Recent Advances in Protein Folding Pathway Prediction through Computational Methods


Cite item

Full Text

Abstract

The protein folding mechanisms are crucial to understanding the fundamental processes of life and solving many biological and medical problems. By studying the folding process, we can reveal how proteins achieve their biological functions through specific structures, providing insights into the treatment and prevention of diseases. With the advancement of AI technology in the field of protein structure prediction, computational methods have become increasingly important and promising for studying protein folding mechanisms. In this review, we retrospect the current progress in the field of protein folding mechanisms by computational methods from four perspectives: simulation of an inverse folding pathway from native state to unfolded state; prediction of early folding residues by machine learning; exploration of protein folding pathways through conformational sampling; prediction of protein folding intermediates based on templates. Finally, the challenges and future perspectives of the protein folding problem by computational methods are also discussed.

About the authors

Kailong Zhao

College of Information Engineering,, Zhejiang University of Technology

Email: info@benthamscience.net

Fang Liang

College of Information Engineering, Zhejiang University of Technology

Email: info@benthamscience.net

Yuhao Xia

College of Information Engineering, Zhejiang University of Technology

Email: info@benthamscience.net

Minghua Hou

College of Information Engineering, Zhejiang University of Technology

Email: info@benthamscience.net

Guijun Zhang

College of Information Engineering, Zhejiang University of Technology

Author for correspondence.
Email: info@benthamscience.net

References

  1. Ding, W.; Nakai, K.; Gong, H. Protein design via deep learning. Brief. Bioinform., 2022, 23(3), bbac102. doi: 10.1093/bib/bbac102 PMID: 35348602
  2. Piana, S.; Lindorff-Larsen, K.; Shaw, D.E. Protein folding kinetics and thermodynamics from atomistic simulation. Proc. Natl. Acad. Sci. USA, 2012, 109(44), 17845-17850. doi: 10.1073/pnas.1201811109 PMID: 22822217
  3. Acharya, N.; Jha, S.K. Dry molten globule-like intermediates in protein folding, function, and disease. J. Phys. Chem. B, 2022, 126(43), 8614-8622. doi: 10.1021/acs.jpcb.2c04991 PMID: 36286394
  4. Huang, L.; Agrawal, T.; Zhu, G.; Yu, S.; Tao, L.; Lin, J.; Marmorstein, R.; Shorter, J.; Yang, X. DAXX represents a new type of protein-folding enabler. Nature, 2021, 597(7874), 132-137. doi: 10.1038/s41586-021-03824-5 PMID: 34408321
  5. Lansbury, P.T., Jr Structural neurology: Are seeds at the root of neuronal degeneration? Neuron, 1997, 19(6), 1151-1154. doi: 10.1016/S0896-6273(00)80406-7 PMID: 9427238
  6. Yuan, Z.; Pan, W.; Zhao, X.; Zhao, F.; Xu, Z.; Li, X.; Zhao, Y.; Zhang, M.Q.; Yao, J. Publisher correction: SODB facilitates comprehensive exploration of spatial omics data. Nat. Methods, 2023, 20(4), 623. doi: 10.1038/s41592-023-01844-9 PMID: 36932185
  7. Yuan, Z.; Li, Y.; Shi, M.; Yang, F.; Gao, J.; Yao, J.; Zhang, M.Q. SOTIP is a versatile method for microenvironment modeling with spatial omics data. Nat. Commun., 2022, 13(1), 7330. doi: 10.1038/s41467-022-34867-5 PMID: 36443314
  8. Zhang, L.; Wang, C.C.; Chen, X. Predicting drug–target binding affinity through molecule representation block based on multi-head attention and skip connection. Brief. Bioinform., 2022, 23(6), bbac468. doi: 10.1093/bib/bbac468 PMID: 36411674
