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

Abstract

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. In 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. Seven such predictors were selected and tested on basic FGF (bFGF) protein, to predict FGF secondary structure. Results were 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) were collected. Optimized Amide I curve-fit parameters of bFGF with low (<5) root mean square deviation (RMSD) in the PDB data and the predictions were obtained. Those parameters were applied in curve-fitting of 1FGFA/50BSA data. Secondary structure was 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. 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 8.38 RMSD in the bFGF and 4.78 RMSD in the 1FGFA/50BSA structures. 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. © (2023), (DergiPark). All rights reserved.