AFP-CMBPred: Computational identification of antifreeze proteins by extending consensus sequences into multi-blocks evolutionary information

2021 ◽  
pp. 105006
Author(s):  
Farman Ali ◽  
Shahid Akbar ◽  
Ghulam Ali ◽  
Zulfikar Ahmed Maher ◽  
Ahsanullah Unar ◽  
...  
Keyword(s):  
Antifreeze Proteins ◽  
Evolutionary Information ◽  
Consensus Sequences ◽  
Computational Identification

A Brief Review of the Computational Identification of Antifreeze Protein

2019 ◽  
Vol 23 (15) ◽  
pp. 1671-1680
Author(s):  
Fang Wang ◽  
Zheng-Xing Guan ◽  
Fu-Ying Dao ◽  
Hui Ding
Keyword(s):  
Machine Learning ◽  
Antifreeze Proteins ◽  
Classification Algorithms ◽  
Ice Crystal ◽  
Cold Adapted ◽  
Machine Learning Methods ◽  
New Ideas ◽  
Computational Identification ◽  
Bacteria And Fungi ◽  
High Diversity

Lots of cold-adapted organisms could produce antifreeze proteins (AFPs) to counter the freezing of cell fluids by controlling the growth of ice crystal. AFPs have been found in various species such as in vertebrates, invertebrates, plants, bacteria, and fungi. These AFPs from fish, insects and plants displayed a high diversity. Thus, the identification of the AFPs is a challenging task in computational proteomics. With the accumulation of AFPs and development of machine meaning methods, it is possible to construct a high-throughput tool to timely identify the AFPs. In this review, we briefly reviewed the application of machine learning methods in antifreeze proteins identification from difference section, including published benchmark dataset, sequence descriptor, classification algorithms and published methods. We hope that this review will produce new ideas and directions for the researches in identifying antifreeze proteins.

Recent Advances in Computational Methods for Identifying Anticancer Peptides

2019 ◽  
Vol 20 (5) ◽  
pp. 481-487 ◽  
Author(s):  
Pengmian Feng ◽  
Zhenyi Wang
Keyword(s):  
Computational Methods ◽  
Clinical Applications ◽  
Future Perspectives ◽  
Small Peptides ◽  
Accurate Identification ◽  
Normal Cells ◽  
Anticancer Peptide ◽  
Anticancer Peptides ◽  
Computational Identification ◽  
Reliable Methods

Anticancer peptide (ACP) is a kind of small peptides that can kill cancer cells without damaging normal cells. In recent years, ACP has been pre-clinically used for cancer treatment. Therefore, accurate identification of ACPs will promote their clinical applications. In contrast to labor-intensive experimental techniques, a series of computational methods have been proposed for identifying ACPs. In this review, we briefly summarized the current progress in computational identification of ACPs. The challenges and future perspectives in developing reliable methods for identification of ACPs were also discussed. We anticipate that this review could provide novel insights into future researches on anticancer peptides.

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.

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