Journal ArticleDOI
Protein disulfide engineering.
TLDR
Progress in disulfide engineering is reviewed, with an emphasis on the issue of stability and computational methods that facilitate engineering efforts.About:
This article is published in FEBS Letters.The article was published on 2014-01-21. It has received 205 citations till now.read more
Citations
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Forces stabilizing proteins
TL;DR: What has been learned about the major forces stabilizing proteins since the late 1980s when site‐directed mutagenesis became possible is summarized.
Journal ArticleDOI
Review: Engineering of thermostable enzymes for industrial applications
TL;DR: The most relevant enzymes that are used for industrial applications are provided and the strategies that are adopted to enhance enzyme stability and/or activity are discussed, along with some of the most relevant achievements.
Journal ArticleDOI
Protein stability: a crystallographer's perspective.
TL;DR: This review will focus on factors affecting protein stability on a somewhat practical level, particularly from the view of a protein crystallographer.
Journal ArticleDOI
Principles of Protein Stability and Their Application in Computational Design
TL;DR: The computational stability design methods have advanced over the past two decades starting from methods that selectively addressed only some aspects of marginal stability, such as thermodynamic, cellular, and evolutionary principles and mechanisms that underlie marginal stability as mentioned in this paper.
Journal ArticleDOI
FoldX as Protein Engineering Tool: Better Than Random Based Approaches?
TL;DR: 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.
References
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Journal ArticleDOI
Prediction of temperature factors from protein sequence
TL;DR: The local primary protein sequence features that in combination can predict the B-value of amino acid residues directly from the protein sequence are analyzed to propose the method based on Support Vector Regression (SVR), which is comparable to existing methods.