AFP-CKSAAP: Prediction of Antifreeze Proteins Using Composition of k-Spaced Amino Acid Pairs with Deep Neural Network

Abstract

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.

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“…In order to make the feature set abundant, we adopted the CKSAAGP descriptor, which is a further extension from the DiC. The descriptor is a modification of the composition of k -spaced amino acid pairs (CKSAAP), which was adopted in several studies of protein prediction [ 8 , 45 ] as an effective descriptor to represent the short motifs of the peptide sequence. At first, 20 amino acid residues are categorized into five groups by their physicochemical properties: aliphatic, aromatic, positive-charged, negative-charged and uncharged residues.…”

AFP-LSE: Antifreeze Proteins Prediction Using Latent Space Encoding of Composition of k-Spaced Amino Acid Pairs

“…However, it has been observed in recent studies that a viable feature extraction method e.g., CKSAAP can equally contribute toward satisfactory prediction performances [43][44][45] . Thus, we utilized CKSAAP encoding scheme in the AFP-CKSAAP method 36 .…”

“…AFP-CKSAAP has been thoroughly evaluated to determine the optimal value of k by manually performing the sequential forward selection method to determine the best-suited feature. The best performance of the classifier was obtained by maintaining the gap value k = 8 36 . It is also evident from the references that an attribute vector obtained from a very large value of k will include redundant features and may not contribute toward prediction 33,47 .…”

“…The classifier is designed to process the latent space variables generated by the auto-encoder module. For the classification, a similar approach as in AFP-CKSAAP 36 i.e., multilayer perceptron (MLP), is implemented. The architecture of the classifier, as shown in Fig.…”

“…In this work, we utilize the composition of k-spaced amino acid pairs (CKSAAP) for the numerical representation of the amino acid sequence, which has been successfully adopted by several researchers to address various prediction problems [33][34][35] . A part of this work was presented in 36 , where we explored the discrimination power of k = 0 to 13-spaced amino acid pairs. More specifically, we observed that a gap of k = 8 provides the best classification performance.…”