融合结构知识的蛋白质预训练模型进展

汤天一; 熊翊名; 张睿格; 张建; 李文飞; 王骏; 王炜

doi:10.7498/aps.73.20240811

摘要

自然语言和图像处理领域引发的人工智能革命给蛋白质计算领域带来了新的思路和研究范式. 其中一个重大的进展是从海量蛋白质序列通过自监督学习得到预训练的蛋白质语言模型. 这类预训练模型编码了蛋白质的序列、进化、结构乃至功能等多种信息, 可方便地迁移至多种下游任务, 并展现了强大的泛化能力. 在此基础上, 人们正进一步发展融合更多种类数据的多模态预训练模型. 考虑到蛋白质结构是决定其功能的主要因素, 融合了结构信息的蛋白质预训练模型可更好地支持下游多种任务, 本文对这一方向的研究工作进行了介绍和总结. 此外, 还简介了融合先验知识的蛋白质预训练模型、RNA语言模型、蛋白质设计等方面的工作, 讨论了这些领域目前的现状、困难及可能的解决方案.

关键词:

Abstract

The AI revolution, sparked by natural language and image processing, has brought new ideas and research paradigms to the field of protein computing. One significant advancement is the development of pre-training protein language models through self-supervised learning from massive protein sequences. These pre-trained models encode various information about protein sequences, evolution, structures, and even functions, which can be easily transferred to various downstream tasks and demonstrate robust generalization capabilities. Recently, researchers have further developed multimodal pre-trained models that integrate more diverse types of data. The recent studies in this direction are summarized and reviewed from the following aspects in this paper. Firstly, the protein pre-training models that integrate protein structures into language models are reviewed: this is particularly important, for protein structure is the primary determinant of its function. Secondly, the pre-trained models that integrate protein dynamic information are introduced. These models may benefit downstream tasks such as protein-protein interactions, soft docking of ligands, and interactions involving allosteric proteins and intrinsic disordered proteins. Thirdly, the pre-trained models that integrate knowledge such as gene ontology are described. Fourthly, we briefly introduce pre-trained models in RNA fields. Finally, we introduce the most recent developments in protein designs and discuss the relationship of these models with the aforementioned pre-trained models that integrate protein structure information.

Keywords:

作者及机构信息

1.
南京大学物理学院, 南京　210093

2.
南京大学脑科学研究院, 南京　210093

通信作者: 张建, jzhang@nju.edu.cn ; 王炜, wangwei@nju.edu.cn

基金项目: 科技部科技创新项目(批准号: 2030-2021ZD0201300)和国家自然科学基金(批准号: 11934008)资助的课题.

Authors and contacts

1.
School of Physics, Nanjing University, Nanjing 210093, China

2.
Institute of Brain Science, Nanjing University, Nanjing 210093, China

Corresponding author: Zhang Jian, jzhang@nju.edu.cn ; Wang Wei, wangwei@nju.edu.cn

Funds: Project supported by the Science and Technology Innovation Project of the Ministry of Science and Technology (Grant No. 2030-2021ZD0201300) and the National Natural Science Foundation of China (Grant No. 11934008).