  9. Anfinsen, C.B.; Haber, E.; Sela, M.; White, F.H., Jr The kinetics of formation of native ribonuclease during oxidation of the reduced polypeptide chain. Proc. Natl. Acad. Sci. USA, 1961, 47(9), 1309-1314. doi: 10.1073/pnas.47.9.1309 PMID: 13683522
  10. Levinthal, C. Are there pathways for protein folding? J. Chim. Phys., 1968, 65, 44-45. doi: 10.1051/jcp/1968650044
  11. Finkelstein, A.V. 50+ years of protein folding. Biochemistry (Mosc.), 2018, 83(S1)(Suppl. 1), S3-S18. doi: 10.1134/S000629791814002X PMID: 29544427
  12. Auer, S.; Miller, M.A.; Krivov, S.V.; Dobson, C.M.; Karplus, M.; Vendruscolo, M. Importance of metastable states in the free energy landscapes of polypeptide chains. Phys. Rev. Lett., 2007, 99(17), 178104. doi: 10.1103/PhysRevLett.99.178104 PMID: 17995375
  13. Englander, S.W.; Mayne, L. The nature of protein folding pathways. Proc. Natl. Acad. Sci. USA, 2014, 111(45), 15873-15880. doi: 10.1073/pnas.1411798111 PMID: 25326421
  14. Greenfield, N.J. Using circular dichroism collected as a function of temperature to determine the thermodynamics of protein unfolding and binding interactions. Nat. Protoc., 2006, 1(6), 2527-2535. doi: 10.1038/nprot.2006.204 PMID: 17406506
  15. Nauli, S.; Kuhlman, B.; Baker, D. Computer-based redesign of a protein folding pathway. Nat. Struct. Biol., 2001, 8(7), 602-605. doi: 10.1038/89638 PMID: 11427890
  16. Wang, J.H.; Tang, Y.L.; Gong, Z.; Jain, R.; Xiao, F.; Zhou, Y.; Tan, D.; Li, Q.; Huang, N.; Liu, S.Q.; Ye, K.; Tang, C.; Dong, M.Q.; Lei, X. Characterization of protein unfolding by fast cross-linking mass spectrometry using di-ortho-phthalaldehyde cross-linkers. Nat. Commun., 2022, 13(1), 1468. doi: 10.1038/s41467-022-28879-4 PMID: 35304446
  17. Outeiral, C.; Nissley, D.A.; Deane, C.M. Current structure predictors are not learning the physics of protein folding. Bioinformatics, 2022, 38(7), 1881-1887. doi: 10.1093/bioinformatics/btab881 PMID: 35099504
  18. Hou, J.; Wu, T.; Cao, R.; Cheng, J. Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13. Proteins, 2019, 87(12), 1165-1178. doi: 10.1002/prot.25697 PMID: 30985027
  19. Pakhrin, S.C.; Shrestha, B.; Adhikari, B.; Kc, D.B. Deep learning-based advances in protein structure prediction. Int. J. Mol. Sci., 2021, 22(11), 5553. doi: 10.3390/ijms22115553 PMID: 34074028
  20. Rohl, C.A.; Strauss, C.E.M.; Misura, K.M.S.; Baker, D. Protein structure prediction using Rosetta. Methods Enzymol., 2004, 383, 66-93. doi: 10.1016/S0076-6879(04)83004-0 PMID: 15063647
  21. Yang, J.; Zhang, Y. I-TASSER server: New development for protein structure and function predictions. Nucleic Acids Res., 2015, 43(W1), W174-W181. doi: 10.1093/nar/gkv342 PMID: 25883148
  22. Zhao, K.L.; Liu, J.; Zhou, X.G.; Su, J.Z.; Zhang, Y.; Zhang, G.J. MMpred: A distance-assisted multimodal conformation sampling for de novo protein structure prediction. Bioinformatics, 2021, 37(23), 4350-4356. doi: 10.1093/bioinformatics/btab484 PMID: 34185079
  23. Kosciolek, T; Jones, DT Accurate contact predictions using covariation techniques and machine learning. Proteins, 2016, 84 Suppl 1(Suppl Suppl 1), 145-151. doi: 10.1002/prot.24863
  24. Joo, K.; Joung, I.; Cheng, Q.; Lee, S.J.; Lee, J. Contact-assisted protein structure modeling by global optimization in CASP11. Proteins, 2016, 84(Suppl. 1), 189-199. doi: 10.1002/prot.24975 PMID: 26677100
  25. Moult, J; Fidelis, K; Kryshtafovych, A; Schwede, T; Tramontano, A Critical assessment of methods of protein structure prediction (CASP)-Round XII. Proteins, 2018, 86 Suppl 1(Suppl 1), 7-15. doi: 10.1002/prot.25415
