Exploring proten’s conformational space by using encoding layer supervised auto-encoder

Chen Guang-Lin; Zhang Zhi-Yong

doi:10.7498/aps.72.20231060

Abstract

Protein function is related to its structure and dynamic change. Molecular dynamics simulation is an important tool for studying protein dynamics by exploring its conformational space, however, conformational sampling is a nontrivial issue, because of the risk of missing key details during sampling. In recent years, deep learning methods, such as auto-encoder, can couple with MD to explore conformational space of protein. After being trained with the MD trajectories, auto-encoder can generate new conformations quickly by inputting random numbers in low dimension space. However, some problems still exist, such as requirements for the quality of the training set, the limitation of explorable area and the undefined sampling direction. In this work, we build a supervised auto-encoder, in which some reaction coordinates are used to guide conformational exploration along certain directions. We also try to expand the explorable area by training through the data generated by the model. Two multi-domain proteins, bacteriophage T4 lysozyme and adenylate kinase, are used to illustrate the method. In the case of the training set consisting of only under-sampled simulated trajectories, the supervised auto-encoder can still explore along the given reaction coordinates. The explored conformational space can cover all the experimental structures of the proteins and be extended to regions far from the training sets. Having been verified by molecular dynamics and secondary structure calculations, most of the conformations explored are found to be plausible. The supervised auto-encoder provides a way to efficiently expand the conformational space of a protein with limited computational resources, although some suitable reaction coordinates are required. By integrating appropriate reaction coordinates or experimental data, the supervised auto-encoder may serve as an efficient tool for exploring conformational space of proteins.

Keywords:

Authors and contacts

Department of Physics, University of Science and Technology of China, Hefei 230026, China

Corresponding author: Zhang Zhi-Yong, zzyzhang@ustc.edu.cn

Funds: Project supported by the National Key Research and Development Program of China (Grant No. 2021YFA1301504), the National Natural Science Foundation of China (Grant No. 91953101), and the Strategic Priority Research Program (B) of the Chinese Academy of Sciences (Grant No. XDB37040202).

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References

[1]	Chu X, Gan L, Wang E, Wang J 2013 Proc. Natl. Acad. Sci. U.S.A. 110 E2342 Google Scholar
[2]	Smyth M S, Martin J H 2000 Mol. Pathol. 53 8 Google Scholar
[3]	Danev R, Yanagisawa H, Kikkawa M 2019 Trends Biochem. Sci. 44 837 Google Scholar
[4]	Vincenzi M, Mercurio F A, Leone M 2021 Curr. Med. Chem. 28 2729 Google Scholar
[5]	Kachala M, Valentini E, Svergun D I 2015 Adv. Exp. Med. Biol. 870 261 Google Scholar
[6]	Chu F, Thornton D T, Nguyen H T 2018 Methods 144 53 Google Scholar
[7]	Bhaumik S R 2021 Emerg. Top Life Sci. 5 49 Google Scholar
[8]	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
[9]	Baek M, DiMaio F, Anishchenko I, Dauparas J, Ovchinnikov S, Lee G R, Wang J, Cong Q, Kinch L N, Schaeffer R D, Millán C, Park H, Adams C, Glassman C R, DeGiovanni A, Pereira J H, Rodrigues A V, van Dijk A A, Ebrecht A C, Opperman D J, Sagmeister T, Buhlheller C, Pavkov-Keller T, Rathinaswamy M K, Dalwadi U, Yip C K, Burke J E, Garcia K C, Grishin N V, Adams P D, Read R J, Baker D 2021 Science 373 871 Google Scholar
[10]	Karplus M, Kuriyan J 2005 Proc. Natl. Acad. Sci. 102 6679 Google Scholar
[11]	Bernardi R C, Melo M C R, Schulten K 2015 Biochim. Biophys. Acta 1850 872 Google Scholar
[12]	Mu J, Liu H, Zhang J, Luo R, Chen H F 2021 J. Chem. Inf. Model. 61 1037 Google Scholar
[13]	Lemke T, Peter C 2019 J. Chem. Theory Comput. 15 1209 Google Scholar
[14]	Zhu J, Wang J, Han W, Xu D 2022 Nat. Commun. 13 1661 Google Scholar
[15]	Hinton G E, Salakhutdinov R R 2006 Science 313 504 Google Scholar
[16]	Degiacomi M T 2019 Structure 27 1034 Google Scholar
[17]	Wen B, Peng J, Zuo X, Gong Q, Zhang Z 2014 Biophysical J. 107 956 Google Scholar
[18]	Giri Rao V V H, Gosavi S 2014 PLOS Computational Biology 10 e1003938 Google Scholar
[19]	Abraham M J, Murtola T, Schulz R, Páll S, Smith J C, Hess B, Lindahl E 2015 SoftwareX 1–2 19 Google Scholar
[20]	Weaver L H, Matthews B W 1987 J. Mol. Biol. 193 189 Google Scholar
[21]	Zhang X J, Wozniak J A, Matthews B W 1995 J. Mol. Biol. 250 527 Google Scholar
[22]	Müller C W, Schulz G E 1992 J. Mol. Biol. 224 159 Google Scholar
[23]	Müller C W, Schlauderer G J, Reinstein J, Schulz G E 1996 Structure 4 147 Google Scholar
[24]	Hornak V, Abel R, Okur A, Strockbine B, Roitberg A, Simmerling C 2006 Proteins Struct. Funct. Bioinf. 65 712 Google Scholar
[25]	Izadi S, Anandakrishnan R, Onufriev A V 2014 J. Phys. Chem. Lett. 5 3863 Google Scholar
[26]	Huang J, Rauscher S, Nawrocki G, Ran T, Feig M, de Groot B L, Grubmüller H, MacKerell A D 2017 Nat. Methods 14 71 Google Scholar
[27]	Bussi G, Donadio D, Parrinello M 2007 J. Chem. Phys. 126 014101 Google Scholar
[28]	Essmann U, Perera L E, Berkowitz M L, Darden T A, Lee H C, Pedersen L G 1995 J. Chem. Phys. 103 8577 Google Scholar
[29]	Kingma D P, Ba J 2014 arXiv:1412.6980 [cs.LG
[30]	Lovell S C, Davis I W, Arendall III W B, de Bakker P I W, Word J M, Prisant M G, Richardson J S, Richardson D C 2003 Proteins Struct. Funct. Bioinf. 50 437 Google Scholar
[31]	Eastman P, Swails J, Chodera J D, McGibbon R T, Zhao Y, Beauchamp K A, Wang L P, Simmonett A C, Harrigan M P, Stern C D, Wiewiora R P, Brooks B R, Pande V S 2017 PLoS Comput. Biol. 13 e1005659 Google Scholar
[32]	Shirts M R, Klein C, Swails J M, Yin J, Gilson M K, Mobley D L, Case D A, Zhong E D 2017 J. Comput. -Aided Mol. Des. 31 147 Google Scholar
[33]	Touw W G, Baakman C, Black J, te Beek T A, Krieger E, Joosten R P, Vriend G 2015 Nucleic Acids Res. 43 D364 Google Scholar

Cited By

图 1 中间层受监督的自编码器示意图

Figure 1. Schematic of supervised-AE.

DownLoad: Full-Size Img PowerPoint

图 2 本研究中使用的两种蛋白质分子的不同结构　(a) T4L的闭合(不透明)和打开(透明)结构, 紫色为α螺旋, 黄色为β折叠; (b) AdK的闭合(不透明)和打开(透明)结构, 不同颜色表示不同的结构域

Figure 2. Different structures of the two proteins in the work. (a) The close (opaque) and open (transparent) state of T4L. α-helix is colored in purple and β-sheet is colored in yellow. (b) The close (opaque) and open (transparent) state of AdK. Different domains are colored in different colors.

DownLoad: Full-Size Img PowerPoint

图 3 T4L的构象空间探索结果　(a) 使用AMBER99SB力场/OPC水模型; (b)使用CHARMM36m力场/TIP3P水模型

Figure 3. Results of conformational space exploration of T4L: (a) With AMBER99SB/OPC; (b) with CHARMM36m/ TIP3P.

DownLoad: Full-Size Img PowerPoint

图 4 探索到的不同T4L构象　(a) PDB编号173L的晶体结构(不透明)与探索到的相似结构(透明); (b) 开合程度不同的两个构象; (c) 扭动情况不同的两个构象; 紫色为α螺旋, 黄色为β折叠

Figure 4. Different T4L conformations explored: (a) PDB:173L (opaque) and a similar structure explored; (b) two conformations with different degrees of opening and closing; (c) two conformations with different degrees of twisting. α-helix is colored in purple and β-sheet is colored in yellow.

DownLoad: Full-Size Img PowerPoint

图 5 T4L构象探索结果的合理性检验　(a) 使用AMBER99SB力场/OPC水模型; (b) 使用CHARMM36m力场/TIP3P水模型; (c) 修复后各代表构象的二级结构含量, 参考值为模拟轨迹的平均值

Figure 5. Plausibility check of T4L conformational exploration results: (a) With AMBER99SB/OPC; (b) with CHARMM36m/TIP3P; (c) secondary structure counts of each representative conformation after fixing, the reference is the average value of the simulated trajectory.

DownLoad: Full-Size Img PowerPoint

图 6 仅从打开状态出发的T4L构象探索结果

Figure 6. Results of T4L conformational exploration from the open state only.

DownLoad: Full-Size Img PowerPoint

图 7 AdK的构象空间探索结果　(a) 使用AMBER99SB力场/OPC水模型; (b)使用CHARMM36m力场/TIP3P水模型

Figure 7. Results of conformational space exploration of AdK: (a) With AMBER99SB/OPC; (b) with CHARMM36m/TIP3P.

DownLoad: Full-Size Img PowerPoint

图 8 探索到的不同AdK构象

Figure 8. Different AdK conformations explored.

DownLoad: Full-Size Img PowerPoint

图 9 AdK构象探索结果的合理性检验　(a) 使用AMBER99SB力场/OPC水模型; (b)使用CHARMM36m力场/TIP3P水模型; (c) 修复后各代表构象的二级结构含量, 参考值为模拟轨迹的平均值

Figure 9. Plausibility check of AdK conformational exploration results: (a) With AMBER99SB/OPC; (b) with CHARMM36m/TIP3P; (c) secondary structure counts of each representative conformation after fixing, the reference is the average value of the simulated trajectory.

DownLoad: Full-Size Img PowerPoint

图 10 使用普通自编码器探索AdK的构象空间

Figure 10. Exploring the conformational space of AdK with a common self-encoder.

DownLoad: Full-Size Img PowerPoint

map

[1]	Chu X, Gan L, Wang E, Wang J 2013 Proc. Natl. Acad. Sci. U.S.A. 110 E2342 Google Scholar
[2]	Smyth M S, Martin J H 2000 Mol. Pathol. 53 8 Google Scholar
[3]	Danev R, Yanagisawa H, Kikkawa M 2019 Trends Biochem. Sci. 44 837 Google Scholar
[4]	Vincenzi M, Mercurio F A, Leone M 2021 Curr. Med. Chem. 28 2729 Google Scholar
[5]	Kachala M, Valentini E, Svergun D I 2015 Adv. Exp. Med. Biol. 870 261 Google Scholar
[6]	Chu F, Thornton D T, Nguyen H T 2018 Methods 144 53 Google Scholar
[7]	Bhaumik S R 2021 Emerg. Top Life Sci. 5 49 Google Scholar
[8]	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
[9]	Baek M, DiMaio F, Anishchenko I, Dauparas J, Ovchinnikov S, Lee G R, Wang J, Cong Q, Kinch L N, Schaeffer R D, Millán C, Park H, Adams C, Glassman C R, DeGiovanni A, Pereira J H, Rodrigues A V, van Dijk A A, Ebrecht A C, Opperman D J, Sagmeister T, Buhlheller C, Pavkov-Keller T, Rathinaswamy M K, Dalwadi U, Yip C K, Burke J E, Garcia K C, Grishin N V, Adams P D, Read R J, Baker D 2021 Science 373 871 Google Scholar
[10]	Karplus M, Kuriyan J 2005 Proc. Natl. Acad. Sci. 102 6679 Google Scholar
[11]	Bernardi R C, Melo M C R, Schulten K 2015 Biochim. Biophys. Acta 1850 872 Google Scholar
[12]	Mu J, Liu H, Zhang J, Luo R, Chen H F 2021 J. Chem. Inf. Model. 61 1037 Google Scholar
[13]	Lemke T, Peter C 2019 J. Chem. Theory Comput. 15 1209 Google Scholar
[14]	Zhu J, Wang J, Han W, Xu D 2022 Nat. Commun. 13 1661 Google Scholar
[15]	Hinton G E, Salakhutdinov R R 2006 Science 313 504 Google Scholar
[16]	Degiacomi M T 2019 Structure 27 1034 Google Scholar
[17]	Wen B, Peng J, Zuo X, Gong Q, Zhang Z 2014 Biophysical J. 107 956 Google Scholar
[18]	Giri Rao V V H, Gosavi S 2014 PLOS Computational Biology 10 e1003938 Google Scholar
[19]	Abraham M J, Murtola T, Schulz R, Páll S, Smith J C, Hess B, Lindahl E 2015 SoftwareX 1–2 19 Google Scholar
[20]	Weaver L H, Matthews B W 1987 J. Mol. Biol. 193 189 Google Scholar
[21]	Zhang X J, Wozniak J A, Matthews B W 1995 J. Mol. Biol. 250 527 Google Scholar
[22]	Müller C W, Schulz G E 1992 J. Mol. Biol. 224 159 Google Scholar
[23]	Müller C W, Schlauderer G J, Reinstein J, Schulz G E 1996 Structure 4 147 Google Scholar
[24]	Hornak V, Abel R, Okur A, Strockbine B, Roitberg A, Simmerling C 2006 Proteins Struct. Funct. Bioinf. 65 712 Google Scholar
[25]	Izadi S, Anandakrishnan R, Onufriev A V 2014 J. Phys. Chem. Lett. 5 3863 Google Scholar
[26]	Huang J, Rauscher S, Nawrocki G, Ran T, Feig M, de Groot B L, Grubmüller H, MacKerell A D 2017 Nat. Methods 14 71 Google Scholar
[27]	Bussi G, Donadio D, Parrinello M 2007 J. Chem. Phys. 126 014101 Google Scholar
[28]	Essmann U, Perera L E, Berkowitz M L, Darden T A, Lee H C, Pedersen L G 1995 J. Chem. Phys. 103 8577 Google Scholar
[29]	Kingma D P, Ba J 2014 arXiv:1412.6980 [cs.LG
[30]	Lovell S C, Davis I W, Arendall III W B, de Bakker P I W, Word J M, Prisant M G, Richardson J S, Richardson D C 2003 Proteins Struct. Funct. Bioinf. 50 437 Google Scholar
[31]	Eastman P, Swails J, Chodera J D, McGibbon R T, Zhao Y, Beauchamp K A, Wang L P, Simmonett A C, Harrigan M P, Stern C D, Wiewiora R P, Brooks B R, Pande V S 2017 PLoS Comput. Biol. 13 e1005659 Google Scholar
[32]	Shirts M R, Klein C, Swails J M, Yin J, Gilson M K, Mobley D L, Case D A, Zhong E D 2017 J. Comput. -Aided Mol. Des. 31 147 Google Scholar
[33]	Touw W G, Baakman C, Black J, te Beek T A, Krieger E, Joosten R P, Vriend G 2015 Nucleic Acids Res. 43 D364 Google Scholar

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