Recent advances in protein conformation sampling by combining machine learning with molecular simulation

doi:10.1088/1674-1056/ad1a92

中国物理B ›› 2024, Vol. 33 ›› Issue (3): 30701-030701.doi: 10.1088/1674-1056/ad1a92

Recent advances in protein conformation sampling by combining machine learning with molecular simulation

Yiming Tang(唐一鸣)^†, Zhongyuan Yang(杨中元), Yifei Yao(姚逸飞), Yun Zhou(周运), Yuan Tan(谈圆),Zichao Wang(王子超), Tong Pan(潘瞳), Rui Xiong(熊瑞), Junli Sun(孙俊力), and Guanghong Wei(韦广红)^‡

Department of Physics, State Key Laboratory of Surface Physics, and Key Laboratory for Computational Physical Sciences (Ministry of Education), Shanghai 200438, China

收稿日期:2023-11-17 修回日期:2023-12-11 接受日期:2024-01-04 出版日期:2024-02-22 发布日期:2024-02-29
通讯作者: Yiming Tang, Guanghong Wei E-mail:ymtang@fudan.edu.cn;ghwei@fudan.edu.cn
基金资助:
Project supported by the National Key Research and Development Program of China (Grant No. 2023YFF1204402), the National Natural Science Foundation of China (Grant Nos. 12074079 and 12374208), the Natural Science Foundation of Shanghai (Grant No. 22ZR1406800), and the China Postdoctoral Science Foundation (Grant No. 2022M720815).

Recent advances in protein conformation sampling by combining machine learning with molecular simulation

Department of Physics, State Key Laboratory of Surface Physics, and Key Laboratory for Computational Physical Sciences (Ministry of Education), Shanghai 200438, China

Received:2023-11-17 Revised:2023-12-11 Accepted:2024-01-04 Online:2024-02-22 Published:2024-02-29
Contact: Yiming Tang, Guanghong Wei E-mail:ymtang@fudan.edu.cn;ghwei@fudan.edu.cn
Supported by:
Project supported by the National Key Research and Development Program of China (Grant No. 2023YFF1204402), the National Natural Science Foundation of China (Grant Nos. 12074079 and 12374208), the Natural Science Foundation of Shanghai (Grant No. 22ZR1406800), and the China Postdoctoral Science Foundation (Grant No. 2022M720815).

摘要/Abstract

摘要： The rapid advancement and broad application of machine learning (ML) have driven a groundbreaking revolution in computational biology. One of the most cutting-edge and important applications of ML is its integration with molecular simulations to improve the sampling efficiency of the vast conformational space of large biomolecules. This review focuses on recent studies that utilize ML-based techniques in the exploration of protein conformational landscape. We first highlight the recent development of ML-aided enhanced sampling methods, including heuristic algorithms and neural networks that are designed to refine the selection of reaction coordinates for the construction of bias potential, or facilitate the exploration of the unsampled region of the energy landscape. Further, we review the development of autoencoder based methods that combine molecular simulations and deep learning to expand the search for protein conformations. Lastly, we discuss the cutting-edge methodologies for the one-shot generation of protein conformations with precise Boltzmann weights. Collectively, this review demonstrates the promising potential of machine learning in revolutionizing our insight into the complex conformational ensembles of proteins.

关键词: machine learning, molecular simulation, protein conformational space, enhanced sampling

Abstract: The rapid advancement and broad application of machine learning (ML) have driven a groundbreaking revolution in computational biology. One of the most cutting-edge and important applications of ML is its integration with molecular simulations to improve the sampling efficiency of the vast conformational space of large biomolecules. This review focuses on recent studies that utilize ML-based techniques in the exploration of protein conformational landscape. We first highlight the recent development of ML-aided enhanced sampling methods, including heuristic algorithms and neural networks that are designed to refine the selection of reaction coordinates for the construction of bias potential, or facilitate the exploration of the unsampled region of the energy landscape. Further, we review the development of autoencoder based methods that combine molecular simulations and deep learning to expand the search for protein conformations. Lastly, we discuss the cutting-edge methodologies for the one-shot generation of protein conformations with precise Boltzmann weights. Collectively, this review demonstrates the promising potential of machine learning in revolutionizing our insight into the complex conformational ensembles of proteins.

