Machine learning for <i>in silico</i> protein research

Zhang Jia-Hui

doi:10.7498/aps.73.20231618

Abstract

In silico protein calculation has been an important research subject for a long time, while its recent combination with machine learning promotes the development greatly in related areas. This review focuses on four major fields of the in silico protein research that combines with machine learning, which are molecular dynamics, structure prediction, property prediction and molecule design. Molecular dynamics depend on the parameters of force field, which is necessary for obtaining accurate results. Machine learning can help researchers to obtain more accurate force field parameters. In molecular dynamics simulation, machine learning can also help to perform the free energy calculation in relatively low cost. Structure prediction is generally used to predict the structure given a protein sequence. Structure prediction is of high complexity and data volume, which is exactly what machine learning is good at. By the help of machine learning, scientists have gained great achievements in three-dimensional structure prediction of proteins. On the other hand, the predicting of protein properties based on its known information is also important to study protein. More challenging, however, is molecule design. Though marching learning has made breakthroughs in drug-like small molecule design and protein design in recent years, there is still plenty of room for exploration. This review focuses on summarizing the above four fields andlooks forward to the application of marching learning to the in silico protein research.

Keywords:

Authors and contacts

Zhang Jia-Hui

School of Life Sciences, University of Science and Technology of China, Hefei 230027, China

Corresponding author: Zhang Jia-Hui, jhzhang@ustc.edu.cn

Funds: Project supported by the National Natural Science Foundation of China (Grant No. 22177107).

FullText HTML : translate this paragraph

References

[1]	Baltoumas F A, Zafeiropoulou S, Karatzas E, et al. 2021 Biomolecules 11 1245 Google Scholar
[2]	Wolf Y I, Katsnelson M I, Koonin E V 2018 Proc. Natl. Acad. Sci. USA 115 E8678 Google Scholar
[3]	Fusco A, Fedele M 2007 Nat. Rev. Cancer 7 899 Google Scholar
[4]	Noble D 2002 Nat. Rev. Mol. Cell Biol. 3 459 Google Scholar
[5]	Markowetz F 2017 PLoS Biology 15 e2002050 Google Scholar
[6]	Hollingsworth S A, Dror R O 2018 Neuron 99 1129 Google Scholar
[7]	Zhang Y 2008 Curr. Opin. Struct. Biol. 18 342 Google Scholar
[8]	Agostini F, Vendruscolo M, Tartaglia G G 2012 J. Mol. Biol. 421 237 Google Scholar
[9]	Chen L, Fan Z, Chang J, et al. 2023 Nat. Commun. 14 4217 Google Scholar
[10]	Geng H, Chen F, Ye J, Jiang F 2019 Computat. Struct. Biotechnol. J. 17 1162 Google Scholar
[11]	Salo-Ahen O M, Alanko I, Bhadane R, et al. 2020 Processes 9 71 Google Scholar
[12]	Norberg J, Nilsson L 2003 Q. Rev. Biophys. 36 257 Google Scholar
[13]	van der Kamp M W, Shaw K E, Woods C J, Mulholland A J 2008 J. R. Soc. Interface 5 173 Google Scholar
[14]	Dror R O, Dirks R M, Grossman J, Xu H, Shaw D E 2012 Annu. Rev. Biophys. 41 429 Google Scholar
[15]	Lin X, Li X, Lin X 2020 Molecules 25 1375 Google Scholar
[16]	Pearce R, Zhang Y 2021 Curr. Opin. Struct. Biol. 68 194 Google Scholar
[17]	Jordan M I, Mitchell T M 2015 Science 349 255 Google Scholar
[18]	Butler K T, Davies D W, Cartwright H, Isayev O, Walsh A 2018 Nature 559 547 Google Scholar
[19]	Liakos K G, Busato P, Moshou D, Pearson S, Bochtis D 2018 Sensors 18 2674 Google Scholar
[20]	Jiang T, Gradus J L, Rosellini A J 2020 Behav. Ther. 51 675 Google Scholar
[21]	Hastie T, Tibshirani R, Friedman J, Hastie T, Tibshirani R, Friedman J 2009 Unsupervised Learning. In: The Elements of Statistical Learning. Springer Series in Statistics (New York: Springer) pp485–585
[22]	Van Engelen J E, Hoos H H 2020 Machine Learning 109 373 Google Scholar
[23]	Wiering M A, Van Otterlo M 2012 Reinforcement Learning (Heidelberg, Berlin: Springer) p729
[24]	LeCun Y, Bengio Y, Hinton G 2015 Nature 521 436 Google Scholar
[25]	Deng L, Yu D 2014 Deep Learning: Methods and Applications (Now Foundations and Trends) p197
[26]	Jones D T 2019 Nat. Rev. Mol. Cell Biol. 20 659 Google Scholar
[27]	Das P, Sercu T, Wadhawan K, et al. 2021 Nat. Biomed. Eng. 5 613 Google Scholar
[28]	Kuhlman B, Bradley P 2019 Nat. Rev. Mol. Cell Biol. 20 681 Google Scholar
[29]	Trevino S R, Scholtz J M, Pace C N 2008 J. Pharm. Sci. 97 4155 Google Scholar
[30]	Kelley K W, Weigent D A, Kooijman R 2007 Brain Behav. Immun. 21 384 Google Scholar
[31]	Babin V, Roland C, Sagui C 2008 J. Chem. Phys. 128 Google Scholar
[32]	Morozov I V, Kazennov A M, Bystryi R, Norman G E, Pisarev V V, Stegailov V V 2011 Comput. Phys. Commun. 182 1974 Google Scholar
[33]	Karplus M, McCammon J A 2002 Nat. Struct. Biol. 9 646 Google Scholar
[34]	Wang Y, Ribeiro J M L, Tiwary P 2020 Curr. Opin. Struct. Biol. 61 139 Google Scholar
[35]	Chmiela S, Tkatchenko A, Sauceda H E, Poltavsky I, Schütt K T, Müller K R 2017 Sci. Adv. 3 e1603015 Google Scholar
[36]	Ponder J W, Case D A 2003 Adv. Protein Chem. 66 27 Google Scholar
[37]	Monticelli L, Tieleman D P 2013 Biomolecular Simulations: Methods and Protocols 197
[38]	Wang J, Wolf R M, Caldwell J W, Kollman P A, Case D A 2004 J. Comput. Chem. 25 1157 Google Scholar
[39]	Hughes Z E, Wright L B, Walsh T R 2013 Langmuir 29 13217 Google Scholar
[40]	Cesari A, Bottaro S, Lindorff-Larsen K, Banáš P, Šponer J, Bussi G 2019 J. Chem. Theory Comput. 15 3425 Google Scholar
[41]	Unke O T, Chmiela S, Sauceda H E, Gastegger M, Poltavsky I, Schütt K T, Tkatchenko A, Müller K R 2021 Chem. Rev. 121 10142 Google Scholar
[42]	Poltavsky I, Tkatchenko A 2021 J. Phys. Chem. Lett. 12 6551 Google Scholar
[43]	Kästner J 2011 WIREs Comput. Mol. Sci. 1 932 Google Scholar
[44]	Izrailev S, Stepaniants S, Isralewitz B, Kosztin D, Lu H, Molnar F, Wriggers W, Schulten K 1999 Computational Molecular Dynamics: Challenges, Methods, Ideas: Proceedings of the 2nd International Symposium on Algorithms for Macromolecular Modelling Berlin, May 21–24, 1997 p39
[45]	Moradi M, Babin V, Roland C, Sagui C 2013 Nucleic Acids Res. 41 33 Google Scholar
[46]	Simonson T, Archontis G, Karplus M 2002 Acc. Chem. Res. 35 430 Google Scholar
[47]	Bitencourt-Ferreira G, de Azevedo W F 2018 Biophys. Chem. 240 63 Google Scholar
[48]	Trott O, Olson A J 2010 J. Comput. Chem. 31 455 Google Scholar
[49]	Besora M, Vidossich P, Lledos A, Ujaque G, Maseras F 2018 J. Phys. Chem. A 122 1392 Google Scholar
[50]	Pan X, Yang J, Van R, Epifanovsky E, Ho J, Huang J, Pu J, Mei Y, Nam K, Shao Y 2021 J. Chem. Theory Comput. 17 5745 Google Scholar
[51]	Senn H M, Thiel W 2009 Angew. Chem. Int. Ed. 48 1198 Google Scholar
[52]	Riniker S 2017 J. Chem. Inf. Model. 57 726 Google Scholar
[53]	Bennett W D, He S, Bilodeau C L, Jones D, Sun D, Kim H, Allen J E, Lightstone F C, Ingólfsson H I 2020 J. Chem. Inf. Model. 60 5375 Google Scholar
[54]	Bertazzo M, Gobbo D, Decherchi S, Cavalli A 2021 J. Chem. Theory Comput. 17 5287 Google Scholar
[55]	Eswar N, John B, Mirkovic N, et al. 2003 Nucleic Acids Research 31 3375 Google Scholar
[56]	Asara J M, Schweitzer M H, Freimark L M, Phillips M, Cantley L C 2007 Science 316 280 Google Scholar
[57]	Greener J G, Kandathil S M, Moffat L, Jones D T 2022 Nat. Rev. Mol. Cell Biol. 23 40 Google Scholar
[58]	Jumper J, Evans R, Pritzel A, et al. 2021 Nature 596 583 Google Scholar
[59]	Wu R, Ding F, Wang R, et al. 2022 bioRxiv 2022.07.21. 500999
[60]	Baek M, DiMaio F, Anishchenko I, et al. 2021 Science 373 871 Google Scholar
[61]	Medsker L R, Jain L 1999 Recurrent Neural Networks: Design and Applications (1st Ed.) (CRC Press) p2
[62]	Kim P 2017 Convolutional Neural Network. In: MATLAB Deep Learning (Berkeley, CA: Apress) p121
[63]	Wardah W, Khan M G, Sharma A, Rashid M A 2019 Comput. Biol. Chem. 81 1 Google Scholar
[64]	Mirabello C, Pollastri G 2013 Bioinformatics 29 2056 Google Scholar
[65]	Heffernan R, Yang Y, Paliwal K, Zhou Y 2017 Bioinformatics 33 2842 Google Scholar
[66]	Wang S, Peng J, Ma J, Xu J 2016 Sci. Rep. 6 1 Google Scholar
[67]	Li Z, Yu Y 2016 arXiv: 1604.07176 [q-bio.BM]
[68]	Wang Y, Mao H, Yi Z 2017 Knowledge-Based Systems 118 115 Google Scholar
[69]	Nishikawa K, Ooi T, Isogai Y, Saitô N 1972 J. Phys. Soc. JPN 32 1331 Google Scholar
[70]	Edgar R C, Batzoglou S 2006 Curr. Opin. Struct. Biol. 16 368 Google Scholar
[71]	Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez A N, Kaiser Ł, Polosukhin I 2017 Advances in Neural Information Processing Systems 30 Long Beach, USA, December 4–9, 2017 p30
[72]	Janin J, Bahadur R P, Chakrabarti P 2008 Q. Rev. Biophys. 41 133 Google Scholar
[73]	Zafferani M, Hargrove A E 2021 Cell Chem. Biol. 28 594 Google Scholar
[74]	Hunter C A 2004 Angew. Chem. Int. Ed. 43 5310 Google Scholar
[75]	Chen R, Li L, Weng Z 2003 Proteins Struct. Funct. Bioinf. 52 80 Google Scholar
[76]	Jingcheng Y, Zhaoming C, Zhaoqun L, Mingliang Z, Wenjun L, He H, Qiwei Y 2022 Code of Open Complex https:// github.com/baaihealth/OpenComplex.
[77]	Evans R, O’ Neill M, Pritzel A, et al. 2021 bioRxiv 2021.10.04.463034
[78]	Moriwaki Y 2021 Twitter https://twitter.com/Ag_smith/ status.
[79]	Ko J, Lee J 2021 bioRxiv 2021.07.27.453972 Ko J, Lee J 2021 bioRxiv 2021.07.27.453972
[80]	Tsaban T, Varga J K, Avraham O, Ben-Aharon Z, Khramushin A, Schueler-Furman O 2022 Nat. Commun. 13 176 Google Scholar
[81]	Bryant P, Pozzati G, Elofsson A 2022 Nat. Commun. 13 1265 Google Scholar
[82]	Zhou T M, Wang S, Xu J 2017 bioRxiv 240754
[83]	Cang Z, Wei G W 2017 PLoS Comput. Biol. 13 e1005690 Google Scholar
[84]	Yagi K, Re S, Mori T, Sugita Y 2022 Curr. Opin. Struct. Biol. 72 88 Google Scholar
[85]	Vendruscolo M, Knowles T P, Dobson C M 2011 CSH Perspect. Biol. 3 a010454 Google Scholar
[86]	Khurana S, Rawi R, Kunji K, Chuang G Y, Bensmail H, Mall R 2018 Bioinformatics 34 2605 Google Scholar
[87]	Wu X, Yu L 2021 Bioinformatics 37 4314 Google Scholar
[88]	Schellekens H 2003 Nephrology Dialysis Transplantation 18 1257 Google Scholar
[89]	Ternette N, Tippler B, Überla K, Grunwald T 2007 Vaccine 25 7271 Google Scholar
[90]	Jefferis R 2016 J. Immunol. Res. 2016 Google Scholar
[91]	Schellekens H 2005 Nephrology Dialysis Transplantation 20 vi3 Google Scholar
[92]	Smith C C, Chai S, Washington A R, et al. 2019 Cancer Immunol. Res. 7 1591 Google Scholar
[93]	Gonzalez-Dias P, Lee E K, Sorgi S, de Lima D S, Urbanski A H, Silveira E L, Nakaya H I 2020 Hum. Vacc. Immunother. 16 269 Google Scholar
[94]	Timr S, Madern D, Sterpone F 2020 Prog. Mol. Biol. Transl. Sci. 170 239 Google Scholar
[95]	Pudžiuvelytė I, Olechnovič K, Godliauskaite E, Sermokas K, Urbaitis T, Gasiunas G, Kazlauskas D 2023 bioRxiv 2023.03.27.534365 Pudžiuvelytė I, Olechnovič K, Godliauskaite E, Sermokas K, Urbaitis T, Gasiunas G, Kazlauskas D 2023 bioRxiv 2023.03.27.534365
[96]	Rives A, Meier J, Sercu T, et al. 2021 Proc. Natl. Acad. Sci. U.S.A. 118 e2016239118 Google Scholar
[97]	Elnaggar A, Heinzinger M, Dallago C, et al. 2022 IEEE Trans. Pattern Anal. Mach. Intell. 44 7112 Google Scholar
[98]	Huang P S, Boyken S E, Baker D 2016 Nature 537 320 Google Scholar
[99]	Huang B, Xu Y, Hu X, Liu Y, Liao S, Zhang J, Huang C, Hong J, Chen Q, Liu H 2022 Nature 602 523 Google Scholar
[100]	Watson J L, Juergens D, Bennett N R, et al. 2023 Nature 620 1089 Google Scholar
[101]	Yang L, Zhang Z, Song Y, Hong S, Xu R, Zhao Y, Shao Y, Zhang W, Cui B, Yang M H 2022 arXiv: 2209.00796 [cs.LG]
[102]	Croitoru F A, Hondru V, Ionescu R T, Shah M 2023 IEEE Trans. Pattern Anal. Mach. Intell. 45 10850 Google Scholar
[103]	Kong Z, Ping W, Huang J, Zhao K, Catanzaro B 2020 arXiv: 2009.09761 [eess.AS]
[104]	Liu Y, Chen L, Liu H 2022 bioRxiv 2022.12.17.52084 Liu Y, Chen L, Liu H 2022 bioRxiv 2022.12.17.52084
[105]	Watson J L, Juergens D, Bennett N R, et al. 2022 bioRxiv 2022.12.09.519842
[106]	Xiong P, Wang M, Zhou X, Zhang T, Zhang J, Chen Q, Liu H 2014 Nat. Commun. 5 5330 Google Scholar
[107]	Xiong P, Hu X, Huang B, Zhang J, Chen Q, Liu H 2020 Bioinformatics 36 136 Google Scholar
[108]	Dauparas J, Anishchenko I, Bennett N, et al. 2022 Science 378 49 Google Scholar
[109]	Zhou J, Cui G, Hu S, Zhang Z, Yang C, Liu Z, Wang L, Li C, Sun M 2020 AI open 1 57 Google Scholar
[110]	Chen Y, Chen Q, Liu H 2022 J. Chem. Inf. Model. 62 971 Google Scholar
[111]	Marchand A, Van Hall-Beauvais A K, Correia B E 2022 Curr. Opin. Struct. Biol. 74 102370 Google Scholar
[112]	Shi C, Wang C, Lu J, Zhong B, Tang J 2022 arXiv: 2210.08761 [q-bio. BM]
[113]	Dixit R, Khambhati K, Supraja K V, Singh V, Lederer F, Show P L, Awasthi M K, Sharma A, Jain R 2022 Bioresour. Technol. 128522 Google Scholar
[114]	Kaptan S, Vattulainen I 2022 Adv. Phys.: X 7 2006080 Google Scholar
[115]	Casadevall G, Duran C, Osuna S 2023 JACS Au 3 1554 Google Scholar
[116]	Webb C, Ip S, Bathula N V, et al. 2022 Mol. Pharmaceutics 19 1047 Google Scholar
[117]	Mauro V P, Chappell S A 2014 Trends Mol. Med. 20 604 Google Scholar
[118]	Sarkar D, Saha S 2019 J. Biosci. 44 104 Google Scholar

