CryoProtect: A Web Server for Classifying Antifreeze Proteins from Nonantifreeze Proteins

Pratiwi, Reny; Malik, Aijaz Ahmad; Schaduangrat, Nalini; Prachayasittikul, Virapong; Wikberg, Jarl E. S.; Nantasenamat, Chanin; Shoombuatong, Watshara

doi:10.1155/2017/9861752

Cited by 44 publications

(75 citation statements)

References 51 publications

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“…Based on the prediction results, AFP-LSE achieved superior performance on all the statistical measures. Particularly, improvements of approximately 2% and 5% in the balanced accuracy and Youden's index, respectively, were observed when compared with the corresponding values for the best classifier in the literature i.e., CryoProtect 29 . Similarly, the best values of the MCC and F-score were demonstrated by AFP_PSSM 24 , whereas the proposed classifier shows improvements of approximately 52% and 68%, respectively, for the aforementioned parameters.…”

Section: Effect Of Latent Variablesmentioning

confidence: 86%

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

Usman

Khan

Lee

2020

Sci Rep

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Species living in extremely cold environments resist the freezing conditions through antifreeze proteins (Afps). Apart from being essential proteins for various organisms living in sub-zero temperatures, Afpshave numerous applications in different industries. They possess very small resemblance to each other and cannot be easily identified using simple search algorithms such as BLAST and PSI-BLAST. Diverse AFPs found in fishes (Type I, II, III, IV and antifreeze glycoproteins (AFGPs)), are sub-types and show low sequence and structural similarity, making their accurate prediction challenging. Although several machine-learning methods have been proposed for the classification of AFPs, prediction methods that have greater reliability are required. In this paper, we propose a novel machine-learning-based approach for the prediction of AFP sequences using latent space learning through a deep auto-encoder method. For latent space pruning, we use the output of the auto-encoder with a deep neural network classifier to learn the non-linear mapping of the protein sequence descriptor and class label. The proposed method outperformed the existing methods, yielding excellent results in comparison. A comprehensive ablation study is performed, and the proposed method is evaluated in terms of widely used performance measures. In particular, the proposed method demonstrated a high Matthews correlation coefficient of 0.52, F-score of 0.49, and Youden's index of 0.81 on an independent test dataset, thereby outperforming the existing methods for Afp prediction.In Antarctic fish, a survival mechanism that prevented them from freezing in seawater at sub-zero temperatures was observed, which led to the discovery of antifreeze proteins (AFP) 1 . AFPs have been identified as a crucial substance for resisting a freezing environment in various species including plants, bacteria, fungi, insects, and animals 2 . Ice exists in different geometric shapes due to the varying arrangements of oxygen atoms; therefore, the structural and sequential arrangements of AFPs largely vary to accommodate this heterogeneity of ice molecules 3 . Ice also exhibits the property of recrystallization, by which small ice crystals bind to the water molecules, thus becoming a large ice lattice, causing severe damage to the cell membrane, which, in some cases, may be lethal 4 . AFPs are commonly categorized into glycoproteins (AFGPs) and non-glycoproteins (AFPs) 5 . They protect the organisms using two mechanisms: (i) thermal hysteresis (TH), by which the freezing point of water is depressed to a few degrees by the adsorption-inhibition effect without altering the melting point 6 ; (ii) ice crystal inhibition, by which the AFP sites bind to the surfaces of ice and inhibit their growth to become a larger ice lattice, developing either small harmless ice crystals or forming a needle-shaped lattice, thus diminishing the recrystallization property of ice 2 .AFPs are indispensable in organisms such as fish 7 , fungi 8 , bacteria 9 , plants 10 , and insects 11 . Furthermo...

