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Identification of Plant Ice-binding Proteins Through Assessment of Ice-recrystallization Inhibition and Isolation Using Ice-affinity Purification
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AFP-CMBPred: Computational identification of antifreeze proteins by extending consensus sequences into multi-blocks
Farman Ali1, Shahid Akbar2, Ali Ghulam3
1School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China.
Computers in Biology and Medicine
|November 8, 2021
Summary
Antifreeze proteins (AFPs) protect organisms from freezing. A new computational model, AFP-CMBPred, accurately predicts AFPs using advanced feature representation and machine learning, improving upon existing methods for research and drug development.
Area of Science:
- Biochemistry and Molecular Biology
- Computational Biology and Bioinformatics
- Biotechnology and Agriculture
Background:
- Organisms in extremely cold environments face intracellular ice formation, leading to cell death.
- Antifreeze proteins (AFPs), or ice-binding proteins, are produced by some species to survive sub-zero temperatures.
- AFPs have significant applications beyond medicine, including biotechnology, agriculture, and the food industry, despite structural and sequential diversity.
Purpose of the Study:
- To address the limitations of existing machine learning models in accurately predicting Antifreeze proteins (AFPs) due to their complex nature.
- To develop a novel and reliable computational predictor for AFPs.
- To enhance the identification and study of AFPs for broader scientific and industrial applications.
Main Methods:
- Employed four distinct feature representation methods: Amphiphilic pseudo amino acid composition (Amp-PseAAC), Dipeptide Deviation from Expected Mean (DDE), Multi-Blocks Position Specific Scoring Matrix (MB-PSSM), and Consensus Sequence-based on Multi-Blocks Position Specific Scoring Matrix (CS-MB-PSSM).
- Utilized Support Vector Machine (SVM) and Random Forest (RF) as classification algorithms to evaluate the extracted feature vectors.
- Validated the prediction performance using rigorous methods: jackknife test, 10-fold cross-validation, and independent testing.
Main Results:
- The proposed AFP-CMBPred model demonstrated superior prediction performance across all validation tests.
- Achieved high prediction accuracies of approximately 92.65% (jackknife), 92.84% (K-fold), and 93.37% (independent test).
- The AFP-CMBPred predictor significantly outperformed existing models in identifying Antifreeze proteins.
Conclusions:
- The developed AFP-CMBPred computational model provides a reliable and accurate method for predicting Antifreeze proteins.
- The novel feature representation and machine learning approach overcomes limitations of previous predictors.
- AFP-CMBPred is expected to be a valuable tool for researchers in academia and the drug development industry.
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