . Journal of Chemical Information and Modeling , 2018 , 58 ( 2 ): 472 - 479 .

REPECKA D , JAUNISKIS V , KARPUS L , et al . Expanding functional protein sequence spaces using generative adversarial networks [J ] . Nature Machine Intelligence , 2021 , 3 ( 4 ): 324 - 333 .

KARIMI M , ZHU S W , CAO Y , et al . De novo protein design for novel folds using guided conditional Wasserstein generative adversarial networks [J ] . Journal of Chemical Information and Modeling , 2020 , 60 ( 12 ): 5667 - 5681 .

DAUPARAS J , ANISHCHENKO I , BENNETT N , et al . Robust deep learning-based protein sequence design using ProteinMPNN [J ] . Science , 2022 , 378 ( 6615 ): 49 - 56 .

LIU Y F , ZHANG L , WANG W L , et al . Rotamer-free protein sequence design based on deep learning and self-consistency [J ] . Nature Computational Science , 2022 , 2 ( 7 ): 451 - 462 .

MOFFAT L , GREENER J G , JONES D T . Using AlphaFold for rapid and accurate fixed backbone protein design [EB/OL ] . bioRxiv , 2021 : 2021 . 08 . 24 . 457549 [ 2023-02-01 ] . https://www.biorxiv.org/content/10.1101/2021.08.24.457549v1 https://www.biorxiv.org/content/10.1101/2021.08.24.457549v1 .

JENDRUSCH M , KORBEL J , SADIQ S . AlphaDesign: a de novo protein design framework based on AlphaFold [EB/OL ] . bioRxiv , 2021 : 2021 . 10 . 11 . 463937 [ 2023-02-01 ] . https://www.biorxiv.org/content/10.1101/2021.10.11.463937v1 https://www.biorxiv.org/content/10.1101/2021.10.11.463937v1 .

NORN C , WICKY B I M , JUERGENS D , et al . Protein sequence design by explicit energy landscape optimization [EB/OL ] . bioRxiv , 2020 : 10 . 1101 /2020. 07 .23. 218917 [ 2023-02-01 ] . https://www.biorxiv.org/content/10.1101/2020.07.23.218917v1 https://www.biorxiv.org/content/10.1101/2020.07.23.218917v1 .

ANISHCHENKO I , PELLOCK S J , CHIDYAUSIKU T M , et al . De novo protein design by deep network hallucination [J ] . Nature , 2021 , 600 ( 7889 ): 547 - 552 .

WANG J , LISANZA S , JUERGENS D , et al . Scaffolding protein functional sites using deep learning [J ] . Science , 2022 , 377 ( 6604 ): 387 - 394 .

GAO Z , TAN C , LI S Z . PiFold: toward effective and efficient protein inverse folding [EB/OL ] . arXiv , 2022 : 2209 . 12643 [ 2023-02-01 ] . https://arxiv.org/abs/2209.12643 https://arxiv.org/abs/2209.12643 .

HUANG B , FAN T W , WANG K Y , et al . Accurate and efficient protein sequence design through learning concise local environment of residues [J ] . Bioinformatics , 2023 , 39 ( 3 ): btad122 .

XIONG P , WANG M , ZHOU X Q , et al . Protein design with a comprehensive statistical energy function and boosted by experimental selection for foldability [J ] . Nature Communications , 2014 , 5 : 5330 .

GOODFELLOW I , POUGET-ABADIE J , MIRZA M , et al . Generative adversarial networks [J ] . Communications of the ACM , 2020 , 63 ( 11 ): 139 - 144 .

RADFORD A , METZ L , CHINTALA S . Unsupervised representation learning with deep convolutional generative adversarial networks [EB/OL ] . arXiv , 2015 : 1511 . 06434 [ 2023-02-01 ] . https://arxiv.org/abs/1511.06434 https://arxiv.org/abs/1511.06434 .

HOCHREITER S , SCHMIDHUBER J . Long short-term memory [J ] . Neural Computation , 1997 , 9 ( 8 ): 1735 - 1780 .

