Abstract

Protein backbone alternation due to insertion/deletion or mutation operation often results in a change of fundamental biophysical properties of proteins. The proposed work intends to encode the protein stability changes associated with single point deletions (SPDs) of amino acids in proteins. The encoding will help in the primary screening of detrimental backbone modifications before opting for expensive in vitro experimentations. In the absence of any benchmark database documenting SPDs, we curate a data set containing SPDs that lead to both folded conformations and unfolded state. We differentiate these SPD instances with the help of simple structural and physicochemical features and eventually classify the foldability resulting out of SPDs using a Random Forest classifier and an Elliptic Envelope based outlier detector. Adhering to leave one out cross validation, the accuracy of the Random Forest classifier and the Elliptic Envelope is of 99.4% and 98.1%, respectively. The newly defined database and the delineation of SPD instances based on its resulting foldability provide a head start toward finding a solution to the given problem.