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Identification of mannose interacting residues using local composition
Sandhya Agarwal1, Nitish Kumar Mishra, Harinder Singh
1Institute of Microbial Technology, Chandigarh, India.
Background:
Mannose binding proteins (MBPs) play a vital role in several biological functions such as defense mechanisms. These proteins bind to mannose on the surface of a wide range of pathogens and help in eliminating these pathogens from our body. Thus, it is important to identify mannose interacting residues (MIRs) in order to understand mechanism of recognition of pathogens by MBPs.
Results:
This paper describes modules developed for predicting MIRs in a protein. Support vector machine (SVM) based models have been developed on 120 mannose binding protein chains, where no two chains have more than 25% sequence similarity. SVM models were developed on two types of datasets: 1) main dataset consists of 1029 mannose interacting and 1029 non-interacting residues, 2) realistic dataset consists of 1029 mannose interacting and 10320 non-interacting residues. In this study, firstly, we developed standard modules using binary and PSSM profile of patterns and got maximum MCC around 0.32. Secondly, we developed SVM modules using composition profile of patterns and achieved maximum MCC around 0.74 with accuracy 86.64% on main dataset. Thirdly, we developed a model on a realistic dataset and achieved maximum MCC of 0.62 with accuracy 93.08%. Based on this study, a standalone program and web server have been developed for predicting mannose interacting residues in proteins (http://www.imtech.res.in/raghava/premier/).
Conclusions:
Compositional analysis of mannose interacting and non-interacting residues shows that certain types of residues are preferred in mannose interaction. It was also observed that residues around mannose interacting residues have a preference for certain types of residues. Composition of patterns/peptide/segment has been used for predicting MIRs and achieved reasonable high accuracy. It is possible that this novel strategy may be effective to predict other types of interacting residues. This study will be useful in annotating the function of protein as well as in understanding the role of mannose in the immune system.
Insights
Identifying mannose interacting residues (MIRs) is crucial for understanding pathogen recognition by mannose binding proteins (MBPs). This study developed machine learning models to accurately predict MIRs, aiding in protein function annotation and immune system research.
Area of Science:
- Biochemistry
- Computational Biology
- Immunology
Background:
- Mannose binding proteins (MBPs) are key in the immune system, recognizing pathogens by binding to mannose on their surfaces.
- Identifying mannose interacting residues (MIRs) is essential for understanding this pathogen recognition mechanism.
Purpose of the Study:
- To develop computational modules for predicting mannose interacting residues (MIRs) in proteins.
- To enhance understanding of protein-carbohydrate interactions and their role in immunity.
Main Methods:
- Support Vector Machine (SVM) models were trained on datasets of mannose binding protein chains.
- Models utilized binary, PSSM, and compositional profiles of residue patterns.
- Performance was evaluated using Matthews Correlation Coefficient (MCC) and accuracy.
Main Results:
- SVM models using compositional profiles achieved high performance, with MCC around 0.74 and 86.64% accuracy on the main dataset.
- A model developed on a realistic dataset yielded an MCC of 0.62 and 93.08% accuracy.
- A web server and standalone program were created for MIR prediction.
Conclusions:
- Compositional analysis reveals specific residue preferences for mannose interaction.
- The developed prediction strategy shows potential for identifying other types of interacting residues.
- This work aids in protein function annotation and understanding mannose's role in the immune system.
