Current Science

Open Access Open Access  Restricted Access Subscription Access

Computational Approaches to Understanding the Biological Behaviour of Intrinsically Disordered Proteins


Affiliations
1 Physical Chemistry Division, CSIR-National Chemical Laboratory, Pune 411 008, India
2 Department of Biological Sciences, Indian Institute of Science Education and Research (IISER) Kolkata, Mohanpur 741 246, India
 

Intrinsically disordered proteins (IDPs) represent a class of proteins that lack a persistent folded conformation and exist as dynamic ensembles in their native state. Inherent lack of a well-defined structure and remarkable structural plasticity have facilitated their functioning in a wide range of crucial cellular processes such as signalling transduction and cell cycle regulation as well as responsible for their aberrant toxic amyloidogenic conformations implicated in a wide range of neurodegenerative diseases, cancer, etc. Their ubiquitous presence in nature, role in biological function and diseases have spurred interest in the biophysical and conformational characterization of IDPs. Conventional methods of structure determination are less feasible owing to structural and spatiotemporal heterogeneity of IDPs, which demand the development of novel biophysical methods as well as rigorous computational techniques for their characterization. In this review, we provide a brief overview of the widely used computational techniques probing the rugged conformational energy landscape of IDPs, their kinetics of structural transitions and molecular interactions key to their functions. Advances in the development of calibrated computational approaches for statistical representation of highly dynamic structural ensemble of IDPs are provided with examples. Challenges in modelling this unique class of proteins as well as the existing and futuristic avenues are also discussed.

Keywords

Chaperones, Free-Energy, Intrinsically Disordered Proteins, Molecular Dynamics, Monte Carlo Method.
User
Notifications
Font Size

  • Fischer, E., Influence of configuration on the action of enzymes. Ber. Dtsch. Chem. Ges.,1894, 27, 2985–2993.

  • Wright, P. E. and Dyson, H. J., Intrinsically unstructured proteins: re-assessing the protein structure-function paradigm. J. Mol. Biol., 1999, 293(2), 321–331.

  • Tompa, P., Intrinsically disordered proteins: a 10-year recap. Trends Biochem. Sci., 2012, 37(12), 509–516.

  • Dyson, H. J., Making sense of intrinsically disordered proteins. Biophys. J., 2016, 110(5), 1013–1016.

  • Tompa, P. and Csermely, P., The role of structural disorder in the function of RNA and protein chaperones. FASEB J., 2004, 18(11), 1169–1175.

  • Kovacs, D. and Tompa, P., Diverse functional manifestations of intrinsic structural disorder in molecular chaperones. Biochem. Soc. Trans., 2012, 40(5), 963–968.

  • Foit, L., George, J. S., Zhang, B. W., Brooks, C. L. and Bardwell, J. C. A., Chaperone activation by unfolding. Proc. Natl. Acad. Sci. USA, 2013, 110(14), E1254–E1262.

  • Oldfield, C. J., Meng, J., Yang, J. Y., Yang, M. Q., Uversky, V. N. and Dunker, A. K., Flexible nets: disorder and induced fit in the associations of p53 and 14-3-3 with their partners. BMC Genomics, 2008, 9(S1), 1–20.

  • Knowles, T. P. J., Vendruscolo, M. and Dobson, C. M., The amyloid state and its association with protein misfolding diseases. Nat. Rev. Mol. Cell. Biol., 2014, 15(6), 384–396.

  • Iakoucheva, L. M., Brown, C. J., Lawson, J. D., Obradović, Z. and Dunker, A. K., Intrinsic disorder in cell-signaling and cancerassociated proteins. J. Mol. Biol., 2002, 323(3), 573–584.

  • Bussell, R. and Eliezer, D., Residual structure and dynamics in Parkinson's disease-associated mutants of -synuclein. J. Biol. Chem., 2001, 276(49), 45996–46003.

  • Eliezer, D., Barré, P., Kobaslija, M., Chan, D., Li, X. and Heend, L., Residual structure in the repeat domain of tau: echoes of microtubule binding and paired helical filament formation. Biochemistry, 2005, 44(3), 1026–1036.

  • Havlin, R. H. and Tycko, R., Probing site-specific conformational distributions in protein folding with solid-state NMR. Proc. Natl. Acad. Sci. USA, 2005, 102(9), 3284–3289.

  • Mylonas, E., Hascher, A., Bernadó, P., Blackledge, M., Mandelkow, E. and Svergun, D. I., Domain conformation of tau protein studied by solution small-angle X-ray scattering. Biochemistry, 2008, 47(39), 10345–10353.

  • Bernado, P. and Svergun, D. I., Structural analysis of intrinsically disordered proteins by small-angle X-ray scattering. Mol. BioSyst. I, 2012, 8(1), 151–167.

