Identification of residues involved in water versus glycerol selectivity in aquaporins by differential residue pair co-evolution

Aquaporins (AQPs) are members of the Major Intrinsic Protein (MIP) family that can transport water or glycerol, as well as other compounds. The rationale for substrate selectivity at the structural level is still incompletely understood. The information present in multiple sequence alignments (MSAs)...

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Bibliographic Details
Main Authors: Lin, Xin, Hong, Tian, Mu, Yuguang, Torres, Jaume
Other Authors: School of Civil and Environmental Engineering
Format: Article
Language:English
Published: 2013
Online Access:https://hdl.handle.net/10356/98837
http://hdl.handle.net/10220/12691
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Institution: Nanyang Technological University
Language: English
Description
Summary:Aquaporins (AQPs) are members of the Major Intrinsic Protein (MIP) family that can transport water or glycerol, as well as other compounds. The rationale for substrate selectivity at the structural level is still incompletely understood. The information present in multiple sequence alignments (MSAs) can help identify both structural and functional features, especially the complex networks of interactions responsible for water or glycerol selectivity. Herein, we have used the method of Statistical Coupling Analysis (SCA) to identify co-evolving pairs of residues in two separate groups of sequences predicted to correspond to water or glycerol transporters. Differentially co-evolved pairs between the two groups were tested by their efficacy in correctly classifying a training set of MSAs, and binary classifiers were built with these pairs. Up to 50% of the residues found in hundreds of binary classifiers corresponded to only ten positions in the MSA of aquaporins. Most of these residues are close to the lining of the aquaporin pore and have been identified previously as important for selectivity. Therefore, this method can shed light on the residues that are important for substrate selectivity of aquaporins and other proteins. SCA requires a very large sequence dataset with relatively low homology amongst its members, and these requirements are met by aquaporins.