Computational Functional Analysis of Lipid Metabolic Enzymes

BAGNATO, CAROLINA; ARJEN TEN HAVE; PRADOS, MARÍA B.; BELIGNI, MARÍA V.

METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.)

Springer

Año: 2017 vol. 1609 p. 195 - 216

1064-3745

p { margin-bottom: 0.25cm; direction: ltr; color: rgb(0, 0, 10); line-height: 120%; text-align: left; }p.western { font-family: "Liberation Serif",serif; font-size: 12pt; }p.cjk { font-family: "Droid Sans Fallback"; font-size: 12pt; }p.ctl { font-family: "FreeSans"; font-size: 12pt; }a:link { }The computational analysis of enzymes that participate in lipid metabolism has both common and unique challenges when compared to the whole protein universe. Some of the hurdles that interfere with the functional annotation of lipid metabolic enzymes that are common toother pathways include the definition of proper starting datasets,the construction of reliable multiple sequence alignments, thedefinition of appropriate evolutionary models and the reconstruction of phylogenetic trees with high statistical support, particularly for large datasets. Most enzymes that take part in lipid metabolism belong to complex superfamilies with many members that are not involved in lipid metabolism. In addition, some enzymes that do not have sequence similarity catalyze similar or even identicalreactions. Some of the challenges that, albeit not unique, are more specific to lipid metabolism refer to the high compartmentalization of the routes, the catalysis in hydrophobic environments and, relatedto this, the function near or in biological membranes. In this work, we provide guidelines intended to assist in the properfunctional annotation of lipid metabolic enzymes, based on previousexperiences related to the phospholipase D superfamily and theannotation of the triglyceride synthesis pathway in algae. Wedescribe a pipeline that starts with the definition of an initial setof sequences to be used in similarity-based searches and ends in thereconstruction of phylogenies. We also mention the main issues thathave to be taken into consideration when using tools to analyzesubcellular localization, hydrophobicity patterns or presence of transmembrane domains in lipid metabolic enzymes.