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


Cite item

Full Text

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.

About the authors

Orkid Coskuner-Weber

Molecular Biotechnology, Türkisch-Deutsche Universität

Author for correspondence.
Email: info@benthamscience.net

Vladimir Uversky

Department of Molecular Medicine and USF Health Byrd Alzheimer’s Research Institute, Morsani College of Medicine, University of South Florida,

Email: info@benthamscience.net

References

  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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 10.1080/0889311X.2023.2208518
  14. Uversky, V.N. Intrinsically disordered proteins and their "Mysterious" (Meta)physics. Front. Phys., 2019, 7, 10. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 10.1016/j.jmb.2021.167232 PMID: 34499920
  65. Baiardi, A.; Christandl, M.; Reiher, M. Quantum computing for molecular biology. arXiv:2212.12220, 2022. doi: 10.48550/ARXIV.2212.12220
  66. Sood, V.; Chauhan, R.P. Archives of Quantum Computing: Research Progress and Challenges; Arch Computat Methods Eng, 2023. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 10.1126/science.aad9480 PMID: 26941315
  72. Long, G.L. Grover algorithm with zero theoretical failure rate. Phys. Rev. A, 2001, 64(2), 022307. doi: 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. doi: 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. doi: 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. doi: 10.1080/00107514.2014.964942

Supplementary files

Supplementary Files
Action
1. JATS XML
Download

Copyright (c) 2024 Bentham Science Publishers