Abstract
BackgroundDisulfide engineering is an important biotechnological tool that has advanced a wide range of research. The introduction of novel disulfide bonds into proteins has been used extensively to improve protein stability, modify functional characteristics, and to assist in the study of protein dynamics. Successful use of this technology is greatly enhanced by software that can predict pairs of residues that will likely form a disulfide bond if mutated to cysteines.ResultsWe had previously developed and distributed software for this purpose: Disulfide by Design (DbD). The original DbD program has been widely used; however, it has a number of limitations including a Windows platform dependency. Here, we introduce Disulfide by Design 2.0 (DbD2), a web-based, platform-independent application that significantly extends functionality, visualization, and analysis capabilities beyond the original program. Among the enhancements to the software is the ability to analyze the B-factor of protein regions involved in predicted disulfide bonds. Importantly, this feature facilitates the identification of potential disulfides that are not only likely to form but are also expected to provide improved thermal stability to the protein.ConclusionsDbD2 provides platform-independent access and significantly extends the original functionality of DbD. A web server hosting DbD2 is provided at http://cptweb.cpt.wayne.edu/DbD2/.
Highlights
Disulfide engineering is an important biotechnological tool that has advanced a wide range of research
Validation of Disulfide by Design 2.0 (DbD2) was performed with a blind test, predicting potential disulfide bonds in the aforementioned 331 non-homologous Protein Data Bank (PDB) structures
We did not split our set of proteins into independent training and test sets because: 1) the DbD2 algorithm uses only the coordinates of the backbone and Cβ carbon atoms for bond predictions; the side chain identities and locations of native disulfides were hidden in the test; and 2) it is preferable to use the largest possible set of disulfides for training the model as evidenced by the energy function improvements implemented in this release based on the expanded training set
Summary
We had previously developed and distributed software for this purpose: Disulfide by Design (DbD). The original DbD program has been widely used; it has a number of limitations including a Windows platform dependency. We introduce Disulfide by Design 2.0 (DbD2), a web-based, platform-independent application that significantly extends functionality, visualization, and analysis capabilities beyond the original program. Among the enhancements to the software is the ability to analyze the B-factor of protein regions involved in predicted disulfide bonds. This feature facilitates the identification of potential disulfides that are likely to form but are expected to provide improved thermal stability to the protein
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