  • Maji, S. K. et al., Functional amyloids as natural storage of peptide hormones in pituitary secretory granules. Science, 2009, 325(5938), 328.

  • Singh, P. K. and Maji, S. K., Amyloid-like fibril formation by tachykinin neuropeptides and its relevance to amyloid β-protein aggregation and toxicity. Cell Biochem. Biophys., 2012, 64(1), 29–44.

  • Schmidt, M., Rohou, A., Lasker, K., Yadav, J. K., SchieneFischer, C., Fändrich, M. and Grigorieff, N., Peptide dimer structure in an A(1–42) fibril visualized with cryo-EM. Proc. Natl. Acad. Sci. USA, 2015, 112(38), 11858–11863.

  • Bhowmik, D., MacLaughlin, C. M., Chandrakesan, M., Ramesh, P., Venkatramani, R., Walker, G. C. and Maiti, S., pH changes the aggregation propensity of amyloid-[small beta] without altering the monomer conformation. Phys. Chem. Chem. Phys., 2014, 16(3), 885–889.

  • Chandrakesan, M. et al., Steric crowding of the turn region alters the tertiary fold of amyloid-β18–35 and makes it soluble. J. Biol. Chem., 2015, 290(50), 30099–30107.

  • Elbaum-Garfinkle, S. and Rhoades, E., Identification of an aggregation-prone structure of tau. J. Am. Chem. Soc., 2012, 134(40), 16607–16613.

  • Arya, S. and Mukhopadhyay, S., Ordered water within the collapsed globules of an amyloidogenic intrinsically disordered protein. J. Phys. Chem. B, 2014, 118(31), 9191–9198.

  • Zerze, G. H., Miller, C. M., Granata, D. and Mittal, J., Free energy surface of an intrinsically disordered protein: comparison between temperature replica exchange molecular dynamics and bias-exchange metadynamics. J. Chem. Theory Comput., 2015, 11(6), 2776–2782.

  • Ithuralde, R. E., Roitberg, A. E. and Turjanski, A. G., Structured and unstructured binding of an intrinsically disordered protein as revealed by atomistic simulations. J. Am. Chem. Soc., 138(28), 8742–8751.

  • Vitalis, A., Wang, X. and Pappu, R. V., Quantitative characterization of intrinsic disorder in polyglutamine: insights from analysis based on polymer theories. Biophys. J., 2007, 93(6), 1923–1937.

  • Jahn, T. R. and Radford, S. E., The Yin and Yang of protein folding. FEBS J., 2005, 272(23), 5962–5970.

  • Hartl, F. U., Bracher, A. and Hayer-Hartl, M., Molecular chaperones in protein folding and proteostasis. Nature, 2011, 475(7356), 324–332.

  • Wales, D. J. and Bogdan, T. V., Potential energy and free energy landscapes. J. Phys. Chem. B, 2006, 110(42), 20765–20776.

  • Raval, A., Piana, S., Eastwood, M. P. and Shaw, D. E., Assessment of the utility of contact‐based restraints in accelerating the prediction of protein structure using molecular dynamics simulations. Protein Sci., 2016, 25(1), 19–29.

  • Jana, A. K. and Sengupta, N., Adsorption mechanism and collapse propensities of the full-length, monomeric A1–42 on the surface of a single-walled carbon nanotube: a molecular dynamics simulation study. Biophys. J., 2012, 102(8), 1889–1896.

  • Allen, M. P. and Tildesley, D. J., Computer Simulation of Liquids, Oxford University Press, 1989.

  • Frenkel, D. and Smit, B., Understanding Molecular Simulations: From Algorithms to Applications, Academic, San Diego, 1996.

  • Sugita, Y. and Okamoto, Y., Replica-exchange molecular dynamics method for protein folding. Chem. Phys. Lett., 1999, 314(1–2), 141–151.

  • Hamelberg, D., Mongan, J. and McCammon, J. A., Accelerated molecular dynamics: a promising and efficient simulation method for biomolecules. J. Chem. Phys., 2004, 120(24), 11919–11929.

  • Paschek, D., Nymeyer, H. and García, A. E., Replica exchange simulation of reversible folding/unfolding of the Trp-cage miniprotein in explicit solvent: On the structure and possible role of internal water. J. Struct. Biol., 2007, 157(3), 524–533.

  • Tran, H. T., Mao, A. and Pappu, R. V., Role of backbone−solvent interactions in determining conformational equilibria of intrinsically disordered proteins. J. Am. Chem. Soc., 2008, 130(23), 7380–7392.

  • Reddy, G., Straub, J. E. and Thirumalai, D., Dynamics of locking of peptides onto growing amyloid fibrils. Proc. Natl. Acad. Sci. USA, 2009, 106(29), 11948–11953.

  • Canchi, D. R., Paschek, D. and García, A. E., Equilibrium study of protein denaturation by urea. J. Am. Chem. Soc., 2010, 132(7), 2338–2344.

  • Reddy, G., Straub, J. E. and Thirumalai, D., Dry amyloid fibril assembly in a yeast prion peptide is mediated by long-lived structures containing water wires. Proc. Natl. Acad. Sci. USA, 2010, 107(50), 21459–21464.

  • O’Brien, E. P., Straub, J. E., Brooks, B. R. and Thirumalai, D., Influence of nanoparticle size and shape on oligomer formation of an amyloidogenic peptide. J. Phys. Chem. Lett., 2011, 2(10), 1171–1177.

  • Chong, S. H. and Ham, S., Impact of chemical heterogeneity on protein self-assembly in water. Proc. Natl. Acad. Sci. USA, 2012, 109(20), 7636–7641.

  • Thirumalai, D., Reddy, G. and Straub, J. E., Role of water in protein aggregation and amyloid polymorphism. Acc. Chem. Res., 2012, 45(1), 83–92.

  • Reddy, G., Liu, Z. and Thirumalai, D., Denaturant-dependent folding of GFP. Proc. Natl. Acad. Sci. USA, 2012, 109(44), 17832–17838.

  • Das, R. K. and Pappu, R. V., Conformations of intrinsically disordered proteins are influenced by linear sequence distributions of oppositely charged residues. Proc. Natl. Acad. Sci. USA, 2013, 110(33), 13392–13397.

  • Thirumalai, D., Liu, Z., O’Brien, E. P. and Reddy, G., Protein folding: from theory to practice. Curr. Opin. Struct. Biol., 2013, 23(1), 22–29.

  • English, C. A. and Garcia, A. E., Folding and unfolding thermodynamics of the TC10b Trp-cage miniprotein. Phys. Chem. Chem. Phys., 2014, 16(7), 2748–2757.

  • Barz, B., Olubiyi, O. O. and Strodel, B., Early amyloid [small beta]-protein aggregation precedes conformational change. Chem. Commun., 2014, 50(40), 5373–5375.

  • Han, W. and Schulten, K., Fibril elongation by A17–42: kinetic network analysis of hybrid-resolution molecular dynamics simulations. J. Am. Chem. Soc., 2014, 136(35), 12450–12460.

  • Baram, M., Atsmon-Raz, Y., Ma, B., Nussinov, R. and Miller, Y., Amylin-A[small beta] oligomers at atomic resolution using molecular dynamics simulations: a link between Type 2 diabetes and Alzheimer’s disease. Phys. Chem. Chem. Phys., 2016, 18(4), 2330–2338.

  • Muttathukattil, A. N. and Reddy, G., Osmolyte effects on the growth of amyloid fibrils. J. Phys. Chem. B, 2016, 120(42), 10979–10989.

  • Duan, Y. and Kollman, P. A., Pathways to a protein folding intermediate observed in a 1-microsecond simulation in aqueous solution. Science, 1998, 282(5389), 740–744.

  • Freddolino, P. L., Liu, F., Gruebele, M. and Schulten, K., Tenmicrosecond molecular dynamics simulation of a fast-folding WW domain. Biophys. J., 2008, 94(10), L75–L77.

  • Lindorff-Larsen, K., Maragakis, P., Piana, S. and Shaw, D. E., Picosecond to millisecond structural dynamics in human ubiquitin. J. Phys. Chem. B, 2016, 120(33), 8313–8320.

  • Shaw, D. E. et al., Anton, a special-purpose machine for molecular dynamics simulation. Commun. ACM, Commun. ACM, 51(7), 91–97.

  • O’Brien, E. P., Okamoto, Y., Straub, J. E., Brooks, B. R. and Thirumalai, D., Thermodynamic perspective on the Dock–Lock growth mechanism of amyloid fibrils. J. Phys. Chem. B, 2009, 113(43), 14421–14430.

  • Zhu, X., Bora, R. P., Barman, A., Singh, R. and Prabhakar, R., Dimerization of the full-length Alzheimer amyloid β-peptide (Aβ42) in explicit aqueous solution: a molecular dynamics study.

  • J. Phys. Chem. B, 2012, 116(15), 4405–4416.

  • Chong, S. H. and Ham, S., Interaction with the surrounding water plays a key role in determining the aggregation propensity of proteins. Angew. Chemie Inter. Ed., 2014, 53(15), 3961–3964.

