Web Server-based structure prediction as a supplementary tool for basic and acidic FGF secondary structure analysis using FTIR spectroscopy and a case study comparing curve-fit with the model-based structure inspection of the FTIR data

Year 2023, , 70 - 83, 15.05.2023

Filiz Korkmaz Özkan , Ayça Doğan Mollaoğlu , Yekbun Adıgüzel

https://doi.org/10.33435/tcandtc.1195150

Abstract

Aim: Fourier Transform Infrared (FTIR) spectroscopy can provide relative proportion of secondary structure elements in a protein. However, extracting this information from the Amide I band area of an FTIR spectrum is difficult. In addition to experimental methods, several protein secondary structure prediction algorithms serving on the Web can be used as supplementary tools requiring only protein amino acid sequences as inputs. WeIn addition, web-server based docking tools can provide structure information when proteins are mixed and potentially interacting. Accordingly, we aimed to utilize web-server based structure predictors in fibroblast growth factor (FGF) protein structure determination through the FTIR data.
Materials and methods: Seven such predictors arewere selected and tested on basic FGF (bFGF) protein, to predict FGF secondary structure. Results arewere compared to available structure-files deposited in the Protein Data Bank (PDB). Then, FTIR spectra of bFGF and the acidic form of the protein with 50 folds more bovine serum albumin as carrier protein (1FGFA/50BSA) arewere collected. Optimized Amide I curve-fit parameters of bFGF with low (<5) root mean square deviation (RMSD) in the PDB data (3.05) and the predictions (2.39) arewere obtained. Those parameters arewere applied in curve-fitting of 1FGFA/50BSA data. Secondary structure iswas inspected also through applying models derived from the previously established methods. Results of model-based secondary structure estimation from FTIR data were compared with secondary structure calculated as 1 part contribution from 1FGFA/1BSA complex and 49 parts contribution from BSA. Complex structure was obtained through docking.
Results: OptimizedResults: RMSD in the PDB data and the predictions were respectively 3.05 and 2.39 with the optimized parameters. Those parameters did not work well for the 1FGFA/50BSA data. Models are better in this case, wherein one model (Model-1') with the lowest average RMSD has 8.1538 RMSD in the bFGF and 6.644.78 RMSD in the 1FGFA/50BSA structures.
Conclusion: Model-based secondary structure predictions are better for determining bFGF and 1FGFA/50BSA secondary structures through the curve-fit approach that we followed, under non-optimal conditions like protein/BSA mixtures. Web servers can assist experimental studies investigating structures with unknown structures. Any web-based structure prediction supporting the experimental results would be enforcing the findings, but the unsupported results would not necessarily falsify the experimental data.

Keywords

Protein structure, secondary structure, structure prediction, ATR FTIR, FGF, protein docking

References

• Y. Adıgüzel, P.I. Haris, F. Severcan, Screening of proteins in cells and tissues by vibrational spectroscopy, in: Severcan F, Haris PI (Eds.), Vibrational Spectroscopy in Diagnosis and Screening, IOS Press, Amsterdam, 2012, 53–108.
• M. Baldassarre, C. Li, N. Eremina, E. Goormaghtigh, A. Barth, Simultaneous Fitting of Absorption Spectra and Their Second Derivatives for an Improved Analysis of Protein Infrared Spectra, Molecules 20 (2015) 12599–12622.
• N.L. Benbow, S. Karpiniec, M. Krasowska, D.A. Beattie, Incorporation of FGF-2 into Pharmaceutical Grade Fucoidan/Chitosan Polyelectrolyte Multilayers, Mar Drugs 18 (2020) 531.
• D.M. Byler, H. Susi, Examination of the secondary structure of proteins by deconvolved FTIR spectra, Biopolymers 25 (1986) 469–487.
• F. Coulier, P. Pontarotti, R. Roubin, H. Hartung, M. Goldfarb, D. Birnbaum, Of worms and men: an evolutionary perspective on the fibroblast growth factor (FGF) and FGF receptor families, J Mol Evol 44 (1997) 43–56.
• G. Deleage, B. Roux, An algorithm for protein secondary structure prediction based on class prediction, Protein Eng 1(1987) 289–294.
• J. Engele, M. Churchill Bohn, Effects of acidic and basic fibroblast growth factors (aFGF, bFGF) on glial precursor cell proliferation: Age dependency and brain region specificity, Developmental Biology 152 (1992) 363–372.
• A.E. Eriksson, L.S. Cousens, B.W. Matthews, Refinement of the structure of human basic fibroblast growth factor at 1.6 A resolution and analysis of presumed heparin binding sites by selenate substitution, Protein Sci 2(1993) 1274–1284.
• C. Geourjon, G. Deleage, SOPM: a self-optimized method for protein secondary structure prediction, Protein Eng 7 (1994) 157–164.
• E. Goormaghtigh, V. Cabiaux, J.M. Ruysschaert, Determination of soluble and membrane protein structure by Fourier transform infrared spectroscopy, Subcell Biochem 23 (1994) 329–450.
• G. Goormaghtigh, J.M. Ruysschaert, V. Raussens, Evaluation of the information content in infrared spectra for protein secondary structure determination, Biophys. J. 90 (2006) 2946–2957.
• E. Goormaghtigh, R. Gasper, A. Bénard, A. Goldsztein, V. Raussens, Protein secondary structure content in solution, films and tissues: Redundancy and complementarity of the information content in circular dichroism, transmission and ATR FTIR spectra, Biochim. Biophys. Acta 1794 (2009) 1332–1343.
• J. Güldenhaupt, Y. Adiguzel, J. Kuhlmann, H. Waldmann, C. Kötting, et al., Secondary structure of lipidated Ras bound to a lipid bilayer, FEBS J 275 (2008) 5910–5918.
• P. Haris, F. Severcan, FTIR spectroscopic characterization of protein structure in aqueous and non-aqueous media, Journal of Molecular Catalysis B: Enzymatic 7 (1999) 207–221.
• M. Heinig, D. Frishman, STRIDE: a web server for secondary structure assignment from known atomic coordinates of proteins, Nucleic Acids Res 32 (2004) W500–W502.
• J.A. Hering, P.R. Innocent, P.I. Haris, An alternative method for rapid quantification of protein secondary structure from FTIR spectra using neural networks, Spectrosc. Int. J. 16 (2002) 53–69.
• L. Homaeian, L.A. Kurgan, J. Ruan, K.J. Cios, K. Chen, Prediction of protein secondary structure content for the twilight zone sequences, Proteins: Structure, Function, and Bioinformatics 69 (2007) 486–498.
• J.S. Kastrup, E.S. Eriksson, H. Dalboge, H. Flodgaard, X-ray structure of the 154-amino-acid form of recombinant human basic fibroblast growth factor. comparison with the truncated 146-amino-acid form, Acta Crystallogr Sect D 53 (1996) 160–168.
• R.D. King, M.J. Sternberg, Identification and application of the concepts important for accurate and reliable protein secondary structure prediction, Protein Sci 5 (1996) 2298–2310.
• M. Klähn, J. Schlitter, K. Gerwert, Theoretical IR spectroscopy based on QM/MM calculations provides changes in charge distribution, bond lengths, and bond angles of the GTP ligand induced by the Ras-protein, Biophys J 88 (2005) 3829–3844.