  26. Xu, J. Distance-based protein folding powered by deep learning. Proc. Natl. Acad. Sci. USA, 2019, 116(34), 16856-16865. doi: 10.1073/pnas.1821309116 PMID: 31399549
  27. Ovchinnikov, S.; Park, H.; Kim, D.E.; DiMaio, F.; Baker, D. Protein structure prediction using Rosetta in CASP12. Proteins, 2018, 86 Suppl 1(Suppl 1), 113-121. doi: 10.1002/prot.25390
  28. Zhou, X.; Zheng, W.; Li, Y.; Pearce, R.; Zhang, C.; Bell, E.W.; Zhang, G.; Zhang, Y. I-TASSER-MTD: A deep- learning-based platform for multi-domain protein structure and function prediction. Nat. Protoc., 2022, 17(10), 2326-2353. doi: 10.1038/s41596-022-00728-0 PMID: 35931779
  29. Abriata, L.A.; Tamò, G.E.; Dal Peraro, M. A further leap of improvement in tertiary structure prediction in CASP13 prompts new routes for future assessments. Proteins, 2019, 87(12), 1100-1112. doi: 10.1002/prot.25787 PMID: 31344267
  30. Kandathil, S.M.; Greener, J.G.; Jones, D.T. Prediction of interresidue contacts with DeepMetaPSICOV in CASP13. Proteins, 2019, 87(12), 1092-1099. doi: 10.1002/prot.25779 PMID: 31298436
  31. 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. Highly accurate protein structure prediction with AlphaFold. Nature, 2021, 596(7873), 583-589. doi: 10.1038/s41586-021-03819-2 PMID: 34265844
  32. Skolnick, J.; Gao, M.; Zhou, H.; Singh, S. AlphaFold 2: Why it works and its implications for understanding the relationships of protein sequence, structure, and function. J. Chem. Inf. Model., 2021, 61(10), 4827-4831. doi: 10.1021/acs.jcim.1c01114 PMID: 34586808
  33. Lane, T.J.; Shukla, D.; Beauchamp, K.A.; Pande, V.S. To milliseconds and beyond: Challenges in the simulation of protein folding. Curr. Opin. Struct. Biol., 2013, 23(1), 58-65. doi: 10.1016/j.sbi.2012.11.002 PMID: 23237705
  34. Zhou, X.; Peng, C.; Zheng, W.; Li, Y.; Zhang, G.; Zhang, Y. DEMO2: Assemble multi-domain protein structures by coupling analogous template alignments with deep-learning inter-domain restraint prediction. Nucleic Acids Res., 2022, 50(W1), W235-W245. doi: 10.1093/nar/gkac340 PMID: 35536281
  35. de Azevedo, W.F., Jr Molecular dynamics simulations of protein targets identified in Mycobacterium tuberculosis. Curr. Med. Chem., 2011, 18(9), 1353-1366. doi: 10.2174/092986711795029519 PMID: 21366529
  36. Scheraga, H.A.; Khalili, M.; Liwo, A. Protein-folding dynamics: Overview of molecular simulation techniques. Annu. Rev. Phys. Chem., 2007, 58(1), 57-83. doi: 10.1146/annurev.physchem.58.032806.104614 PMID: 17034338
  37. Lindorff-Larsen, K.; Piana, S.; Dror, R.O.; Shaw, D.E. How fast-folding proteins fold. Science, 2011, 334(6055), 517-520. doi: 10.1126/science.1208351 PMID: 22034434
  38. Paci, E.; Vendruscolo, M.; Dobson, C.M.; Karplus, M. Determination of a transition state at atomic resolution from protein engineering data. J. Mol. Biol., 2002, 324(1), 151-163. doi: 10.1016/S0022-2836(02)00944-0 PMID: 12421565
  39. White, G.W.N.; Gianni, S.; Grossmann, J.G.; Jemth, P.; Fersht, A.R.; Daggett, V. Simulation and experiment conspire to reveal cryptic intermediates and a slide from the nucleation-condensation to framework mechanism of folding. J. Mol. Biol., 2005, 350(4), 757-775. doi: 10.1016/j.jmb.2005.05.005 PMID: 15967458
  40. Lindorff-Larsen, K.; Maragakis, P.; Piana, S.; Eastwood, M.P.; Dror, R.O.; Shaw, D.E. Systematic validation of protein force fields against experimental data. PLoS One, 2012, 7(2), e32131. doi: 10.1371/journal.pone.0032131 PMID: 22384157
  41. Pancsa, R.; Varadi, M.; Tompa, P.; Vranken, W.F. Start2Fold: A database of hydrogen/deuterium exchange data on protein folding and stability. Nucleic Acids Res., 2016, 44(D1), D429-D434. doi: 10.1093/nar/gkv1185 PMID: 26582925
  42. Bai, Y. Protein folding pathways studied by pulsed- and native-state hydrogen exchange. Chem. Rev., 2006, 106(5), 1757-1768. doi: 10.1021/cr040432i PMID: 16683753
  43. Konermann, L.; Pan, Y.; Stocks, B.B. Protein folding mechanisms studied by pulsed oxidative labeling and mass spectrometry. Curr. Opin. Struct. Biol., 2011, 21(5), 634-640. doi: 10.1016/j.sbi.2011.05.004 PMID: 21703846
  44. Fazelinia, H.; Xu, M.; Cheng, H.; Roder, H. Ultrafast hydrogen exchange reveals specific structural events during the initial stages of folding of cytochrome c. J. Am. Chem. Soc., 2014, 136(2), 733-740. doi: 10.1021/ja410437d PMID: 24364692
  45. Merstorf, C.; Maciejak, O.; Mathé, J.; Pastoriza-Gallego, M.; Thiebot, B.; Clément, M.J.; Pelta, J.; Auvray, L.; Curmi, P.A.; Savarin, P. Mapping the conformational stability of maltose binding protein at the residue scale using nuclear magnetic resonance hydrogen exchange experiments. Biochemistry, 2012, 51(44), 8919-8930. doi: 10.1021/bi3003605 PMID: 23046344
  46. Greene, L.H.; Li, H.; Zhong, J.; Zhao, G.; Wilson, K. Folding of an all-helical Greek-key protein monitored by quenched-flow hydrogen–deuterium exchange and NMR spectroscopy. Eur. Biophys. J., 2012, 41(1), 41-51. doi: 10.1007/s00249-011-0756-6 PMID: 22130896
  47. Pancsa, R.; Raimondi, D.; Cilia, E.; Vranken, W.F. Early folding events, local interactions, and conservation of protein backbone rigidity. Biophys. J., 2016, 110(3), 572-583. doi: 10.1016/j.bpj.2015.12.028 PMID: 26840723
  48. Manavalan, B.; Kuwajima, K.; Lee, J. PFDB: A standardized protein folding database with temperature correction. Sci. Rep., 2019, 9(1), 1588. doi: 10.1038/s41598-018-36992-y PMID: 30733462
  49. Kuwajima, K. The molten globule, and two-state vs. non-two-state folding of globular proteins. Biomolecules, 2020, 10(3), 407. doi: 10.3390/biom10030407 PMID: 32155758
  50. Bilsel, O.; Robert Matthews, C. Barriers in protein folding reactions. Adv. Protein Chem., 2000, 53, 153-207. doi: 10.1016/S0065-3233(00)53004-6 PMID: 10751945
  51. Perl, D.; Welker, C.; Schindler, T.; Schröder, K.; Marahiel, M.A.; Jaenicke, R.; Schmid, F.X. Conservation of rapid two-state folding in mesophilic, thermophilic and hyperthermophilic cold shock proteins. Nat. Struct. Biol., 1998, 5(3), 229-235. doi: 10.1038/nsb0398-229 PMID: 9501917
  52. Martinez, J.C.; Pisabarro, M.T.; Serrano, L. Obligatory steps in protein folding and the conformational diversity of the transition state. Nat. Struct. Mol. Biol., 1998, 5(8), 721-729. doi: 10.1038/1418 PMID: 9699637
  53. Jain, R.; Muneeruddin, K.; Anderson, J.; Harms, M.J.; Shaffer, S.A.; Matthews, C.R. A conserved folding nucleus sculpts the free energy landscape of bacterial and archaeal orthologs from a divergent TIM barrel family. Proc. Natl. Acad. Sci. USA, 2021, 118(17), e2019571118. doi: 10.1073/pnas.2019571118 PMID: 33875592
  54. Grantcharova, V.P.; Riddle, D.S.; Santiago, J.V.; Baker, D. Important role of hydrogen bonds in the structurally polarized transition state for folding of the src SH3 domain. Nat. Struct. Mol. Biol., 1998, 5(8), 714-720. doi: 10.1038/1412 PMID: 9699636
  55. Portman, J.J.; Takada, S.; Wolynes, P.G. Variational theory for site resolved protein folding free energy surfaces. Phys. Rev. Lett., 1998, 81(23), 5237-5240. doi: 10.1103/PhysRevLett.81.5237
  56. Galzitskaya, O.V.; Finkelstein, A.V. A theoretical search for folding/unfolding nuclei in three-dimensional protein structures. Proc. Natl. Acad. Sci. USA, 1999, 96(20), 11299-11304. doi: 10.1073/pnas.96.20.11299 PMID: 10500171
  57. Muñoz, V.; Eaton, W.A. A simple model for calculating the kinetics of protein folding from three-dimensional structures. Proc. Natl. Acad. Sci. USA, 1999, 96(20), 11311-11316. doi: 10.1073/pnas.96.20.11311 PMID: 10500173
  58. Alm, E.; Baker, D. Prediction of protein-folding mechanisms from free-energy landscapes derived from native structures. Proc. Natl. Acad. Sci. USA, 1999, 96(20), 11305-11310. doi: 10.1073/pnas.96.20.11305 PMID: 10500172
  59. Alm, E.; Morozov, A.V.; Kortemme, T.; Baker, D. Simple physical models connect theory and experiment in protein folding kinetics. J. Mol. Biol., 2002, 322(2), 463-476. doi: 10.1016/S0022-2836(02)00706-4 PMID: 12217703
  60. Jacobs, W.M.; Shakhnovich, E.I. Accurate protein-folding transition-path statistics from a simple free-energy landscape. J. Phys. Chem. B, 2018, 122(49), 11126-11136. doi: 10.1021/acs.jpcb.8b05842 PMID: 30091592
  61. Feng, Q.; Hou, M.; Liu, J.; Zhao, K.; Zhang, G. Construct a variable-length fragment library for de novo protein structure prediction. Brief. Bioinform., 2022, 23(3), bbac086. doi: 10.1093/bib/bbac086 PMID: 35284936
  62. Jacobs, W.M.; Shakhnovich, E.I. Structure-based prediction of protein-folding transition paths. Biophys. J., 2016, 111(5), 925-936. doi: 10.1016/j.bpj.2016.06.031 PMID: 27602721
  63. Best, R.B.; Hummer, G. Microscopic interpretation of folding ϕ-values using the transition path ensemble. Proc. Natl. Acad. Sci. USA, 2016, 113(12), 3263-3268. doi: 10.1073/pnas.1520864113 PMID: 26957599
  64. de los Rios, M.A.; Daneshi, M.; Plaxco, K.W. Experimental investigation of the frequency and substitution dependence of negative phi-values in two-state proteins. Biochemistry, 2005, 44(36), 12160-12167. doi: 10.1021/bi0505621 PMID: 16142914
  65. Schuler, B.; Hofmann, H. Single-molecule spectroscopy of protein folding dynamics-expanding scope and timescales. Curr. Opin. Struct. Biol., 2013, 23(1), 36-47. doi: 10.1016/j.sbi.2012.10.008 PMID: 23312353
  66. Sosnick, T.R.; Barrick, D. The folding of single domain proteins-have we reached a consensus? Curr. Opin. Struct. Biol., 2011, 21(1), 12-24. doi: 10.1016/j.sbi.2010.11.002 PMID: 21144739
  67. Nickson, A.A.; Wensley, B.G.; Clarke, J. Take home lessons from studies of related proteins. Curr. Opin. Struct. Biol., 2013, 23(1), 66-74. doi: 10.1016/j.sbi.2012.11.009 PMID: 23265640
  68. Plaxco, K.W.; Simons, K.T.; Baker, D. Contact order, transition state placement and the refolding rates of single domain proteins 1 1Edited by P. E. Wright. J. Mol. Biol., 1998, 277(4), 985-994. doi: 10.1006/jmbi.1998.1645 PMID: 9545386
  69. Cilia, E.; Pancsa, R.; Tompa, P.; Lenaerts, T.; Vranken, W.F. From protein sequence to dynamics and disorder with DynaMine. Nat. Commun., 2013, 4(1), 2741. doi: 10.1038/ncomms3741 PMID: 24225580
  70. Walsh, I.; Martin, A.J.M.; Di Domenico, T.; Tosatto, S.C.E. ESpritz: Accurate and fast prediction of protein disorder. Bioinformatics, 2012, 28(4), 503-509. doi: 10.1093/bioinformatics/btr682 PMID: 22190692
  71. Bittrich, S.; Schroeder, M.; Labudde, D. Characterizing the relation of functional and early folding residues in protein structures using the example of aminoacyl-tRNA synthetases. PLoS One, 2018, 13(10), e0206369. doi: 10.1371/journal.pone.0206369 PMID: 30376559
  72. Raimondi, D.; Orlando, G.; Pancsa, R.; Khan, T.; Vranken, W.F. Exploring the sequence-based prediction of folding initiation sites in proteins. Sci. Rep., 2017, 7(1), 8826. doi: 10.1038/s41598-017-08366-3 PMID: 28821744
