Detecting motifs for computational classification of dockerin and cohesin sequences

Official URL: http://risc01.sabanciuniv.edu/record=b1505282 (Table of Contents)

Cellulose is the most abundant biopolymer in nature. It has several usage areas in industry. The initial hydrolysis of cellulose is the rate determining step in cellulose degradation. Cellulosomes are the complex structures composed of non-catalytic units and enzymes that take part in cellulose degradation. Cellulosomal units are attached via the interaction between cohesin and dockerin domains which are divided into three subclasses; type I, type II and type III. Development and rational design of novel cohesin and dockerin domains to enhance synergistic actions is very important research topic for biotechnological applications. In this aspect, accurate classification of the subunits and identification of key interaction sites are of great importance for design purposes. In this thesis, we propose a multiple sequence alignment and information theory based classification method that identifies potential key interaction sites. Based on the multiple sequence alignments, the residues that are conserved only in one subclass are determined as the motifs. Classification performance of these motifs is determined using a majority voting based normalized scoring scheme. In addition, reduced amino acid alphabets are introduced to capture the similarities that are invisible in 20-letter alphabet. In this work, we classify cohesin sequences with 99% accuracy, 96% sensitivity and 97% specificity, on average. For dockerin, the sequences are classified with up to 95% accuracy. 76% sensitivity and 92% specificity are observed on average. Potential interaction sites between cohesins and dockerins are determined from the correlated mutation analysis