KINGMA D P , WELLING M . Auto-encoding variational bayes [EB/OL ] . arXiv , 2013 : 1312 . 6114 [ 2023-02-01 ] . https://arxiv.org/abs/1312.6114 https://arxiv.org/abs/1312.6114 .

VASWANI A , SHAZEER N , PARMAR N , et al . Attention is all you need [C ] // Proceedings of the 31st International Conference on Neural Information Processing Systems . December 4-9, 2017 , Long Beach, California, USA . New York : ACM , 2017 : 6000 - 6010 .

INGRAHAM J , GARG V K , BARZILAY R , et al . Generative models for graph-based protein design [C/OL ] // Advances in Neural Information Processing Systems 32 (NeurIPS 2019), 2019 , 32[2023-02-01] . https://proceedings.neurips.cc/paper_files/paper/2019/hash/f3a4ff4839c56a5f460c88cce3666a2b-Abstract.html https://proceedings.neurips.cc/paper_files/paper/2019/hash/f3a4ff4839c56a5f460c88cce3666a2b-Abstract.html .

MCPARTLON M , LAI B , XU J B . A deep SE(3)-equivariant model for learning inverse protein folding [EB/OL ] . bioRxiv , 2022 [ 2023-02-01 ] . https://www.biorxiv.org/content/10.1101/2022.04. 15.488492v1 https://www.biorxiv.org/content/10.1101/2022.04.15.488492v1 .

HOU J , ADHIKARI B , CHENG J L . DeepSF: deep convolutional neural network for mapping protein sequences to folds [J ] . Bioinformatics , 2018 , 34 ( 8 ): 1295 - 1303 .

ANAND N , EGUCHI R , MATHEWS I I , et al . Protein sequence design with a learned potential [J ] . Nature Communications , 2022 , 13 : 746 .

SUH D , LEE J W , CHOI S , et al . Recent applications of deep learning methods on evolution- and contact-based protein structure prediction [J ] . International Journal of Molecular Sciences , 2021 , 22 ( 11 ): 6032 .

BROOKS B R , BRUCCOLERI R E , OLAFSON B D , et al . CHARMM: a program for macromolecular energy, minimization, and dynamics calculations [J ] . Journal of Computational Chemistry , 1983 , 4 ( 2 ): 187 - 217 .

Klepeis J L , Floudas C A . ASTRO-FOLD: a combinatorial and global optimization framework for Ab initio prediction of three-dimensional structures of proteins from the amino acid sequence [J ] . Biophysical Journal , 2003 , 85 ( 4 ): 2119 - 2146 .

SUBRAMANI A , WEI Y , FLOUDAS C A . ASTRO-FOLD 2.0: an enhanced framework for protein structure prediction [J ] . AIChE Journal , 2012 , 58 ( 5 ): 1619 - 1637 .

BURLEY S K , BERMAN H M , KLEYWEGT G J , et al . Protein data bank (PDB): the single global macromolecular structure archive [J ] . Methods in Molecular Biology , 2017 , 1607 : 627 - 641 .

XU D , ZHANG Y . Ab initio protein structure assembly using continuous structure fragments and optimized knowledge-based force field [J ] . Proteins: Structure, Function, and Bioinformatics , 2012 , 80 ( 7 ): 1715 - 1735 .

YANG J Y , ZHANG Y . I-TASSER server: new development for protein structure and function predictions [J ] . Nucleic Acids Research , 2015 , 43 ( W1 ): W174 - W181 .

YANG J Y , YAN R X , ROY A , et al . The I-TASSER Suite: protein structure and function prediction [J ] . Nature Methods , 2015 , 12 ( 1 ): 7 - 8 .

LEAVER-FAY A , TYKA M , LEWIS S M , et al . R OSETTA 3: an object-oriented software suite for the simulation and design of macromolecules [M ] //Computer Methods, Part C-Methods in Enzymology . Amsterdam : Elsevier , 2011 : 545 - 574 .

JONES D T , BUCHAN D W A , COZZETTO D , et al . PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments [J ] . Bioinformatics , 2012 , 28 ( 2 ): 184 - 190 .