  • Tarus, B., Tran, T. T., Nasica-Labouze, J., Sterpone, F., Nguyen, P. H. and Derreumaux, P., Structures of the Alzheimer’s wildtype A 1–40 dimer from atomistic simulations. J. Phys. Chem. B, 2015, 119(33), 10478–10487.

  • Dupuis, N. F., Wu, C., Shea, J.-E. and Bowers, M. T., Human islet amyloid polypeptide monomers form ordered β -hairpins: a possible direct amyloidogenic precursor. J. Am. Chem. Soc., 2009, 131(51), 18283–18292.

  • Sgourakis, N. G., Merced-Serrano, M., Boutsidis, C., Drineas, P., Du, Z., Wang, C. and Garcia, A. E., Atomic-level characterization of the ensemble of the Aβ(1–42) monomer in water using unbiased molecular dynamics simulations and spectral algorithms. J. Mol. Biol., 2011, 405(2), 570–583.

  • Wu, C. and Shea, J. E., Structural similarities and differences between amyloidogenic and non-amyloidogenic islet amyloid polypeptide (IAPP) sequences and implications for the dual physiological and pathological activities of these peptides. PLoS Comput. Biol., 2013, 9(8), e1003211.

  • Mittal, J., Yoo, T. H., Georgiou, G. and Truskett, T. M., Structural ensemble of an intrinsically disordered polypeptide. J. Phys. Chem. B, 2013, 117(1), 118–124.

  • Miller, C., Zerze, G. H. and Mittal, J., Molecular simulations indicate marked differences in the structure of amylin mutants, correlated with known aggregation propensity. J. Phys. Chem. B, 2013, 117(50), 16066–16075.

  • Qi, R., Luo, Y., Wei, G., Nussinov, R. and Ma, B., A ‘stretching-and-tacking’ cross-seeding mechanism can trigger tau protein aggregation. J. Phys. Chem. Lett., 2015, 6(16), 3276– 3282.

  • Zou, Y., Sun, Y., Zhu, Y., Ma, B., Nussinov, R. and Zhang, Q., Critical nucleus structure and aggregation mechanism of the C-terminal fragment of copper–zinc superoxide dismutase protein. ACS Chem. Neurosci., 2016, 7(3), 286–296.

  • Das, P., Chacko, A. R. and Belfort, G., Alzheimer’s protective cross–interaction between wild-type and A2T variants alters A42 dimer structure. ACS Chem. Neurosci., 2017, 8(3), 606–618.

  • Sgourakis, N. G., Yan, Y., McCallum, S. A., Wang, C. and Garcia, A. E., The Alzheimer’s peptides A40 and 42 adopt distinct conformations in water: a combined MD/NMR study. J. Mol. Biol., 2007, 368(5), 1448–1457.

  • Mukrasch, M. D. et al., Highly populated turn conformations in natively unfolded tau protein identified from residual dipolar couplings and molecular simulation. J. Am. Chem. Soc., 2007, 129(16), 5235–5243.

  • Jose, J. C., Khatua, P., Bansal, N., Sengupta, N. and Bandyopadhyay, S., Microscopic hydration properties of the A1–42 peptide monomer and the globular protein ubiquitin: a comparative molecular dynamics study. J. Phys. Chem. B, 2014, 118(40), 11591–11604.

  • Khatua, P., Jose, J. C., Sengupta, N. and Bandyopadhyay, S., Conformational features of the A42 peptide monomer and its interaction with the surrounding solvent. Phys. Chem. Chem. Phys., 2016, 18(43), 30144–30159.

  • Torrie, G. M. and Valleau, J. P., Nonphysical sampling distributions in Monte Carlo free-energy estimation: umbrella sampling. J. Comput. Phys., 1977, 23(2), 187–199.

  • Fraternali, F. and Van Gunsteren, W. F., Conformational transitions of a dipeptide in water: effects of imposed pathways using umbrella sampling techniques. Biopolymers, 1994, 34(3), 347–355.

  • Lemkul, J. A. and Bevan, D. R., Assessing the stability of Alzheimer’s amyloid protofibrils using molecular dynamics. J. Phys. Chem. B, 2010, 114(4), 1652–1660.

  • Darve, E., Rodríguez-Gómez, D. and Pohorille, A., Adaptive biasing force method for scalar and vector free energy calculations. J. Chem. Phys., 2008, 128(14), 144120.

  • Pellarin, R. and Caflisch, A., Interpreting the aggregation kinetics of amyloid peptides. J. Mol. Biol., 2006, 360(4), 882–892.

  • Hyeon, C. and Thirumalai, D., Capturing the essence of folding and functions of biomolecules using coarse-grained models. Nature Commun., 2011, 2(487), 1–11.

