» Articles » PMID: 18029422

Computational Design of the Fyn SH3 Domain with Increased Stability Through Optimization of Surface Charge Charge Interactions

Overview
Journal Protein Sci
Specialty Biochemistry
Date 2007 Nov 22
PMID 18029422
Citations 20
Authors
Affiliations
Soon will be listed here.
Abstract

Computational design of surface charge-charge interactions has been demonstrated to be an effective way to increase both the thermostability and the stability of proteins. To test the robustness of this approach for proteins with predominantly beta-sheet secondary structure, the chicken isoform of the Fyn SH3 domain was used as a model system. Computational analysis of the optimal distribution of surface charges showed that the increase in favorable energy per substitution begins to level off at five substitutions; hence, the designed Fyn sequence contained four charge reversals at existing charged positions and one introduction of a new charge. Three additional variants were also constructed to explore stepwise contributions of these substitutions to Fyn stability. The thermodynamic stabilities of the variants were experimentally characterized using differential scanning calorimetry and far-UV circular dichroism spectroscopy and are in very good agreement with theoretical predictions from the model. The designed sequence was found to have increased the melting temperature, DeltaT (m) = 12.3 +/- 0.2 degrees C, and stability, DeltaDeltaG(25 degrees C) = 7.1 +/- 2.2 kJ/mol, relative to the wild-type protein. The experimental data suggest that a significant increase in stability can be achieved through a very small number of amino acid substitutions. Consistent with a number of recent studies, the presented results clearly argue for a seminal role of surface charge-charge interactions in determining protein stability and suggest that the optimization of surface interactions can be an attractive strategy to complement algorithms optimizing interactions in the protein core to further enhance protein stability.

Citing Articles

A Practical Guide to Computational Tools for Engineering Biocatalytic Properties.

Vega A, Planas A, Biarnes X Int J Mol Sci. 2025; 26(3).

PMID: 39940748 PMC: 11817184. DOI: 10.3390/ijms26030980.


Rational Design of Adenylate Kinase Thermostability through Coevolution and Sequence Divergence Analysis.

Chang J, Zhang C, Cheng H, Tan Y Int J Mol Sci. 2021; 22(5).

PMID: 33803409 PMC: 7967156. DOI: 10.3390/ijms22052768.


Effects of Topology and Sequence in Protein Folding Linked via Conformational Fluctuations.

Trotter D, Wallin S Biophys J. 2020; 118(6):1370-1380.

PMID: 32061276 PMC: 7091472. DOI: 10.1016/j.bpj.2020.01.020.


Creating a Homeodomain with High Stability and DNA Binding Affinity by Sequence Averaging.

Tripp K, Sternke M, Majumdar A, Barrick D J Am Chem Soc. 2017; 139(14):5051-5060.

PMID: 28326770 PMC: 5617789. DOI: 10.1021/jacs.6b11323.


Improvement of the thermostability and catalytic efficiency of a highly active β-glucanase from Talaromyces leycettanus JCM12802 by optimizing residual charge-charge interactions.

You S, Tu T, Zhang L, Wang Y, Huang H, Ma R Biotechnol Biofuels. 2016; 9:124.

PMID: 27303445 PMC: 4906821. DOI: 10.1186/s13068-016-0544-8.


References
1.
Dill K . Dominant forces in protein folding. Biochemistry. 1990; 29(31):7133-55. DOI: 10.1021/bi00483a001. View

2.
Antosiewicz J, McCammon J, Gilson M . The determinants of pKas in proteins. Biochemistry. 1996; 35(24):7819-33. DOI: 10.1021/bi9601565. View

3.
Matthew J, Gurd F, Flanagan M, March K, Shire S . pH-dependent processes in proteins. CRC Crit Rev Biochem. 1985; 18(2):91-197. DOI: 10.3109/10409238509085133. View

4.
Bolon D, Marcus J, Ross S, Mayo S . Prudent modeling of core polar residues in computational protein design. J Mol Biol. 2003; 329(3):611-22. DOI: 10.1016/s0022-2836(03)00423-6. View

5.
Makhatadze G, Privalov P . Energetics of protein structure. Adv Protein Chem. 1995; 47:307-425. DOI: 10.1016/s0065-3233(08)60548-3. View