文章全文 : translate this paragraph

参考文献

[1]	Senior A W, Evans R, Jumper J, Kirkpatrick J, Sifre L, Green T, Qin C, Žídek A, Nelson A W, Bridgland A, Penedones H, Petersen S, Simonyan K, Crossan S, Kohli P, Jones D T, Silver D, Kavukcuoglu K, Hassabis D 2020 Nature 577 706 Google Scholar
[2]	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 S A A, Ballard A J, 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 A W, Kavukcuoglu K, Kohli P, Hassabis D 2021 Nature 596 583 Google Scholar
[3]	Radford A, Narasimhan K, Salimans T, Sutskever I 2018 Improving Language Understanding by Generative Pre-Training [2024-6-9]
[4]	Radford A, Wu J, Child R, Luan D, Amodei D, Sutskever I 2019 Language Models are Unsupervised Multitask Learners [2024-6-9]
[5]	Brown T B, Mann B, Ryder N, Subbiah M, Kaplan J, Dhariwal P, Neelakantan A, Shyam P, Sastry G, Askell A, Agarwal S, Herbert-Voss A, Krueger G, Henighan T, Child R, Ramesh A, Ziegler D M, Wu J, Winter C, Hesse C, Chen M, Sigler E, Litwin M, Gray S, Chess B, Clark J, Berner C, McCandlish S, Radford A, Sutskever I, Amodeis D 2020 arXiv: 2005.14165[cs.CV]
[6]	Ouyang L, Wu J, Jiang X, Almeida D, Wainwright C L, Mishkin P, Zhang C, Agarwal S, Slama K, Ray A, Schulman J, Hilton J, Kelton F, Miller L, Simens M, Askell A, Welinder P, Christiano P, Leike J, Low R 2022 arXiv: 2203.02155[cs.CV]
[7]	Devlin J, Chang M W, Lee K, Toutanova K 2018 arXiv: 1810.04805[cs.CV]
[8]	Ma Z, He J, Qiu J, Cao H, Wang Y, Sun Z, Zheng L, Wang H, Tang S, Zheng T, Lin J, Feng G, Huang Z, Gao J, Zeng A, Zhang J, Zhong R, Shi T, Liu S, Zheng W, Tang J, Yang H, Liu X, Zhai J, Chen W 2022 Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming Seoul, Republic of Korea, April 2–6, 2022 p192
[9]	Han X, Zhang Z, Ding N, Gu Y, Liu X, Huo Y, Qiu J, Yao Y, Zhang A, Zhang L, Han W, Huang M, Jin Q, Lan Y, Liu Y, Liu Z, Lu Z, Qiu X, Song R, Tang J, Wen J R, Yuan J, Zhao W X, Zhu J 2021 arXiv: 2106.07139[AI]
[10]	Yuan S, Zhao H, Zhao S, et al. 2022 arXiv: 2203.14101 [cs.LG]
[11]	Wei J, Tay Y, Bommasani R, Raffel C, Zoph B, Borgeaud S, Yogatama D, Bosma M, Zhou D, Metzler D, Chi E H, Hashimoto T, Vinyals O, Liang P, Dean J, Fedus W 2022 arXiv: 2206.07682[cs.CV]
[12]	Alayrac J B, Donahue J, Luc P, Miech A, Barr I, Hasson Y, Lenc K, Mensch A, Millican K, Reynolds M, Ring R, Rutherford E, Cabi S, Han T, Gong Z, Samangooei S, Monteiro M, Menick J, Borgeaud S, Brock A, Nematzadeh A, Sharifzadeh S, Binkowski M, Barreira R, Vinyals O, Zisserman A, Simonyan K 2022 arXiv: 2204.14198[cs.CV]
[13]	OpenAI, Achiam J, Adler S, et al. 2024 arXiv: 2303.08774 [cs.CV]
[14]	Driess D, Xia F, Sajjadi M S M, Lynch C, Chowdhery A, Ichter B, Wahid A, Tompson J, Vuong Q, Yu T, Huang W, Chebotar Y, Sermanet P, Duckworth D, Levine S, Vanhoucke V, Hausman K, Toussaint M, Greff K, Zeng A, Mordatch I, Florence P 2023 arXiv: 2303.03378[cs.LG]
[15]	Touvron H, Lavril T, Izacard G, Martinet X, Lachaux M A, Lacroix T, Rozière B, Goyal N, Hambro E, Azhar F, Rodriguez A, Joulin A, Grave E, Lample G 2023 arXiv: 2302.13971[cs.CV]
[16]	Gemini Team Google, Anil R, Borgeaud S, et al. 2024 arXiv: 2312.11805[cs.CV]
[17]	Chen F, Han M, Zhao H, Zhang Q, Shi J, Xu S, Xu B 2023 arXiv: 2305.04160[cs.CV]
[18]	Li K, He Y, Wang Y, Li Y, Wang W, Luo P, Wang Y, Wang L, Qiao Y 2023 arXiv: 2305.06355[cs.CV]
[19]	Bepler T, Berger B 2019 arXiv: 1902.08661[cs.LG]
[20]	Heinzinger M, Elnaggar A, Wang Y, Dallago C, Nechaev D, Matthes F, Rost B 2019 bioRxiv: 614313[Bioinformatics]
[21]	Alley E C, Khimulya G, Biswas S, Alquraishi M, Church G M 2019 Nat. Methods 16 1315 Google Scholar
[22]	Rives A, Meier J, Sercu T, Goyal S, Lin Z, Liu J, Guo D, Ott M, Zitnick C L, Ma J, Fergus R 2021 Proc. Natl. Acad. Sci. 118 e2016239118 Google Scholar
[23]	Rao R, Liu J, Verkuil R, et al. 2021 bioRxiv: 2021.02.12. 430858 [Synthetic Biology]
[24]	Meier J, Rao R, Verkuil R, Liu J, Sercu T, Rives A 2021 Advances in Neural Information Processing Systems 34 29287 Google Scholar
[25]	Lin Z, Akin H, Rao R, Hie B, Zhu Z, Lu W, Smetanin N, Verkuil R, Kabeli O, Shmueli Y, dos Santos Costa A, Fazel-Zarandi M, Sercu T, Candido S, Rives A 2023 Science 379 1123 Google Scholar
[26]	Lin Z, Akin H, Rao R, Hie B, Zhu Z, Lu W, Santos Costa A d, Fazel-Zarandi M, Sercu T, Candido S, Rives A 2022 bioRxiv: 2022.07.20.500902[Synthetic Biology]
[27]	Madani A, McCann B, Naik N, Keskar N S, Anand N, Eguchi R R, Huang P S, Socher R 2020 arXiv: 2004.03497[q-bio.QM]
[28]	Madani A, Krause B, Greene E R, Subramanian S, Mohr B P, Holton J M, Olmos J L, Xiong C, Sun Z Z, Socher R, Fraser J S, Naik N 2023 Nat. Biotechnol. 41 1099 Google Scholar
[29]	He L, Zhang S, Wu L, Xia H, Ju F, Zhang H, Liu S, Xia Y, Zhu J, Deng P, Shao B, Qin T, Liu T Y 2021 arXiv: 2110.15527[cs.CV]
[30]	Elnaggar A, Heinzinger M, Dallago C, Rihawi G, Wang Y, Jones L, Gibbs T, Feher T, Angerer C, Steinegger M, Bhowmik D, Rost B 2021 arXiv: 2007.06225[cs.LG]
[31]	Chen B, Cheng X, Li P, Geng Y, Gong J, Li S, Bei Z, Tan X, Wang B, Zeng X, Liu C, Zeng A, Dong Y, Tang J, Song L 2024 arXiv: 2401.06199[q-bio.QM]
