Screening and Production of Antifreeze Proteins from Actinobacteria

2022 ◽  
pp. 467-470
Author(s):  
Abirami Baskaran ◽  
Radhakrishnan Manikkam ◽  
Manigundan Kaari ◽  
Gopikrishnan Venugopal ◽  
Somasundaram Thirugnanasambandham ◽  
...  
Keyword(s):  
Antifreeze Proteins

iAFP-gap-SMOTE: An Efficient Feature Extraction Scheme Gapped Dipeptide Composition is Coupled with an Oversampling Technique for Identification of Antifreeze Proteins

2019 ◽  
Vol 16 (4) ◽  
pp. 294-302 ◽  
Author(s):  
Shahid Akbar ◽  
Maqsood Hayat ◽  
Muhammad Kabir ◽  
Muhammad Iqbal
Keyword(s):  
Feature Extraction ◽  
Amino Acid ◽  
Antifreeze Proteins ◽  
Protein Sequences ◽  
Sampling Technique ◽  
Lower Class ◽  
Success Rates ◽  
Throughput Model ◽  
Extraction Scheme ◽  
Living Organisms

Antifreeze proteins (AFPs) perform distinguishable roles in maintaining homeostatic conditions of living organisms and protect their cell and body from freezing in extremely cold conditions. Owing to high diversity in protein sequences and structures, the discrimination of AFPs from non- AFPs through experimental approaches is expensive and lengthy. It is, therefore, vastly desirable to propose a computational intelligent and high throughput model that truly reflects AFPs quickly and accurately. In a sequel, a new predictor called “iAFP-gap-SMOTE” is proposed for the identification of AFPs. Protein sequences are expressed by adopting three numerical feature extraction schemes namely; Split Amino Acid Composition, G-gap di-peptide Composition and Reduce Amino Acid alphabet composition. Usually, classification hypothesis biased towards majority class in case of the imbalanced dataset. Oversampling technique Synthetic Minority Over-sampling Technique is employed in order to increase the instances of the lower class and control the biasness. 10-fold cross-validation test is applied to appraise the success rates of “iAFP-gap-SMOTE” model. After the empirical investigation, “iAFP-gap-SMOTE” model obtained 95.02% accuracy. The comparison suggested that the accuracy of” iAFP-gap-SMOTE” model is higher than that of the present techniques in the literature so far. It is greatly recommended that our proposed model “iAFP-gap-SMOTE” might be helpful for the research community and academia.

PoGB-Pred: Prediction of Antifreeze Proteins Sequences using Amino Acid Composition with Feature Selection followed by a Sequential based Ensemble Approach

2020 ◽  
Vol 15 ◽  
Author(s):  
Affan Alim ◽  
Abdul Rafay ◽  
Imran Naseem
Keyword(s):  
Amino Acid ◽  
Dimension Reduction ◽  
Protein Identification ◽  
Cold Water ◽  
Genomic Sequence ◽  
Sequence Data ◽  
Antifreeze Proteins ◽  
Building Blocks ◽  
Gradient Boosting ◽  
Proposed Model

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 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 the competitive performance on highly distinct AFP structure. Methods: In this study, we propose to use machine learning-based algorithms Principal Component Analysis (PCA) followed by Gradient Boosting (GB) for antifreeze protein identification. To analyze the performance and validation of the proposed model, various combinations of two segments composition of amino acid and dipeptide are used. PCA, in particular, is proposed to dimension reduction and high variance retaining of data which is followed by an ensemble method named gradient boosting for modelling and classification. Results: The proposed method obtained the superfluous performance on PDB, Pfam and Uniprot dataset as compared with 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 the 87.41 utilizing 300 significant features reported for the RAFP-Pred method. Experiment-2 is conducted using two different dataset such that non-AFP from the PISCES server and AFPs from Protein data bank. In this experiment-2, our proposed method attained high sensitivity of 79.16 which is 12.50 better than state-of-the-art the RAFP-pred method. Conclusion: AFPs have a common function with distinct structure. Therefore, the development of a single model for different sequences often fails to AFPs. A robust results have been shown by our proposed model on the diversity of training and testing dataset. The results of the proposed model outperformed compared to the previous AFPs prediction method such as RAFP-Pred. Our model consists of PCA for dimension reduction followed by gradient boosting for classification. Due to simplicity, scalability properties and high performance result our model can be easily extended for analyzing the proteomic and genomic dataset.

Incorporation of antifreeze proteins into zebrafish embryos by a non-invasive method

Cryobiology ◽  
2008 ◽  
Vol 56 (3) ◽  
pp. 216-222 ◽  
Author(s):  
S. Martínez-Páramo ◽  
S. Pérez-Cerezales ◽  
V. Robles ◽  
L. Anel ◽  
M.P. Herráez
Keyword(s):  
Antifreeze Proteins ◽  
Zebrafish Embryos ◽  
Non Invasive ◽  
Invasive Method

Effect of carrot (Daucus carota) antifreeze proteins on texture properties of frozen dough and volatile compounds of crumb

LWT ◽  
2008 ◽  
Vol 41 (6) ◽  
pp. 1029-1036 ◽  
Author(s):  
Chao Zhang ◽  
Hui Zhang ◽  
Li Wang ◽  
Xiaona Guo
Keyword(s):  
Volatile Compounds ◽  
Daucus Carota ◽  
Antifreeze Proteins ◽  
Frozen Dough ◽  
Texture Properties

Hyperactive Antifreeze Proteins Promote Ice Growth before Binding to It

2021 ◽  
Author(s):  
Shaoli Cui ◽  
Weijia Zhang ◽  
Xueguang Shao ◽  
Wensheng Cai
Keyword(s):  
Antifreeze Proteins ◽  
Ice Growth

Effects of antifreeze proteins on cryopreserved sterlet, Acipenser ruthenus sperm quality

Cryobiology ◽  
2018 ◽  
Vol 85 ◽  
pp. 184
Author(s):  
Miaomiao Xin ◽  
Jan Sterba ◽  
Anna Shaliutina-Kolesova ◽  
Borys Dzyuba ◽  
Serhii Boryshpolets ◽  
...  
Keyword(s):  
Sperm Quality ◽  
Antifreeze Proteins ◽  
Acipenser Ruthenus ◽  
Sterlet Acipenser Ruthenus
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