Assessing scoring metrics for AlphaFold2 and AlphaFold3 protein complex predictions.

ABSTRACT: Recent breakthroughs in AI-driven protein structure prediction have revolutionized structural biology, unlocking new possibilities to model complex biomolecular interactions. We evaluated widely used scoring metrics for assessing models predicted by ColabFold with templates, ColabFold without templates, and AlphaFold3. We benchmarked the optimal cutoffs for these assessment scores using a set of 223 heterodimeric, high-resolution protein structures and their predictions. Our results show that ColabFold with templates and AlphaFold3 perform similarly, and both outperform ColabFold without templates. However, the assessment scores perform best on ColabFold without templates. Furthermore, interface-specific scores are more reliable for evaluating protein complex predictions compared to the co

SUBMITTER: Genz LR 

PROVIDER: S-EPMC12516916 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature