Recent Advances in Protein Folding Pathway Prediction through
Computational Methods

Kailong      Zhao; Fang      Liang; Yuhao      Xia; Minghua      Hou; Guijun      Zhang
doi:10.2174/0109298673265249231004193520
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.
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[1]
Ding, W.; Nakai, K.; Gong, H. Protein design via deep learning. Brief. Bioinform.,  2022, 23(3), bbac102.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/10.1073/pnas.47.9.1309] [PMID: 13683522]

[10]
Levinthal, C. Are there pathways for protein folding? J. Chim. Phys.,  1968, 65, 44-45.
 [http://dx.doi.org/10.1051/jcp/1968650044]

[11]
Finkelstein, A.V. 50+ years of protein folding. Biochemistry (Mosc.),  2018, 83(S1)(Suppl. 1), S3-S18.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/10.3390/biom10030407] [PMID: 32155758]

[50]
Bilsel, O.; Robert Matthews, C. Barriers in protein folding reactions. Adv. Protein Chem.,  2000, 53, 153-207.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/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.
 [http://dx.doi.org/10.1016/S0022-2836(03)00627-2] [PMID: 12842473]
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DOI https://dx.doi.org/10.2174/0109298673265249231004193520	Print ISSN 0929-8673
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Recent Advances in Protein Folding Pathway Prediction through Computational Methods

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