Cited By

图 1 量子力学与机器学习间的相似性. 从左到右, 从上到下的图片分别是: Chignolin蛋白质在(a)无水环境和(b)有水环境下的情况, 使用SchNet模型得到的(c)可视化电荷密度和(d)局部化学势, (e)氢原子的波函数以及(f)Müller-Brown势能. 图片引自文献[42] (版权属于美国化学会)

Figure 1. Similarity between quantum mechanics and machine learning. Images from left to right from top to bottom: Chignolin protein (a) without and (b) with the water environment, (c) visualized total charge densities and (d) local chemical potentials obtained using the SchNet model, (e) wave functions for hydrogen atom and (f) Müller-Brown potential. Reprinted with permission from Ref. [42] (Copyright 2021 American Chemical Society).

DownLoad: Full-Size Img PowerPoint

图 2 AlphaFold2的结构图

Figure 2. Architecture of AlphaFold2.

DownLoad: Full-Size Img PowerPoint

图 3 AlphaFold-Multimer的多序列比对构建方法

Figure 3. Construction of MSA used in AlphaFold-Multimer.

DownLoad: Full-Size Img PowerPoint

图 4 ProteinMPNN模型核心思想示意图

Figure 4. Main idea of ProteinMPNN.

DownLoad: Full-Size Img PowerPoint

图 5 蛋白质结构序列协同设计的一种机器学习模型示意图

Figure 5. Illustration of a machine learning model of protein structure-sequence co-design.

