Large genome sequencing projects generate huge number of protein sequences in their primary structures that is difficult for conventional biological techniques to determine their corresponding 3D structures and then their functions. Protein secondary structure prediction is a prerequisite step in determining the 3D structure of a protein. In this research a method for prediction of protein secondary structure has been proposed and implemented together with other known accurate methods in this domain. The method has been discussed and presented in a comparative analysis progression to allow easy comparison and clear conclusions. A benchmark data set is exploited in training and testing the methods under the same hardware, platforms, and environments. The newly developed method utilizes the knowledge of the GORV information theory and the power of the neural network to classify a novel protein sequence in one of its three secondary structures classes. NN-GORV-I is developed and implemented to predict proteins secondary structure using the biological information conserved in neighboring residues and related

My first thought when I saw a preliminary version of this book was: Too bad there was nothing like this book when I really needed it. Around 20 years ago, I decided it was time to change my research directions. After exploring a number of possibilities, I decided that the area of overlap between molecular biology and computer science (which later came to be called ”bioinformatics”) was my best bet for an exciting career. The next decision was to select a specific class of problems to work on, and the main criterion for me was that algorithmic methods would be the main key to success. I decided to work on sequence analysis. A book like this could have, so to speak, straightened my learning curve. It is amazing to me that those two conclusions still apply: bioinformatics is a tremendously vibrant and rewarding field to be in, and sequence comparison is (arguably, at least) the subfield of bioinformatics where algorithmic techniques play the largest role in achieving success. The importance of sequence-analysis methods in bioinformatics can be measured objectively, simply by looking at the numbers of citations in the scientific literature for papers that describe successful developments;

Abstract—Substitution matrices are among the most widely used scoring techniques: BLAST, Muscle and other alignment packages, all use them. However these matrices are general; they ignore organism specific properties and do not provide customized scoring schemes. We present a Φhage-specific scoring matrix based on the abundances of aligned substitutions. These matrices use information from approximately five and a half million similar protein alignments from over five hundred Φhage genomes. Our scoring matrix is different from the existing PAM and BLOSUM matrices. This indicates the need for similar treatments for other groups of organisms.

Protein sequence alignment is the task of identifying evolutionarily or structurally related positions in a collection of amino acid sequences. Although the protein alignment problem has been studied for several decades, many recent studies have demonstrated considerable progress in improving the accuracy or scalability of multiple and pairwise alignment tools, or in expanding the scope of tasks handled by an alignment program. In this chapter, we review state-of-the-art protein sequence alignment and provide practical advice for users of alignment tools.

issues in computer-assisted analysis of biological data. The main emphasis is on current scientific developments and innovative techniques in computational biology (bioinformatics), bringing to light methods from mathematics, statistics and computer science that directly address biological problems currently under investigation. The series offers publications that present the state-of-the-art regarding the problems in question; show computational biology/bioinformatics methods at work; and finally discuss anticipated demands regarding developments in future methodology. Titles can range from focused monographs, to undergraduate and graduate textbooks, and professional text/reference works.

Pairwise alignment incorporating dipeptide covariation