Computationally Constructing Peptide Sequences for Protein Signaling

Opportunity

Proteins and peptides perform an array of vital functions in the human body. The variety of factors that dictate the function of a protein include its amino acid sequence, with each amino acid differentiated by a unique side chain. Specific combinations of amino acids with specific side chains join together to create proteins with unique shapes, compositions, and configurations, all of which greatly influence what the protein does in the body and how efficiently the function is carried out. Of great interest in medical research is the creation of computational models to assist in sequence predictions and the creation of synthetic proteins and peptides. Signal peptides, which utilize a specific amino acid sequence to facilitate protein localization and transport, have been the subject of computational research for the last twenty years. The sheer number of possible amino acid sequences and configurations is the main challenge in generating functional signal peptide proteins.  Developing a robust computational methodology for constructing novel and functional signal peptide sequences could contribute to advancements in nutritional science, cancer treatment and prevention, and pharmaceutical research.

Breakthrough in Signal Peptide Sequencing Technology

Researchers at the University of South Alabama have developed a computational methodology for constructing novel signal peptide sequences from sets of known signal peptides and their amino acids. The method utilizes sets of ordered amino acid pairs to act as “building blocks” from which peptide sequences are constructed. These “building blocks” can then be layered and rearranged in a three-dimensional space. Because the number of “building blocks” can be quite large, a variety of novel signal peptide sequences can be constructed through an algorithmic procedure of combining and ordering amino acid pairs. Not only can constructed sequences be accurately identified as functional signal proteins through cutting-edge prediction platforms, but a large variety of constructions can also be identified as existing in living organisms.

Competitive Advantages

• Provides a simple, functional method to understand and assemble novel peptide configurations;
• Potential to assist synthetic protein development in cancer research, nutritional science, and pharmaceutical development;
• Allows for increased yield in creating synthetic proteins through greater sequence accuracy;
• Allows for a broader range of experimentation with peptides used in drug synthesis, such as secretion signal peptides used to produce insulin.

Intellectual Property Status

Patent Pending