Development of an aggregation prediction program using instrinsic chemical and structural properties of amino acids

CHED 816

Jessica Gagnon, tigerbyte87@hotmail.com, Christopher Retlich, Chia-Ching Chang, and Jeffery Schwehm, schwehmjm@lakeland.edu. Natural Sciences Division, Lakeland College, P.O. Box 359, Sheboygan, WI 53082
Diseases such as Alzheimer's disease, Creutzfeld-Jacobs disease, and Parkinson's disease are associated with the phenomenon of protein aggregation. The Lakeland College Aggregation Prediction Program (LC-APP) attempts to predict the likelihood of a protein to aggregate based on its primary sequence. The sequence is evaluated by an algorithm based on the intrinsic chemical and structural properties such as hydrophobicity, aggregation propensity, and the secondary structure propensities of the 20 naturally occurring amino acids. Using graphical profiles and a variable sliding window LC-APP recognizes experimentally identified “hot spots” proficiently. LC-APP suggests that one can identify “hot spots” through the use of simple intrinsic chemical and structural properties of the amino acids. Results demonstrate that further refinement of the algorithm is necessary before proteins and peptides can be ranked according to some type of aggregation potential.