Variant effect predictor correlation with functional assays is reflective of clinical classification performance.

ABSTRACT:

Background

Understanding the relationship between protein sequence and function is crucial for accurate classification of missense variants. Variant effect predictors (VEPs) play a vital role in deciphering this complex relationship, yet evaluating their performance remains challenging for several reasons, including data circularity, where the same or related data is used for training and assessment. High-throughput experimental strategies like deep mutational scanning (DMS) offer a promising solution.

Results

In this study, we extend upon our previous benchmarking approach, assessing the performance of 97 VEPs using missense DMS measurements from 36 different human proteins. In addition, a new pairwise, VEP-centric approach mitigates the impact of missing predictions on ove

SUBMITTER: Livesey BJ 

PROVIDER: S-EPMC12016141 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature