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Current Protein & Peptide Science

Editor-in-Chief

ISSN (Print): 1389-2037
ISSN (Online): 1875-5550

Review Article

Current Stage and Future Perspectives for Homology Modeling, Molecular Dynamics Simulations, Machine Learning with Molecular Dynamics, and Quantum Computing for Intrinsically Disordered Proteins and Proteins with Intrinsically Disordered Regions

Author(s): Orkid Coskuner-Weber* and Vladimir N. Uversky

Volume 25, Issue 2, 2024

Published on: 02 January, 2024

Page: [163 - 171] Pages: 9

DOI: 10.2174/0113892037281184231123111223

Price: $65

Abstract

The structural ensembles of intrinsically disordered proteins (IDPs) and proteins with intrinsically disordered regions (IDRs) cannot be easily characterized using conventional experimental techniques. Computational techniques complement experiments and provide useful insights into the structural ensembles of IDPs and proteins with IDRs. Herein, we discuss computational techniques such as homology modeling, molecular dynamics simulations, machine learning with molecular dynamics, and quantum computing that can be applied to the studies of IDPs and hybrid proteins with IDRs. We also provide useful future perspectives for computational techniques that can be applied to IDPs and hybrid proteins containing ordered domains and IDRs.

Graphical Abstract

[1]
Coskuner, O.; Uversky, V.N. Tyrosine regulates β-sheet structure formation in amyloid-β 42 : A new clustering algorithm for disordered proteins. J. Chem. Inf. Model., 2017, 57(6), 1342-1358.
[http://dx.doi.org/10.1021/acs.jcim.6b00761] [PMID: 28474890]
[2]
Coskuner, O.; Uversky, V.N. Intrinsically disordered proteins in various hypotheses on the pathogenesis of alzheimer’s and parkinson’s diseases. In: Progress in Molecular Biology and Translational Science; Elsevier, 2019; Vol. 166, pp. 145-223.
[3]
Coskuner, O.; Wise-Scira, O. Arginine and disordered amyloid-β peptide structures: Molecular level insights into the toxicity in Alzheimer’s disease. ACS Chem. Neurosci., 2013, 4(12), 1549-1558.
[http://dx.doi.org/10.1021/cn4001389] [PMID: 24041422]
[4]
Coskuner-Weber, O.; Mirzanli, O.; Uversky, V.N. Intrinsically disordered proteins and proteins with intrinsically disordered regions in neurodegenerative diseases. Biophys. Rev., 2022, 14(3), 679-707.
[http://dx.doi.org/10.1007/s12551-022-00968-0] [PMID: 35791387]
[5]
Burger, V.; Gurry, T.; Stultz, C. Intrinsically disordered proteins: Where computation meets experiment. Polymers, 2014, 6(10), 2684-2719.
[http://dx.doi.org/10.3390/polym6102684]
[6]
Rezaei-Ghaleh, N.; Blackledge, M.; Zweckstetter, M. Intrinsically disordered proteins: From sequence and conformational properties toward drug discovery. ChemBioChem, 2012, 13(7), 930-950.
[http://dx.doi.org/10.1002/cbic.201200093] [PMID: 22505141]
[7]
Trivedi, R.; Nagarajaram, H.A. Intrinsically disordered proteins: An overview. Int. J. Mol. Sci., 2022, 23(22), 14050.
[http://dx.doi.org/10.3390/ijms232214050] [PMID: 36430530]
[8]
Gibbs, E.B.; Showalter, S.A. Quantitative biophysical characterization of intrinsically disordered proteins. Biochemistry, 2015, 54(6), 1314-1326.
[http://dx.doi.org/10.1021/bi501460a] [PMID: 25631161]
[9]
Oldfield, C.J.; Uversky, V.N.; Dunker, A.K.; Kurgan, L. Introduction to intrinsically disordered proteins and regions. In: Intrinsically Disordered Proteins; Elsevier, 2019; pp. 1-34.
[http://dx.doi.org/10.1016/B978-0-12-816348-1.00001-6]
[10]
Tompa, P.; Schad, E.; Tantos, A.; Kalmar, L. Intrinsically disordered proteins: Emerging interaction specialists. Curr. Opin. Struct. Biol., 2015, 35, 49-59.
[http://dx.doi.org/10.1016/j.sbi.2015.08.009] [PMID: 26402567]
[11]
Oldfield, C.J.; Dunker, A.K. Intrinsically disordered proteins and intrinsically disordered protein regions. Annu. Rev. Biochem., 2014, 83(1), 553-584.
[http://dx.doi.org/10.1146/annurev-biochem-072711-164947] [PMID: 24606139]
[12]
Uversky, V.N. A decade and a half of protein intrinsic disorder: Biology still waits for physics. Protein Sci., 2013, 22(6), 693-724.
[http://dx.doi.org/10.1002/pro.2261] [PMID: 23553817]
[13]
Zanotti, G. Intrinsic disorder and flexibility in proteins: A challenge for structural biology and drug design. Crystallogr. Rev., 2023, 29(2), 48-75.
[http://dx.doi.org/10.1080/0889311X.2023.2208518]
[14]
Uversky, V.N. Intrinsically disordered proteins and their “Mysterious” (Meta)physics. Front. Phys., 2019, 7, 10.
[http://dx.doi.org/10.3389/fphy.2019.00010]
[15]
Wei, G.; Xi, W.; Nussinov, R.; Ma, B. Protein ensembles: How does nature harness thermodynamic fluctuations for life? the diverse functional roles of conformational ensembles in the cell. Chem. Rev., 2016, 116(11), 6516-6551.
[http://dx.doi.org/10.1021/acs.chemrev.5b00562] [PMID: 26807783]
[16]
Siltberg-Liberles, J.; Grahnen, J.A.; Liberles, D.A. The evolution of protein structures and structural ensembles under functional constraint. Genes, 2011, 2(4), 748-762.
[http://dx.doi.org/10.3390/genes2040748] [PMID: 24710290]
[17]
Akbayrak, I.Y.; Caglayan, S.I.; Ozcan, Z.; Uversky, V.N.; Coskuner-Weber, O. Current challenges and limitations in the studies of intrinsically disordered proteins in neurodegenerative diseases by computer simulations. Curr. Alzheimer Res., 2021, 17(9), 805-818.
[http://dx.doi.org/10.2174/1567205017666201109094908] [PMID: 33167839]
[18]
Na, J.H.; Lee, W.K.; Yu, Y. How do we study the dynamic structure of unstructured proteins: A case study on Nopp140 as an example of a large, intrinsically disordered protein. Int. J. Mol. Sci., 2018, 19(2), 381.
[http://dx.doi.org/10.3390/ijms19020381] [PMID: 29382046]
[19]
Bourne, P.E.; Weissig, H. Structural Bioinformatics. In: Methods of Biochemical Analysis, 1st ed.; Wiley, 2003; p. 44.
[20]
Wallner, B.; Elofsson, A. All are not equal: A benchmark of different homology modeling programs. Protein Sci., 2005, 14(5), 1315-1327.
[http://dx.doi.org/10.1110/ps.041253405] [PMID: 15840834]
[21]
Kopp, J.; Schwede, T. Automated protein structure homology modeling: A progress report. Pharmacogenomics, 2004, 5(4), 405-416.
[http://dx.doi.org/10.1517/14622416.5.4.405] [PMID: 15165176]
[22]
Alexandrov, N.N.; Luethy, R. Alignment algorithm for homology modeling and threading. Protein Sci., 1998, 7(2), 254-258.
[http://dx.doi.org/10.1002/pro.5560070204] [PMID: 9521100]
[23]
