PSSM-Suc: Accurately predicting succinylation using position specific scoring matrix into bigram for feature
Abdollah Dehzangi1, Yosvany López2, Sunil Pranit Lal3
1Department of Psychiatry, Carver College of Medicine, University of Iowa, Iowa, USA.
This study introduces PSSM-Suc, a novel computational tool for identifying lysine succinylation, a crucial protein modification. PSSM-Suc significantly improves prediction accuracy, aiding proteomic research.
Area of Science:
- Biochemistry
- Proteomics
- Bioinformatics
Background:
- Post-translational modifications (PTMs) diversify the proteome, with lysine succinylation gaining research interest.
- Experimental identification of lysine succinylation is challenging due to cost and time constraints.
- Existing computational predictors for succinylation have limited performance.
Purpose of the Study:
- To develop a novel computational predictor for identifying lysine succinylation.
- To improve the accuracy and efficiency of succinylated lysine residue prediction.
Main Methods:
- Developed PSSM-Suc, a predictor utilizing evolutionary information from amino acids.
- Represented lysine residues using profile bigrams from position-specific scoring matrices.
- Evaluated PSSM-Suc against existing predictors on a benchmark dataset.
Main Results:
- PSSM-Suc demonstrated significant performance improvements over state-of-the-art predictors.
- Achieved high sensitivity (0.8159), accuracy (0.8199), and Matthews correlation coefficient (0.6396).
Conclusions:
- PSSM-Suc offers a more effective computational approach for predicting lysine succinylation.
- The method enhances the study of protein modifications and cellular functions.
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