  • Ruff, Kiersten M., Khan, Siddique, J., Pappu and Rohit, V., A coarse-grained model for polyglutamine aggregation modulated by amphipathic flanking sequences. Biophys. J., 2014, 107(5), 1226–1235.

  • Saric, A., Chebaro, Y. C., Knowles, T. P. J. and Frenkel, D., Crucial role of nonspecific interactions in amyloid nucleation. Proc. Natl. Acad. Sci. USA, 2014, 111(50), 17869–17874.

  • Zhuravlev, P. I., Reddy, G., Straub, J. E. and Thirumalai, D., Propensity to form amyloid fibrils is encoded as excitations in the free energy landscape of monomeric proteins. J. Mol. Biol., 2014, 426(14), 2653–2666.

  • Dominguez, L., Foster, L., Meredith, S. C., Straub, J. E. and Thirumalai, D., Structural heterogeneity in transmembrane amyloid precursor protein homodimer is a consequence of environmental selection. J. Am. Chem. Soc., 2014, 136(27), 9619–9626.

  • Kmiecik, S., Gront, D., Kolinski, M., Wieteska, L., Dawid, A. E. and Kolinski, A., Coarse-grained protein models and their applications. Chem. Rev., 2016, 116(14), 7898–7936.

  • Maity, H. and Reddy, G., Folding of protein L with implications for collapse in the denatured state ensemble. J. Am. Chem. Soc., 2016, 138(8), 2609–2616.

  • Kurcinski, M., Kolinski, A. and Kmiecik, S., Mechanism of folding and binding of an intrinsically disordered protein as revealed by ab initio simulations. J. Chem. Theory Comput., 2014, 10(6), 2224–2231.

  • Sieradzan, A. K., Liwo, A. and Hansmann, U. H. E., Folding and self-assembly of a small protein complex. J. Chem. Theory Comput., 2012, 8(9), 3416–3422.

  • Ranganathan, S., Maji, S. K. and Padinhateeri, R., Defining a physical basis for diversity in protein self-assemblies using a minimal model. J. Am. Chem. Soc., 2016, 138(42), 13911–13922.

  • Earl, D. J. and Deem, M. W., Monte Carlo simulations. In Molecular Modeling of Proteins (ed. Kukol, A.), Springer, 2008, pp. 25–36.

  • Harrison, R. L., Introduction to Monte Carlo simulation. AIP Conf. Proc., 2010, 1204, 17–21.

  • Lomakin, A., Teplow, D. B., Kirschner, D. A. and Benedek, G. B., Kinetic theory of fibrillogenesis of amyloid β-protein. Proc. Natl. Acad. Sci. USA, 1997, 94(15), 7942–7947.

  • Pallitto, M. M. and Murphy, R. M., A mathematical model of the kinetics of beta-amyloid fibril growth from the denatured state. Biophys. J., 2001, 81(3), 1805–1822.

  • Powers, E. T. and Powers, D. L., The kinetics of nucleated polymerizations at high concentrations: amyloid fibril formation near and above the ‘supercritical concentration’. Biophys. J., 2006, 91(1), 122–132.

  • Ranganathan, S., Ghosh, D., Maji, S. K. and Padinhateeri, R., A minimal conformational switching-dependent model for amyloid self-assembly. Sci. Rep., 2016, 6, 21103(1–14).

  • Wales, D. J., Doye, J. P. K., Miller, M. A., Mortenson, P. N. and Walsh, T. R., Energy landscapes: from clusters to biomolecules. In Advances in Chemical Physics, John Wiley, 2000, pp. 1–111.

  • Wales, D. J. and Doye, J. P. K., Global optimization by basinhopping and the lowest energy structures of Lennard–Jones clusters containing up to 110 atoms. J. Phys. Chem. A, 1997, 101(28), 5111–5116.

  • Wales, D. J. and Scheraga, H. A., Global optimization of clusters, crystals, and biomolecules. Science, 1999, 285(5432), 1368.

  • Wales, D. J., Some further applications of discrete path sampling to cluster isomerization. Mol. Phys., 2004, 102(9–10), 891–908.

  • Wales, D. J., Discrete path sampling. Mol. Phys., 2002, 100(20), 3285–3305.

  • Strodel, B. and Wales, D. J., Implicit solvent models and the energy landscape for aggregation of the amyloidogenic KFFE peptide. J. Chem. Theory Comput., 2008, 4(4), 657–672.

  • Barz, B., Wales, D. J. and Strodel, B., A kinetic approach to the sequence–aggregation relationship in disease-related protein assembly. J. Phys. Chem. B, 2014, 118(4), 1003–1011.

  • Strodel, B., Lee, J. W. L., Whittleston, C. S. and Wales, D. J., Transmembrane structures for Alzheimer’s Aβ1–42 oligomers. J. Am. Chem. Soc., 2010, 132(38), 13300–13312.

  • MacKerell, A. D., Protein force fields. In Encyclopedia of Computational Chemistry, John Wiley & Sons, Ltd, 2002.

  • Lindorff-Larsen, K., Maragakis, P., Piana, S., Eastwood, M. P., Dror, R. O. and Shaw, D. E., Systematic validation of protein force fields against experimental data. PLOS ONE, 2012, 7(2), e32131.

  • Sgourakis, N. G., Merced-Serrano, M., Boutsidis, C., Drineas, P., Du, Z., Wang, C. and Garcia, A. E., Atomic-level characterization of the ensemble of the A(1–42) monomer in water using unbiased molecular dynamics simulations and spectral algorithms. J. Mol. Biol., 2011, 405(2), 570–583.

  • Jana, A. K., Jose, J. C. and Sengupta, N., Critical roles of key domains in complete adsorption of A[small beta] peptide on single-walled carbon nanotubes: insights with point mutations and MD simulations. Phys. Chem. Chem. Phys., 2013, 15(3), 837– 844.

  • Kim, S. and Klimov, D. K., Binding to the lipid monolayer induces conformational transition in Aβ monomer. J. Mol. Model., 2013, 19(2), 737–750.

  • Shemetov, A. A., Nabiev, I. and Sukhanova, A., Molecular interaction of proteins and peptides with nanoparticles. ACS Nano, 2012, 6(6), 4585–4602.

  • Arce, F. T., Jang, H., Ramachandran, S., Landon, P. B., Nussinov, R. and Lal, R., Polymorphism of amyloid [small beta] peptide in different environments: implications for membrane insertion and pore formation. Soft Matter, 2011, 7(11), 5267–5273.

  • Tofoleanu, F. and Buchete, N.-V., Alzheimer A peptide interactions with lipid membranes: fibrils, oligomers and polymorphic amyloid channels. Prion, 2012, 6(4), 339–345.

  • Jana, A. K. and Sengupta, N., A [small beta] self-association and adsorption on a hydrophobic nanosurface: competitive effects and the detection of small oligomers via electrical response. Soft Matter, 2015, 11(2), 269–279.

  • Sciacca, Michele F. M., Lolicato, F., Di Mauro, G., Milardi, D., D’Urso, L., Satriano, C., Ramamoorthy, A. and La Rosa, C., The role of cholesterol in driving IAPP-membrane interactions. Biophys. J., 2016, 111(1), 140–151.

  • Chen, Y., Cruz-Chu, E. R., Woodard, J., Gartia, M. R., Schulten, K. and Liu, L., Electrically induced conformational change of peptides on metallic nano-surfaces. ACS Nano, 2012, 6(10), 8847–8856.

  • Liu, J., Liao, C. and Zhou, J., Multiscale simulations of protein G B1 adsorbed on charged self-assembled monolayers. Langmuir, 2013, 29(36), 11366–11374.

  • Hawe, A., Sutter, M. and Jiskoot, W., Extrinsic fluorescent dyes as tools for protein characterization. Pharm. Res., 2008, 25(7), 1487–1499.

  • Wu, C., Wang, Z., Lei, H., Duan, Y., Bowers, M. T. and Shea, J.-E., The binding of thioflavin-T and its neutral analog BTA-1 to protofibrils of the Alzheimer A(16–22) peptide probed by molecular dynamics simulations. J. Mol. Biol., 2008, 384(3), 718–729.

  • Rodriguez-Rodriguez, C., Rimola, A., Rodriguez-Santiago, L., Ugliengo, P., Alvarez-Larena, A., Gutierrez-de-Teran, H., Sodupe, M. and Gonzalez-Duarte, P., Crystal structure of thioflavin-T and its binding to amyloid fibrils: insights at the molecular level. Chem. Commun., 2010, 46(7), 1156–1158.

  • Dyson, H. J. and Wright, P. E., Intrinsically unstructured proteins and their functions. Nat. Rev. Mol. Cell Biol., 2005, 6(3), 197– 208.

  • Uversky, V. N., Intrinsically disordered proteins from A to Z. Int. J. Biochem. Cell Biol., 2011, 43(8), 1090–1103.

  • Tompa, P., Unstructural biology coming of age. Curr. Opin. Struct. Biol., 2011, 21(3), 419–425.

  • Zhang, Y., Cao, H. and Liu, Z., Binding cavities and druggability of intrinsically disordered proteins. Prot. Sci., 2015, 24(5), 688– 705.

  • Chen, J., Intrinsically disordered p53 extreme C-terminus binds to S100B() through ‘fly-casting’. J. Am. Chem. Soc., 2009, 131(6), 2088–2089.

  • Ganguly, D. and Chen, J., Atomistic details of the disordered states of KID and pKID. Implications in coupled binding and folding. J. Am. Chem. Soc., 2009, 131(14), 5214–5223.

  • Soucek, L. et al., Modelling Myc inhibition as a cancer therapy. Nature, 2008, 455(7213), 679–683.

  • Michel, J. and Cuchillo, R., The impact of small molecule binding on the energy landscape of the intrinsically disordered protein C-Myc. PLoS ONE, 2012, 7(7), e41070.

  • Jin, F., Yu, C., Lai, L. and Liu, Z., Ligand clouds around protein clouds: a scenario of ligand binding with intrinsically disordered proteins. PLoS Comput. Biol., 2013, 9(10), e1003249.

  • Lührs, T., Ritter, C., Adrian, M., Riek-Loher, D., Bohrmann, B., Döbeli, H., Schubert, D. and Riek, R., 3D structure of Alzheimer’s amyloid-(1–42) fibrils. Proc. Natl. Acad. Sci. USA, 2005, 102(48), 17342–17347.

  • Shewmaker, F., Wickner, R. B. and Tycko, R., Amyloid of the prion domain of Sup35p has an in-register parallel -sheet structure. Proc. Natl. Acad. Sci. USA, 2006, 103(52), 19754– 19759.

  • Sugase, K., Dyson, H. J. and Wright, P. E., Mechanism of coupled folding and binding of an intrinsically disordered protein. Nature, 2007, 447(7147), 1021–1025.

  • Jensen, M. R., Salmon, L., Nodet, G. and Blackledge, M., Defining conformational ensembles of intrinsically disordered and partially folded proteins directly from chemical shifts. J. Am. Chem. Soc., 2010, 132(4), 1270–1272.

  • Ozenne, V. et al., Mapping the potential energy landscape of intrinsically disordered proteins at amino acid resolution. J. Am. Chem. Soc., 2012, 134(36), 15138–15148.

  • Bernadó, P., Mylonas, E., Petoukhov, M. V., Blackledge, M. and Svergun, D. I., Structural characterization of flexible proteins using small-angle X-ray scattering. J. Am. Chem. Soc., 2007, 129(17), 5656–5664.

  • Shell, S. S., Putnam, C. D. and Kolodner, R. D., The N terminus of Saccharomyces cerevisiae Msh6 is an unstructured tether to PCNA. Mol. Cell, 2007, 26(4), 565–578.

  • Tidow, H. et al., Quaternary structures of tumor suppressor p53 and a specific p53–DNA complex. Proc. Natl. Acad. Sci. USA, 2007, 104(30), 12324–12329.

  • Wells, M. et al., Structure of tumor suppressor p53 and its intrinsically disordered N-terminal transactivation domain. Proc. Natl. Acad. Sci. USA, 2008, 105(15), 5762–5767.

  • Giehm, L., Svergun, D. I., Otzen, D. E. and Vestergaard, B., Low-resolution structure of a vesicle disrupting -synuclein oligomer that accumulates during fibrillation. Proc. Natl. Acad. Sci. USA, 2011, 108(8), 3246–3251.

  • Bourhis, J.-M. et al., The intrinsically disordered C-terminal domain of the measles virus nucleoprotein interacts with the C-terminal domain of the phosphoprotein via two distinct sites and remains predominantly unfolded. Prot. Sci., 2005, 14(8), 1975–1992.

  • Sengupta, P., Garai, K., Sahoo, B., Shi, Y., Callaway, D. J. E. and Maiti, S., The Amyloid  peptide (A1–40) is thermodynamically soluble at physiological concentrations. Biochemistry, 2003, 42(35), 10506–10513.

  • Mukhopadhyay, S., Krishnan, R., Lemke, E. A., Lindquist, S. and Deniz, A. A., A natively unfolded yeast prion monomer adopts an ensemble of collapsed and rapidly fluctuating structures. Proc. Natl. Acad. Sci. USA, 2007, 104(8), 2649–2654.

  • Sahoo, B., Balaji, J., Nag, S., Kaushalya, S. K. and Maiti, S., Protein aggregation probed by two-photon fluorescence correlation spectroscopy of native tryptophan. J. Chem. Phys., 2008, 129(7), 08B608.

  • Nag, S. et al., Nature of the Amyloid- monomer and the monomer–oligomer equilibrium. J. Biol. Chem., 2011, 286(16), 13827–13833.

  • Hofmann, H., Soranno, A., Borgia, A., Gast, K., Nettels, D. and Schuler, B., Polymer scaling laws of unfolded and intrinsically disordered proteins quantified with single-molecule spectroscopy. Proc. Natl. Acad. Sci. USA, 2012, 109(40), 16155–16160.

  • Soranno, A. et al., Quantifying internal friction in unfolded and intrinsically disordered proteins with single-molecule spectroscopy. Proc. Natl. Acad. Sci. USA, 2012, 109(44), 17800– 17806.

  • Dalal, V., Arya, S., Bhattacharya, M. and Mukhopadhyay, S., Conformational switching and nanoscale assembly of human prion protein into polymorphic amyloids via structurally labile oligomers. Biochemistry, 2015, 54(51), 7505–7513.

  • Maji, S. K., Amsden, J. J., Rothschild, K. J., Condron, M. M. and Teplow, D. B., Conformational dynamics of amyloid -protein assembly probed using intrinsic fluorescence. Biochemistry, 2005, 44(40), 13365–13376.

  • Ghosh, D. et al., Structure based aggregation studies reveal the presence of helix-rich intermediate during -synuclein aggregation. Sci. Rep., 2015, 5, 9228.

  • Sahay, S., Anoop, A., Krishnamoorthy, G. and Maji, S. K., Sitespecific fluorescence dynamics of -synuclein fibrils using timeresolved fluorescence studies: effect of familial Parkinson’s disease-associated mutations. Biochemistry, 2014, 53(5), 807– 809.

  • Yu, J., Malkova, S. and Lyubchenko, Y. L., -Synuclein misfolding: single molecule AFM force spectroscopy study. J. Mol. Biol., 2008, 384(4), 992–1001.

  • Sandal, M. et al., Conformational equilibria in monomeric synuclein at the single-molecule level. PLoS Biol., 2008, 6(1), e6.

  • Jiménez, J. L., Nettleton, E. J., Bouchard, M., Robinson, C. V., Dobson, C. M. and Saibil, H. R., The protofilament structure of insulin amyloid fibrils. Proc. Natl. Acad. Sci. USA, 2002, 99(14), 9196–9201.

  • Sumner Makin, O. and Serpell, L. C., Structural characterisation of islet amyloid polypeptide fibrils. J. Mol. Biol., 2004, 335(5), 1279–1288.

  • Krotee, P. et al., Atomic structures of fibrillar segments of hIAPP suggest tightly mated  -sheets are important for cytotoxicity. eLife, 2017, 6, e19273.

  • Nelson, R., Sawaya, M. R., Balbirnie, M., Madsen, A. O., Riekel, C., Grothe, R. and Eisenberg, D., Structure of the cross-[beta] spine of amyloid-like fibrils. Nature, 2005, 435(7043), 773–778.

  • Kishimoto, A., Hasegawa, K., Suzuki, H., Taguchi, H., Namba, K. and Yoshida, M., β -helix is a likely core structure of yeast prion Sup35 amyloid fibers. Biochem. Biophys. Res. Commun., 2004, 315(3), 739–745.


Abstract Views: 312

PDF Views: 103




  • Computational Approaches to Understanding the Biological Behaviour of Intrinsically Disordered Proteins

Abstract Views: 312  |  PDF Views: 103

Authors

Sneha Menon
Physical Chemistry Division, CSIR-National Chemical Laboratory, Pune 411 008, India
Neelanjana Sengupta
Department of Biological Sciences, Indian Institute of Science Education and Research (IISER) Kolkata, Mohanpur 741 246, India

Abstract


Intrinsically disordered proteins (IDPs) represent a class of proteins that lack a persistent folded conformation and exist as dynamic ensembles in their native state. Inherent lack of a well-defined structure and remarkable structural plasticity have facilitated their functioning in a wide range of crucial cellular processes such as signalling transduction and cell cycle regulation as well as responsible for their aberrant toxic amyloidogenic conformations implicated in a wide range of neurodegenerative diseases, cancer, etc. Their ubiquitous presence in nature, role in biological function and diseases have spurred interest in the biophysical and conformational characterization of IDPs. Conventional methods of structure determination are less feasible owing to structural and spatiotemporal heterogeneity of IDPs, which demand the development of novel biophysical methods as well as rigorous computational techniques for their characterization. In this review, we provide a brief overview of the widely used computational techniques probing the rugged conformational energy landscape of IDPs, their kinetics of structural transitions and molecular interactions key to their functions. Advances in the development of calibrated computational approaches for statistical representation of highly dynamic structural ensemble of IDPs are provided with examples. Challenges in modelling this unique class of proteins as well as the existing and futuristic avenues are also discussed.

Keywords


Chaperones, Free-Energy, Intrinsically Disordered Proteins, Molecular Dynamics, Monte Carlo Method.

References





DOI: https://doi.org/10.18520/cs%2Fv112%2Fi07%2F1444-1454