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Updated: May 21, 2026
06:50
Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
Published on: January 26, 2024
Predicting protein-ATP binding sites from primary sequence through fusing bi-profile sampling of multi-view features
Ya-Nan Zhang1, Dong-Jun Yu, Shu-Sen Li
1Department of Automation, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China.
BMC Bioinformatics
|June 2, 2012
Summary
We developed a new method to predict protein binding sites for Adenosine-5'-triphosphate (ATP). This approach combines sequence and structural features for accurate identification of functional residues in protein-ATP complexes.
Area of Science:
- Biochemistry
- Structural Biology
- Bioinformatics
Background:
- Adenosine-5'-triphosphate (ATP) is a crucial nucleotide involved in numerous cellular processes as a coenzyme.
- Understanding protein-ATP interactions is vital for elucidating protein function and complex mechanisms.
Purpose of the Study:
- To propose a novel framework for accurately predicting protein residues that bind to ATP.
- To enhance the understanding of protein-ATP binding site characteristics.
Main Methods:
- Integration of sequence evolutional information.
- Application of bi-profile sampling for multi-view sequential features.
- Inclusion of sequence-derived structural features.
Main Results:
- The proposed protocol demonstrates high prediction performance on benchmark datasets.
- Identified distinct structural characteristics of ATP binding sites, including low solvent accessibility and preference for secondary structure junctions.
- Performance is sensitive to training dataset balance and benefits from increased scale.
Conclusions:
- The novel framework effectively predicts protein-ATP binding sites.
- Structural features offer significant insights into the nature of these binding interactions.
- Dataset scale and balance are critical factors for optimizing prediction accuracy.
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