[32]	Nguyen E, Poli M, Durrant M G, Thomas A W, Kang B, Sullivan J, Ng M Y, Lewis A, Patel A, Lou A, Ermon S, Baccus S A, Hernandez-Boussard T, Ré C, Hsu P D, Hie B L 2024 bioRxiv: 2024.02.27.582234[Synthetic Biology]
[33]	Gao W, Mahajan S P, Sulam J, Gray J J 2020 Patterns 1 100142 Google Scholar
[34]	Unsal S, Atas H, Albayrak M, Turhan K, Acar A C, Doğan T 2022 Nature Machine Intelligence 4 227 Google Scholar
[35]	Zhang Q, Ding K, Lyv T, Wang X, Yin Q, Zhang Y, Yu J, Wang Y, Li X, Xiang Z, Feng K, Zhuang X, Wang Z, Qin M, Zhang M, Zhang J, Cui J, Huang T, Yan P, Xu R, Chen H, Li X, Fan X, Xing H, Chen H 2024 arXiv: 2401. 14656[cs.CV]
[36]	管星悦, 黄恒焱, 彭华祺, 刘彦航, 李文飞, 王炜 2023 72 248708 Google Scholar Guan X Y, Huang H Y, Peng H Q, Liu Y H, Li W F, Wang W 2023 Acta Phys. Sin. 72 248708 Google Scholar
[37]	陈光临, 张志勇 2023 72 248705 Google Scholar Chen G L, Zhang Z Y 2023 Acta Phys. Sin. 72 248705 Google Scholar
[38]	张嘉晖 2024 73 069301 Google Scholar Zhang J H 2024 Acta Phys. Sin. 73 069301 Google Scholar
[39]	Zeng C, Jian Y, Vosoughi S, Zeng C, Zhao Y 2023 Nat. Commun. 14 1060 Google Scholar
[40]	Zeng C, Zhao Y 2023 Scientia Sinica Physica, Mechanica & Astronomica 53 290018 Google Scholar
[41]	Huh M, Cheung B, Wang T, Isola P 2024 arXiv: 2405.07987 [cs.LG]
[42]	Bepler T, Berger B 2021 Cell Systems 12 654 Google Scholar
[43]	Guo Y, Wu J, Ma H, Huang J 2022 Proceedings of the AAAI Conference on Artificial Intelligence 36 6801 Google Scholar
[44]	Hermosilla P, Ropinski T 2022 arXiv: 2205.15675[q-bio.BM]
[45]	Zhang Z, Xu M, Jamasb A, Chenthamarakshan V, Lozano A, Das P, Tang J 2022 arXiv: 2203.06125[cs.LG]
[46]	Zhang Z, Xu M, Lozano A, Chenthamarakshan V, Das P, Tang J 2023 arXiv: 2303.06275[q-bio.QM]
[47]	Gligorijević V, Renfrew P D, Kosciolek T, Leman J K, Berenberg D, Vatanen T, Chandler C, Taylor B C, Fisk I M, Vlamakis H, Xavier R J, Knight R, Cho K, Bonneau R 2021 Nat. Commun. 12 3168 Google Scholar
[48]	Wang Z, Combs S A, Brand R, Calvo M R, Xu P, Price G, Golovach N, Salawu E O, Wise C J, Ponnapalli S P, Clark P M 2022 Sci. Rep. 12 6832 Google Scholar
[49]	Chen C, Zhou J, Wang F, Liu X, Dou D 2023 arXiv: 2204.04213[cs.LG]
[50]	Zhou G, Gao Z, Ding Q, Zheng H, Xu H, Wei Z, Zhang L, Ke G 2022 DOI: 10.26434/chemrxiv-2022-jjm0j-v4
[51]	Su J, Han C, Zhou Y, Shan J, Zhou X, Yuan F 2023 bioRxiv: 2023.10.01.560349[Bioinformatics]
[52]	Su J, Li Z, Han C, Zhou Y, Shan J, Zhou X, Ma D, OPMC T, Ovchinnikov S, Yuan F 2024 bioRxiv: 2024.05.24.595648[Bioinformatics]
[53]	Hu M Y, Yuan F J, Yang K K, Ju F S, Su J, Wang H, Yang F, Ding Q Y 2022 arXiv:2206.06583 [q-bio.QM]
[54]	Abramson J, Adler J, Dunger J, et al. 2024 Nature 630 493 Google Scholar
[55]	Wang L, Liu H, Liu Y, Kurtin J, Ji S 2022 arXiv: 2207.12600[cs.LG]
[56]	Somnath V R, Bunne C, Krause A 2021 arXiv: 2204.02337[cs.LG]
[57]	Gainza P, Sverrisson F, Monti F, Rodola E, Boscaini D, Bronstein M M, Correia B E 2020 Nat. Methods 17 184 Google Scholar
[58]	Wu F, Jin S, Jiang Y, Jin X, Tang B, Niu Z, Liu X, Zhang Q, Zeng X, Li S Z 2022 arXiv: 2204.08663[CE]
[59]	Meyer T, D'Abramo M, Rueda M, Ferrer-Costa C, Pérez A, Carrillo O, Camps J, Fenollosa C, Repchevsky D, Gelpí J L, Orozco M 2010 Structure 18 1399 Google Scholar
[60]	Zhang N, Bi Z, Liang X, Cheng S, Hong H, Deng S, Lian J, Zhang Q, Chen H 2022 arXiv: 2201.11147[q-bio.BM]
[61]	Gu Y, Tinn R, Cheng H, Lucas M, Usuyama N, Liu X, Naumann T, Gao J, Poon H 2021 arXiv: 2007.15779[cs.CV]
[62]	Zhou H Y, Fu Y, Zhang Z, Bian C, Yu Y 2023 arXiv: 2301.13154[cs.LG]
[63]	Xu M, Yuan X, Miret S, Tang J 2023 arXiv: 2301.12040 [q-bio.BM]
[64]	Singh J, Hanson J, Paliwal K, Zhou Y 2019 Nat. Commun. 10 5407 Google Scholar
[65]	Singh J, Paliwal K, Zhang T, Singh J, Litfin T, Zhou Y 2021 Bioinformatics 37 2589 Google Scholar
[66]	Wang J, Mao K, Zhao Y, Zeng C, Xiang J, Zhang Y, Xiao Y 2017 Nucleic Acids Res. 45 6299 Google Scholar
[67]	Wang J, Xiao Y 2017 Current Protocols in Bioinformatics 57 5 Google Scholar
[68]	Wang J, Wang J, Huang Y, Xiao Y 2019 Int. J. Mol. Sci. 20 4116 Google Scholar
[69]	Tan Y L, Wang X, Shi Y Z, Zhang W, Tan Z J 2022 Biophys. J. 121 142 Google Scholar
[70]	Zhou L, Wang X, Yu S, Tan Y L, Tan Z J 2022 Biophys. J. 121 3381 Google Scholar
[71]	Wang X, Tan Y L, Yu S, Shi Y Z, Tan Z J 2023 Biophys. J. 122 1503 Google Scholar
[72]	Li J, Zhu W, Wang J, Li W, Gong S, Zhang J, Wang W 2018 PLoS Comput. Biol. 14 e1006514 Google Scholar
[73]	Fu L, Cao Y, Wu J, Peng Q, Nie Q, Xie X 2022 Nucleic Acids Res. 50 e14 Google Scholar
[74]	Pearce R, Omenn G S, Zhang Y 2022 bioRxiv: 2022. 05.15.491755[Bioinformatics]
[75]	Baek M, McHugh R, Anishchenko I, Baker D, DiMaio F 2022 bioRxiv: 2022.09.09.507333[Bioinformatics]
[76]	Zhang J, Lang M, Zhou Y, Zhang Y 2024 Trends in Genetics 40 94 Google Scholar
[77]	Li J, Zhou Y, Chen S J 2024 Curr. Opin. Struct. Biol. 87 102847 Google Scholar
[78]	Chen J, Hu Z, Sun S, Tan Q, Wang Y, Yu Q, Zong L, Hong L, Xiao J, Shen T, King I, Li Y 2022 arXiv: 2204.00300[q-bio.QM]
[79]	Chen K, Zhou Y, Ding M, Wang Y, Ren Z, Yang Y 2023 bioRxiv: 2023.01.31.526427[Bioinformatics]
