ECOD: integrating classifications of protein domains from experimental and predicted structures
R. Dustin Schaeffer, Kirill E. Medvedev, Antonina Andreeva, Sara Chuguransky, Beatriz Lazaro Pinto, Jing Zhang, Qian Cong, Alex Bateman 等 9 位
The University of Texas Southwestern Medical Center European Bioinformatics Institute
内容与影响
The evolutionary classification of protein domains (ECOD) classifies protein domains using a combination of sequence and structural data (http://prodata.swmed.edu/ecod). Here we present the culmination of our previous efforts at classifying domains from predicted structures, principally from the AlphaFold Database (AFDB), by integrating these domains with our existing classification of PDB structures. This combined classification includes both domains from our previous, purely experimental, classification of domains as well as domains from our provisional classification of 48 proteomes in AFDB predicted from model organisms and organisms of concern to global health. ECOD classifies over 1.8 M domains from over 1000 000 proteins collectively deposited in the PDB and AFDB. Additionally, we have changed the F-group classification reference used for ECOD, deprecating our original ECODf library and instead relying on direct collaboration with the Pfam sequence family database to inform our classification. Pfam provides similar coverage of ECOD with family classification while being more accurate and less redundant. By eliminating duplication of effort, we can improve both classifications. Finally, we discuss the initial deployment of DrugDomain, a database of domain-ligand interactions, on ECOD and discuss future plans.
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生物医学Protein Structure and Dynamics
Machine Learning in Bioinformatics · RNA and protein synthesis mechanisms
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