Mapping allosteric pathway in NIa-Pro using computational approach

Abstract

Background: Computer simulation studies complement in vitro experiments and provide avenue to understand allosteric regulation in the absence of other molecular viewing techniques. Molecular dynamics captures internal motion within the protein and enables tracing the communication path between a catalytic site and a distal allosteric site. In this article, we have identified the communication pathway between the viral protein genome linked (VPg) binding region and catalytic active site in nuclear inclusion protein-a protease (NIa-Pro).

Methods: Molecular dynamics followed by in silico analyses have been used to map the allosteric pathway.

Results: This study delineates the residue interaction network involved in allosteric regulation of NIa-Pro activity by VPg. Simulation studies indicate that point mutations in the VPg interaction interface of NIa-Pro lead to disruption in these networks and change the orientation of catalytic residues. His142Ala and His167Ala mutations do not show a substantial change in the overall protease structure, but rather in the residue interaction network and catalytic site geometry.

Conclusion: Our mutagenic study delineates the allosteric pathway and facilitates the understanding of the modulation of NIa-Pro activity on a molecular level in the absence of the structure of its complex with the known regulator VPg. Additionally, our in silico analysis explains the molecular concepts and highlights the dynamics behind the previously reported wet lab study findings.

Graphical abstract

Keywords

NIa-Pro / VPg / simulation / residue interaction network / allostery

References

[1] nez, F., Rodrigo, G., Ruiz, M., Lodewijk, I., ndez, U., Elena, S. F. (2016). Interaction network of tobacco etch potyvirus NIa protein with the host proteome during infection. BMC Genomics, 17: 87

[2] Anindya, R., Joseph, J., Gowri, T. D. Savithri, H. (2004). Complete genomic sequence of pepper vein banding virus (PVBV): a distinct member of the genus Potyvirus. Arch. Virol., 149: 625–632

[3] Joseph, J. Savithri, H. (2000). Mutational analysis of the NIa protease from pepper vein banding potyvirus. Arch. Virol., 145: 2493–2502

[4] Anindya, R., Chittori, S. Savithri, H. (2005). Tyrosine 66 of pepper vein banding virus genome-linked protein is uridylylated by RNA-dependent RNA polymerase. Virology, 336: 154–162

[5] Urcuqui-Inchima, S., Haenni, A. L. (2001). Potyvirus proteins: a wealth of functions. Virus Res., 74: 157–175

[6] Ghabrial, S. A., Smith, H. A., Parks, T. D. Dougherty, W. (1990). Molecular genetic analyses of the soybean mosaic virus NIa proteinase. J. Gen. Virol., 71: 1921–1927

[7] Dougherty, W. G. Parks, T. (1989). Molecular genetic and biochemical evidence for the involvement of the heptapeptide cleavage sequence in determining the reaction profile at two tobacco etch virus cleavage sites in cell-free assays. Virology, 172: 145–155

[8] Merits, A., ki, M. L., Lindholm, P., Runeberg-Roos, P., Kekarainen, T., Puustinen, P., inen, K., Valkonen, J. P. T. (2002). Proteolytic processing of potyviral proteins and polyprotein processing intermediates in insect and plant cells. J. Gen. Virol., 83: 1211–1221

[9] Pasin, F., n-Mateo, C. (2014). The hypervariable amino-terminus of P1 protease modulates potyviral replication and host defense responses. PLoS Pathog., 10: e1003985

[10] Riechmann, J. L., (1992). Highlights and prospects of potyvirus molecular biology. J. Gen. Virol., 73: 1–16

[11] Schaad, M. C., Haldeman-Cahill, R., Cronin, S. Carrington, J. (1996). Analysis of the VPg-proteinase (NIa) encoded by tobacco etch potyvirus: effects of mutations on subcellular transport, proteolytic processing, and genome amplification. J. Virol., 70: 7039–7048

