DRP-PSM: Multi-Level Feature Integration Reveals Hierarchical Contributions to Pathogenic Synonymous Mutation...

Area of Science:

• Genetics
• Molecular Biology
• Bioinformatics

Background:

• Synonymous mutations do not alter amino acid sequences but can affect protein function.
• These subtle genetic variants are implicated in disease initiation and progression.
• Accurate prediction of pathogenic synonymous mutations is vital for clinical diagnosis and treatment.

Purpose of the Study:

• To develop a novel method, DRP-PSM, for predicting pathogenic synonymous mutations.
• To integrate DNA, RNA, and protein-level biological features for a comprehensive prediction framework.
• To elucidate the contribution of different biological levels to synonymous mutation pathogenicity.

Main Methods:

• Developed DRP-PSM, a prediction method incorporating DNA, RNA, and protein features.
• Systematically integrated sequence and structural features from DNA, RNA, and protein levels.
• Evaluated the predictive power of features from distinct biological levels.

Main Results:

• DNA-level features provided the largest contribution to prediction accuracy, followed by RNA-level features.
• Protein-level features offered marginal predictive utility.
• DNA conservation and splicing effect were dominant biological features, outperforming sequence/structure descriptors.

Conclusions:

• Synonymous mutations primarily cause pathogenicity through effects on splicing or transcriptional efficiency.
• Translational or post-translational processes are less significant contributors.
• Understanding these regulatory mechanisms offers potential therapeutic targets.