BITBOL A F , DWYER R S , COLWELL L J , et al . Inferring interaction partners from protein sequences [J ] . Proceedings of the National Academy of Sciences of the United States of America , 2016 , 113 ( 43 ): 12180 - 12185 .

MORCOS F , PAGNANI A , LUNT B , et al . Direct-coupling analysis of residue coevolution captures native contacts across many protein families [J ] . Proceedings of the National Academy of Sciences of the United States of America , 2011 , 108 ( 49 ): E1293 - E1301 .

SEEMAYER S , GRUBER M , SÖDING J . CCMpred—fast and precise prediction of protein residue-residue contacts from correlated mutations [J ] . Bioinformatics , 2014 , 30 ( 21 ): 3128 - 3130 .

WEIGT M , WHITE R A , SZURMANT H , et al . Identification of direct residue contacts in protein-protein interaction by message passing [J ] . Proceedings of the National Academy of Sciences of the United States of America , 2009 , 106 ( 1 ): 67 - 72 .

KAMISETTY H , OVCHINNIKOV S , BAKER D . Assessing the utility of coevolution-based residue-residue contact predictions in a sequence- and structure-rich era [J ] . Proceedings of the National Academy of Sciences of the United States of America , 2013 , 110 ( 39 ): 15674 - 15679 .

WANG S , SUN S Q , LI Z , et al . Accurate de novo prediction of protein contact map by ultra-deep learning model [J ] . PLoS Computational Biology , 2017 , 13 ( 1 ): e1005324 .

XU J B . Distance-based protein folding powered by deep learning [J ] . Proceedings of the National Academy of Sciences of the United States of America , 2019 , 116 ( 34 ): 16856 - 16865 .

GREENER J G , KANDATHIL S M , JONES D T . Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints [J ] . Nature Communications , 2019 , 10 : 3977 .

BRUNGER A T . Version 1.2 of the crystallography and NMR system [J ] . Nature Protocols , 2007 , 2 ( 11 ): 2728 - 2733 .

Zheng W , WUYUN Q Q G , Zhou X G , et al . Integrating deep neural network models with I-TASSER for accurate protein structure prediction [EB/OL ] . 2022[ 2023-02-01 ] . https://zhanggroup.org/D-I-TASSER https://zhanggroup.org/D-I-TASSER .

LI Y , ZHANG C X , YU D J , et al . Deep learning geometrical potential for high-accuracy ab initio protein structure prediction [J ] . iScience , 2022 , 25 ( 6 ): 104425 .

ALQURAISHI M . End-to-end differentiable learning of protein structure [J ] . Cell Systems , 2019 , 8 ( 4 ): 292 - 301.e3 .

LIN Z M , AKIN H , RAO R , et al . Language models of protein sequences at the scale of evolution enable accurate structure prediction [EB/OL ] . bioRxiv , 2022 : 10 . 1101 /2022. 07 .20. 500902 [ 2023-02-01 ] . https://www.biorxiv.org/content/10.1101/2022.07.20.500902v1 https://www.biorxiv.org/content/10.1101/2022.07.20.500902v1 .

WANG W K , PENG Z L , YANG J Y . Single-sequence protein structure prediction using supervised transformer protein language models [J ] . Nature Computational Science , 2022 , 2 ( 12 ): 804 - 814 .

WU R D , DING F , WANG R , et al . High-resolution de novo structure prediction from primary sequence [EB/OL ] . bioRxiv , 2022 [ 2023-02-01 ] . https://www.biorxiv.org/content/10.1101/2022.07.21.500999v1 https://www.biorxiv.org/content/10.1101/2022.07.21.500999v1 .

CHOWDHURY R , BOUATTA N , BISWAS S , et al . Single-sequence protein structure prediction using a language model and deep learning [J ] . Nature Biotechnology , 2022 , 40 ( 11 ): 1617 - 1623 .

BAEK M , DIMAIO F , ANISHCHENKO I , et al . Accurate prediction of protein structures and interactions using a three-track neural network [J ] . Science , 2021 , 373 ( 6557 ): 871 - 876 .