Key words: machine learning, molecular simulation, protein conformational space, enhanced sampling

中图分类号: (Neural networks, fuzzy logic, artificial intelligence)

07.05.Mh

87.15.ap (Molecular dynamics simulation) 87.14.E- (Proteins) 87.15.B- (Structure of biomolecules)

Yiming Tang(唐一鸣), Zhongyuan Yang(杨中元), Yifei Yao(姚逸飞), Yun Zhou(周运), Yuan Tan(谈圆),Zichao Wang(王子超), Tong Pan(潘瞳), Rui Xiong(熊瑞), Junli Sun(孙俊力), and Guanghong Wei(韦广红). Recent advances in protein conformation sampling by combining machine learning with molecular simulation[J]. 中国物理B, 2024, 33(3): 30701-030701.

参考文献

[1] Karplus M and McCammon J A 1983 Annu. Rev. Biochem. 52 263
[2] Eisenberg D, Marcotte E M, Xenarios I and Yeates T O 2000 Nature 405 823
[3] Gregersen N, Bross P, Vang S and Christensen J H 2006 Annu. Rev. Genom. Hum. Genet. 7 103
[4] Vendruscolo M and Fuxreiter M 2022 Nat. Commun. 13 5550
[5] Ubarretxena-Belandia I and Stokes D L 2010 Adv. Protein Chem. Struct. Biol. 81 33
[6] Orts J and Gossert A D 2018 Methods 138 3
[7] Ikeya T, Guntert P and Ito Y 2019 Int. J. Mol. Sci. 20 2442
[8] Kulkarni P, Bhattacharya S, Achuthan S, Behal A, Jolly M K, Kotnala S, Mohanty A, Rangarajan G, Salgia R and Uversky V 2022 Chem. Rev. 122 6614
[9] Kulkarni P, Salgia R and Rangarajan G 2023 iScience 26 107109
[10] Patil A, Strom A R, Paulo J A, Collings C K, Ruff K M, Shinn M K, Sankar A, Cervantes K S, Wauer T, St Laurent J D, Xu G, Becker L A, Gygi S P, Pappu R V, Brangwynne C P and Kadoch C 2023 Cell 186 4936
[11] Wei G, Xi W, Nussinov R and Ma B 2016 Chem. Rev. 116 6516
[12] Prasad P A, Kanagasabai V, Arunachalam J and Gautham N 2007 J. Biosci. 32 909
[13] Liwo A, Czaplewski C, Oldziej S and Scheraga H A 2008 Curr. Opin. Struct. Biol. 18 134
[14] Compiani M and Capriotti E 2013 Biochemistry 52 8601
[15] Biswas P 2019 Adv. Protein Chem. Struct. Biol. 118 1
[16] Shea J E, Best R B and Mittal J 2021 Curr. Opin. Struct. Biol. 67 219
[17] Lindorff-Larsen K, Piana S, Dror R O and Shaw D E 2011 Science 334 517
[18] Robustelli P, Piana S and Shaw D E 2018 Proc. Natl. Acad. Sci. USA 115 E4758
[19] Ruff K M, Pappu R V and Holehouse A S 2019 Curr. Opin. Struct. Biol. 56 1
[20] Atilgan A R and Atilgan C 2022 Curr. Opin. Struct. Biol. 72 79
[21] Lee T S, Cerutti D S, Mermelstein D, Lin C, LeGrand S, Giese T J, Roitberg A, Case D A, Walker R C and York D M 2018 J. Chem. Inf. Model. 58 2043
[22] Pall S, Zhmurov A, Bauer P, Abraham M, Lundborg M, Gray A, Hess B and Lindahl E 2020 J. Chem. Phys. 153 134110
[23] Huang J and MacKerell A D 2018 Curr. Opin. Struct. Biol. 48 40
[24] Mu J, Liu H, Zhang J, Luo R and Chen H F 2021 J. Chem. Inf. Model. 61 1037
[25] Qi R, Wei G, Ma B and Nussinov R 2018 Peptide self-assembly: Methods and protocols (New York: Humana Press) p.101
[26] Bernardi R C, Melo M C R and Schulten K 2015 Biochim. Biophys. Acta. 1850 872
[27] Hamelberg D, Mongan J and McCammon J A 2004 J. Chem. Phys. 120 11919
[28] Zheng S and Pfaendtner J 2014 Mol. Simul. 41 55
[29] Bussi G and Laio A 2020 Nat. Rev. Phys. 2 200
[30] Ovchinnikov S and Huang P S 2021 Curr. Opin. Chem. Biol. 65 136
[31] Strokach A and Kim P M 2022 Curr. Opin. Struct. Biol. 72 226
[32] Pakhrin S C, Shrestha B, Adhikari B and Kc D B 2021 Int. J. Mol. Sci. 22 5553
[33] Dong T, Gong T and Li W 2021 J. Phys. Chem. B 125 9490
[34] Zeng X, Wang T, Kang Y, Bai G and Ma B 2023 Antibodies 12 58
[35] Lin P, Yan Y and Huang S Y 2023 Brief. Bioinform. 24 bbac499
[36] Lin P, Yan Y, Tao H and Huang S Y 2023 Nat. Commun. 14 4935
[37] Jumper J, Evans R, Pritzel A, et al. 2021 Nature 596 583
[38] Jumper J, Evans R, Pritzel A, et al. 2021 Proteins 89 1711
[39] Ruff K M and Pappu R V 2021 J. Mol. Biol. 433 167208
[40] Noe F, Tkatchenko A, Muller K R and Clementi C 2020 Annu. Rev. Phys. Chem. 71 361
[41] Wang Y, Lamim R J M and Tiwary P 2020 Curr. Opin. Struct. Biol. 61 139
[42] Miron R A and Fichthorn K A 2004 Phys. Rev. Lett. 93 128301
[43] Qin Z and Buehler M J 2010 Phys. Rev. Lett. 104 198304
[44] Abyzov A, Blackledge M and Zweckstetter M 2022 Chem. Rev. 122 6719
[45] Alberti S, Gladfelter A and Mittag T 2019 Cell 176 419
[46] Yang Y I, Shao Q, Zhang J, Yang L and Gao Y Q 2019 J. Chem. Phys. 151 070902
[47] Tiwary P and van de Walle A 2016 A Review of Enhanced Sampling Approaches for Accelerated Molecular Dynamics (Cham: Springer International Publishing) p. 195
[48] Kätner J 2011 Wiley Interdiscip. Rev. Comput. Mol. Sci. 1 932
[49] Isralewitz B, Gao M and Schulten K 2001 Curr. Opin. Struct. Biol. 11 224
[50] Laio A and Gervasio F L 2008 Rep. Prog. Phys. 71 126601
[51] Allison J R 2020 Biochem. Soc. Trans. 48 1707
[52] Chen M 2021 Eur. Phys. J. B 94 211
[53] Pan A C, Weinreich T M, Shan Y, Scarpazza D P and Shaw D E 2014 J. Chem. Theory Comput. 10 2860
[54] Wehmeyer C and Noé F 2018 J. Chem. Phys. 148 241703
[55] Bonati L, Zhang Y Y and Parrinello M 2019 Proc. Natl. Acad. Sci. USA 116 17641
[56] Bonati L, Rizzi V and Parrinello M 2020 J. Phys. Chem. Lett. 11 2998
[57] Sultan M M, Wayment-Steele H K and Pande V S 2018 J. Chem. Theory Comput. 14 1887
[58] Perez-Hernandez G, Paul F, Giorgino T, De Fabritiis G and Noe F 2013 J. Chem. Phys. 139 015102
[59] Schwantes C R and Pande V S 2015 J. Chem. Theory Comput. 11 600
[60] Odstrcil R E, Dutta P and Liu J 2022 J. Chem. Theory Comput. 18 6297
[61] Odstrcil R E, Dutta P and Liu J 2023 J. Chem. Theory Comput. 19 6500
[62] Ribeiro J M L, Bravo P, Wang Y and Tiwary P 2018 J. Chem. Phys. 149 072301
[63] Itoh S G, Okumura H and Okamoto Y 2007 Mol. Simul. 33 47
[64] Stariolo D A and Cugliandolo L F 2020 Phys. Rev. E 102 022126
[65] Harada R and Shigeta Y 2017 J. Comput. Chem. 38 1921
[66] Harada R and Shigeta Y 2017 J. Chem. Inf. Model. 57 3070
[67] Shkurti A, Styliari I D, Balasubramanian V, Bethune I, Pedebos C, Jha S and Laughton C A 2019 J. Chem. Theory Comput. 15 2587
[68] Harada R and Kitao A 2013 J. Chem. Phys. 139 035103
[69] Shamsi Z, Cheng K J and Shukla D 2018 J. Phys. Chem. B 122 8386
[70] Kleiman D E and Shukla D 2022 J. Chem. Theory Comput. 18 5422
[71] Zhang J and Gong H 2020 J. Chem. Theory Comput. 16 4813
[72] Li W and Takada S 2010 Biophys. J. 99 3029
[73] Kmiecik S, Gront D, Kolinski M, Wieteska L, Dawid A E and Kolinski A 2016 Chem. Rev. 116 7898
[74] Joshi S Y and Deshmukh S A 2020 Mol. Simul. 47 786
[75] de Jong D H, Singh G, Bennett W F, Arnarez C, Wassenaar T A, Schafer L V, Periole X, Tieleman D P and Marrink S J 2013 J. Chem. Theory Comput. 9 687
[76] Souza P C T, Alessandri R, Barnoud J, et al. 2021 Nat. Methods 18 382
[77] Machado M R, Barrera E E, Klein F, Sonora M, Silva S and Pantano S 2019 J. Chem. Theory Comput. 15 2719
[78] Wang J, Olsson S, Wehmeyer C, Perez A, Charron N E, de Fabritiis G, Noe F and Clementi C 2019 ACS Cent. Sci. 5 755
[79] Majewski M, Perez A, Tholke P, Doerr S, Charron N E, Giorgino T, Husic B E, Clementi C, Noe F and De Fabritiis G 2023 Nat. Commun. 14 5739
[80] Kuhlman B and Bradley P 2019 Nat. Rev. Mol. Cell Biol. 20 681
[81] Soleymani F, Paquet E, Viktor H, Michalowski W and Spinello D 2022 Comput. Struct. Biotechnol. J. 20 5316
[82] Ferruz N, Heinzinger M, Akdel M, Goncearenco A, Naef L and Dallago C 2023 Comput. Struct. Biotechnol. J. 21 238
[83] Zheng L E, Barethiya S, Nordquist E and Chen J 2023 Molecules 28 4047
[84] Wayment-Steele H K and Pande V S 2018 J. Chem. Phys. 149 216101
[85] Degiacomi M T 2019 Structure 27 1034
[86] Ramaswamy V K, Musson S C, Willcocks C G and Degiacomi M T 2021 Phys. Rev. X 11 011052
[87] Jin Y, Johannissen L O and Hay S 2021 Proteins 89 915
[88] Gupta A, Dey S, Hicks A and Zhou H X 2022 Commun. Biol. 5 610
[89] Zhu J J, Zhang N J, Wei T and Chen H F 2023 Int. J. Mol. Sci. 24 6896
[90] Wetzel S J 2017 Phys. Rev. E 96 022140
[91] Song Z, Zhou H, Tian H, Wang X and Tao P 2020 Commun. Chem. 3 134
[92] Noé F, Olsson S, Kohler J and Wu H 2019 Science 365 eaaw1147
[93] Mahmoud A H, Masters M, Lee S J and Lill M A 2022 J. Chem. Inf. Model. 62 1602
[94] Dinh L, Krueger D and Bengio Y 2014 arXiv:1410.8516 [cs.LG]
[95] Dinh L, Sohl-Dickstein J and Bengio S 2016 arXiv:1605.08803 [cs.LG]
[96] Kullback S and Leibler R A 1951 Ann. Math. Stat. 22 79
[97] Kingma D P and Ba J 2014 arXiv:1412.6980 [cs.LG]

Recent advances in protein conformation sampling by combining machine learning with molecular simulation

Recent advances in protein conformation sampling by combining machine learning with molecular simulation

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