Background: Proteins contribute significantly in every task of cellular life. Their
functions encompass the building and repairing of tissues in human bodies and other organisms.
Hence they are the building blocks of bones, muscles, cartilage, skin, and blood. Similarly, antifreeze
proteins are of prime significance for organisms that live in very cold areas. With the help of
these proteins, the cold water organisms can survive below zero temperature and resist the water
crystallization process, which may cause the rupture in the internal cells and tissues. AFP’s have
also attracted attention and interest in food industries and cryopreservation.
Objective: With the increase in the availability of genomic sequence data of protein, an automated
and sophisticated tool for AFP recognition and identification is in dire need. The sequence and
structures of AFP are highly distinct, therefore, most of the proposed methods fail to show
promising results on different structures. A consolidated method is proposed to produce a
competitive performance on a highly distinct AFP structure.
Methods: In this study, machine learning-based algorithms including Principal Component Analysis
(PCA) followed by Gradient Boosting (GB) were proposed to be used for anti-freeze protein
identification. To analyze the performance and validation of the proposed model, various
combinations of two segments' composition of amino acid and dipeptides are used. PCA, in
particular, is proposed for dimension reduction and high variance retaining of data, which is
followed by an ensemble method named gradient boosting for modeling and classification.
Results: The proposed method obtained a superfluous performance on PDB, Pfam, and Uniprot
datasets as compared to the RAFP-Pred method. In experiment-3, by utilizing only 150 PCA
components, a high accuracy of 89.63% was achieved, which is superior to 87.41% utilizing 300
significant features reported for the RAFP-Pred method. Experiment-2 is conducted using two
different datasets such that non-AFP from the PISCES server and AFPs from Protein data bank. In
this experiment-2, the proposed method attained high sensitivity of 79.16% which is 12.50% better
than state-of-the-art RAFP-pred method.
Conclusion: AFPs have a common function with a distinct structure. Therefore, the development of
a single model for different sequences often fails for AFPs. Robust results have been shown by the
proposed model on the diversity of training and testing datasets. The results of the proposed model
outperformed compared to the previous AFPs prediction method, such as RAFP-Pred. The proposed
model consists of PCA for dimension reduction, followed by gradient boosting for classification.
Due to simplicity, scalability properties, and high performance result, this model can be easily
extended for analyzing the proteomic and genomic datasets.