• F. Korkmaz, S. Köster, O. Yildiz, W. Mäntele, In situ opening/closing of OmpG from E. coli and the splitting of β-sheet signals in ATR-FTIR spectroscopy, Spectrochim. Acta. A. Mol. Biomol. Spectrosc. 91 (2012) 395–401.
• S. Krimm, J. Bandekar, Vibrational spectroscopy and conformation of peptides, polypeptides, and proteins, Adv Protein Chem 38 (1986) 181–363.
• J.G. Lees, R.W. Janes, Combining sequence-based prediction methods and circular dichroism and infrared spectroscopic data to improve protein secondary structure determinations, BMC Bioinformatics 9 (2008) 24.
• K. Lin, V.A. Simossis, W.R. Taylor, J. Heringa, A simple and fast secondary structure prediction method using hidden neural networks, Bioinformatics 21 (2005) 152–159.
• M.R. Lozano, M. Redondo-Horcajo, M.Á. Jiménez, L. Zilberberg, P. Cuevas, et al., Solution Structure and Interaction with Basic and Acidic Fibroblast Growth Factor of a 3-kDa Human Platelet Factor-4 Fragment with Antiangiogenic Activity, J Biol Chem 276 (2001) 35723–35734.
• G. Macindoe, L. Mavridis, V. Venkatraman, M.-D. Devignes, D.W. Ritchie, HexServer: an FFT-based protein docking server powered by graphics processors, Nucleic Acids Res 38 (2010) W445–W449.
• C.N. Magnan, P. Baldi, SSpro/ACCpro 5: almost perfect prediction of protein secondary structure and relative solvent accessibility using profiles, machine learning, and structural similarity, Bioinformatics 30 (2014) 2592–2597.
• S. Navea, R. Tauler, A. de Juan, Application of the local regression method interval partial least-squares to the elucidation of protein secondary structure, Anal Biochem 336 (2005) 231–242.
• S. Noji, T. Matsuo, E. Koyama, T. Yamaai, T. Nohno, et al., Expression pattern of acidic and basic fibroblast growth factor genes in adult rat eyes, Biochemical and Biophysical Research Communications 168 (1990)343–349.
• J. Ollesch, E. Kuennemann, R. Glockshuber, K. Gerwert, Prion protein α-to-β transition monitored by Time-resolved Fourier Transform Infrared Spectroscopy, Applied Spectroscopy 61 (2007) 1025–1031.
• G. Pollastri, D. Przybylski, B. Rost, P. Baldi, Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles, Proteins 47 (2002) 228–235.
• D.W. Ritchie, Recent progress and future directions in protein-protein docking, Current Protein and Peptide Science 9 (2008) 1–15.
• D.W. Ritchie, S. Grudinin, Spherical polar Fourier assembly of protein complexes with arbitrary point group symmetry, J Appl Cryst 49 (2016) 158–167.
• B. Rost, G. Yachdav, J. Liu, The PredictProtein server, Nucleic Acids Res 32 (2004) W321–W326.
• S. Sen-Britain, W. Hicks, R. Hard, G.A. Gardella Jr, The mechanism of secondary structural changes in Keratinocyte Growth Factor during uptake and release from a hydroxyethyl(methacrylate) hydrogel revealed by 2D Correlation Spectroscopy, arXiv 1808.03670 (2018) [cond-mat.soft].
• M. Severcan, P.I. Haris, F. Severcan, Using artificially generated spectral data to improve protein secondary structure prediction from Fourier transform infrared spectra of proteins, Anal Biochem 332 (2004) 238–244.
• V.A. Shashilov, I.K. Lednev, Advanced statistical and numerical methods for spectroscopic characterization of protein structural evolution, Chem Rev 110 (2010) 5692–5713.
• J. Smith, A. Yelland, R. Baillie, R.C. Coombes, Acidic and basic fibroblast growth factors in human breast tissue, European Journal of Cancer 30 (1994) 496–503.
• I. Tooyama, H.P.H. Kremer, M.R. Hayden, H. Kimura, E.G. McGeer,P.L. McGeer, Acidic and basic fibroblast growth factor-like immunoreactivity in the striatum and midbrain in Huntington's disease, Brain Research 610 (1993) 1–7.
• A. Tovchigrechko, I.A. Vakser, GRAMM-X public web server for protein–protein docking, Nucleic Acids Res 34 (2006) W310–W314.
• I.A. Vakser, Protein-protein docking: from interaction to interactome, Biophys J 107 (2014) 1785–1793.
• S.J. Wodak, J. Janin, Computer analysis of protein-protein interaction, J Mol Biol 124 (1978) 323–342.