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Comprehensive Prediction of Lipocalin Proteins Using Artificial Intelligence Strategy
Hasan Zulfiqar1, Zahoor Ahmed1, Cai-Yi Ma1
1School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, 610054 Chengdu, Sichuan, China.
Frontiers in Bioscience (Landmark Edition)
|March 29, 2022
Summary
This study developed a machine learning model to accurately identify lipocalins, proteins involved in stress responses and allergic inflammation. The model achieved high accuracy, aiding in the functional study of these important proteins.
Area of Science:
- Proteomics
- Bioinformatics
- Computational Biology
Background:
- Lipocalins are stable, multifunctional extracellular proteins belonging to the calcyin family.
- They play crucial roles in stress responses and allergic inflammations.
- Accurate identification of lipocalins is essential for understanding their functions.
Purpose of the Study:
- To develop a machine learning-based model for accurate lipocalin identification.
- To distinguish lipocalin proteins from non-lipocalin proteins.
Main Methods:
- Protein sequences were encoded using six feature types: amino acid composition (AAC), composition of k-spaced amino acid pairs (CKSAAP), pseudo amino acid composition (PseAAC), Geary correlation (GD), normalized Moreau-Broto autocorrelation (NMBroto), and composition/transition/distribution (CTD).
- Feature selection techniques were employed to optimize the descriptors.
- A random forest classifier was trained using the optimal feature subset.
Main Results:
- The model achieved 95.03% accuracy and an area under the curve of 0.987 in 10-fold cross-validation.
- On an independent dataset, the model demonstrated 89.90% accuracy, surpassing existing models by 4.17%.
Conclusions:
- An advanced computational model was successfully developed to discriminate lipocalin proteins.
- The model leverages sequence-derived features and feature selection for high predictive performance.
- The random forest classifier, based on an optimal feature subset, yielded superior prediction results.