DownLoad: Full-Size Img PowerPoint

[1]	Baltoumas F A, Zafeiropoulou S, Karatzas E, et al. 2021 Biomolecules 11 1245 Google Scholar
[2]	Wolf Y I, Katsnelson M I, Koonin E V 2018 Proc. Natl. Acad. Sci. USA 115 E8678 Google Scholar
[3]	Fusco A, Fedele M 2007 Nat. Rev. Cancer 7 899 Google Scholar
[4]	Noble D 2002 Nat. Rev. Mol. Cell Biol. 3 459 Google Scholar
[5]	Markowetz F 2017 PLoS Biology 15 e2002050 Google Scholar
[6]	Hollingsworth S A, Dror R O 2018 Neuron 99 1129 Google Scholar
[7]	Zhang Y 2008 Curr. Opin. Struct. Biol. 18 342 Google Scholar
[8]	Agostini F, Vendruscolo M, Tartaglia G G 2012 J. Mol. Biol. 421 237 Google Scholar
[9]	Chen L, Fan Z, Chang J, et al. 2023 Nat. Commun. 14 4217 Google Scholar
[10]	Geng H, Chen F, Ye J, Jiang F 2019 Computat. Struct. Biotechnol. J. 17 1162 Google Scholar
[11]	Salo-Ahen O M, Alanko I, Bhadane R, et al. 2020 Processes 9 71 Google Scholar
[12]	Norberg J, Nilsson L 2003 Q. Rev. Biophys. 36 257 Google Scholar
[13]	van der Kamp M W, Shaw K E, Woods C J, Mulholland A J 2008 J. R. Soc. Interface 5 173 Google Scholar
[14]	Dror R O, Dirks R M, Grossman J, Xu H, Shaw D E 2012 Annu. Rev. Biophys. 41 429 Google Scholar
[15]	Lin X, Li X, Lin X 2020 Molecules 25 1375 Google Scholar
[16]	Pearce R, Zhang Y 2021 Curr. Opin. Struct. Biol. 68 194 Google Scholar
[17]	Jordan M I, Mitchell T M 2015 Science 349 255 Google Scholar
[18]	Butler K T, Davies D W, Cartwright H, Isayev O, Walsh A 2018 Nature 559 547 Google Scholar
[19]	Liakos K G, Busato P, Moshou D, Pearson S, Bochtis D 2018 Sensors 18 2674 Google Scholar
[20]	Jiang T, Gradus J L, Rosellini A J 2020 Behav. Ther. 51 675 Google Scholar
[21]	Hastie T, Tibshirani R, Friedman J, Hastie T, Tibshirani R, Friedman J 2009 Unsupervised Learning. In: The Elements of Statistical Learning. Springer Series in Statistics (New York: Springer) pp485–585
[22]	Van Engelen J E, Hoos H H 2020 Machine Learning 109 373 Google Scholar
[23]	Wiering M A, Van Otterlo M 2012 Reinforcement Learning (Heidelberg, Berlin: Springer) p729
[24]	LeCun Y, Bengio Y, Hinton G 2015 Nature 521 436 Google Scholar
[25]	Deng L, Yu D 2014 Deep Learning: Methods and Applications (Now Foundations and Trends) p197
[26]	Jones D T 2019 Nat. Rev. Mol. Cell Biol. 20 659 Google Scholar
[27]	Das P, Sercu T, Wadhawan K, et al. 2021 Nat. Biomed. Eng. 5 613 Google Scholar
[28]	Kuhlman B, Bradley P 2019 Nat. Rev. Mol. Cell Biol. 20 681 Google Scholar
[29]	Trevino S R, Scholtz J M, Pace C N 2008 J. Pharm. Sci. 97 4155 Google Scholar
[30]	Kelley K W, Weigent D A, Kooijman R 2007 Brain Behav. Immun. 21 384 Google Scholar
[31]	Babin V, Roland C, Sagui C 2008 J. Chem. Phys. 128 Google Scholar
[32]	Morozov I V, Kazennov A M, Bystryi R, Norman G E, Pisarev V V, Stegailov V V 2011 Comput. Phys. Commun. 182 1974 Google Scholar
[33]	Karplus M, McCammon J A 2002 Nat. Struct. Biol. 9 646 Google Scholar
[34]	Wang Y, Ribeiro J M L, Tiwary P 2020 Curr. Opin. Struct. Biol. 61 139 Google Scholar
[35]	Chmiela S, Tkatchenko A, Sauceda H E, Poltavsky I, Schütt K T, Müller K R 2017 Sci. Adv. 3 e1603015 Google Scholar
[36]	Ponder J W, Case D A 2003 Adv. Protein Chem. 66 27 Google Scholar
[37]	Monticelli L, Tieleman D P 2013 Biomolecular Simulations: Methods and Protocols 197
[38]	Wang J, Wolf R M, Caldwell J W, Kollman P A, Case D A 2004 J. Comput. Chem. 25 1157 Google Scholar
[39]	Hughes Z E, Wright L B, Walsh T R 2013 Langmuir 29 13217 Google Scholar
[40]	Cesari A, Bottaro S, Lindorff-Larsen K, Banáš P, Šponer J, Bussi G 2019 J. Chem. Theory Comput. 15 3425 Google Scholar
[41]	Unke O T, Chmiela S, Sauceda H E, Gastegger M, Poltavsky I, Schütt K T, Tkatchenko A, Müller K R 2021 Chem. Rev. 121 10142 Google Scholar
