Despite significant advances in the understanding of protein structure-function relationships, revealing protein folding pathways still poses a challenge due to a limited number of relevant experimental tools. Widely-used experimental techniques, such as calorimetry or spectroscopy, critically depend on a proper data analysis. Currently, there are only separate data analysis tools available for each type of experiment with a limited model selection. To address this problem, we have developed the CalFitter web server to be a unified platform for comprehensive data fitting and analysis of protein thermal denaturation data. The server allows simultaneous global data fitting using any combination of input data types and offers 12 protein unfolding pathway models for selection, including irreversible transitions often missing from other tools. The data fitting produces optimal parameter values, their confidence intervals, and statistical information to define unfolding pathways. The server provides an interactive and easy-to-use interface that allows users to directly analyse input datasets and simulate modelled output based on the model parameters. CalFitter web server is available free at https://loschmidt.chemi.muni.cz/calfitter/.

Department of Information Systems Faculty of Information Technology Brno University of Technology Brno Czech Republic

International Centre for Clinical Research St Anne's University Hospital Brno Brno Czech Republic

Loschmidt Laboratories Department of Experimental Biology and Research Centre for Toxic Compounds in the Environment Faculty of Science Masaryk University Brno Czech Republic

Zobrazit více v PubMed

Dill K.A., MacCallum J.L.. The protein-folding problem, 50 years on. Science. 2012; 338:1042–1046. PubMed

Orengo C.A., Pearl F.M.G., Bray J.E., Todd A.E., Martin A., Lo Conte L., Thornton J.M.. The CATH database provides insights into protein structure/function relationships. Nucleic Acids Res. 1999; 27:275–279. PubMed PMC

Fersht A.R., Matouschek A., Serrano L.. The folding of an enzyme: I. Theory of protein engineering analysis of stability and pathway of protein folding. J. Mol. Biol. 1992; 224:771–782. PubMed

Yang H., Liu L., Li J., Chen J., Du G.. Rational design to improve protein thermostability: recent advances and prospects. ChemBioEng Rev. 2015; 2:87–94.

Knowles T.P., Vendruscolo M., Dobson C.M.. The amyloid state and its association with protein misfolding diseases. Nat. Rev. Mol. Cell Biol. 2014; 15:384–396. PubMed

Sancho J. The stability of 2-state, 3-state and more-state proteins from simple spectroscopic techniques… plus the structure of the equilibrium intermediates at the same time. Arch. Biochem. Biophys. 2013; 531:4–13. PubMed

Temel D.B., Landsman P., Brader M.L.. Orthogonal methods for characterizing the unfolding of therapeutic monoclonal antibodies: differential scanning calorimetry, isothermal chemical denaturation, and intrinsic fluorescence with concomitant static light scattering. Methods Enzymol. 2016; 567:359–389. PubMed

Gelman H., Gruebele M.. Fast protein folding kinetics. Q. Rev. Biophys. 2014; 47:95–142. PubMed PMC

Goyal M., Chaudhuri T.K., Kuwajima K.. Irreversible denaturation of maltodextrin glucosidase studied by differential scanning calorimetry, circular dichroism, and turbidity measurements. PloS One. 2014; 9:e115877. PubMed PMC

Dimitriadis G., Drysdale A., Myers J.K., Arora P., Radford S.E., Oas T.G., Smith D.A.. Microsecond folding dynamics of the F13W G29A mutant of the B domain of staphylococcal protein A by laser-induced temperature jump. Proc. Natl. Acad. Sci. U.S.A. 2004; 101:3809–3814. PubMed PMC

Lepock J.R., Ritchie K.P., Kolios M.C., Rodahl A.M., Heinz K.A., Kruuv J.. Influence of transition rates and scan rate on kinetic simulations of differential scanning calorimetry profiles of reversible and irreversible protein denaturation. Biochemistry. 1992; 31:12706–12712. PubMed

Sanchez-Ruiz J.M. Theoretical analysis of Lumry-Eyring models in differential scanning calorimetry. Biophys. J. 1992; 61:921–935. PubMed PMC

Privalov P.L., Dragan A.I.. Microcalorimetry of biological macromolecules. Biophys. Chem. 2007; 126:16–24. PubMed

