Work

Protein complex prediction using Rosetta, AlphaFold, and mass spectrometry covalent labeling

Year: 2022

Type: article

Abstract: Covalent labeling (CL) in combination with mass spectrometry can be used as an analytical tool to study and determine structural properties of protein-protein complexes. However, data from these experiments is sparse and does not unambiguously elucidate protein structure. Thus, computational algorithms are needed to deduce structure from the CL data. In this work, we present a hybrid method that combines models of protein complex subunits generated with AlphaFold with differential CL data via a CL-guided protein-protein docking in Rosetta. In a benchmark set, the RMSD (root-mean-square deviation) of the best-scoring models was below 3.6 Å for 5/5 complexes with inclusion of CL data, whereas the same quality was only achieved for 1/5 complexes without CL data. This study suggests that our integrated approach can successfully use data obtained from CL experiments to distinguish between nativelike and non-nativelike models.

Source: Nature Communications

DOI: 10.1038/s41467-022-35593-8

Authors: Zachary C. Drake, Justin T. Seffernick, Steffen Lindert

Institution: The Ohio State University

Language: English

Topic: Protein Structure and Dynamics

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $5,890

Funders: U.S. Department of Health and Human Services, Alfred P. Sloan Foundation, National Institutes of Health, National Institute of General Medical Sciences