[42]	Poltavsky I, Tkatchenko A 2021 J. Phys. Chem. Lett. 12 6551 Google Scholar
[43]	Kästner J 2011 WIREs Comput. Mol. Sci. 1 932 Google Scholar
[44]	Izrailev S, Stepaniants S, Isralewitz B, Kosztin D, Lu H, Molnar F, Wriggers W, Schulten K 1999 Computational Molecular Dynamics: Challenges, Methods, Ideas: Proceedings of the 2nd International Symposium on Algorithms for Macromolecular Modelling Berlin, May 21–24, 1997 p39
[45]	Moradi M, Babin V, Roland C, Sagui C 2013 Nucleic Acids Res. 41 33 Google Scholar
[46]	Simonson T, Archontis G, Karplus M 2002 Acc. Chem. Res. 35 430 Google Scholar
[47]	Bitencourt-Ferreira G, de Azevedo W F 2018 Biophys. Chem. 240 63 Google Scholar
[48]	Trott O, Olson A J 2010 J. Comput. Chem. 31 455 Google Scholar
[49]	Besora M, Vidossich P, Lledos A, Ujaque G, Maseras F 2018 J. Phys. Chem. A 122 1392 Google Scholar
[50]	Pan X, Yang J, Van R, Epifanovsky E, Ho J, Huang J, Pu J, Mei Y, Nam K, Shao Y 2021 J. Chem. Theory Comput. 17 5745 Google Scholar
[51]	Senn H M, Thiel W 2009 Angew. Chem. Int. Ed. 48 1198 Google Scholar
[52]	Riniker S 2017 J. Chem. Inf. Model. 57 726 Google Scholar
[53]	Bennett W D, He S, Bilodeau C L, Jones D, Sun D, Kim H, Allen J E, Lightstone F C, Ingólfsson H I 2020 J. Chem. Inf. Model. 60 5375 Google Scholar
[54]	Bertazzo M, Gobbo D, Decherchi S, Cavalli A 2021 J. Chem. Theory Comput. 17 5287 Google Scholar
[55]	Eswar N, John B, Mirkovic N, et al. 2003 Nucleic Acids Research 31 3375 Google Scholar
[56]	Asara J M, Schweitzer M H, Freimark L M, Phillips M, Cantley L C 2007 Science 316 280 Google Scholar
[57]	Greener J G, Kandathil S M, Moffat L, Jones D T 2022 Nat. Rev. Mol. Cell Biol. 23 40 Google Scholar
[58]	Jumper J, Evans R, Pritzel A, et al. 2021 Nature 596 583 Google Scholar
[59]	Wu R, Ding F, Wang R, et al. 2022 bioRxiv 2022.07.21. 500999
[60]	Baek M, DiMaio F, Anishchenko I, et al. 2021 Science 373 871 Google Scholar
[61]	Medsker L R, Jain L 1999 Recurrent Neural Networks: Design and Applications (1st Ed.) (CRC Press) p2
[62]	Kim P 2017 Convolutional Neural Network. In: MATLAB Deep Learning (Berkeley, CA: Apress) p121
[63]	Wardah W, Khan M G, Sharma A, Rashid M A 2019 Comput. Biol. Chem. 81 1 Google Scholar
[64]	Mirabello C, Pollastri G 2013 Bioinformatics 29 2056 Google Scholar
[65]	Heffernan R, Yang Y, Paliwal K, Zhou Y 2017 Bioinformatics 33 2842 Google Scholar
[66]	Wang S, Peng J, Ma J, Xu J 2016 Sci. Rep. 6 1 Google Scholar
[67]	Li Z, Yu Y 2016 arXiv: 1604.07176 [q-bio.BM]
[68]	Wang Y, Mao H, Yi Z 2017 Knowledge-Based Systems 118 115 Google Scholar
[69]	Nishikawa K, Ooi T, Isogai Y, Saitô N 1972 J. Phys. Soc. JPN 32 1331 Google Scholar
[70]	Edgar R C, Batzoglou S 2006 Curr. Opin. Struct. Biol. 16 368 Google Scholar
[71]	Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez A N, Kaiser Ł, Polosukhin I 2017 Advances in Neural Information Processing Systems 30 Long Beach, USA, December 4–9, 2017 p30
[72]	Janin J, Bahadur R P, Chakrabarti P 2008 Q. Rev. Biophys. 41 133 Google Scholar
[73]	Zafferani M, Hargrove A E 2021 Cell Chem. Biol. 28 594 Google Scholar
[74]	Hunter C A 2004 Angew. Chem. Int. Ed. 43 5310 Google Scholar
[75]	Chen R, Li L, Weng Z 2003 Proteins Struct. Funct. Bioinf. 52 80 Google Scholar
[76]	Jingcheng Y, Zhaoming C, Zhaoqun L, Mingliang Z, Wenjun L, He H, Qiwei Y 2022 Code of Open Complex https:// github.com/baaihealth/OpenComplex.
[77]	Evans R, O’ Neill M, Pritzel A, et al. 2021 bioRxiv 2021.10.04.463034
[78]	Moriwaki Y 2021 Twitter https://twitter.com/Ag_smith/ status.
[79]	Ko J, Lee J 2021 bioRxiv 2021.07.27.453972 Ko J, Lee J 2021 bioRxiv 2021.07.27.453972
[80]	Tsaban T, Varga J K, Avraham O, Ben-Aharon Z, Khramushin A, Schueler-Furman O 2022 Nat. Commun. 13 176 Google Scholar
