The Influence of Dipeptide Composition on Protein Folding Rates

Guo, Jian Xiu; Rao, Ni Ni

doi:10.4028/www.scientific.net/amr.378-379.157

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…The three-dimensional structure of proteins is important in determining how they interact with other proteins [28]. Differences in dipeptide compositions might also lead to the variations in protein folding [16, 40], which, in turn, can affect their ability to interact with other proteins. Second, dipeptide compositions may contain specific amino acid pairs that serve as critical binding sites for PPIs.…”

Section: Discussionmentioning

confidence: 99%

Physical-Chemical Features Selection Reveals That Differences in Dipeptide Compositions Correlate Most with Protein-Protein Interactions

Teimouri,

Medvedeva,

Kolomeisky

2024

Preprint

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The ability to accurately predict protein-protein interactions is critically important for our understanding of major cellular processes. However, current experimental and computational approaches for identifying them are technically very challenging and still have limited success. We propose a new computational method for predicting protein-protein interactions using only primary sequence information. It utilizes a concept of physical-chemical similarity to determine which interactions will most probably occur. In our approach, the physical-chemical features of protein are extracted using bioinformatics tools for different organisms, and then they are utilized in a machine-learning method to identify successful protein-protein interactions via correlation analysis. It is found that the most important property that correlates most with the protein-protein interactions for all studied organisms is dipeptide amino acid compositions. The analysis is specifically applied to the bacterial two-component system that includes histidine kinase and transcriptional response regulators. Our theoretical approach provides a simple and robust method for quantifying the important details of complex mechanisms of biological processes.

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Section: Discussionmentioning

confidence: 99%

Physical-Chemical Features Selection Reveals That Differences in Dipeptide Compositions Correlate Most with Protein-Protein Interactions

Teimouri,

Medvedeva,

Kolomeisky

2024

Preprint

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“…As a remedy, Di-Peptide Composition (DPC) is used that not only extracts the occurrence frequency information but also exploits the structural information of amino acid residues in a protein sequence. DPC considers amino acid residues in pairs with some specified gap [20]. Dipeptide D i is calculated using (1).…”

Section: ) Feature Extraction Strategymentioning

confidence: 99%

Discrimination of Golgi Proteins Through Efficient Exploitation of Hybrid Feature Spaces Coupled With SMOTE and Ensemble of Support Vector Machine

et al. 2020

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Unraveling the role of physicochemical differences in predicting protein–protein interactions

Teimouri,

Medvedeva,

Kolomeisky

2024

The Journal of Chemical Physics

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The ability to accurately predict protein–protein interactions is critically important for understanding major cellular processes. However, current experimental and computational approaches for identifying them are technically very challenging and still have limited success. We propose a new computational method for predicting protein–protein interactions using only primary sequence information. It utilizes the concept of physicochemical similarity to determine which interactions will most likely occur. In our approach, the physicochemical features of proteins are extracted using bioinformatics tools for different organisms. Then they are utilized in a machine-learning method to identify successful protein–protein interactions via correlation analysis. It was found that the most important property that correlates most with the protein–protein interactions for all studied organisms is dipeptide amino acid composition (the frequency of specific amino acid pairs in a protein sequence). While current approaches often overlook the specificity of protein–protein interactions with different organisms, our method yields context-specific features that determine protein–protein interactions. The analysis is specifically applied to the bacterial two-component system that includes histidine kinase and transcriptional response regulators, as well as to the barnase–barstar complex, demonstrating the method’s versatility across different biological systems. Our approach can be applied to predict protein–protein interactions in any biological system, providing an important tool for investigating complex biological processes’ mechanisms.

show abstract

The Influence of Dipeptide Composition on Protein Folding Rates

Cited by 3 publications

References 16 publications

Physical-Chemical Features Selection Reveals That Differences in Dipeptide Compositions Correlate Most with Protein-Protein Interactions

Physical-Chemical Features Selection Reveals That Differences in Dipeptide Compositions Correlate Most with Protein-Protein Interactions

Discrimination of Golgi Proteins Through Efficient Exploitation of Hybrid Feature Spaces Coupled With SMOTE and Ensemble of Support Vector Machine

Unraveling the role of physicochemical differences in predicting protein–protein interactions

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