LIPSH-SOKOLIK R , KHERSONSKY O , SCHRÖDER S P , et al . Combinatorial assembly and design of enzymes [J ] . Science , 2023 , 379 ( 6628 ): 195 - 201 .

MOFFAT L , KANDATHIL S M , JONES D T . Design in the DARK: learning deep generative models for de novo protein design [EB/OL ] . bioRxiv , 2022 : 2022 . 01 . 27 . 478087 [ 2023-02-01 ] . https://www.biorxiv.org/content/10.1101/2022.01.27.478087v1 https://www.biorxiv.org/content/10.1101/2022.01.27.478087v1 .

ZHANG Y , SKOLNICK J . TM-align: a protein structure alignment algorithm based on the TM-score [J ] . Nucleic Acids Research , 2005 , 33 ( 7 ): 2302 - 2309 .

BENNETT N , COVENTRY B , GORESHNIK I , et al . Improving de novo protein binder design with deep learning [EB/OL ] . bioRxiv , 2022 : 2022 . 06 . 15 . 495993 [ 2023-02-01 ] . https://www.biorxiv.org/content/10.1101/2022.06.15.495993v1 https://www.biorxiv.org/content/10.1101/2022.06.15.495993v1 .

STEIN R A , MCHAOURAB H S . Modeling alternate conformations with Alphafold2 via modification of the multiple sequence alignment [EB/OL ] . bioRxiv , 2021 : 2021 . 11 . 29 . 470469 [ 2023-02-01 ] . https://www.biorxiv.org/content/10.1101/2021.11. 29.470469v1 https://www.biorxiv.org/content/10.1101/2021.11.29.470469v1 .

CASADEVALL G , DURAN C , ESTÉVEZ-GAY M , et al . Estimating conformational heterogeneity of tryptophan synthase with a template-based Alphafold2 approach [J ] . Protein Science , 2022 , 31 ( 10 ): e4426 .

GOULET A , CAMBILLAU C , ROUSSEL A , et al . Structure prediction and analysis of hepatitis E virus non-structural proteins from the replication and transcription machinery by AlphaFold2 [J ] . Viruses , 2022 , 14 ( 7 ): 1537 .

LI H , BAO Q Q , ZHAO J F , et al . Directed evolution engineering to improve activity of glucose dehydrogenase by increasing pocket hydrophobicity [J ] . Frontiers in Microbiology , 2022 , 13 : 1044226 .

BURNIM A A , XU D , SPENCE M A , et al . Analysis of insertions and extensions in the functional evolution of the ribonucleotide reductase family [J ] . Protein Science , 2022 , 31 ( 12 ): e4483 .

WU Y T , LIU J Q , HAN X , et al . Eliminating host-guest incompatibility via enzyme mining enables the high-temperature production of N -acetylglucosamine [J ] . iScience , 2023 , 26 ( 1 ): 105774 .

BARTAS M , SLYCHKO K , BRÁZDA V , et al . Searching for new Z-DNA/Z-RNA binding proteins based on structural similarity to experimentally validated zα domain [J ] . International Journal of Molecular Sciences , 2022 , 23 ( 2 ): 768 .

SHEN Y , WANG Y L , WEI X , et al . Engineering the active site pocket to enhance the catalytic efficiency of a novel feruloyl esterase derived from human intestinal bacteria Dorea formicigenerans [J ] . Frontiers in Bioengineering and Biotechnology , 2022 , 10 : 936914 .

TSABAN T , VARGA J K , AVRAHAM O , et al . Harnessing protein folding neural networks for peptide-protein docking [J ] . Nature Communications , 2022 , 13 ( 1 ): 176 .

LI G , BURIC F , ZRIMEC J , et al . Learning deep representations of enzyme thermal adaptation [J ] . Protein Science , 2022 , 31 ( 12 ): e4480 .

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于人工智能和计算生物学的合成生物学元件设计

微生物组生物合成基因簇发掘方法及应用前景

人工智能蛋白质结构设计算法研究进展