[81]	Bryant P, Pozzati G, Elofsson A 2022 Nat. Commun. 13 1265 Google Scholar
[82]	Zhou T M, Wang S, Xu J 2017 bioRxiv 240754
[83]	Cang Z, Wei G W 2017 PLoS Comput. Biol. 13 e1005690 Google Scholar
[84]	Yagi K, Re S, Mori T, Sugita Y 2022 Curr. Opin. Struct. Biol. 72 88 Google Scholar
[85]	Vendruscolo M, Knowles T P, Dobson C M 2011 CSH Perspect. Biol. 3 a010454 Google Scholar
[86]	Khurana S, Rawi R, Kunji K, Chuang G Y, Bensmail H, Mall R 2018 Bioinformatics 34 2605 Google Scholar
[87]	Wu X, Yu L 2021 Bioinformatics 37 4314 Google Scholar
[88]	Schellekens H 2003 Nephrology Dialysis Transplantation 18 1257 Google Scholar
[89]	Ternette N, Tippler B, Überla K, Grunwald T 2007 Vaccine 25 7271 Google Scholar
[90]	Jefferis R 2016 J. Immunol. Res. 2016 Google Scholar
[91]	Schellekens H 2005 Nephrology Dialysis Transplantation 20 vi3 Google Scholar
[92]	Smith C C, Chai S, Washington A R, et al. 2019 Cancer Immunol. Res. 7 1591 Google Scholar
[93]	Gonzalez-Dias P, Lee E K, Sorgi S, de Lima D S, Urbanski A H, Silveira E L, Nakaya H I 2020 Hum. Vacc. Immunother. 16 269 Google Scholar
[94]	Timr S, Madern D, Sterpone F 2020 Prog. Mol. Biol. Transl. Sci. 170 239 Google Scholar
[95]	Pudžiuvelytė I, Olechnovič K, Godliauskaite E, Sermokas K, Urbaitis T, Gasiunas G, Kazlauskas D 2023 bioRxiv 2023.03.27.534365 Pudžiuvelytė I, Olechnovič K, Godliauskaite E, Sermokas K, Urbaitis T, Gasiunas G, Kazlauskas D 2023 bioRxiv 2023.03.27.534365
[96]	Rives A, Meier J, Sercu T, et al. 2021 Proc. Natl. Acad. Sci. U.S.A. 118 e2016239118 Google Scholar
[97]	Elnaggar A, Heinzinger M, Dallago C, et al. 2022 IEEE Trans. Pattern Anal. Mach. Intell. 44 7112 Google Scholar
[98]	Huang P S, Boyken S E, Baker D 2016 Nature 537 320 Google Scholar
[99]	Huang B, Xu Y, Hu X, Liu Y, Liao S, Zhang J, Huang C, Hong J, Chen Q, Liu H 2022 Nature 602 523 Google Scholar
[100]	Watson J L, Juergens D, Bennett N R, et al. 2023 Nature 620 1089 Google Scholar
[101]	Yang L, Zhang Z, Song Y, Hong S, Xu R, Zhao Y, Shao Y, Zhang W, Cui B, Yang M H 2022 arXiv: 2209.00796 [cs.LG]
[102]	Croitoru F A, Hondru V, Ionescu R T, Shah M 2023 IEEE Trans. Pattern Anal. Mach. Intell. 45 10850 Google Scholar
[103]	Kong Z, Ping W, Huang J, Zhao K, Catanzaro B 2020 arXiv: 2009.09761 [eess.AS]
[104]	Liu Y, Chen L, Liu H 2022 bioRxiv 2022.12.17.52084 Liu Y, Chen L, Liu H 2022 bioRxiv 2022.12.17.52084
[105]	Watson J L, Juergens D, Bennett N R, et al. 2022 bioRxiv 2022.12.09.519842
[106]	Xiong P, Wang M, Zhou X, Zhang T, Zhang J, Chen Q, Liu H 2014 Nat. Commun. 5 5330 Google Scholar
[107]	Xiong P, Hu X, Huang B, Zhang J, Chen Q, Liu H 2020 Bioinformatics 36 136 Google Scholar
[108]	Dauparas J, Anishchenko I, Bennett N, et al. 2022 Science 378 49 Google Scholar
[109]	Zhou J, Cui G, Hu S, Zhang Z, Yang C, Liu Z, Wang L, Li C, Sun M 2020 AI open 1 57 Google Scholar
[110]	Chen Y, Chen Q, Liu H 2022 J. Chem. Inf. Model. 62 971 Google Scholar
[111]	Marchand A, Van Hall-Beauvais A K, Correia B E 2022 Curr. Opin. Struct. Biol. 74 102370 Google Scholar
[112]	Shi C, Wang C, Lu J, Zhong B, Tang J 2022 arXiv: 2210.08761 [q-bio. BM]
[113]	Dixit R, Khambhati K, Supraja K V, Singh V, Lederer F, Show P L, Awasthi M K, Sharma A, Jain R 2022 Bioresour. Technol. 128522 Google Scholar
[114]	Kaptan S, Vattulainen I 2022 Adv. Phys.: X 7 2006080 Google Scholar
[115]	Casadevall G, Duran C, Osuna S 2023 JACS Au 3 1554 Google Scholar
[116]	Webb C, Ip S, Bathula N V, et al. 2022 Mol. Pharmaceutics 19 1047 Google Scholar
[117]	Mauro V P, Chappell S A 2014 Trends Mol. Med. 20 604 Google Scholar
[118]	Sarkar D, Saha S 2019 J. Biosci. 44 104 Google Scholar

