Potential aggregation hot spots in recombinant human keratinocyte growth factor: a computational study

References

• Ahmed, A. B., & Kajava, AV. (2013). Breaking the amyloidogenicity code: Methods to predict amyloids from amino acid sequence. FEBS Letters, 587(8), 1089–1095. https://doi.org/10.1016/j.febslet.2012.12.006
   PubMed Web of Science ®Google Scholar
• Benington, L., Rajan, G., Locher, C., & Lim, L. Y. (2020). Fibroblast growth factor 2—A review of stabilisation approaches for clinical applications. Pharmaceutics, 12(6), 508. https://doi.org/10.3390/pharmaceutics12060508
   PubMed Web of Science ®Google Scholar
• Buck, P. M. (2012). Computational methods to predict therapeutic protein aggregation. In Voynov Vladimir & Caravella Justin A. (Eds.), Therapeutic proteins (pp. 425–451). Springer.
   Google Scholar
• Chen, B. L., Arakawa, T., Hsu, E., Narhi, L. O., Tressel, T. J., & Chien, S. L. (1994). Strategies to suppress aggregation of recombinant keratinocyte growth factor during liquid formulation development. Journal of Pharmaceutical Sciences, 83(12), 1657–1661. https://doi.org/10.1002/jps.2600831204
   PubMed Web of Science ®Google Scholar
• Chennamsetty, N., Voynov, V., Kayser, V., Helk, B., & Trout, B. L. (2010). Prediction of aggregation prone regions of therapeutic proteins. The Journal of Physical Chemistry B, 114(19), 6614–6624. https://doi.org/10.1021/jp911706q
   PubMed Web of Science ®Google Scholar
• Chiti, F. (2003). Rationalization of the effects of mutations on peptide andprotein aggregation rates. Nature, 424(6950), 805–808.
   Google Scholar
• Chiti, F., Calamai, M., Taddei, N., Stefani, M., Ramponi, G., & Dobson, C. M. (2002). Studies of the aggregation of mutant proteins in vitro provide insights into the genetics of amyloid diseases. Proceedings of the National Academy of Sciences, 99(Suppl. 4), 16419–16426. https://doi.org/10.1073/pnas.212527999
   PubMed Web of Science ®Google Scholar
• Conchillo-Solé, O., de Groot, N. S., Avilés, F. X., Vendrell, J., Daura, X., & Ventura, S. (2007). AGGRESCAN: A server for the prediction and evaluation of “hot spots” of aggregation in polypeptides. BMC Bioinformatics, 8(1), 65. https://doi.org/10.1186/1471-2105-8-65
   PubMed Web of Science ®Google Scholar
• Courtois, F., Agrawal, N. J., Lauer, T. M., & Trout, B. L. (2016). Rational design of therapeutic mAbs against aggregation through protein engineering and incorporation of glycosylation motifs applied to bevacizumab. mAbs, 8(1), 99–112. https://doi.org/10.1080/19420862.2015.1112477 26514585
   PubMed Web of Science ®Google Scholar
• Dastjerdeh, M. S., Marashiyan, M., Boroujeni, M. B., Golkar, M., Shokrgozar, M. A., & Rahimi, H. (2019). In silico analysis of different signal peptides for the secretory production of recombinant human keratinocyte growth factor in Escherichia coli. Computational Biology and Chemistry, 80, 225–233. https://doi.org/10.1016/j.compbiolchem.2019.03.003
   PubMed Web of Science ®Google Scholar
• Garvey, M., Meehan, S., Gras, S. L., Schirra, H. J., Craik, D. J., Van der Weerden, N. L., Anderson, M. A., Gerrard, J. A., & Carver, J. A. (2013). A radish seed antifungal peptide with a high amyloid fibril-forming propensity. Biochimica et Biophysica Acta, 1834(8), 1615–1623. https://doi.org/10.1016/j.bbapap.2013.04.030
   PubMed Web of Science ®Google Scholar
• Hsu, E., Osslund, T., Nybo, R., Chen, B.-L., Kenney, W. C., Morris, C. F., Arakawa, T., & Narhi, L. O. (2006). Enhanced stability of recombinant keratinocyte growth factor by mutagenesis. Protein Engineering, Design & Selection: PEDS, 19(4), 147–153. https://doi.org/10.1093/protein/gzj013
   PubMed Web of Science ®Google Scholar
• Huang, Z. (2012). A novel solid-phase site-specific PEGylation enhances the in vitro and in vivo biostabilty of recombinant human keratinocyte growth factor 1. PloS one, 7(5), e36423.
