Abstract
Improving protein stability is an important goal for basic research as well as for clinical and industrial applications but no commonly accepted and widely used strategy for efficient engineering is known. Beside random approaches like error prone PCR or physical techniques to stabilize proteins, e.g. by immobilization, in silico approaches are gaining more attention to apply target-oriented mutagenesis. In this review different algorithms for the prediction of beneficial mutation sites to enhance protein stability are summarized and the advantages and disadvantages of FoldX are highlighted. The question whether the prediction of mutation sites by the algorithm FoldX is more accurate than random based approaches is addressed.
Highlights
Increasing protein stability is a desirable goal for different life science purposes, this includes design of therapeutic proteins like antibodies, human cell biology and biotechnology
FoldX shows a good performance in most of the studies compared to other algorithms, but it is necessary to increase the number of experimental mutations above 3 to achieve probable true positive results for protein engineering experiments
The performance of FoldX depends drastically on the quality of the crystal structure and it is unclear if the protein source might have an influence on the accuracy of such algorithms
Summary
Increasing protein stability is a desirable goal for different life science purposes, this includes design of therapeutic proteins like antibodies, human cell biology and biotechnology. In case of enzymes, selection might be performed by heating up unpurified crude extracts from cells [42] Using this technique, protein melting temperature Tm can be improved in the best screenings by far N10 °C [42,43,44,45,46,47]. Directed evolution can be a successful strategy but might not be applicable at any time, especially when missing a high-throughput screening or protein purification for stability measurements is necessary. This mini-review focusses on protein/enzyme engineering for thermostabilization using structure guided site-directed mutagenesis. Command-line based in silico approaches, which are without graphical interface, scientists not familiar with programming languages like python, Java, R-script and so on and hide a larger workload for these kind of approaches
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