[12] Carrington, J. C., Haldeman, R., Dolja, V. V. Restrepo-Hartwig, M. (1993). Internal cleavage and trans-proteolytic activities of the VPg-proteinase (NIa) of tobacco etch potyvirus in vivo. J. Virol., 67: 6995–7000

[13] Sabharwal, P., Srinivas, S. Savithri, H. (2018). Mapping the domain of interaction of PVBV VPg with NIa-Pro: Role of N-terminal disordered region of VPg in the modulation of structure and function. Virology, 524: 18–31

[14] Mathur, C. Savithri, H. (2012). Novel ATPase activity of the polyprotein intermediate, viral protein genome-linked-nuclear inclusion-a protease, of pepper vein banding potyvirus. Biochem. Biophys. Res. Commun., 427: 113–118

[15] Mathur, C., Jimsheena, V. K., Banerjee, S., Makinen, K., Gowda, L. R. Savithri, H. (2012). Functional regulation of PVBV nuclear inclusion protein—a protease activity upon interaction with viral protein genome-linked and phosphorylation. Virology, 422: 254–264

[16] Boerner, J. E., Lyle, J. M., Daijogo, S., Semler, B. L., Schultz, S. C., Kirkegaard, K. Richards, O. (2005). Allosteric effects of ligands and mutations on poliovirus RNA-dependent RNA polymerase. J. Virol., 79: 7803–7811

[17] Changeux, J. (2012). Allostery and the Monod-Wyman-Changeux model after 50 years. Annu. Rev. Biophys., 41: 103–133

[18] del Sol, A., Fujihashi, H., Amoros, D. (2006). Residues crucial for maintaining short paths in network communication mediate signaling in proteins. Mol. Syst. Biol., 2: 0019

[19] Tyukhtenko, S., Ma, X., Rajarshi, G., Karageorgos, I., Anderson, K. W., Hudgens, J. W., Guo, J. J., Nasr, M. L., Zvonok, N., Vemuri, K. . (2020). Conformational gating, dynamics and allostery in human monoacylglycerol lipase. Sci. Rep., 10: 18531

[20] Cooper, A. Dryden, D. (1984). Allostery without conformational change. A plausible model. Eur. Biophys. J., 11: 103–109

[21] Popovych, N., Sun, S., Ebright, R. H. Kalodimos, C. (2006). Dynamically driven protein allostery. Nat. Struct. Mol. Biol., 13: 831–838

[22] Blacklock, K. Verkhivker, G. (2014). Computational modeling of allosteric regulation in the hsp90 chaperones: a statistical ensemble analysis of protein structure networks and allosteric communications. PLOS Comput. Biol., 10: e1003679

[23] Ghosh, A. (2007). A study of communication pathways in methionyl-tRNA synthetase by molecular dynamics simulations and structure network analysis. Proc. Natl. Acad. Sci. USA., 104: 15711–15716

[24] Atilgan, A. R., Turgut, D. (2007). Screened nonbonded interactions in native proteins manipulate optimal paths for robust residue communication. Biophys. J., 92: 3052–3062

[25] Ghosh, A., Sakaguchi, R., Liu, C., Vishveshwara, S. Hou, Y. (2011). Allosteric communication in cysteinyl tRNA synthetase: a network of direct and indirect readout. J. Biol. Chem., 286: 37721–37731

[26] Taverna, D. M. Goldstein, R. (2002). Why are proteins so robust to site mutations? J. Mol. Biol., 315: 479–484

[27] Sun, P., Austin, B. P., Waugh, D. (2010). Structural determinants of tobacco vein mottling virus protease substrate specificity. Protein Sci., 19: 2240–2251

[28] Phan, J., Zdanov, A., Evdokimov, A. G., Tropea, J. E., Peters, H. K. Kapust, R. B., Li, M., Wlodawer, A. Waugh, D. (2002). Structural basis for the substrate specificity of tobacco etch virus protease. J. Biol. Chem., 277: 50564–50572

[29] Wenthur, C. J., Gentry, P. R., Mathews, T. P. Lindsley, C. (2014). Drugs for allosteric sites on receptors. Annu. Rev. Pharmacol. Toxicol., 54: 165–184

[30] Sheik Amamuddy, O., Veldman, W., Manyumwa, C., Khairallah, A., Agajanian, S., Oluyemi, O., Verkhivker, G. (2020). Integrated computational approaches and tools forallosteric drug discovery. Int. J. Mol. Sci., 21: 847

[31] Sinha, N. (2001). Point mutations and sequence variability in proteins: redistributions of preexisting populations. Proc. Natl. Acad. Sci. USA., 98: 3139–3144

[32] Vaish, N. K., Dong, F., Andrews, L., Schweppe, R. E., Ahn, N. G., Blatt, L. Seiwert, S. (2002). Monitoring post-translational modification of proteins with allosteric ribozymes. Nat. Biotechnol., 20: 810–815

[33] Ngo, K. Dunn, M. (1992). Allosteric interactions coordinate catalytic activity between successive metabolic enzymes in the tryptophan synthase bienzyme complex. Biochemistry, 31: 3831–3839

[34] Hilser, V. J. Thompson, E. (2007). Intrinsic disorder as a mechanism to optimize allosteric coupling in proteins. Proc. Natl. Acad. Sci. USA, 104: 8311–8315

[35] Schlessinger, J. (1988). Signal transduction by allosteric receptor oligomerization. Trends Biochem. Sci., 13: 443–447

[36] Caldo, K. M. P., Acedo, J. Z., Panigrahi, R., Vederas, J. C., Weselake, R. J. Lemieux, M. (2017). Diacylglycerol acyltransferase 1 is regulated by its N-terminal domain in response to allosteric effectors. Plant Physiol., 175: 667–680

[37] Panigrahi, R., Matsui, T., Song, A. H., Caldo, K. M. P., Young, H. S., Weselake, R. J. Lemieux, M. (2018). Intrinsic disorder in the regulatory N-terminal domain of diacylglycerol acyltransferase 1 from Brassica napus. Sci. Rep., 8: 1–13

[38] Buller, A. R., Van Roye, P., Cahn, J. K., Scheele, R. A., Herger, M. Arnold, F. (2018). Directed evolution mimics allosteric activation by stepwise tuning of the conformational ensemble. J. Am. Chem. Soc., 140: 7256–7266

[39] GreenerJ. G.SternbergM.. (2015) AlloPred: Prediction of allosteric pockets on proteins using normal mode perturbation analysis. BMC Bioinformatics, 16, 335

[40] Panjkovich, A. Daura, X. J. (2014). PARS: A web server for the prediction of protein allosteric and regulatory sites. Bioinformatics, 30: 1314–1315

[41] Huang, Q., Song, P., Chen, Y., Liu, Z. (2021). Allosteric type and pathways are governed by the forces of protein-ligand binding. J. Phys. Chem. Lett., 12: 5404–5412

[42] NiD.,ChaiZ.,WangY.,LiM.,YuZ.,LiuY.,LuS.. (2021) Along the allostery stream: Recent advances in computational methods for allosteric drug discovery. Wires Comput. Mol. Sci., e1585

[43] Huang, W., Lu, S., Huang, Z., Liu, X., Mou, L., Luo, Y., Zhao, Y., Liu, Y., Chen, Z., Hou, T. . (2013). Allosite: a method for predicting allosteric sites. Bioinformatics, 29: 2357–2359

[44] Song, K., Liu, X., Huang, W., Lu, S., Shen, Q., Zhang, L. (2017). Improved method for the identification and validation of allosteric sites. J. Chem. Inf. Model., 57: 2358–2363

