A software tool for finding locally optimal alignments in protein and nucleic acid sequences

Area of Science:

• Bioinformatics
• Computational Biology
• Molecular Biology

Background:

• Sequence alignment is crucial for understanding protein and nucleic acid function.
• Identifying short, conserved regions within largely dissimilar sequences presents a significant challenge.
• Existing methods may not efficiently detect localized similarities relevant to biological function.

Purpose of the Study:

• To present novel software for sequence alignment based on match density.
• To enable the identification of short, biologically significant similarities between long, dissimilar sequences.
• To demonstrate the software's utility in discovering conserved functional sites.

Main Methods:

• Development of software utilizing the 'match density' concept for sequence alignment.
• Implementation of user-controlled matching parameters for local alignment selection.
• Employment of a novel, efficient algorithm for collecting and ranking alignments.

Main Results:

• The software successfully identifies biologically interesting similarities in local sequence alignments.
• Demonstrated ability to locate short similarity regions within dissimilar sequences, exemplified by active site identification.
• Successfully identified a new conserved sequence in viral DNA polymerases, potentially at a functional site.

Conclusions:

• The developed software provides an effective tool for detecting subtle sequence similarities.
• This approach enhances the discovery of functionally important conserved regions in biological sequences.
• The software has potential applications in identifying novel enzymatic sites and understanding protein/nucleic acid evolution.