AFP-SRC: identification of antifreeze proteins using sparse representation classifier

Species living in the extreme cold environment fight against the harsh conditions using antifreeze proteins (AFPs), which manipulate the freezing mechanism of water in more than one way. This amazing nature of AFPs turns out to be extremely useful in several industrial and medical applications. The lack of similarity in their structure and sequence makes their prediction an arduous task, and identifying them experimentally in the wet laboratory is time-consuming and expensive. In this research, we propose a computational framework for the prediction of AFPs, which is essentially based on a sample-specific classification method using sparse reconstruction. A linear model and an over-complete dictionary matrix (ODM) of known AFPs are used to predict a sparse class-label vector that provides a sample-association score. Delta rule is applied for the reconstruction of two pseudo-samples using lower and upper parts of the sample-association vector and based on the minimum recovery score, class labels are assigned. We compare our approach with contemporary methods on a standard dataset. The proposed method outperforms the contemporary methods in terms of balanced accuracy and Youden’s index. The MATLAB implementation of the proposed method is available at the author’s GitHub page (https://github.com/Shujaat123/AFP-SRC).