[45] Xu, Y., Wang, S., Hu, Q., Gao, S., Ma, X., Zhang, W., Shen, Y., Chen, F., Lai, L. (2018). CavityPlus: a web server for protein cavity detection with pharmacophore modelling, allosteric site identification and covalent ligand binding ability prediction. Nucleic Acids Res., 46: W374–W379

[46] Tan, Z. W., Guarnera, E., Tee, W. Berezovsky, I. (2020). Allosigma 2: Paving the way to designing allosteric effectors and to exploring allosteric effects of mutations. Nucleic Acids Res., 48: W116–W124

[47] Guarnera, E., Tan, Z. W., Zheng, Z. Berezovsky, I. (2017). AlloSigMA: Allosteric signaling and mutation analysis server. Bioinformatics, 33: 3996–3998

[48] Huang, M., Song, K., Liu, X., Lu, S., Shen, Q., Wang, R., Gao, J., Hong, Y., Li, Q., Ni, D. . (2018). Allofinder: A strategy for allosteric modulator discovery and allosterome analyses. Nucleic Acids Res., 46: W451–W458

[49] Shukla, D., Meng, Y., Roux, B. Pande, V. (2014). Activation pathway of Src kinase reveals intermediate states as targets for drug design. Nat. Commun., 5: 3397

[50] Ghosh, A. (2007). A study of communication pathways in methionyl-tRNA synthetase by molecular dynamics simulations and structure network analysis. Proc. Natl. Acad. Sci. USA., 104: 15711–15716

[51] Yang, L., Song, G. Jernigan, R. (2009). Protein elastic network models and the ranges of cooperativity. Proc. Natl. Acad. Sci. USA. 106, 12347–12352

[52] Atilgan, A. R., Durell, S. R., Jernigan, R. L., Demirel, M. C., Keskin, O. (2001). Anisotropy of fluctuation dynamics of proteins with an elastic network model. Biophys. J., 80: 505–515

[53] Stolzenberg, S., Michino, M., LeVine, M. V., Weinstein, H. (2016). Computational approaches to detect allosteric pathways in transmembrane molecular machines. Biochim. Biophys. Acta, 1858: 1652–1662

[54] Wang, J., Jain, A., McDonald, L. R., Gambogi, C., Lee, A. L. Dokholyan, N. (2020). Mapping allosteric communications within individual proteins. Nat. Commun., 11: 3862

[55] michL.,WuN.,BarahonaM.YalirakiS.. (2022) Allosteric hotspots in the main protease of SARS-CoV-2. J. Mol. Biol., 434, 167748

[56] Wu, N., mich, L. Yaliraki, S. (2022). Prediction of allosteric sites and signaling: Insights from benchmarking datasets. Patterns, 3: 100408

[57] Kravats, A. N., Tonddast-Navaei, S. (2016). Coarse-grained simulations of topology-dependent mechanisms of protein unfolding and translocation mediated by ClpY ATPase nanomachines. PLOS Comput. Biol., 12: e1004675

[58] Chandramohan, A., Krishnamurthy, S., Larsson, A., Nordlund, P., Jansson, A. Anand, G. (2016). Predicting allosteric effects from orthosteric binding in hsp90-ligand interactions: Implications for fragment-based drug design. PLOS Comput. Biol., 12: e1004840

[59] Wootten, D., Reynolds, C. A., Smith, K. J., Mobarec, J. C., Furness, S. G., Miller, L. J., Christopoulos, A. Sexton, P. (2016). Key interactions by conserved polar amino acids located at the transmembrane helical boundaries in Class B GPCRs modulate activation, effector specificity and biased signalling in the glucagon-like peptide-1 receptor. Biochem. Pharmacol., 118: 68–87

[60] SabharwalP.. (2017) Molecular insights into the structure and function of pepper vein banding virus encoded proteins and endocytic uptake pathway of virus-like particles into mammalian cells. Doctorate of Philosophy, Bangalore: Indian Institute of Science

