Improving the solubility, activity, and stability of reteplase using in silico design of new variants
Abstract
Reteplase (recombinant plasminogen activator, r-PA) is a thrombolytic agent recombined from tissue-type plasminogen activator (t-PA), which has several prominent features such as strong thrombolytic ability and E. coli expressibility. Despite these outstanding features, it demonstrates reduced fibrin binding affinity, reduced stimulation of protease activity, and lower solubility, hence higher aggregation propensity, compared to t-PA. The present study was devoted to design r-PA variants with comparable structural stability, enhanced biological activity, and high solubility. For this purpose, computational molecular modeling techniques were utilized. The supercharging technique was applied for r-PA to designing new species of the protein. Based on the results from in silico evaluation of selected mutations in comparison to the wild-type r-PA, the designed supercharged mutant (S7 variant) exhibited augmented stability, decreased solvation energy, as well as enhanced binding affinity to fibrin. The data also implied increased plasminogen cleavage activity of the new variant. These findings have implications to therapies which involve removal of intravascular blood clots, including the treatment of acute myocardial infarction.
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Shafiee F, Moazen F, Rabbani M, Mir Mohammad Sadeghi H. Expression and activity evaluation of Reteplase in Escherichia coli TOP10. J Param Sci. 2015;6(3):58-64.
Kumar A, Pulicherla KK, Ram KS, Rao KR. Evolutionary trend of thrombolytics. Int J Bio Sci Bio Technol. 2010;2(4):51-68.
Renatus M, Bode W, Huber R, Stürzebecher J, Prasa D, Fischer S, et al. Structural mapping of the active site specificity determinants of human tissue-type plasminogen activator. Implications for the design of low molecular weight substrates and inhibitors. J Biol Chem. 1997;272(35):21713-21719.
Hudson NE. Biophysical mechanisms mediating fibrin fiber lysis. BioMed Res Int. 2017;(2017). Article ID 2748340, 17 pages.
Mandi N, Sundaram KR, Tandra SK, Bandyopadhyay S, Padmanabhan S. Asn12 and Asn278: critical residues for in vitro biological activity of reteplase. Adv Hematol. 2010;(2010). Article ID 172484, 9 pages.
Kohnert U, Rudolph R, Verheijen JH, Weening-Verhoeff EJ, Stern A, Opitz U, et al. Biochemical properties of the kringle 2 and protease domains are maintained in the refolded t-PA deletion variant BM 06.022. Protein Eng. 1992;5(1):93-100.
Lawrence MS, Phillips KJ, Liu DR. Supercharging proteins can impart unusual resilience. J Am Chem Soc. 2007;129(33):10110-10112.
Adamczak M, Krishna SH. Strategies for improving enzymes for efficient biocatalysis. Food Technol Biotechnol. 2004;42(4):251-264.
Chang CC, Song J, Tey BT, Ramanan RN. Bioinformatics approaches for improved recombinant protein production in Escherichia coli: protein solubility prediction. Brief Bioinform. 2013;15(6):953-962.
Laskowski RA, MacArthur MW, Moss DS, Thornton JM. PROCHECK: a program to check the stereochemical quality of protein structures. J Appl Crystallogr. 1993;26(2):283-291.
Willard L, Ranjan A, Zhang H, Monzavi H, Boyko RF, Sykes BD, et al. VADAR: a web server for quantitative evaluation of protein structure quality. Nucleic Acids Res. 2003;31(13):3316-3319.
Moretti R, Lyskov S, Das R, Meiler J, Gray JJ. Web‐accessible molecular modeling with Rosetta: the Rosetta online server that includes everyone (ROSIE). Protein Sci. 2018;27(1):259-268.
Brenner S. The molecular evolution of genes and proteins: a tale of two serines. Nature. 1988;334(6182):528-530.
Dehouck Y, Kwasigroch JM, Gilis D, Rooman M. PoPMuSiC 2.1: a web server for the estimation of protein stability changes upon mutation and sequence optimality. BMC Bioinformatics. 2011;12(1):151-162.
Yin S, Ding F, Dokholyan NV. Eris: an automated estimator of protein stability. Nat Methods. 2007;4(6):466-467.