[80]	Babjac A N, Lu Z, Emrich S J 2023 Proceedings of the 14th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics New York, United States, September 3–6, 2023 p1
[81]	Chu Y, Yu D, Li Y, Huang K, Shen Y, Cong L, Zhang J, Wang M 2024 Nature Machine Intelligence 6 449 Google Scholar
[82]	Yang Y, Li G, Pang K, Cao W, Li X, Zhang Z 2023 bioRxiv: 2023.09.08.556883[Bioinformatics]
[83]	Zhang Y, Lang M, Jiang J, Gao Z, Xu F, Litfin T, Chen K, Singh J, Huang X, Song G, Tian Y, Zhan J, Chen J, Zhou Y 2024 Nucleic Acids Res. 52 e3 Google Scholar
[84]	Wang X, Gu R, Chen Z, Li Y, Ji X, Ke G, Wen H 2023 bioRxiv: 2023.07.11.548588[Bioinformatics]
[85]	Wang N, Bian J, Li Y, Li X, Mumtaz S, Kong L, Xiong H 2024 Nature Machine Intelligence 6 548 Google Scholar
[86]	Akiyama M, Sakakibara Y 2022 NAR Genomics and Bioinformatics 4 lqac012 Google Scholar
[87]	Shen T, Hu Z, Peng Z, Chen J, Xiong P, Hong L, Zheng L, Wang Y, King I, Wang S, Siqi S, Yu L 2022 arXiv: 2207.01586[q-bio.QM]
[88]	Li Y, Zhang C, Feng C, Pearce R, Lydia Freddolino P, Zhang Y 2023 Nat. Commun. 14 5745 Google Scholar
[89]	Ferruz N, Schmidt S, Höcker B 2022 Nat. Commun. 13 4348 Google Scholar
[90]	Wang J, Lisanza S, Juergens D, Tischer D, Watson J L, Castro K M, Ragotte R, Saragovi A, Milles L F, Baek M, Anishchenko I, Yang W, Hicks D R, Expòsit M, Schlichthaerle T, Chun J H, Dauparas J, Bennett N, Wicky B I M, Muenks A, DiMaio F, Correia B, Ovchinnikov S, Baker D 2022 Science 377 387 Google Scholar
[91]	Trippe B L, Yim J, Tischer D, Baker D, Broderick T, Barzilay R, Jaakkola T 2022 arXiv: 2206.04119[q-bio.BM]
[92]	Anishchenko I, Pellock S J, Chidyausiku T M, Ramelot T A, Ovchinnikov S, Hao J, Bafna K, Norn C, Kang A, Bera A K, DiMaio F, Carter L, Chow C M, Montelione G T, Baker D 2021 Nature 600 547 Google Scholar
[93]	Wicky B I M, Milles L F, Courbet A, Ragotte R J, Dauparas J, Kinfu E, Tipps S, Kibler R D, Baek M, DiMaio F, Li X, Carter L, Kang A, Nguyen H, Bera A K, Baker D 2022 Science 378 56 Google Scholar
[94]	Anand N, Achim T 2022 arXiv: 2205.15019[q-bio.QM]
[95]	Luo S, Su Y, Peng X, Wang S, Peng J, Ma J 2022 Advances in Neural Information Processing Systems 35 9754 Google Scholar
[96]	Cao L, Coventry B, Goreshnik I, et al 2022 Nature 605 551 Google Scholar
[97]	Kuhlman B, Bradley P 2019 Nat. Rev. Mol. Cell Biol. 20 681 Google Scholar
[98]	Pan X, Kortemme T 2021 J. Biol. Chem. 296 100558 Google Scholar
[99]	Khakzad H, Igashov I, Schneuing A, Goverde C, Bronstein M, Correia B 2023 Cell Systems 14 925 Google Scholar
[100]	Malbranke C, Bikard D, Cocco S, Monasson R, Tubiana J 2023 Curr. Opin. Struct. Biol. 80 102571 Google Scholar
[101]	Kortemme T 2024 Cell 187 526 Google Scholar
[102]	Notin P, Rollins N, Gal Y, Sander C, Marks D 2024 Nat. Biotechnol. 42 216 Google Scholar
[103]	Listov D, Goverde C A, Correia B E, Fleishman S J 2024 Nat. Rev. Mol. Cell Biol. 25 639 Google Scholar
[104]	Ingraham J, Garg V K, Barzilay R, Jaakkola T 2019 Proceedings of the 33rd International Conference on Neural Information Processing Systems Vancouver, BC, Canada, December 8–14, 2019 p15820
[105]	Dauparas J, Anishchenko I, Bennett N, Bai H, Ragotte R J, Milles L F, Wicky B I M, Courbet A, de Haas R J, Bethel N, Leung P J Y, Huddy T F, Pellock S, Tischer D, Chan F, Koepnick B, Nguyen H, Kang A, Sankaran B, Bera A K, King N P, Baker D 2022 Science 378 49 Google Scholar
[106]	Hsu C, Verkuil R, Liu J, Lin Z, Hie B, Sercu T, Lerer A, Rives A 2022 bioRxiv: 2022.04.10.487779[Systems Biology]
[107]	Sohl-Dickstein J, Weiss E A, Maheswaranathan N, Ganguli S 2015 arXiv: 1503.03585[cs.LG]
[108]	Ho J, Jain A, Abbeel P 2020 Advances in Neural Information Processing Systems 33 6840 Google Scholar
[109]	Watson J L, Juergens D, Bennett N R, et al 2023 Nature 620 1089 Google Scholar
[110]	Song Y, Sohl-Dickstein J, Kingma D P, Kumar A, Ermon S, Poole B 2020 arXiv: 2011.13456[cs.LG]
[111]	Lee J S, Kim J, Kim P M 2023 Nature Computational Science 3 382 Google Scholar
[112]	Liu Y, Chen L, Liu H 2023 bioRxiv: 2023.11.18.567666 [Bioinformatics]
[113]	Zheng Z, Deng Y, Xue D, Zhou Y, YE F, Gu Q 2023 arXiv: 2302.01649[cs.LG]
[114]	Yang K K, Zanichelli N, Yeh H 2023 Protein Eng. Des. Sel. 36 gzad015 Google Scholar
[115]	Kaplan J, McCandlish S, Henighan T, Brown T B, Chess B, Child R, Gray S, Radford A, Wu J, Amodei D 2020 arXiv: 2001.08361[cs.LG]
[116]	He K, Chen X, Xie S, Li Y, Dollár P, Girshick R 2021 arXiv: 2111.06377[cs.CV]
[117]	Chen T, Kornblith S, Norouzi M, Hinton G 2020 arXiv: 2002.05709[cs.LG]
[118]	Wang Z, Wang Z, Srinivasan B, Ioannidis V N, Rangwala H, Anubhai R 2023 arXiv: 2310.03320[cs.LG]
[119]	Von Rueden L, Mayer S, Beckh K, Georgiev B, Giesselbach S, Heese R, Kirsch B, Walczak M, Pfrommer J, Pick A, Ramamurthy R, Garcke J, Bauckhage C, Schuecker J 2021 IEEE Trans. Knowl. Data Eng. 35 614
[120]	Bao L, Zhang X, Jin L, Tan Z J 2015 Chin. Phys. B 25 018703 Google Scholar
[121]	Qiang X W, Zhang C, Dong H L, Tian F J, Fu H, Yang Y J, Dai L, Zhang X H, Tan Z J 2022 Phys. Rev. Lett. 128 108103 Google Scholar
[122]	Dong H L, Zhang C, Dai L, Zhang Y, Zhang X H, Tan Z J 2024 Nucleic Acids Res. 52 2519 Google Scholar

