Predicting subcellular localization of multisite proteins using differently weighted multi-label k-nearest neighbors sets.

Background: For a protein to execute its function, ensuring its correct subcellular localization is essential. In addition to biological experiments, bioinformatics is widely used to predict and determine the subcellular localization of proteins. However, single-feature extraction methods cannot effectively handle the huge amount of data and multisite localization of proteins. Thus, we developed a pseudo amino acid composition (PseAAC) method and an entropy density technique to extract feature fusion information from subcellular multisite proteins.

Objective: Predicting multiplex protein subcellular localization and achieve high prediction accuracy.

Method: To improve the efficiency of predicting multiplex protein subcellular localization, we used the multi-label k-nearest neighbors algorithm and assigned different weights to various attributes. The method was evaluated using several performance metrics with a dataset consisting of protein sequences with single-site and multisite subcellular localizations.

Results: Evaluation experiments showed that the proposed method significantly improves the optimal overall accuracy rate of multiplex protein subcellular localization.

Conclusion: This method can help to more comprehensively predict protein subcellular localization toward better understanding protein function, thereby bridging the gap between theory and application toward improved identification and monitoring of drug targets.