Pronk S, Páll S, Schulz R, Larsson P, Bjelkmar P, Apostolov R, et al. GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics. 2013;29(7):845-854.
Darden T, York D, Pedersen L. Particle mesh Ewald: An N.log (N) method for Ewald sums in large systems. J Chem Phys. 1993;98(12): 10089-10092.
Basu S, Sen S. Do homologous thermophilic-mesophilic proteins exhibit similar structures and dynamics at optimal growth temperatures?A molecular dynamics simulation study. J Chem Inf Model. 2013;53(2):423-434.
Zeiske T, Stafford KA, Palmer AG. Thermostability of enzymes from molecular dynamics simulations. J Chem Theory Comput. 2016;12(6):2489-2492.
Cerutti DS, Jain T, McCammon JA. CIRSE: A solvation energy estimator compatible with flexible protein docking and design applications. Protein Sci. 2006;15(7):1579-1596.
Medved L, Nieuwenhuizen W. Molecular mechanisms of initiation of fibrinolysis by fibrin. Thromb Haemost. 2003;89(03):409-419.
Dominguez C, Boelens R, Bonvin AM. HADDOCK: a protein-protein docking approach based on biochemical or biophysical information. J Am Chem Soc. 2003;125(7):1731-1737.
Khodabakhsh F, Dehghani Z, Zia MF, Rabbani M, Sadeghi HM. Cloning and expression of functional reteplase in Escherichia coli top10. Avicenna J Med Biotechnol. 2013;5(3):168-175.
Ghosh S, Rasheedi S, Rahim SS, Banerjee S, Choudhary RK, Chakhaiyar P, et al. Method for enhancing solubility of the expressed recombinant proteins in Escherichia coli. Biotechniques. 2004;37(3):418-423.
Prasad S, Khadatare PB, Roy I. Effect of chemical chaperones in improving the solubility of recombinant proteins in Escherichia coli. Appl Environ Microbiol. 2011;77(13):4603-469.
Trésaugues L, Collinet B, Minard P, Henckes G, Aufrère R, Blondeau K, et al. Refolding strategies from inclusion bodies in a structural genomics project. J Struct Funct Genomics. 2004;5(3): 195-204.
Costa S, Almeida A, Castro A, Domingues L. Fusion tags for protein solubility, purification and immunogenicity in Escherichia coli: the novel Fh8 system. Front Microbiol. 2014;5:63-82.
Aghaabdollahian S, Rabbani M, Ghaedi K, Sadeghi HM. Molecular cloning of reteplase and its expression in E. coli using tac promoter.Adv Biomed Res. 2014;3:190.
Der BS, Kluwe C, Miklos AE, Jacak R, Lyskov S, Gray JJ, et al. Alternative computational protocols for supercharging protein surfaces for reversible unfolding and retention of stability. PLoS One. 2013;8(5):e64363.
Simeonov P, Berger-Hoffmann R, Hoffmann R, Sträter N, Zuchner T. Surface supercharged human enteropeptidase light chain shows improved solubility and refolding yield. Protein Eng Des Sel. 2010;24(3):261-268.
Mosavi LK, Peng ZY. Structure‐based substitutions for increased solubility of a designed protein. Protein Eng. 2003;16(10):739-745.
Tsumoto K, Umetsu M, Kumagai I, Ejima D,Philo JS, Arakawa T. Role of arginine in protein refolding, solubilization, and purification. Biotechnol Prog. 2004;20(5):1301-1308.
Das U, Hariprasad G, Ethayathulla AS, Manral P, Das TK, Pasha S, et al. Inhibition of protein aggregation: supramolecular assemblies of arginine hold the key. PLoS One. 2007;2(11):e1176.
Shehu A, Kavraki LE. Modeling structures and motions of loops in protein molecules. Entropy. 2012;14(2):252-290.
Miklos AE, Kluwe C, Der BS, Pai S, Sircar A, Hughes RA, et al. Structure-based design of supercharged, highly thermoresistant antibodies. Chem Biol. 2012;19(4):449-455.
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