Recognizing and validating ligands with CheckMyBlob
Author(s) -
Dariusz Brzeziński,
Przemyslaw Porebski,
Marcin Kowiel,
Joanna M. Macnar,
W. Minor
Publication year - 2021
Publication title -
nucleic acids research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 9.008
H-Index - 537
eISSN - 1362-4954
pISSN - 0305-1048
DOI - 10.1093/nar/gkab296
Subject(s) - biology , web server , protein data bank (rcsb pdb) , ranking (information retrieval) , identification (biology) , protein data bank , plug in , ligand (biochemistry) , computational biology , computer science , the internet , bioinformatics , data mining , protein structure , information retrieval , genetics , world wide web , biochemistry , operating system , ecology , receptor
Structure-guided drug design depends on the correct identification of ligands in crystal structures of protein complexes. However, the interpretation of the electron density maps is challenging and often burdened with confirmation bias. Ligand identification can be aided by automatic methods such as CheckMyBlob, a machine learning algorithm that learns to generalize ligand descriptions from sets of moieties deposited in the Protein Data Bank. Here, we present the CheckMyBlob web server, a platform that can identify ligands in unmodeled fragments of electron density maps or validate ligands in existing models. The server processes PDB/mmCIF and MTZ files and returns a ranking of 10 most likely ligands for each detected electron density blob along with interactive 3D visualizations. Additionally, for each prediction/validation, a plugin script is generated that enables users to conduct a detailed analysis of the server results in Coot. The CheckMyBlob web server is available at https://checkmyblob.bioreproducibility.org.
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