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Section: Effect Of Latent Variablesmentioning

confidence: 86%

“…(12) and Fig. 2, that the CKSAAP encoding scheme utilizes the the trivial information from the preceding features including AAC, DPC, and TPC, which have been proven to play a vital role in AFP prediction in earlier studies 22,28,29 .…”

Section: Methods Evaluation Parametersmentioning

confidence: 99%

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

Usman

Khan

Lee

2020

Sci Rep

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“…With the advancements of genome sequencing, a large number of sequenced proteins have been accumulated and need to be functionally annotated. Many auto-annotation tools exist to identify antifreeze proteins, such as TargetFreeze (He et al, 2015), AFP_PSSM (Zhao et al, 2012), CryoProtect (Pratiwi et al, 2017), and afpCOOL (Eslami et al, 2018). However, these tools use too many features ( Table 2), which may often be redundant and lead to overfitting.…”

Section: Comparison Of Our Seven Key Featuresmentioning

confidence: 99%

“…Later studies on predicting antifreeze proteins used modern machine learning algorithms, which have demonstrated their ability in other protein-related research, such as identifying membrane proteins and their subcategories (Chou and Shen, 2007), predicting subcellular localization of multi-label proteins (Javed and Hayat, 2019), and classifying protein secondary structures (Ge et al, 2019). Most of these studies focused on amino acid compositionrelated features, and various physicochemical properties of amino acid sequences have been extensively used to identify antifreeze proteins (Kandaswamy et al, 2011;Yu and Lu, 2011;Mondal and Pai, 2014;Pratiwi et al, 2017). In contrast, despite the presumed convergent evolution of antifreeze proteins, Zhao et al (2012) built a classifier with high performance solely based on evolutionary features derived from position-specific scoring matrices (PSSMs), suggesting that evolutionary information is also important for identifying antifreeze proteins.…”

Section: Introductionmentioning

confidence: 99%

Identifying Antifreeze Proteins Based on Key Evolutionary Information

Sun

Ding

Wang

et al. 2020

Front. Bioeng. Biotechnol.

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Antifreeze proteins are important antifreeze materials that have been widely used in industry, including in cryopreservation, de-icing, and food storage applications. However, the quantity of some commercially produced antifreeze proteins is insufficient for large-scale industrial applications. Further, many antifreeze proteins have properties such as cytotoxicity, severely hindering their applications. Understanding the mechanisms underlying the protein-ice interactions and identifying novel antifreeze proteins are, therefore, urgently needed. In this study, to uncover the mechanisms underlying protein-ice interactions and provide an efficient and accurate tool for identifying antifreeze proteins, we assessed various evolutionary features based on position-specific scoring matrices (PSSMs) and evaluated their importance for discriminating of antifreeze and non-antifreeze proteins. We then parsimoniously selected seven key features with the highest importance. We found that the selected features showed opposite tendencies (regarding the conservation of certain amino acids) between antifreeze and non-antifreeze proteins. Five out of the seven features had relatively high contributions to the discrimination of antifreeze and non-antifreeze proteins, as revealed by a principal component analysis, i.e., the conservation of the replacement of Cys, Trp, and Gly in antifreeze proteins by Ala, Met, and Ala, respectively, in the related proteins, and the conservation of the replacement of Arg in non-antifreeze proteins by Ser and Arg in the related proteins. Based on the seven parsimoniously selected key features, we established a classifier using support vector machine, which outperformed the state-of-the-art tools. These results suggest that understanding evolutionary information is crucial to designing accurate automated methods for discriminating antifreeze and non-antifreeze proteins. Our classifier, therefore, is an efficient tool for annotating new proteins with antifreeze functions based on sequence information and can facilitate their application in industry.

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“…Antifreeze proteins have variety of applications in the food industry, biotechnological applications, medicine, preservation of organs and cell lines etc. Therefore, their reliable prediction is of utter importance [8].…”

Section: Introductionmentioning

confidence: 99%

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

Usman

Lee

2019

2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)

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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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CryoProtect: A Web Server for Classifying Antifreeze Proteins from Nonantifreeze Proteins

Cited by 44 publications

References 51 publications

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

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

Identifying Antifreeze Proteins Based on Key Evolutionary Information

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

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