Abstract

β-barrel membrane proteins perform a variety of functions, such as mediating non-specific, passive transport of ions and small molecules, selectively passing the molecules like maltose and sucrose and are involved in voltage dependent anion channels. Understanding the structural features of β-barrel membrane proteins and detecting them in genomic sequences are challenging tasks in structural and functional genomics. In this review, with the survey of experimentally known amino acid sequences and structures, the characteristic features of amino acid residues in β-barrel membrane proteins and novel parameters for understanding their folding and stability will be described. The development of statistical methods and machine learning techniques for discriminating β-barrel membrane proteins from other folding types of globular and membrane proteins will be explained along with their relative importance. Further, different methods including hydrophobicity profiles, rule based approach, amino acid properties, neural networks, hidden Markov models etc. for predicting membrane spanning segments of β-barrel membrane proteins will be discussed. In addition, the applications of discrimination techniques for detecting β-barrel membrane proteins in genomic sequences will be outlined. In essence, this comprehensive review would provide an overall picture about β-barrel membrane proteins starting from the construction of datasets to genome-wide applications.

Keywords: β-Barrel membrane protein, TMB, OMP, structural analysis, amino acid sequence, conformational parameter, hydrophobicity, machine learning techniques, discrimination, prediction