施引文献

图 1 蛋白质多模态基础(预训练)模型及其应用 (只示意性列出若干下游任务)

Fig. 1. Protein multi-modal foundation (pre-trained) models and the downstream tasks.

下载: 全尺寸图片幻灯片

表 1 多模态蛋白质预训练模型

Table 1. Multimodal protein pre-trained models.

模型名	时间	模型	数据模态	预训练方法	训练集	参数量	算力要求	下游任务	文献
融合了结构信息的通用蛋白质预训练模型
Bepler &Berger	2019	Bi-LSTM	Sequence, structure	MLM for sequences, supervised learning for 3D structures	76M sequences, 28K structures	—	1X 32G-V100, 13 to 51 days	Fold classification transmembrane region prediction	[19,42]
Guo model	2022	CNN	Structure	Self-supervised pre-training on noised pair-distance	73K structures	—	—	QA, PPI	[43]
New IEConv	2022	GCN	Sequence, structure	Contrastive learning between randomly sampled 3D substructures	476K chains	30M	—	protein function prediction, protein fold classification, structural similarity prediction, protein-ligand binding affinity prediction	[44]
GearNet	2023	ESM-1b, GearNet	Sequence, structure	PLM, contrastive learning	805K structures from AlphaFoldDB	—	4X A100	Fold classification, EC, GO
STEPS	2023	BERT, GCN	Sequence, structure	PLM, supervised learning from 3D structures	40K structures	—	—	Membrane protein classification, cellular location prediction, EC
UNI-MOL	2023	Transformer	Sequence, structure	Atom 3D position denoise, masked atom type prediction	209M molecule conformations, 3.2M protein pockets structure	—	8X 32G-V100, 3 days	molecular property prediction, molecular conformation generation, pocket property prediction, protein-ligand binding pose prediction
SaProt	2023	BERT	Sequence, structure	Convert structures to structure-aware vocabulary, MLM	40M sequences and structures from PDB/AlphaFoldDB	650M	64X 80G-A100, 3 months	Thermostability, HumanPPI, Metal Ion Binding, EC, GO, DeepLoc, contact prediction	[51]
融合了结构信息的非通用蛋白质预训练模型
Evoformer	2021	Evoformer	Sequence, structure	MLM, Supervised learning	BPD+Uniclust30, PDB	—	128TPU-v3, 11 days	Structure prediction	[2]
DeepFRI	2021	LSTM+GCN	Sequence, structure	PLM(pretrained, frozen), supervised learning for 3D structures	10M sequences for pre-training	—	—	GO, EC, PPI interaction sites	[47]
LM-GVP	2022	Transformer +GVP	Sequence, structure	PLM(changeable), supervised learning for 3D structures	—	—	8X 32G-V100	fluorescence, protease stability, GO, mutational effects	[48]
ProNet	2023	GCN	Sequence, structure	Supervised learning	—	—	—	Fold classification, reaction classification, binding affinity, PI
HoloProt	2022	MPN	Sequence, structure surface	Supervised learning	—	1.8M	1X 1080Ti, 1 day	Ligand binding affinity, EC	[56]
编码动态三维结构信息的预训练模型
ProtMD	2022	E(3)-Equivariant Graph Matching Network	Sequence, structure trajectory	Self-supervised learning, atom-level prompt-based denoising generative task, conformation-level snapshot ordering task	62.8K snapshots from MD for 64 protein-ligand pairs	5.2M	4X V100	Binding affinity prediction, binary classification of ligand efficacy	[58]
融合了知识的蛋白质预训练模型
OntoProtein	2022	ProtBert, Gu-model	Sequence, knowledge	MLM, contrastive learning	ProteinKG25 with 5M knowledge triples	—	V100	TAPE, PPI, Protein function prediction	[60]
KeAP	2023	ProtBert, Gu-model	Sequence, knowledge	MLM	ProteinKG25	—	—	TAPE, PPI, Protein function prediction	[62]
ProtST	2023	ProtBert, ESM-1b, ESM-2, PubMedBert	Sequence, knowledge	MLM, Multimodal Representation Alignment, Multimodal Mask Prediction	ProtDescribe with 553K sequence-property pairs	—	4X V100	Protein localization prediction, Fitness landscape prediction, Protein function annotation	[63]
RNA语言模型
RNA-FM	2022.8	BERT	Sequence	MLM	RNAcentral, 23.7M ncRNA sequences	—	8X A100 80G, 1 month	SS prediction, 3D contact/distance map, 3D reconstruction, evolution study, RNA-protein interaction, MRL prediction	[78]
RNABert	2022	BERT	Sequence	MLM	RNAcentral (762K) & Rfam 14.3 dataset	—	V100	structural alignment, clustering	[86]
SpliceBERT	2023	BERT	Sequence	MLM	Pre-mRNA of 72 vertebrates, 2M sequences, 64B nucleotides	19.4M	8X V100, 1 week	multi-species splice site prediction, human branch point prediction	[79]
RNA-MSM	2023	MSA-transformer	Sequence	MLM	4069 RNA families from Rfam 14.7	—	8X V100 32G	SS prediction, solvent accessibility prediction	[83]
Uni-RNA	2023	BERT	Sequence	MLM	RNAcentral & nt & GWH (1billion sequences)	25—400M	128X A100	SS prediction, 3D structure prediction, MRL, Isoform percentage prediction on 3’UTR, splice site prediction, classification of ncRNA functional families, modification site prediction	[84]
RNAErnie	2024	ERNIE	Sequence, motif information	MLM at base/subsequence/motif level masking	RNAcentral, 23M ncRNA sequences	105M	4X V100 32G, 250 hours	sequence classification, RNA–RNA interaction, SS prediction	[85]
*PLM, protein language model; MLM, masked language model; GCN, graph convolutional network; GVP, geometric vector perceptrons; EC, enzyme commission number prediction; GO, gene ontology term prediction; PPI, protein-protein interaction; TAPE, the tasks assessing protein embeddings database; QA, quality assessment of structures; SS, secondary structure; MRL, mean ribosome load prediction in mRNA.

