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Updated: Jun 21, 2026

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
Published on: January 26, 2024
Improved prediction of protein binding sites from sequences using genetic algorithm
Xiuquan Du1, Jiaxing Cheng, Jie Song
1Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, Anhui University, 230039, Hefei, China. duxiuquan2@sina.com
Abstract:
We undertook this project in response to the rapidly increasing number of protein structures with unknown functions in the Protein Data Bank. Here, we combined a genetic algorithm with a support vector machine to predict protein-protein binding sites. In an experiment on a testing dataset, we predicted the binding sites for 66% of our datasets, made up of 50 testing hetero-complexes. This classifier achieved greater sensitivity (60.17%), specificity (58.17%), accuracy (64.08%), and F-measure (54.79%), and a higher correlation coefficient (0.2502) than those of the support vector machine. This result can be used to guide biologists in designing specific experiments for protein analysis.
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