Rosace-AA: enhancing interpretation of deep mutational scanning data with amino acid substitution and position-specific insights.

ABSTRACT:

Motivation

Proteins are dynamic systems whose function and behavior are sensitive to environmental conditions and often involve multiple cellular roles. Deep mutational scanning (DMS) experiments generate extensive datasets to capture the functional consequences of mutations. However, the sheer volume of data presents challenges in visualization and interpretation. Current approaches often rely on heatmaps, but these methods fail to capture the nuanced effects of amino acid substitutions, which are essential for understanding mutational impact.

Results

To address this, we extend the Rosace framework with Rosace-AA, a model that incorporates both position-specific information and amino acid substitution trends. Using substitution matrices like BLOSUM90, Rosace-AA infers an in

SUBMITTER: Rao J 

PROVIDER: S-EPMC12809776 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature