  Antifreeze proteins (AFPs) are the sub-set of ice binding proteins
indispensable for the species living in extreme cold weather. These proteins
bind to the ice crystals, hindering their growth into large ice lattice that
could cause physical damage. There are variety of AFPs found in numerous
organisms and due to the heterogeneous sequence characteristics, AFPs are found
to demonstrate a high degree of diversity, which makes their prediction a
challenging task. Herein, we propose a machine learning framework to deal with
this vigorous and diverse prediction problem using the manifolding learning
through composition of k-spaced amino acid pairs. We propose to use the deep
neural network with skipped connection and ReLU non-linearity to learn the
non-linear mapping of protein sequence descriptor and class label. The proposed
antifreeze protein prediction method called AFP-CKSAAP has shown to outperform
the contemporary methods, achieving excellent prediction scores on standard
dataset. The main evaluater for the performance of the proposed method in this
study is Youden's index whose high value is dependent on both sensitivity and
specificity. In particular, AFP-CKSAAP yields a Youden's index value of 0.82 on
the independent dataset, which is better than previous methods.