Annalora, A.J.; Bobrovnikov-Marjon, E.; Serda, R.; Pastuszyn, A.; Graham, S.E.; Marcus, C.B.; Omdahl, J.L. Hybrid homology modeling and mutational analysis of cytochrome P450C24A1 (CYP24A1) of the Vitamin D pathway: Insights into substrate specificity and membrane bound structure–function. Arch. Biochem. Biophys., 2007, 460(2), 262-273.
[http://dx.doi.org/10.1016/j.abb.2006.11.018] [PMID: 17207766]
[24]
Taverner, T.; Hernández, H.; Sharon, M.; Ruotolo, B.T.; Matak-Vinković, D.; Devos, D.; Russell, R.B.; Robinson, C.V. Subunit architecture of intact protein complexes from mass spectrometry and homology modeling. Acc. Chem. Res., 2008, 41(5), 617-627.
[http://dx.doi.org/10.1021/ar700218q] [PMID: 18314965]
[25]
Hameduh, T.; Haddad, Y.; Adam, V.; Heger, Z. Homology modeling in the time of collective and artificial intelligence. Comput. Struct. Biotechnol. J., 2020, 18, 3494-3506.
[http://dx.doi.org/10.1016/j.csbj.2020.11.007] [PMID: 33304450]
[26]
Park, H.; Ovchinnikov, S.; Kim, D.E.; DiMaio, F.; Baker, D. Protein homology model refinement by large-scale energy optimization. Proc. Natl. Acad. Sci., 2018, 115(12), 3054-3059.
[http://dx.doi.org/10.1073/pnas.1719115115] [PMID: 29507254]
[27]
Ranganathan, A.; Stoddart, L.A.; Hill, S.J.; Carlsson, J. Fragment-based discovery of subtype-selective adenosine receptor ligands from homology models. J. Med. Chem., 2015, 58(24), 9578-9590.
[http://dx.doi.org/10.1021/acs.jmedchem.5b01120] [PMID: 26592528]
[28]
Oshiro, C.; Bradley, E.K.; Eksterowicz, J.; Evensen, E.; Lamb, M.L.; Lanctot, J.K.; Putta, S.; Stanton, R.; Grootenhuis, P.D.J. Performance of 3D-database molecular docking studies into homology models. J. Med. Chem., 2004, 47(3), 764-767.
[http://dx.doi.org/10.1021/jm0300781] [PMID: 14736258]
[29]
Sunuwar, J.; Azad, R.K. Identification of novel antimicrobial resistance genes using machine learning, homology modeling, and molecular docking. Microorganisms, 2022, 10(11), 2102.
[http://dx.doi.org/10.3390/microorganisms10112102] [PMID: 36363694]
[30]
Advances in bioinformatics and computational biology. In: Bazzan, A.L.C.; Craven, M.; Martins, N.F.; Eds.; Third International Brazilian Symposium on Bioinformatics, BSB 2008; Santo André, Brazil, August 28-30, 2008, 978-3-540-85556-9
[31]
Yang, J.; Yan, R.; Roy, A.; Xu, D.; Poisson, J.; Zhang, Y. The I-TASSER Suite: Protein structure and function prediction. Nat. Methods, 2015, 12(1), 7-8.
[http://dx.doi.org/10.1038/nmeth.3213] [PMID: 25549265]
[32]
Cramer, P. AlphaFold2 and the future of structural biology. Nat. Struct. Mol. Biol., 2021, 28(9), 704-705.
[http://dx.doi.org/10.1038/s41594-021-00650-1] [PMID: 34376855]
[33]
Zhang, Y. I-TASSER server for protein 3D structure prediction. BMC Bioinformatics, 2008, 9(1), 40.
[http://dx.doi.org/10.1186/1471-2105-9-40] [PMID: 18215316]
[34]
Yang, J.; Zhang, Y. Protein structure and function prediction using I-TASSER. Curr. Protoc. Bioinformatics, 2015, 52(1), 8.1-, 15.
[http://dx.doi.org/10.1002/0471250953.bi0508s52] [PMID: 26678386]
[35]
Zheng, W.; Zhang, C.; Li, Y.; Pearce, R.; Bell, E.W.; Zhang, Y. Folding non-homologous proteins by coupling deep-learning contact maps with I-TASSER assembly simulations. Cell Reports Methods, 2021, 1(3), 100014.
[http://dx.doi.org/10.1016/j.crmeth.2021.100014] [PMID: 34355210]
[36]
Bryant, P.; Pozzati, G.; Elofsson, A. Improved prediction of protein-protein interactions using AlphaFold2. Nat. Commun., 2022, 13(1), 1265.
[http://dx.doi.org/10.1038/s41467-022-28865-w] [PMID: 35273146]
[37]
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]
[38]
Alici, H.; Uversky, V.N.; Kang, D.E.; Woo, J.A.; Coskuner-Weber, O. Effects of the Jokela type of spinal muscular atrophy-related G66V mutation on the structural ensemble characteristics of CHCHD10. Proteins, 2023, 91(6), 739-749.
[http://dx.doi.org/10.1002/prot.26463] [PMID: 36625206]
[39]
Ait-El-Mkadem Saadi, S.; Chaussenot, A.; Bannwarth, S.; Rouzier, C.; Paquis-Flucklinger, V. CHCHD10-Related Disorders. In: GeneReviews; Adam, M.P.; Ardinger, H.H; Pagon, R.A.; Wallace, S.E.; Bean, L.J.; Mirzaa, G.; Amemiya, A., Eds.; University of Washington, Seattle: Seattle (WA), 1993.
[40]
Aras, S.; Bai, M.; Lee, I.; Springett, R.; Hüttemann, M.; Grossman, L.I. MNRR1 (formerly CHCHD2) is a bi-organellar regulator of mitochondrial metabolism. Mitochondrion, 2015, 20, 43-51.
[http://dx.doi.org/10.1016/j.mito.2014.10.003] [PMID: 25315652]
[41]
Alici, H.; Uversky, V.N.; Kang, D.E.; Woo, J.A.; Coskuner-Weber, O. Structures of the wild-type and S59L mutant CHCHD10 proteins important in amyotrophic lateral sclerosis–frontotemporal dementia. ACS Chem. Neurosci., 2022, 13(8), 1273-1280.
[http://dx.doi.org/10.1021/acschemneuro.2c00011] [PMID: 35349255]
[42]
Metallic Systems: A Quantum Chemist’s Perspective; Allison, T.C.; Coskuner, O.; Gonzalez, C.A., Eds.; CRC Press, 2011.
[43]
Hansson, T.; Oostenbrink, C.; van Gunsteren, W. Molecular dynamics simulations. Curr. Opin. Struct. Biol., 2002, 12(2), 190-196.
[http://dx.doi.org/10.1016/S0959-440X(02)00308-1] [PMID: 11959496]
[44]
Coskuner-Weber, O.; Habiboglu, M.G.; Teplow, D.; Uversky, V.N. From quantum mechanics, classical mechanics, and bioinformatics to artificial intelligence studies in neurodegenerative diseases. Methods Mol. Biol., 2022, 2340, 139-173.
[http://dx.doi.org/10.1007/978-1-0716-1546-1_8] [PMID: 35167074]
[45]
Alici, H.; Hasekioglu, O.; Uversky, V.N.; Coskuner-Weber, O. Methods to study the effect of solution variables on the conformational dynamics of intrinsically disordered proteins. In: Advances in Protein Molecular and Structural Biology Methods; Elsevier, 2022; pp. 551-563.
[http://dx.doi.org/10.1016/B978-0-323-90264-9.00033-7]
[46]
Bernardi, R.C.; Melo, M.C.R.; Schulten, K. Enhanced sampling techniques in molecular dynamics simulations of biological systems. Biochim. Biophys. Acta, Gen. Subj., 2015, 1850(5), 872-877.
[http://dx.doi.org/10.1016/j.bbagen.2014.10.019] [PMID: 25450171]
[47]
Kästner, J. Umbrella sampling. Wiley Interdiscip. Rev. Comput. Mol. Sci., 2011, 1(6), 932-942.
[http://dx.doi.org/10.1002/wcms.66]
[48]
Abrams, C.; Bussi, G. Enhanced sampling in molecular dynamics using metadynamics, replica-exchange, and temperature-acceleration. Entropy, 2013, 16(1), 163-199.
[http://dx.doi.org/10.3390/e16010163]
[49]
Zheng, S.; Pfaendtner, J. Enhanced sampling of chemical and biochemical reactions with metadynamics. Mol. Simul., 2015, 41(1-3), 55-72.
[http://dx.doi.org/10.1080/08927022.2014.923574]
[50]
Fatafta, H.; Samantray, S.; Sayyed-Ahmad, A.; Coskuner-Weber, O.; Strodel, B. Molecular simulations of IDPs: From ensemble generation to IDP interactions leading to disorder-to-order transitions. Progress in Molecular Biology and Translational Science; Elsevier, 2021, Vol. 183, pp. 135-185.
[51]
Strodel, B.; Coskuner-Weber, O. Transition metal ion interactions with disordered amyloid-β peptides in the pathogenesis of alzheimer’s disease: Insights from computational chemistry studies. J. Chem. Inf. Model., 2019, 59(5), 1782-1805.
[http://dx.doi.org/10.1021/acs.jcim.8b00983] [PMID: 30933519]
[52]
Perez, D.; Uberuaga, B.P.; Shim, Y.; Amar, J.G.; Voter, A.F. Accelerated molecular dynamics methods: Introduction and recent developments. In: Annual Reports in Computational Chemistry; Elsevier, 2009; Vol. 5, pp. 79-98.
[53]
Do, T.N.; Choy, W.Y.; Karttunen, M. Accelerating the conformational sampling of intrinsically disordered proteins. J. Chem. Theory Comput., 2014, 10(11), 5081-5094.
[http://dx.doi.org/10.1021/ct5004803] [PMID: 26584388]
[54]
Weber, O.C.; Uversky, V.N. How accurate are your simulations? Effects of confined aqueous volume and AMBER FF99SB and CHARMM22/CMAP force field parameters on structural ensembles of intrinsically disordered proteins: Amyloid-β 42 in water. Intrinsically Disord. Proteins, 2017, 5(1), e1377813.
[http://dx.doi.org/10.1080/21690707.2017.1377813] [PMID: 30250773]
[55]
Wang, Y.; Lamim Ribeiro, J.M.; Tiwary, P. Machine learning approaches for analyzing and enhancing molecular dynamics simulations. Curr. Opin. Struct. Biol., 2020, 61, 139-145.
[http://dx.doi.org/10.1016/j.sbi.2019.12.016] [PMID: 31972477]
[56]
Glazer, D.S.; Radmer, R.J.; Altman, R.B. Combining molecular dynamics and machine learning to improve protein function recognition. Proceedings of the Biocomputing, 2008, , pp. 332-343.
[57]
Noé, F.; Tkatchenko, A.; Müller, K.R.; Clementi, C. Machine learning for molecular simulation. Annu. Rev. Phys. Chem., 2020, 71(1), 361-390.
[http://dx.doi.org/10.1146/annurev-physchem-042018-052331] [PMID: 32092281]
[58]
Bai, Q.; Liu, S.; Tian, Y.; Xu, T.; Banegas-Luna, A.J.; Pérez-Sánchez, H.; Huang, J.; Liu, H.; Yao, X. Application advances of deep learning methods for de novo drug design and molecular dynamics simulation. Wiley Interdiscip. Rev. Comput. Mol. Sci., 2022, 12(3), e1581.
[http://dx.doi.org/10.1002/wcms.1581]
[59]
Shin, K.; Tran, D.P.; Takemura, K.; Kitao, A.; Terayama, K.; Tsuda, K. Enhancing biomolecular sampling with reinforcement learning: A tree search molecular dynamics simulation method. ACS Omega, 2019, 4(9), 13853-13862.
[http://dx.doi.org/10.1021/acsomega.9b01480] [PMID: 31497702]
[60]
Shmilovich, K.; Mansbach, R.A.; Sidky, H.; Dunne, O.E.; Panda, S.S.; Tovar, J.D.; Ferguson, A.L. Discovery of self-assembling π-conjugated peptides by active learning-directed coarse-grained molecular simulation. J. Phys. Chem. B, 2020, 124(19), 3873-3891.
[http://dx.doi.org/10.1021/acs.jpcb.0c00708] [PMID: 32180410]
[61]
Pratt, L.R.; Haan, S.W. Effects of periodic boundary conditions on equilibrium properties of computer simulated fluids. I. Theory. J. Chem. Phys., 1981, 74(3), 1864-1872.
[http://dx.doi.org/10.1063/1.441276]
[62]
Demerdash, O.; Shrestha, U.R.; Petridis, L.; Smith, J.C.; Mitchell, J.C.; Ramanathan, A. Using small-angle scattering data and parametric machine learning to optimize force field parameters for intrinsically disordered proteins. Front. Mol. Biosci., 2019, 6, 64.
[http://dx.doi.org/10.3389/fmolb.2019.00064] [PMID: 31475155]
[63]
Ahmed, S.S.; Rifat, Z.T.; Lohia, R.; Campbell, A.J.; Dunker, A.K.; Rahman, M.S.; Iqbal, S. Characterization of intrinsically disordered regions in proteins informed by human genetic diversity. PLOS Comput. Biol., 2022, 18(3), e1009911.
[http://dx.doi.org/10.1371/journal.pcbi.1009911] [PMID: 35275927]
[64]
Morgunov, A.S.; Saar, K.L.; Vendruscolo, M.; Knowles, T.P.J. New frontiers for machine learning in protein science. J. Mol. Biol., 2021, 433(20), 167232.
[http://dx.doi.org/10.1016/j.jmb.2021.167232] [PMID: 34499920]
[65]
Baiardi, A.; Christandl, M.; Reiher, M. Quantum computing for molecular biology. arXiv:2212.12220, 2022.
[http://dx.doi.org/10.48550/ARXIV.2212.12220]
[66]
Sood, V.; Chauhan, R.P. Archives of Quantum Computing: Research Progress and Challenges; Arch Computat Methods Eng, 2023.
[http://dx.doi.org/10.1007/s11831-023-09973-2]
[67]
Verstraete, F.; Porras, D.; Cirac, J.I. Density matrix renormalization group and periodic boundary conditions: A quantum information perspective. Phys. Rev. Lett., 2004, 93(22), 227205.
[http://dx.doi.org/10.1103/PhysRevLett.93.227205] [PMID: 15601115]
[68]
Ajagekar, A.; You, F. New frontiers of quantum computing in chemical engineering. Korean J. Chem. Eng., 2022, 39(4), 811-820.
[http://dx.doi.org/10.1007/s11814-021-1027-6]
[69]
Shepherd, D.J. On the role of hadamard gates in quantum circuits. Quantum Inform. Process., 2006, 5(3), 161-177.
[http://dx.doi.org/10.1007/s11128-006-0023-4]
[70]
Sarfaraj, M.N.; Mukhopadhyay, S. All-optical scheme for implementation of tri-state Pauli-X, Y and Z quantum gates using phase encoding. Optoelectron. Lett., 2021, 17(12), 746-750.
[http://dx.doi.org/10.1007/s11801-021-1037-y]
[71]
Monz, T.; Nigg, D.; Martinez, E.A.; Brandl, M.F.; Schindler, P.; Rines, R.; Wang, S.X.; Chuang, I.L.; Blatt, R. Realization of a scalable Shor algorithm. Science, 2016, 351(6277), 1068-1070.
[http://dx.doi.org/10.1126/science.aad9480] [PMID: 26941315]
[72]
Long, G.L. Grover algorithm with zero theoretical failure rate. Phys. Rev. A, 2001, 64(2), 022307.
[http://dx.doi.org/10.1103/PhysRevA.64.022307]
[73]
Hauke, P.; Katzgraber, H.G.; Lechner, W.; Nishimori, H.; Oliver, W.D. Perspectives of quantum annealing: Methods and implementations. Rep. Prog. Phys., 2020, 83(5), 054401.
[http://dx.doi.org/10.1088/1361-6633/ab85b8] [PMID: 32235066]
[74]
Rebentrost, P.; Mohseni, M.; Lloyd, S. Quantum support vector machine for big data classification. Phys. Rev. Lett., 2014, 113(13), 130503.
[http://dx.doi.org/10.1103/PhysRevLett.113.130503] [PMID: 25302877]
[75]
Schuld, M.; Sinayskiy, I.; Petruccione, F. An introduction to quantum machine learning. Contemp. Phys., 2015, 56(2), 172-185.
[http://dx.doi.org/10.1080/00107514.2014.964942]

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