[1]	Tang Tian-Yi, Xiong Yi-Ming, Zhang Rui-Ge, Zhang Jian, Li Wen-Fei, Wang Jun, Wang Wei. Progress in protein pre-training models integrating structural knowledge. Acta Physica Sinica, 2024, 73(18): 188701. doi: 10.7498/aps.73.20240811
[2]	Ouyang Xin-Jian, Zhang Yan-Xing, Wang Zhi-Long, Zhang Feng, Chen Wei-Jia, Zhuang Yuan, Jie Xiao, Liu Lai-Jun, Wang Da-Wei. Modeling ferroelectric phase transitions with graph convolutional neural networks. Acta Physica Sinica, 2024, 73(8): 086301. doi: 10.7498/aps.73.20240156
[3]	Song Rui, Liu Xue-Mei, Wang Hai-Bin, Lü Hao, Song Xiao-Yan. Hardness prediction of WC-Co cemented carbide based on machine learning model. Acta Physica Sinica, 2024, 73(12): 126201. doi: 10.7498/aps.73.20240284
[4]	Zhang Qiao, Tan Wei, Ning Yong-Qi, Nie Guo-Zheng, Cai Meng-qiu, Wang Jun-Nian, Zhu Hui-Ping, Zhao Yu-Qing. Prediction of Magnetic Janus Materials Based on Machine Learning and First-Principles Calculations. Acta Physica Sinica, 2024, 73(23): 230201. doi: 10.7498/aps.73.20241278
[5]	Lin Kai-Dong, Lin Xiao-Qian, Lin Xu-Bo. Virtual screening of drugs targeting PD-L1 protein. Acta Physica Sinica, 2023, 72(24): 240501. doi: 10.7498/aps.72.20231068
[6]	Chen Guang-Lin, Zhang Zhi-Yong. Exploring proten’s conformational space by using encoding layer supervised auto-encoder. Acta Physica Sinica, 2023, 72(24): 248705. doi: 10.7498/aps.72.20231060
[7]	Yang Zhang-Zhang, Liu Li, Wan Zhi-Tao, Fu Jia, Fan Qun-Chao, Xie Feng, Zhang Yi, Ma Jie. Combining machine learning algorithm to improve prediction performance of ab initio method for vibrational energy spectra of HF/HBr/H³⁵Cl/Na³⁵Cl. Acta Physica Sinica, 2023, 72(7): 073101. doi: 10.7498/aps.72.20221953
[8]	Luo Qi-Rui, Shen Yi-Fan, Luo Meng-Bo. Computer simulation and machine learning of polymer collapse and critical adsorption phase transitions. Acta Physica Sinica, 2023, 72(24): 240502. doi: 10.7498/aps.72.20231058
[9]	Luo Fang-Fang, Cai Zhi-Tao, Huang Yan-Dong. Progress in protein pK_a prediction. Acta Physica Sinica, 2023, 72(24): 248704. doi: 10.7498/aps.72.20231356
[10]	Zhang Yi-Fan, Ren Wei, Wang Wei-Li, Ding Shu-Jian, Li Nan, Chang Liang, Zhou Qian. Machine learning combined with solid solution strengthening model for predicting hardness of high entropy alloys. Acta Physica Sinica, 2023, 72(18): 180701. doi: 10.7498/aps.72.20230646
[11]	Guan Xing-Yue, Huang Heng-Yan, Peng Hua-Qi, Liu Yan-Hang, Li Wen-Fei, Wang Wei. Machine learning in molecular simulations of biomolecules. Acta Physica Sinica, 2023, 72(24): 248708. doi: 10.7498/aps.72.20231624
[12]	Ai Fei, Liu Zhi-Bing, Zhang Yuan-Tao. Numerical study of discharge characteristics of atmospheric dielectric barrier discharges by integrating machine learning. Acta Physica Sinica, 2022, 71(24): 245201. doi: 10.7498/aps.71.20221555
[13]	Zhou Jia-Jian, Zhang Yu-Wen, He Chao-Yu, Ouyang Tao, Li Jin, Tang Chao. First-principles study of structure prediction and electronic properties of two-dimensional SiP₂ allotropes. Acta Physica Sinica, 2022, 71(23): 236101. doi: 10.7498/aps.71.20220853
[14]	Li Wei, Long Lian-Chun, Liu Jing-Yi, Yang Yang. Classification of magnetic ground states and prediction of magnetic moments of inorganic magnetic materials based on machine learning. Acta Physica Sinica, 2022, 71(6): 060202. doi: 10.7498/aps.71.20211625
[15]	Lin Jian, Ye Meng, Zhu Jia-Wei, Li Xiao-Peng. Machine learning assisted quantum adiabatic algorithm design. Acta Physica Sinica, 2021, 70(14): 140306. doi: 10.7498/aps.70.20210831
[16]	Liu Chun-Jie, Zhao Xin-Jun, Gao Zhi-Fu, Jiang Zhong-Ying. Modeling study of adsorption/desorption of proteins by polymer mixed brush. Acta Physica Sinica, 2021, 70(22): 224701. doi: 10.7498/aps.70.20211219
[17]	Yan Xiao, Xin Zi-Hua, Zhang Jiao-Jiao. Molecular dynamics study on the structure and properties of silicon-graphdiyne. Acta Physica Sinica, 2013, 62(23): 238101. doi: 10.7498/aps.62.238101
[18]	Xia Dong, Wang Xin-Qiang. Structures and melting behaviors of ultrathin platinum nanowires. Acta Physica Sinica, 2012, 61(13): 130510. doi: 10.7498/aps.61.130510
[19]	Yang Hong, Chen Min. A molecular dynamics simulation of thermodynamic properties of undercooled liquid Ni2TiAl alloy. Acta Physica Sinica, 2006, 55(5): 2418-2421. doi: 10.7498/aps.55.2418
[20]	Wang Chang-Qing, Jia Yu, Ma Bing-Xian, Wang Song-You, Qin Zhen, Wang Fei, Wu Le-Ke, Li Xin-Jian. Molecular dynamics simulations of various metastable structures on Si(001) at different temperatures. Acta Physica Sinica, 2005, 54(9): 4313-4318. doi: 10.7498/aps.54.4313

Catalog

Vol. 73, Issue 6 - 2024-03-20

Metrics

Abstract views: 3088
PDF Downloads: 97
Cited By: 0

姓名
邮箱
手机号码
标题
留言内容
验证码

Search

Article

留言板

Machine learning for in silico protein research

Abstract

Authors and contacts

Corresponding author: Zhang Jia-Hui, jhzhang@ustc.edu.cn

FullText HTML : translate this paragraph

References

Cited By

Catalog

Metrics

Authors

Referees

About Journal

About

Search

Article

留言板

Machine learning for in silico protein research

Abstract

Authors and contacts

Corresponding author: Zhang Jia-Hui, jhzhang@ustc.edu.cn

FullText HTML : translate this paragraph

References

Cited By

Catalog

Metrics

Publishing process

Authors

Referees

About Journal

About