qNABpredict

qNABpredict predicts the fraction of nucleic acid (NA)-binding amino acids in protein sequences to provide rapid, taxonomy-aware quantification of NA-binding content for comparative and proteome-scale analyses.

Key Features:

• Content Prediction Approach: Predicts the fraction (content) of NA-binding residues in a protein sequence rather than classifying individual residues, reducing computational cost relative to residue-level predictors.
• Feature Set: Employs a small, rationally designed set of sequence-derived features that capture relevant characteristics for NA-binding content prediction.
• Prediction Model: Uses a well-parametrized support vector regression (SVR) model to predict NA-binding amino acid content.
• Taxonomy-Aware Models: Provides a taxonomy-agnostic model and kingdom-specific models for archaea, bacteria, eukaryota, and viruses to account for evolutionary differences.
• Performance: Demonstrated on a low-similarity test dataset to be approximately 100-fold faster than traditional residue-level predictors while yielding statistically more accurate content predictions and enabling refinement of residue-level method outputs.

Scientific Applications:

• Proteome-scale screening: Rapidly estimates NA-binding content across entire proteomes to prioritize proteins for detailed analysis.
• Comparative and evolutionary analyses: Enables comparison of NA-binding content across taxa using taxonomy-aware models.
• Structural biology and functional annotation: Assists identification of proteins and regions likely involved in protein–nucleic acid interactions to inform experimental studies.
• Genomics and systems biology: Supports large-scale studies of protein–NA interaction trends across gene families and biological networks.

Methodology:

Uses a small, rationally designed sequence-derived feature set and a well-parametrized support vector regression model with taxonomy-agnostic and kingdom-specific (archaea, bacteria, eukaryota, viruses) variants; performance was evaluated on a low-similarity test dataset.