Augmenting Biological Fitness Prediction Benchmarks with Landscapes Features from GraphFLA
Mingyu Huang, Shasha Zhou, Ke Li
Machine learning models increasingly map biological sequence-fitness landscapes across various modalities to predict mutational effects. Effective evaluation of these models requires benchmarks derived from empirical data. However, current benchmarks often lack detailed topographical information regarding the underlying fitness landscape, which hampers interpretation and comparison of model performance. Here, we introduce GraphFLA, a Python framework that constructs and analyzes fitness landscapes from diverse modalities (DNA, RNA, protein, and beyond), accommodating datasets up to millions of mutants. GraphFLA calculates 20 biologically relevant features that characterize 4 fundamental aspects of landscape topography. By applying GraphFLA to over 5,300 landscapes from ProteinGym, RNAGym, and UniProbe, we demonstrate its utility in interpreting and comparing the performance of dozens of fitness prediction models, highlighting factors influencing model accuracy. Additionally, we release 155 combinatorially complete landscapes, encompassing over 2.2 million sequences across various modalities. All resources are available at https://github.com/COLA-Laboratory/GraphFLA.