下载: 导出CSV

map

[1]	Senior A W, Evans R, Jumper J, Kirkpatrick J, Sifre L, Green T, Qin C, Žídek A, Nelson A W, Bridgland A, Penedones H, Petersen S, Simonyan K, Crossan S, Kohli P, Jones D T, Silver D, Kavukcuoglu K, Hassabis D 2020 Nature 577 706 Google Scholar
[2]	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 S A A, Ballard A J, 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 A W, Kavukcuoglu K, Kohli P, Hassabis D 2021 Nature 596 583 Google Scholar
[3]	Radford A, Narasimhan K, Salimans T, Sutskever I 2018 Improving Language Understanding by Generative Pre-Training [2024-6-9]
[4]	Radford A, Wu J, Child R, Luan D, Amodei D, Sutskever I 2019 Language Models are Unsupervised Multitask Learners [2024-6-9]
[5]	Brown T B, Mann B, Ryder N, Subbiah M, Kaplan J, Dhariwal P, Neelakantan A, Shyam P, Sastry G, Askell A, Agarwal S, Herbert-Voss A, Krueger G, Henighan T, Child R, Ramesh A, Ziegler D M, Wu J, Winter C, Hesse C, Chen M, Sigler E, Litwin M, Gray S, Chess B, Clark J, Berner C, McCandlish S, Radford A, Sutskever I, Amodeis D 2020 arXiv: 2005.14165[cs.CV]
[6]	Ouyang L, Wu J, Jiang X, Almeida D, Wainwright C L, Mishkin P, Zhang C, Agarwal S, Slama K, Ray A, Schulman J, Hilton J, Kelton F, Miller L, Simens M, Askell A, Welinder P, Christiano P, Leike J, Low R 2022 arXiv: 2203.02155[cs.CV]
[7]	Devlin J, Chang M W, Lee K, Toutanova K 2018 arXiv: 1810.04805[cs.CV]
[8]	Ma Z, He J, Qiu J, Cao H, Wang Y, Sun Z, Zheng L, Wang H, Tang S, Zheng T, Lin J, Feng G, Huang Z, Gao J, Zeng A, Zhang J, Zhong R, Shi T, Liu S, Zheng W, Tang J, Yang H, Liu X, Zhai J, Chen W 2022 Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming Seoul, Republic of Korea, April 2–6, 2022 p192
[9]	Han X, Zhang Z, Ding N, Gu Y, Liu X, Huo Y, Qiu J, Yao Y, Zhang A, Zhang L, Han W, Huang M, Jin Q, Lan Y, Liu Y, Liu Z, Lu Z, Qiu X, Song R, Tang J, Wen J R, Yuan J, Zhao W X, Zhu J 2021 arXiv: 2106.07139[AI]
[10]	Yuan S, Zhao H, Zhao S, et al. 2022 arXiv: 2203.14101 [cs.LG]
[11]	Wei J, Tay Y, Bommasani R, Raffel C, Zoph B, Borgeaud S, Yogatama D, Bosma M, Zhou D, Metzler D, Chi E H, Hashimoto T, Vinyals O, Liang P, Dean J, Fedus W 2022 arXiv: 2206.07682[cs.CV]
[12]	Alayrac J B, Donahue J, Luc P, Miech A, Barr I, Hasson Y, Lenc K, Mensch A, Millican K, Reynolds M, Ring R, Rutherford E, Cabi S, Han T, Gong Z, Samangooei S, Monteiro M, Menick J, Borgeaud S, Brock A, Nematzadeh A, Sharifzadeh S, Binkowski M, Barreira R, Vinyals O, Zisserman A, Simonyan K 2022 arXiv: 2204.14198[cs.CV]
[13]	OpenAI, Achiam J, Adler S, et al. 2024 arXiv: 2303.08774 [cs.CV]
[14]	Driess D, Xia F, Sajjadi M S M, Lynch C, Chowdhery A, Ichter B, Wahid A, Tompson J, Vuong Q, Yu T, Huang W, Chebotar Y, Sermanet P, Duckworth D, Levine S, Vanhoucke V, Hausman K, Toussaint M, Greff K, Zeng A, Mordatch I, Florence P 2023 arXiv: 2303.03378[cs.LG]
[15]	Touvron H, Lavril T, Izacard G, Martinet X, Lachaux M A, Lacroix T, Rozière B, Goyal N, Hambro E, Azhar F, Rodriguez A, Joulin A, Grave E, Lample G 2023 arXiv: 2302.13971[cs.CV]
[16]	Gemini Team Google, Anil R, Borgeaud S, et al. 2024 arXiv: 2312.11805[cs.CV]
[17]	Chen F, Han M, Zhao H, Zhang Q, Shi J, Xu S, Xu B 2023 arXiv: 2305.04160[cs.CV]
[18]	Li K, He Y, Wang Y, Li Y, Wang W, Luo P, Wang Y, Wang L, Qiao Y 2023 arXiv: 2305.06355[cs.CV]
[19]	Bepler T, Berger B 2019 arXiv: 1902.08661[cs.LG]
[20]	Heinzinger M, Elnaggar A, Wang Y, Dallago C, Nechaev D, Matthes F, Rost B 2019 bioRxiv: 614313[Bioinformatics]
[21]	Alley E C, Khimulya G, Biswas S, Alquraishi M, Church G M 2019 Nat. Methods 16 1315 Google Scholar
[22]	Rives A, Meier J, Sercu T, Goyal S, Lin Z, Liu J, Guo D, Ott M, Zitnick C L, Ma J, Fergus R 2021 Proc. Natl. Acad. Sci. 118 e2016239118 Google Scholar
[23]	Rao R, Liu J, Verkuil R, et al. 2021 bioRxiv: 2021.02.12. 430858 [Synthetic Biology]
[24]	Meier J, Rao R, Verkuil R, Liu J, Sercu T, Rives A 2021 Advances in Neural Information Processing Systems 34 29287 Google Scholar
[25]	Lin Z, Akin H, Rao R, Hie B, Zhu Z, Lu W, Smetanin N, Verkuil R, Kabeli O, Shmueli Y, dos Santos Costa A, Fazel-Zarandi M, Sercu T, Candido S, Rives A 2023 Science 379 1123 Google Scholar
[26]	Lin Z, Akin H, Rao R, Hie B, Zhu Z, Lu W, Santos Costa A d, Fazel-Zarandi M, Sercu T, Candido S, Rives A 2022 bioRxiv: 2022.07.20.500902[Synthetic Biology]
[27]	Madani A, McCann B, Naik N, Keskar N S, Anand N, Eguchi R R, Huang P S, Socher R 2020 arXiv: 2004.03497[q-bio.QM]
[28]	Madani A, Krause B, Greene E R, Subramanian S, Mohr B P, Holton J M, Olmos J L, Xiong C, Sun Z Z, Socher R, Fraser J S, Naik N 2023 Nat. Biotechnol. 41 1099 Google Scholar
[29]	He L, Zhang S, Wu L, Xia H, Ju F, Zhang H, Liu S, Xia Y, Zhu J, Deng P, Shao B, Qin T, Liu T Y 2021 arXiv: 2110.15527[cs.CV]
[30]	Elnaggar A, Heinzinger M, Dallago C, Rihawi G, Wang Y, Jones L, Gibbs T, Feher T, Angerer C, Steinegger M, Bhowmik D, Rost B 2021 arXiv: 2007.06225[cs.LG]
[31]	Chen B, Cheng X, Li P, Geng Y, Gong J, Li S, Bei Z, Tan X, Wang B, Zeng X, Liu C, Zeng A, Dong Y, Tang J, Song L 2024 arXiv: 2401.06199[q-bio.QM]
[32]	Nguyen E, Poli M, Durrant M G, Thomas A W, Kang B, Sullivan J, Ng M Y, Lewis A, Patel A, Lou A, Ermon S, Baccus S A, Hernandez-Boussard T, Ré C, Hsu P D, Hie B L 2024 bioRxiv: 2024.02.27.582234[Synthetic Biology]
[33]	Gao W, Mahajan S P, Sulam J, Gray J J 2020 Patterns 1 100142 Google Scholar
[34]	Unsal S, Atas H, Albayrak M, Turhan K, Acar A C, Doğan T 2022 Nature Machine Intelligence 4 227 Google Scholar
[35]	Zhang Q, Ding K, Lyv T, Wang X, Yin Q, Zhang Y, Yu J, Wang Y, Li X, Xiang Z, Feng K, Zhuang X, Wang Z, Qin M, Zhang M, Zhang J, Cui J, Huang T, Yan P, Xu R, Chen H, Li X, Fan X, Xing H, Chen H 2024 arXiv: 2401. 14656[cs.CV]
[36]	管星悦, 黄恒焱, 彭华祺, 刘彦航, 李文飞, 王炜 2023 72 248708 Google Scholar Guan X Y, Huang H Y, Peng H Q, Liu Y H, Li W F, Wang W 2023 Acta Phys. Sin. 72 248708 Google Scholar
[37]	陈光临, 张志勇 2023 72 248705 Google Scholar Chen G L, Zhang Z Y 2023 Acta Phys. Sin. 72 248705 Google Scholar
[38]	张嘉晖 2024 73 069301 Google Scholar Zhang J H 2024 Acta Phys. Sin. 73 069301 Google Scholar
[39]	Zeng C, Jian Y, Vosoughi S, Zeng C, Zhao Y 2023 Nat. Commun. 14 1060 Google Scholar
[40]	Zeng C, Zhao Y 2023 Scientia Sinica Physica, Mechanica & Astronomica 53 290018 Google Scholar
[41]	Huh M, Cheung B, Wang T, Isola P 2024 arXiv: 2405.07987 [cs.LG]
[42]	Bepler T, Berger B 2021 Cell Systems 12 654 Google Scholar
[43]	Guo Y, Wu J, Ma H, Huang J 2022 Proceedings of the AAAI Conference on Artificial Intelligence 36 6801 Google Scholar
[44]	Hermosilla P, Ropinski T 2022 arXiv: 2205.15675[q-bio.BM]
[45]	Zhang Z, Xu M, Jamasb A, Chenthamarakshan V, Lozano A, Das P, Tang J 2022 arXiv: 2203.06125[cs.LG]
[46]	Zhang Z, Xu M, Lozano A, Chenthamarakshan V, Das P, Tang J 2023 arXiv: 2303.06275[q-bio.QM]
[47]	Gligorijević V, Renfrew P D, Kosciolek T, Leman J K, Berenberg D, Vatanen T, Chandler C, Taylor B C, Fisk I M, Vlamakis H, Xavier R J, Knight R, Cho K, Bonneau R 2021 Nat. Commun. 12 3168 Google Scholar
[48]	Wang Z, Combs S A, Brand R, Calvo M R, Xu P, Price G, Golovach N, Salawu E O, Wise C J, Ponnapalli S P, Clark P M 2022 Sci. Rep. 12 6832 Google Scholar
[49]	Chen C, Zhou J, Wang F, Liu X, Dou D 2023 arXiv: 2204.04213[cs.LG]
[50]	Zhou G, Gao Z, Ding Q, Zheng H, Xu H, Wei Z, Zhang L, Ke G 2022 DOI: 10.26434/chemrxiv-2022-jjm0j-v4
[51]	Su J, Han C, Zhou Y, Shan J, Zhou X, Yuan F 2023 bioRxiv: 2023.10.01.560349[Bioinformatics]
[52]	Su J, Li Z, Han C, Zhou Y, Shan J, Zhou X, Ma D, OPMC T, Ovchinnikov S, Yuan F 2024 bioRxiv: 2024.05.24.595648[Bioinformatics]
[53]	Hu M Y, Yuan F J, Yang K K, Ju F S, Su J, Wang H, Yang F, Ding Q Y 2022 arXiv:2206.06583 [q-bio.QM]
[54]	Abramson J, Adler J, Dunger J, et al. 2024 Nature 630 493 Google Scholar
[55]	Wang L, Liu H, Liu Y, Kurtin J, Ji S 2022 arXiv: 2207.12600[cs.LG]
[56]	Somnath V R, Bunne C, Krause A 2021 arXiv: 2204.02337[cs.LG]
[57]	Gainza P, Sverrisson F, Monti F, Rodola E, Boscaini D, Bronstein M M, Correia B E 2020 Nat. Methods 17 184 Google Scholar
[58]	Wu F, Jin S, Jiang Y, Jin X, Tang B, Niu Z, Liu X, Zhang Q, Zeng X, Li S Z 2022 arXiv: 2204.08663[CE]
[59]	Meyer T, D'Abramo M, Rueda M, Ferrer-Costa C, Pérez A, Carrillo O, Camps J, Fenollosa C, Repchevsky D, Gelpí J L, Orozco M 2010 Structure 18 1399 Google Scholar
[60]	Zhang N, Bi Z, Liang X, Cheng S, Hong H, Deng S, Lian J, Zhang Q, Chen H 2022 arXiv: 2201.11147[q-bio.BM]
[61]	Gu Y, Tinn R, Cheng H, Lucas M, Usuyama N, Liu X, Naumann T, Gao J, Poon H 2021 arXiv: 2007.15779[cs.CV]
[62]	Zhou H Y, Fu Y, Zhang Z, Bian C, Yu Y 2023 arXiv: 2301.13154[cs.LG]
[63]	Xu M, Yuan X, Miret S, Tang J 2023 arXiv: 2301.12040 [q-bio.BM]
[64]	Singh J, Hanson J, Paliwal K, Zhou Y 2019 Nat. Commun. 10 5407 Google Scholar
[65]	Singh J, Paliwal K, Zhang T, Singh J, Litfin T, Zhou Y 2021 Bioinformatics 37 2589 Google Scholar
[66]	Wang J, Mao K, Zhao Y, Zeng C, Xiang J, Zhang Y, Xiao Y 2017 Nucleic Acids Res. 45 6299 Google Scholar
[67]	Wang J, Xiao Y 2017 Current Protocols in Bioinformatics 57 5 Google Scholar
[68]	Wang J, Wang J, Huang Y, Xiao Y 2019 Int. J. Mol. Sci. 20 4116 Google Scholar
[69]	Tan Y L, Wang X, Shi Y Z, Zhang W, Tan Z J 2022 Biophys. J. 121 142 Google Scholar
[70]	Zhou L, Wang X, Yu S, Tan Y L, Tan Z J 2022 Biophys. J. 121 3381 Google Scholar
[71]	Wang X, Tan Y L, Yu S, Shi Y Z, Tan Z J 2023 Biophys. J. 122 1503 Google Scholar
[72]	Li J, Zhu W, Wang J, Li W, Gong S, Zhang J, Wang W 2018 PLoS Comput. Biol. 14 e1006514 Google Scholar
[73]	Fu L, Cao Y, Wu J, Peng Q, Nie Q, Xie X 2022 Nucleic Acids Res. 50 e14 Google Scholar
[74]	Pearce R, Omenn G S, Zhang Y 2022 bioRxiv: 2022. 05.15.491755[Bioinformatics]
[75]	Baek M, McHugh R, Anishchenko I, Baker D, DiMaio F 2022 bioRxiv: 2022.09.09.507333[Bioinformatics]
[76]	Zhang J, Lang M, Zhou Y, Zhang Y 2024 Trends in Genetics 40 94 Google Scholar
[77]	Li J, Zhou Y, Chen S J 2024 Curr. Opin. Struct. Biol. 87 102847 Google Scholar
[78]	Chen J, Hu Z, Sun S, Tan Q, Wang Y, Yu Q, Zong L, Hong L, Xiao J, Shen T, King I, Li Y 2022 arXiv: 2204.00300[q-bio.QM]
[79]	Chen K, Zhou Y, Ding M, Wang Y, Ren Z, Yang Y 2023 bioRxiv: 2023.01.31.526427[Bioinformatics]
[80]	Babjac A N, Lu Z, Emrich S J 2023 Proceedings of the 14th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics New York, United States, September 3–6, 2023 p1
[81]	Chu Y, Yu D, Li Y, Huang K, Shen Y, Cong L, Zhang J, Wang M 2024 Nature Machine Intelligence 6 449 Google Scholar
[82]	Yang Y, Li G, Pang K, Cao W, Li X, Zhang Z 2023 bioRxiv: 2023.09.08.556883[Bioinformatics]
[83]	Zhang Y, Lang M, Jiang J, Gao Z, Xu F, Litfin T, Chen K, Singh J, Huang X, Song G, Tian Y, Zhan J, Chen J, Zhou Y 2024 Nucleic Acids Res. 52 e3 Google Scholar
[84]	Wang X, Gu R, Chen Z, Li Y, Ji X, Ke G, Wen H 2023 bioRxiv: 2023.07.11.548588[Bioinformatics]
[85]	Wang N, Bian J, Li Y, Li X, Mumtaz S, Kong L, Xiong H 2024 Nature Machine Intelligence 6 548 Google Scholar
[86]	Akiyama M, Sakakibara Y 2022 NAR Genomics and Bioinformatics 4 lqac012 Google Scholar
[87]	Shen T, Hu Z, Peng Z, Chen J, Xiong P, Hong L, Zheng L, Wang Y, King I, Wang S, Siqi S, Yu L 2022 arXiv: 2207.01586[q-bio.QM]
[88]	Li Y, Zhang C, Feng C, Pearce R, Lydia Freddolino P, Zhang Y 2023 Nat. Commun. 14 5745 Google Scholar
[89]	Ferruz N, Schmidt S, Höcker B 2022 Nat. Commun. 13 4348 Google Scholar
[90]	Wang J, Lisanza S, Juergens D, Tischer D, Watson J L, Castro K M, Ragotte R, Saragovi A, Milles L F, Baek M, Anishchenko I, Yang W, Hicks D R, Expòsit M, Schlichthaerle T, Chun J H, Dauparas J, Bennett N, Wicky B I M, Muenks A, DiMaio F, Correia B, Ovchinnikov S, Baker D 2022 Science 377 387 Google Scholar
[91]	Trippe B L, Yim J, Tischer D, Baker D, Broderick T, Barzilay R, Jaakkola T 2022 arXiv: 2206.04119[q-bio.BM]
[92]	Anishchenko I, Pellock S J, Chidyausiku T M, Ramelot T A, Ovchinnikov S, Hao J, Bafna K, Norn C, Kang A, Bera A K, DiMaio F, Carter L, Chow C M, Montelione G T, Baker D 2021 Nature 600 547 Google Scholar
[93]	Wicky B I M, Milles L F, Courbet A, Ragotte R J, Dauparas J, Kinfu E, Tipps S, Kibler R D, Baek M, DiMaio F, Li X, Carter L, Kang A, Nguyen H, Bera A K, Baker D 2022 Science 378 56 Google Scholar
[94]	Anand N, Achim T 2022 arXiv: 2205.15019[q-bio.QM]
[95]	Luo S, Su Y, Peng X, Wang S, Peng J, Ma J 2022 Advances in Neural Information Processing Systems 35 9754 Google Scholar
[96]	Cao L, Coventry B, Goreshnik I, et al 2022 Nature 605 551 Google Scholar
[97]	Kuhlman B, Bradley P 2019 Nat. Rev. Mol. Cell Biol. 20 681 Google Scholar
[98]	Pan X, Kortemme T 2021 J. Biol. Chem. 296 100558 Google Scholar
[99]	Khakzad H, Igashov I, Schneuing A, Goverde C, Bronstein M, Correia B 2023 Cell Systems 14 925 Google Scholar
[100]	Malbranke C, Bikard D, Cocco S, Monasson R, Tubiana J 2023 Curr. Opin. Struct. Biol. 80 102571 Google Scholar
[101]	Kortemme T 2024 Cell 187 526 Google Scholar
[102]	Notin P, Rollins N, Gal Y, Sander C, Marks D 2024 Nat. Biotechnol. 42 216 Google Scholar
[103]	Listov D, Goverde C A, Correia B E, Fleishman S J 2024 Nat. Rev. Mol. Cell Biol. 25 639 Google Scholar
[104]	Ingraham J, Garg V K, Barzilay R, Jaakkola T 2019 Proceedings of the 33rd International Conference on Neural Information Processing Systems Vancouver, BC, Canada, December 8–14, 2019 p15820
[105]	Dauparas J, Anishchenko I, Bennett N, Bai H, Ragotte R J, Milles L F, Wicky B I M, Courbet A, de Haas R J, Bethel N, Leung P J Y, Huddy T F, Pellock S, Tischer D, Chan F, Koepnick B, Nguyen H, Kang A, Sankaran B, Bera A K, King N P, Baker D 2022 Science 378 49 Google Scholar
[106]	Hsu C, Verkuil R, Liu J, Lin Z, Hie B, Sercu T, Lerer A, Rives A 2022 bioRxiv: 2022.04.10.487779[Systems Biology]
[107]	Sohl-Dickstein J, Weiss E A, Maheswaranathan N, Ganguli S 2015 arXiv: 1503.03585[cs.LG]
[108]	Ho J, Jain A, Abbeel P 2020 Advances in Neural Information Processing Systems 33 6840 Google Scholar
[109]	Watson J L, Juergens D, Bennett N R, et al 2023 Nature 620 1089 Google Scholar
[110]	Song Y, Sohl-Dickstein J, Kingma D P, Kumar A, Ermon S, Poole B 2020 arXiv: 2011.13456[cs.LG]
[111]	Lee J S, Kim J, Kim P M 2023 Nature Computational Science 3 382 Google Scholar
[112]	Liu Y, Chen L, Liu H 2023 bioRxiv: 2023.11.18.567666 [Bioinformatics]
[113]	Zheng Z, Deng Y, Xue D, Zhou Y, YE F, Gu Q 2023 arXiv: 2302.01649[cs.LG]
[114]	Yang K K, Zanichelli N, Yeh H 2023 Protein Eng. Des. Sel. 36 gzad015 Google Scholar
[115]	Kaplan J, McCandlish S, Henighan T, Brown T B, Chess B, Child R, Gray S, Radford A, Wu J, Amodei D 2020 arXiv: 2001.08361[cs.LG]
[116]	He K, Chen X, Xie S, Li Y, Dollár P, Girshick R 2021 arXiv: 2111.06377[cs.CV]
[117]	Chen T, Kornblith S, Norouzi M, Hinton G 2020 arXiv: 2002.05709[cs.LG]
[118]	Wang Z, Wang Z, Srinivasan B, Ioannidis V N, Rangwala H, Anubhai R 2023 arXiv: 2310.03320[cs.LG]
[119]	Von Rueden L, Mayer S, Beckh K, Georgiev B, Giesselbach S, Heese R, Kirsch B, Walczak M, Pfrommer J, Pick A, Ramamurthy R, Garcke J, Bauckhage C, Schuecker J 2021 IEEE Trans. Knowl. Data Eng. 35 614
[120]	Bao L, Zhang X, Jin L, Tan Z J 2015 Chin. Phys. B 25 018703 Google Scholar
[121]	Qiang X W, Zhang C, Dong H L, Tian F J, Fu H, Yang Y J, Dai L, Zhang X H, Tan Z J 2022 Phys. Rev. Lett. 128 108103 Google Scholar
[122]	Dong H L, Zhang C, Dai L, Zhang Y, Zhang X H, Tan Z J 2024 Nucleic Acids Res. 52 2519 Google Scholar

[1]	张旭, 丁进敏, 侯晨阳, 赵一鸣, 刘鸿维, 梁生. 基于机器学习的激光匀光整形方法. , 2024, 73(16): 164205. doi: 10.7498/aps.73.20240747
[2]	杨添, 欧阳颀. 利用冷冻电镜研究蛋白质机器的非平衡统计物理. , 2024, 73(13): 138701. doi: 10.7498/aps.73.20240592
[3]	张嘉晖. 蛋白质计算中的机器学习. , 2024, 73(6): 069301. doi: 10.7498/aps.73.20231618
[4]	刘烨, 牛赫然, 李兵兵, 马欣华, 崔树旺. 机器学习在宇宙线粒子鉴别中的应用. , 2023, 72(14): 140202. doi: 10.7498/aps.72.20230334
[5]	管星悦, 黄恒焱, 彭华祺, 刘彦航, 李文飞, 王炜. 生物分子模拟中的机器学习方法. , 2023, 72(24): 248708. doi: 10.7498/aps.72.20231624
[6]	刘栋, 崔新月, 王浩东, 张贵军. 蛋白质结构模型质量评估方法综述. , 2023, 72(24): 248702. doi: 10.7498/aps.72.20231071
[7]	林开东, 林晓倩, 林绪波. 靶向PD-L1蛋白的计算机辅助药物筛选. , 2023, 72(24): 240501. doi: 10.7498/aps.72.20231068
[8]	张逸凡, 任卫, 王伟丽, 丁书剑, 李楠, 常亮, 周倩. 机器学习结合固溶强化模型预测高熵合金硬度. , 2023, 72(18): 180701. doi: 10.7498/aps.72.20230646
[9]	陈光临, 张志勇. 使用中间层受监督的自编码器探索蛋白质的构象空间. , 2023, 72(24): 248705. doi: 10.7498/aps.72.20231060
[10]	罗方芳, 蔡志涛, 黄艳东. 蛋白质pK_a预测模型研究进展. , 2023, 72(24): 248704. doi: 10.7498/aps.72.20231356
[11]	林键, 叶梦, 朱家纬, 李晓鹏. 机器学习辅助绝热量子算法设计. , 2021, 70(14): 140306. doi: 10.7498/aps.70.20210831
[12]	陈江芷, 杨晨温, 任捷. 基于波动与扩散物理系统的机器学习. , 2021, 70(14): 144204. doi: 10.7498/aps.70.20210879
[13]	刘春杰, 赵新军, 高志福, 蒋中英. 高分子混合刷吸附/脱附蛋白质的模型化研究. , 2021, 70(22): 224701. doi: 10.7498/aps.70.20211219
[14]	史晨阳, 闵光宗, 刘向阳. 蛋白质基忆阻器研究进展. , 2020, 69(17): 178702. doi: 10.7498/aps.69.20200617
[15]	袁飞, 张传彪, 周昕, 黎明. 基于氨基酸位置特异性的蛋白质Loop区结构预测改进方法. , 2016, 65(15): 158701. doi: 10.7498/aps.65.158701
[16]	邓海游, 贾亚, 张阳. 蛋白质结构预测. , 2016, 65(17): 178701. doi: 10.7498/aps.65.178701
[17]	万茜, 周进, 刘曾荣. 蛋白质相互作用网络特征的理论再现. , 2012, 61(1): 010203. doi: 10.7498/aps.61.010203
[18]	丁玮, 江凡. 蛋白质晶体结构刚体优化的新方法. , 2011, 60(4): 046103. doi: 10.7498/aps.60.046103
[19]	闫循领, 董瑞新, 王伯运. α螺旋蛋白质螺旋线模型的耦合孤立子. , 1999, 48(4): 751-756. doi: 10.7498/aps.48.751
[20]	阎循领, 董瑞新, 王伯运, 胡海泉, 徐炳振. α螺旋蛋白质分子Raman光谱的选择定则. , 1998, 47(12): 1963-1967. doi: 10.7498/aps.47.1963

计量

文章访问数: 543
PDF下载量: 26
被引次数: 0

姓名
邮箱
手机号码
标题
留言内容
验证码

搜索

留言板