Bioinformatic analysis of protein families to select function-related variable positionsстатья Глава в книге

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[1] Suplatov D., Kirilin E., Švedas V. Bioinformatic analysis of protein families to select function-related variable positions // Understanding Enzymes: Function, Design, Engineering and Analysis (ed. Allan Svendsen). — Pan Stanford Publishing, 2016. — P. 351–385. Understanding how changes in protein sequence affect biological function is one of the most challenging problems of modern structural biology. During evolution of proteins from a common ancestor one functional property can be preserved, while others can vary, leading to functional diversity. For example, homologous enzymes within a superfamily can share a common structural framework and overall reaction chemistry but differ in other catalytic properties such as substrate specificity, enantio- and regioselectivity or even catalyze promiscuous chemical conversions. Why do similar active sites in homologous enzymes perform different chemical transformations? How should we study the structure‒function relationship and predict particular structural changes that lead to functional diversity? Our understanding of the fundamental molecular mechanisms in practice can be evaluated by the ability to design protein functions that have important biotechnological applications in a rational way. However, as yet no clear methodology has been suggested to change the structure of an enzyme in order to improve its catalytic properties or modify its function. While sequence data and crystallographic structures of proteins and protein complexes reveal amino acid composition and organization of the active sites, as well as binding interfaces, they do not provide information about the importance of individual residues. Consequently, in this chapter we suggest a systematic analysis of adaptive mutations that survived evolution as a key to understand the impact of amino acid substitutions on enzyme function. An overview is given of bioinformatic methods, which can be used to identify variable amino acid residues that seem to play an important role in protein function. We describe algorithmic assumptions behind them and outline biological problems to which they can be applied. We then focus on the most recent advances in the field and discuss the bioinformatic methods that can help to select a focused set of variable positions to be used as hotspots for directed evolution or rational design of the protein function. [ DOI ]

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