[61] Roy, A., Xu, D., Poisson, J. (2011). A protocol for computer-based protein structure and function prediction. J. Vis. Exp., 57: e3259

[62] Zhang, Y. (2008). I-TASSER server for protein 3D structure prediction. BMC Bioinformatics, 9: 40

[63] Roy, A., Kucukural, A. (2010). I-TASSER: a unified platform for automated protein structure and function prediction. Nat. Protoc., 5: 725–738

[64] Roy, A., Yang, J. (2012). COFACTOR: an accurate comparative algorithm for structure-based protein function annotation. Nucleic Acids Res., 40: W471–477

[65] Chung, S. Y. (1996). A structural explanation for the twilight zone of protein sequence homology. Structure, 4: 1123–1127

[66] Venclovas, C. (2005). Comparative modeling in CASP6 using consensus approach to template selection, sequence-structure alignment, and structure assessment. Proteins, 61: 99–105

[67] Lovell, S. C., Davis, I. W., Arendall, W. B. de Bakker, P. I., Word, J. M., Prisant, M. G., Richardson, J. S. Richardson, D. (2003). Structure validation by Calpha geometry: phi, psi and Cbeta deviation. Proteins, 50: 437–450

[68] tz, A. W., Williamson, M. J., Xu, D., Poole, D., Le Grand, S. Walker, R. (2012). Routine microsecond molecular dynamics simulations with amber on GPUS. 1. Generalized born. J. Chem. Theory Comput., 8: 1542–1555

[69] CaseD. A.,DardenT. A.,CheathamT. E.,SimmerlingC. L.,WangJ.,DukeR. E.,LuoR.,WalkerR. C.,ZhangW.,MerzK. M.,. (2012) Amber 12. San Francisco: University of California

[70] Case, D. A., Cheatham, T. E. Darden, T., Gohlke, H., Luo, R., Merz, K. M. Onufriev, A., Simmerling, C., Wang, B. Woods, R. (2005). The Amber biomolecular simulation programs. J. Comput. Chem., 26: 1668–1688

[71] Jorgensen, W. L., Chandrasekhar, J., Madura, J. D., Impey, R. W. Klein, M. (1983). Comparison of simple potential functions for simulating liquid water. J. Chem. Phys., 79: 926–935

[72] Ryckaert, J. Ciccotti, G. Berendsen, H. J. (1977). Numerical integration of the cartesian equations of motion of a system with constraints: Molecular dynamics of n-alkanes. J. Comput. Phys., 23: 327–341

[73] Salomon-Ferrer, R., tz, A. W., Poole, D., Le Grand, S. Walker, R. (2013). Routine microsecond molecular dynamics simulations with amber on GPUS. 2. Explicit solvent particle Mesh Ewald. J. Chem. Theory Comput., 9: 3878–3888

[74] McDonald, I. K. Thornton, J. (1994). Satisfying hydrogen bonding potential in proteins. J. Mol. Biol., 238: 777–793

[75] Wallace, A. C., Laskowski, R. A. Thornton, J. (1995). LIGPLOT: a program to generate schematic diagrams of protein-ligand interactions. Protein Eng., 8: 127–134

[76] HumphreyW.,DalkeA.. (1996) VMD: visual molecular dynamics. J. Mol. Graph., 14, 33–38., 27–28

[77] Amadei, A., Linssen, A. B. Berendsen, H. (1993). Essential dynamics of proteins. Proteins, 17: 412–425

[78] Pronk, S., ll, S., Schulz, R., Larsson, P., Bjelkmar, P., Apostolov, R., Shirts, M. R., Smith, J. C., Kasson, P. M., van der Spoel, D. . (2013). GROMACS 4. 5: a high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics, 29: 845–854

[79] DeLanoW.. (2002) The pymol molecular graphics system. DeLano Scientific, San Carlos, CA, USA

RIGHTS & PERMISSIONS

The Author(s). Published by Higher Education Press.