NetAcet

NetAcet predicts substrates of N-acetyltransferase A (NatA) and N-terminal acetylation in eukaryotic proteins to support analysis of protein stability, localization, and interaction.

Key Features:

• Neural Network Approach: Employs a neural network-based predictive model to identify potential NatA substrates.
• Training Data: Trained using a comprehensive yeast dataset of known NatA-mediated N-terminal acetylation examples.
• Cross-Species Applicability: Demonstrates applicability to mammalian NatA orthologs for cross-eukaryotic predictions.
• Performance Metrics: Achieves a correlation coefficient close to 0.7 on yeast test data and a sensitivity of up to 74% on mammalian data.

Scientific Applications:

• Protein Function Analysis: Predicts N-terminal acetylation to inform studies of protein function, stability, and interactions.
• Pathway Elucidation: Identifies NatA-mediated modifications relevant to signaling pathways and regulatory mechanisms.
• Comparative Genomics: Enables exploration of evolutionary conservation of NatA substrates across eukaryotic species.

Methodology:

Uses a neural network-based model trained on a yeast-derived dataset and evaluated on yeast and mammalian data.