   Google Scholar
• Ko, J., Park, H., & Seok, C. (2012). GalaxyTBM: Template-based modeling by building a reliable core and refining unreliable local regions. BMC Bioinformatics, 13(1), 198. https://doi.org/10.1186/1471-2105-13-198
   PubMed Web of Science ®Google Scholar
• Kuhlman, B., & Baker, D. (2000). Native protein sequences are close to optimal for their structures. Proceedings of the National Academy of Sciences of the United States of America, 97(19), 10383–10388. https://doi.org/10.1073/pnas.97.19.10383
   PubMed Web of Science ®Google Scholar
• Lee, C. C., Perchiacca, J. M., & Tessier, P. (2013). Toward aggregation-resistant antibodies by design. Trends in Biotechnology, 31(11), 612–620. https://doi.org/10.1016/j.tibtech.2013.07.002
   PubMed Web of Science ®Google Scholar
• Maurer-Stroh, S., Debulpaep, M., Kuemmerer, N., Lopez de la Paz, M., Martins, I. C., Reumers, J., Morris, K. L., Copland, A., Serpell, L., Serrano, L., Schymkowitz, J. W. H., & Rousseau, F. (2010). Exploring the sequence determinants of amyloid structure using position-specific scoring matrices. Nature Methods, 7(3), 237–242. https://doi.org/10.1038/nmeth.1432
   PubMed Web of Science ®Google Scholar
• Meric, G., Robinson, A. S., & Roberts, C. J. (2017). Driving forces for nonnative protein aggregation and approaches to predict aggregation-prone regions. Annual Review of Chemical and Biomolecular Engineering, 8, 139–159. https://doi.org/10.1146/annurev-chembioeng-060816-101404
   PubMed Web of Science ®Google Scholar
• Nelson, R., Sawaya, M. R., Balbirnie, M., Madsen, A. Ø., Riekel, C., Grothe, R., & Eisenberg, D. (2005). Structure of the cross-beta spine of amyloid-like fibrils. Nature, 435(7043), 773–778. https://doi.org/10.1038/nature03680
   PubMed Web of Science ®Google Scholar
• Oliveberg, M. (2010). Waltz, an exciting new move in amyloid prediction. Nature Methods, 7(3), 187–188. https://doi.org/10.1038/nmeth0310-187
   PubMed Web of Science ®Google Scholar
• Paladin, L., Piovesan, D., & Tosatto, S. (2017). SODA: Prediction of protein solubility from disorder and aggregation propensity. Nucleic Acids Research, 45(W1), W236–W240. https://doi.org/10.1093/nar/gkx412
   PubMed Web of Science ®Google Scholar
• Pawlicki, S., Béchec, A. L., & Delamarche, C. (2008). AMYPdb: A database dedicated to amyloid precursor proteins. BMC Bioinformatics, 9(1), 273. https://doi.org/10.1186/1471-2105-9-273
   PubMed Web of Science ®Google Scholar
• Poorebrahim, M., Sadeghi, S., Ghorbani, R., Asghari, M., Abazari, M. F., Kalhor, H., & Rahimi, H. (2017). In silico enhancement of the stability and activity of keratinocyte growth factor. Journal of Theoretical Biology, 418, 111–121. https://doi.org/10.1016/j.jtbi.2017.01.009
   PubMed Web of Science ®Google Scholar
• Raja, M., & Kinne, R. (2020). Mechanistic insights into protein stability and self-aggregation in GLUT1 genetic variants causing GLUT1-deficiency syndrome. The Journal of membrane biology, 1–13.
   Google Scholar
• Rajan, R. S., Li, T., & Arakawa, T. (2010). Case studies involving protein aggregation. In Wang Wei & J. Roberts Christopher (Eds.), Aggregation of therapeutic proteins (pp. 367–401). Wiley.
