Computational method for identifying adhesin and adhesin-like proteins of therapeutic potential

Abstract

A computational method for identifying adhesin and adhesin-like proteins, said method comprising steps of computing the sequence-based attributes of a neural network software wherein the attributes are (i) amino acid frequencies, (ii) multiplet frequency, (iii) dipeptide frequencies, (iv) charge composition, and (v) hydrophobic composition, training the artificial neural Network (ANN) for each of the computed five attributes, and identifying the adhesin and adhesin-like proteins having probability of being an adhesin (Pad) as >= 0.51; a computer system for performing the method; and genes and proteins encoding adhesin and adhesin-like proteins.

Description

Technical field A calculation method for recognizing adhesin or adhesin-like protein; a computer system for performing the method; genes and proteins encoding adhesin and adhesin-like protein. Background technique The development of genome sequencing projects has produced a large number of putative protein sequences from different organisms. It is expected that the complete set of protein information obtained from easily infectious human pathogens will enable us to develop novel molecular methods to deal with these pathogens. A necessary step in the successful colonization of microbial pathogens and subsequent disease manifestation is the ability of pathogens to adhere to host cells. Microbial pathogens encode several known adhesin proteins, which mediate the adhesion of pathogens to host cell surface receptors, cell membranes or extracellular matrix in order to successfully colonize. In the past few decades, research on the primary activity of host-pathogen interaction has reve...

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