Tsytlonok M., Itzhaki L.S.. The how's and why's of protein folding intermediates. Arch. Biochem. Biophys. 2013; 531:14–23. PubMed

Neudecker P., Robustelli P., Cavalli A., Walsh P., Lundstrom P., Zarrine-Afsar A., Sharpe S., Vendruscolo M., Kay L.E.. Structure of an intermediate state in protein folding and aggregation. Science. 2012; 336:362–366. PubMed

Bowman G.R., Beauchamp K.A., Boxer G., Pande V.S.. Progress and challenges in the automated construction of Markov state models for full protein systems. J. Chem. Phys. 2009; 131:124101. PubMed PMC

Wei G., Xi W., Nussinov R., Ma B.. Protein ensembles: how does nature harness thermodynamic fluctuations for life? the diverse functional roles of conformational ensembles in the cell. Chem. Rev. 2016; 116:6516–6551. PubMed PMC

Johnson K.A. Fitting enzyme kinetic data with KinTek global kinetic explorer. Methods Enzymol. 2009; 467:601–626. PubMed

Kuzmič P. DynaFit—a software package for enzymology. Methods Enzymol. 2009; 467:247–280. PubMed

Niklasson M., Andresen C., Helander S., Roth M.G., Zimdahl Kahlin A., Lindqvist Appell M., Mårtensson L., Lundström P.. Robust and convenient analysis of protein thermal and chemical stability. Prot. Sci. 2015; 24:2055–2062. PubMed PMC

Harder M.E., Deinzer M.L., Leid M.E., Schimerlik M.I.. Global analysis of threestate protein unfolding data. Prot. Sci. 2004; 13:2207–2222. PubMed PMC

Li A., Ziehr J.L., Johnson K.A.. A new general method for simultaneous fitting of temperature and concentration dependence of reaction rates yields kinetic and thermodynamic parameters for HIV reverse transcriptase specificity. J. Biol. Chem. 2017; 292:6695–6702. PubMed PMC

Yi Q., Scalley M.L., Simons K.T., Gladwin S.T., Baker D.. Characterization of the free energy spectrum of peptostreptococcal protein L. Fold. Des. 1997; 2:271–280. PubMed

Mazurenko S., Kunka A., Beerens K., Johnson C.M., Damborsky J., Prokop Z.. Exploration of protein unfolding by modelling calorimetry data from reheating. Sci. Rep. 2017; 7:16321. PubMed PMC

Ibarra-Molero B., Naganathan A.N., Sanchez-Ruiz J.M., Muñoz V.. Modern analysis of protein folding by differential scanning calorimetry. Methods Enzymol. 2016; 567:281–318. PubMed

Rodriguez‐Larrea D., Ibarra‐Molero B., de Maria L., Borchert T.V., Sanchez-Ruiz J.M.. Beyond Lumry–Eyring: an unexpected pattern of operational reversibility/irreversibility in protein denaturation. Prot. Struct. Funct. Bioinf. 2008; 70:19–24. PubMed

Lyubarev A.E., Kurganov B.I.. Analysis of DSC data relating to proteins undergoing irreversible thermal denaturation. J. Therm. Anal. Cal. 2000; 62:51–62.

Milardi D., La Rosa C., Grasso D.. Extended theoretical analysis of irreversible protein thermal unfolding. Biophys. Chem. 1994; 52:183–189. PubMed

Kirk P., Thorne T., Stumpf M.P.. Model selection in systems and synthetic biology. Curr. Opin. Biotechnol. 2013; 24:767–774. PubMed

Dvorak P., Bednar D., Vanacek P., Balek L., Eiselleova L., Stepankova V., Sebestova E., Kunova Bosakova M., Konecna Z., Mazurenko S. et al. . Computer-assisted engineering of hyperstable fibroblast growth factor 2. Biotechnol. Bioeng. 2018; 115:850–862. PubMed

Advancing Enzyme's Stability and Catalytic Efficiency through Synergy of Force-Field Calculations, Evolutionary Analysis, and Machine Learning

CalFitter 2.0: Leveraging the power of singular value decomposition to analyse protein thermostability