   Google Scholar
• Rousseau, F., Schymkowitz, J., & Serrano, L. J. (2006). Protein aggregation and amyloidosis: Confusion of the kinds? Current Opinion in Structural Biology, 16(1), 118–126. https://doi.org/10.1016/j.sbi.2006.01.011
   PubMed Web of Science ®Google Scholar
• Sawaya, M. R., Sambashivan, S., Nelson, R., Ivanova, M. I., Sievers, S. A., Apostol, M. I., Thompson, M. J., Balbirnie, M., Wiltzius, J. J. W., McFarlane, H. T., Madsen, A. Ø., Riekel, C., & Eisenberg, D. (2007). Atomic structures of amyloid cross-beta spines reveal varied steric zippers. Nature, 447(7143), 453–457. https://doi.org/10.1038/nature05695
   PubMed Web of Science ®Google Scholar
• Siepen, J. A., Radford, S. E., & Westhead, D. (2003). Beta edge strands in protein structure prediction and aggregation. Protein Science, 12(10), 2348–2359. https://doi.org/10.1110/ps.03234503
   PubMed Web of Science ®Google Scholar
• Sormanni, P., Aprile, F. A., & Vendruscolo, M. (2015). The CamSol method of rational design of protein mutants with enhanced solubility. Journal of Molecular Biology, 427(2), 478–490. https://doi.org/10.1016/j.jmb.2014.09.026
   PubMed Web of Science ®Google Scholar
• Tartaglia, G. G., & Vendruscolo, M. J. C. S. R. (2008). The zyggregator method for predicting protein aggregation propensities. Chemical Society Reviews, 37(7), 1395–1401. https://doi.org/10.1039/b706784b
   PubMed Web of Science ®Google Scholar
• Trovato, A., Chiti, F., Maritan, A., & Seno, F. (2006). Insight into the structure of amyloid fibrils from the analysis of globular proteins. PLoS Computational Biology, 2(12), e170. https://doi.org/10.1371/journal.pcbi.0020170
   PubMed Web of Science ®Google Scholar
• Tsolis, A. C. (2013). A consensus method for the prediction of ‘aggregation-prone’ peptides in globular proteins. PloS one, 8(1), p.e54175.
   Google Scholar
• Tsolis, A. C., Papandreou, N. C., Iconomidou, V. A., & Hamodrakas, S. J. (2013). A consensus method for the prediction of ‘aggregation-prone’ peptides in globular proteins. PLoS One, 8(1), e54175. https://doi.org/10.1371/journal.pone.0054175
   PubMed Web of Science ®Google Scholar
• van der Kant, R. (2019). Solubis: Optimizing protein solubility by minimal point mutations. In Protein misfolding diseases (pp. 317–333). Springer.
   Google Scholar
• van der Kant, R., Karow-Zwick, A. R., Van Durme, J., Blech, M., Gallardo, R., Seeliger, D., Aßfalg, K., Baatsen, P., Compernolle, G., Gils, A., Studts, J. M., Schulz, P., Garidel, P., Schymkowitz, J., & Rousseau, F. (2017). Prediction and reduction of the aggregation of monoclonal antibodies. Journal of Molecular Biology, 429(8), 1244–1261. https://doi.org/10.1016/j.jmb.2017.03.014
   PubMed Web of Science ®Google Scholar
• Walsh, I., Seno, F., Tosatto, S. C. E., & Trovato, A. (2014). PASTA 2.0: An improved server for protein aggregation prediction. Nucleic Acids Research, 42(Web Server issue), W301–W307. https://doi.org/10.1093/nar/gku399
   PubMedGoogle Scholar
• Wang, W., & Roberts, C. J. (2010). Aggregation of therapeutic proteins. Vol. 100. Wiley Online Library.
   Google Scholar
• Wang, X., Das, T. K., Singh, S. K., & Kumar, S. (2009). Potential aggregation prone regions in biotherapeutics: A survey of commercial monoclonal antibodies. mAbs, 1(3), 254–267. https://doi.org/10.4161/mabs.1.3.8035 20065649
   PubMed Web of Science ®Google Scholar
• Zahra, B., Reza, K. H., & Javad, M. S. (2016). Expression of the full-length human recombinant keratinocyte growth factor in Pichia pastoris. Journal of Cell and Molecular Research, 8(1), 1–7.
   Google Scholar
• Zambrano, R., Jamroz, M., Szczasiuk, A., Pujols, J., Kmiecik, S., & Ventura, S. (2015). AGGRESCAN3D (A3D): Server for prediction of aggregation properties of protein structures. Nucleic Acids Research, 43(W1), W306–W313. https://doi.org/10.1093/nar/gkv359
   PubMed Web of Science ®Google Scholar
• Zibaee, S., Makin, O. S., Goedert, M., & Serpell, L. C. (2007). A simple algorithm locates beta-strands in the amyloid fibril core of alpha-synuclein, abeta, and tau using the amino acid sequence alone. Protein Science: A Publication of the Protein Society, 16(5), 906–918. https://doi.org/10.1110/ps.062624507
   PubMed Web of Science ®Google Scholar