  73. Roca-Martinez, J.; Lazar, T.; Gavalda-Garcia, J.; Bickel, D.; Pancsa, R.; Dixit, B.; Tzavella, K.; Ramasamy, P.; Sanchez-Fornaris, M.; Grau, I.; Vranken, W.F. Challenges in describing the conformation and dynamics of proteins with ambiguous behavior. Front. Mol. Biosci., 2022, 9, 959956. doi: 10.3389/fmolb.2022.959956 PMID: 35992270
  74. Grau, I.; Nowé, A.; Vranken, W. Interpreting a black box predictor to gain insights into early folding mechanisms. Comput. Struct. Biotechnol. J., 2021, 19, 4919-4930. doi: 10.1016/j.csbj.2021.08.041 PMID: 34527196
  75. Bittrich, S.; Kaden, M.; Leberecht, C.; Kaiser, F.; Villmann, T.; Labudde, D. Application of an interpretable classification model on early folding residues during protein folding. BioData Min., 2019, 12(1), 1. doi: 10.1186/s13040-018-0188-2 PMID: 30627219
  76. Liwo, A.; Khalili, M.; Scheraga, H.A. Ab initio simulations of protein-folding pathways by molecular dynamics with the united-residue model of polypeptide chains. Proc. Natl. Acad. Sci. USA, 2005, 102(7), 2362-2367. doi: 10.1073/pnas.0408885102 PMID: 15677316
  77. Zhou, R.; Maisuradze, G.G.; Suñol, D.; Todorovski, T.; Macias, M.J.; Xiao, Y.; Scheraga, H.A.; Czaplewski, C.; Liwo, A. Folding kinetics of WW domains with the united residue force field for bridging microscopic motions and experimental measurements. Proc. Natl. Acad. Sci. USA, 2014, 111(51), 18243-18248. doi: 10.1073/pnas.1420914111 PMID: 25489078
  78. Maisuradze, G.G.; Senet, P.; Czaplewski, C.; Liwo, A.; Scheraga, H.A. Investigation of protein folding by coarse- grained molecular dynamics with the UNRES force field. J. Phys. Chem. A, 2010, 114(13), 4471-4485. doi: 10.1021/jp9117776 PMID: 20166738
  79. Sterpone, F.; Derreumaux, P.; Melchionna, S. Protein simulations in fluids: Coupling the OPEP coarse-grained force field with hydrodynamics. J. Chem. Theory Comput., 2015, 11(4), 1843-1853. doi: 10.1021/ct501015h PMID: 26574390
  80. Adhikari, A.N.; Freed, K.F.; Sosnick, T.R. De novo prediction of protein folding pathways and structure using the principle of sequential stabilization. Proc. Natl. Acad. Sci. USA, 2012, 109(43), 17442-17447. doi: 10.1073/pnas.1209000109 PMID: 23045636
  81. Adhikari, A.N.; Freed, K.F.; Sosnick, T.R. Simplified protein models: Predicting folding pathways and structure using amino acid sequences. Phys. Rev. Lett., 2013, 111(2), 028103. doi: 10.1103/PhysRevLett.111.028103 PMID: 23889448
  82. Kmiecik, S.; Gront, D.; Kolinski, M.; Wieteska, L.; Dawid, A.E.; Kolinski, A. Coarse-grained protein models and their applications. Chem. Rev., 2016, 116(14), 7898-7936. doi: 10.1021/acs.chemrev.6b00163 PMID: 27333362
  83. Becerra, D.; Butyaev, A.; Waldispühl, J. Fast and flexible coarse-grained prediction of protein folding routes using ensemble modeling and evolutionary sequence variation. Bioinformatics, 2020, 36(5), 1420-1428. doi: 10.1093/bioinformatics/btz743 PMID: 31584628
  84. Huang, Z.; Cui, X.; Xia, Y.; Zhao, K.; Zhang, G. Pathfinder: Protein folding pathway prediction based on conformational sampling. bioRxiv, 2023, 2023.2004. doi: 10.1101/2023.04.20.537604
  85. Hou, M.; Peng, C.; Zhou, X.; Zhang, B.; Zhang, G. Multi contact-based folding method for de novo protein structure prediction. Brief. Bioinform., 2022, 23(1), bbab463. doi: 10.1093/bib/bbab463 PMID: 34849573
  86. Bitran, A.; Jacobs, W.M.; Shakhnovich, E. Validation of DBFOLD: An efficient algorithm for computing folding pathways of complex proteins. PLOS Comput. Biol., 2020, 16(11), e1008323. doi: 10.1371/journal.pcbi.1008323 PMID: 33196646
  87. Faísca, P.F. The nucleation mechanism of protein folding: a survey of computer simulation studies. J. Phys. Condens Matter., 2009, 21(37), 373102. doi: 10.1088/0953-8984/21/37/373102
  88. Guzenko, D.; Burley, S.K.; Duarte, J.M. Real time structural search of the protein data bank. PLOS Comput. Biol., 2020, 16(7), e1007970. doi: 10.1371/journal.pcbi.1007970 PMID: 32639954
  89. Cheng, H.; Schaeffer, R.D.; Liao, Y.; Kinch, L.N.; Pei, J.; Shi, S.; Kim, B.H.; Grishin, N.V. ECOD: An evolutionary classification of protein domains. PLOS Comput. Biol., 2014, 10(12), e1003926. doi: 10.1371/journal.pcbi.1003926 PMID: 25474468
  90. Chandonia, J.M.; Guan, L.; Lin, S.; Yu, C.; Fox, N.K.; Brenner, S.E. SCOPe: Improvements to the structural classification of proteins – extended database to facilitate variant interpretation and machine learning. Nucleic Acids Res., 2022, 50(D1), D553-D559. doi: 10.1093/nar/gkab1054 PMID: 34850923
  91. Sillitoe, I.; Bordin, N.; Dawson, N.; Waman, V.P.; Ashford, P.; Scholes, H.M.; Pang, C.S.M.; Woodridge, L.; Rauer, C.; Sen, N.; Abbasian, M.; Le Cornu, S.; Lam, S.D.; Berka, K.; Varekova, I.H.; Svobodova, R.; Lees, J.; Orengo, C.A. CATH: Increased structural coverage of functional space. Nucleic Acids Res., 2021, 49(D1), D266-D273. doi: 10.1093/nar/gkaa1079 PMID: 33237325
  92. Schwarz, D.; Georges, G.; Kelm, S.; Shi, J.; Vangone, A.; Deane, C.M. Co-evolutionary distance predictions contain flexibility information. Bioinformatics, 2021, 38(1), 65-72. doi: 10.1093/bioinformatics/btab562 PMID: 34383892
  93. Zhao, K.; Xia, Y.; Zhang, F.; Zhou, X.; Li, S.Z.; Zhang, G. Protein structure and folding pathway prediction based on remote homologs recognition using PAthreader. Commun. Biol., 2023, 6(1), 243. doi: 10.1038/s42003-023-04605-8 PMID: 36871126
  94. Bittrich, S.; Rose, Y.; Segura, J.; Lowe, R.; Westbrook, J.D.; Duarte, J.M.; Burley, S.K. RCSB Protein Data Bank: Improved annotation, search and visualization of membrane protein structures archived in the PDB. Bioinformatics, 2022, 38(5), 1452-1454. doi: 10.1093/bioinformatics/btab813 PMID: 34864908
  95. Varadi, M.; Anyango, S.; Deshpande, M.; Nair, S.; Natassia, C.; Yordanova, G.; Yuan, D.; Stroe, O.; Wood, G.; Laydon, A.; Žídek, A.; Green, T.; Tunyasuvunakool, K.; Petersen, S.; Jumper, J.; Clancy, E.; Green, R.; Vora, A.; Lutfi, M.; Figurnov, M.; Cowie, A.; Hobbs, N.; Kohli, P.; Kleywegt, G.; Birney, E.; Hassabis, D.; Velankar, S. Alphafold protein structure database: Massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Res., 2022, 50(D1), D439-D444. doi: 10.1093/nar/gkab1061 PMID: 34791371
  96. Liu, J.; Zhao, K.; Zhang, G. Improved model quality assessment using sequence and structural information by enhanced deep neural networks. Brief. Bioinform., 2023, 24(1), bbac507. doi: 10.1093/bib/bbac507 PMID: 36460624
  97. He, G.; Liu, J.; Liu, D.; Zhang, G. GraphGPSM: A global scoring model for protein structure using graph neural networks. bioRxiv, 2023, 2023.2001.
  98. Englander, S.W.; Mayne, L. The case for defined protein folding pathways. Proc. Natl. Acad. Sci. USA, 2017, 114(31), 8253-8258. doi: 10.1073/pnas.1706196114 PMID: 28630329
  99. Yao, Y; Qian, C; Ye, K; Wang, J; Bai, Z; Tang, W. Solution structure of cyanoferricytochrome c: Ligand-controlled conformational flexibility and electronic structure of the heme moiety. J Biol Inorg Chem, 2002, 7(4-5), 539-47. doi: 10.1007/s00775-001-0334-y
  100. Baldwin, R. The nature of protein folding pathways: The classical versus the new view. J. Biomol. NMR, 1995, 5(2), 103-109. doi: 10.1007/BF00208801 PMID: 7703696
  101. Nussinov, R.; Zhang, M.; Liu, Y.; Jang, H. AlphaFold, Artificial Intelligence (AI), and allostery. J. Phys. Chem. B, 2022, 126(34), 6372-6383. doi: 10.1021/acs.jpcb.2c04346 PMID: 35976160
  102. Roney, J.P.; Ovchinnikov, S. State-of-the-art estimation of protein model accuracy using AlphaFold. Phys. Rev. Lett., 2022, 129(23), 238101. doi: 10.1103/PhysRevLett.129.238101 PMID: 36563190
  103. Dill, K.A.; MacCallum, J.L. The protein-folding problem, 50 years on. Science, 2012, 338(6110), 1042-1046. doi: 10.1126/science.1219021 PMID: 23180855
  104. Zeng, J.; Huang, Z. From Levinthal’s paradox to the effects of cell environmental perturbation on protein folding. Curr. Med. Chem., 2020, 26(42), 7537-7554. doi: 10.2174/0929867325666181017160857 PMID: 30332937
  105. Hartl, F.U.; Bracher, A.; Hayer-Hartl, M. Molecular chaperones in protein folding and proteostasis. Nature, 2011, 475(7356), 324-332. doi: 10.1038/nature10317 PMID: 21776078
  106. Elcock, A.H. Models of macromolecular crowding effects and the need for quantitative comparisons with experiment. Curr. Opin. Struct. Biol., 2010, 20(2), 196-206. doi: 10.1016/j.sbi.2010.01.008 PMID: 20167475
  107. Jacobs, W.M.; Shakhnovich, E.I. Evidence of evolutionary selection for cotranslational folding. Proc. Natl. Acad. Sci. USA, 2017, 114(43), 11434-11439. doi: 10.1073/pnas.1705772114 PMID: 29073068
  108. Tsao, D.; Dokholyan, N.V. Macromolecular crowding induces polypeptide compaction and decreases folding cooperativity. Phys. Chem. Chem. Phys., 2010, 12(14), 3491-3500. doi: 10.1039/b924236h PMID: 20355290
  109. Jones, D.T.; Thornton, J.M. The impact of AlphaFold2 one year on. Nat. Methods, 2022, 19(1), 15-20. doi: 10.1038/s41592-021-01365-3 PMID: 35017725
  110. Peng, C.X.; Zhou, X.G.; Xia, Y.H.; Liu, J.; Hou, M.H.; Zhang, G.J. Structural analogue-based protein structure domain assembly assisted by deep learning. Bioinformatics, 2022, 38(19), 4513-4521. doi: 10.1093/bioinformatics/btac553 PMID: 35962986
  111. Karami, Y.; Guyon, F.; De Vries, S.; Tufféry, P. DaReUS-Loop: Accurate loop modeling using fragments from remote or unrelated proteins. Sci. Rep., 2018, 8(1), 13673. doi: 10.1038/s41598-018-32079-w PMID: 30209260
  112. Park, H.; Lee, G.R.; Heo, L.; Seok, C. Protein loop modeling using a new hybrid energy function and its application to modeling in inaccurate structural environments. PLoS One, 2014, 9(11), e113811. doi: 10.1371/journal.pone.0113811 PMID: 25419655
  113. Barozet, A.; Molloy, K.; Vaisset, M.; Siméon, T.; Cortés, J. A reinforcement-learning-based approach to enhance exhaustive protein loop sampling. Bioinformatics, 2020, 36(4), 1099-1106. doi: 10.1093/bioinformatics/btz684 PMID: 31504192
  114. Wang, J; Wang, W; Shang, Y. Protein loop modeling using AlphaFold2. IEEE/ACM Trans Comput Biol Bioinform, 2023. doi: 10.1109/TCBB.2023.3264899
  115. 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. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, 2023, 379(6637), 1123-1130. doi: 10.1126/science.ade2574 PMID: 36927031
  116. Calloni, G.; Taddei, N.; Plaxco, K.W.; Ramponi, G.; Stefani, M.; Chiti, F. Comparison of the folding processes of distantly related proteins. Importance of hydrophobic content in folding. J. Mol. Biol., 2003, 330(3), 577-591. doi: 10.1016/S0022-2836(03)00627-2 PMID: 12842473

Supplementary files

Supplementary Files
Action
1. JATS XML
Download

Copyright (c) 2024 Bentham Science Publishers