A fully automated benchmarking suite to compare macromolecular complexes.

ABSTRACT: Protein structure prediction has a long history of benchmarking efforts such as critical assessment of structure prediction, continuous automated model evaluation and critical assessment of prediction of interactions. With the rise of artificial intelligence-based methods for prediction of macromolecular complexes, benchmarking with large datasets and robust, unsupervised scores to compare predictions against a reference has become essential. Also, the increasing size and complexity of experimentally determined reference structures by crystallography or cryogenic electron microscopy poses challenges for structure comparison methods. Here we review the current state of the art in scoring methodologies, identify existing limitations and present more suitable approaches for scoring of tertiar

SUBMITTER: Studer G 

PROVIDER: S-EPMC12904798 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature