Recognizing and validating ligands with CheckMyBlob

Author:

Brzezinski Dariusz123ORCID,Porebski Przemyslaw J1ORCID,Kowiel Marcin3ORCID,Macnar Joanna M145ORCID,Minor Wladek1ORCID

Affiliation:

1. Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA 22908, USA

2. Institute of Computing Science, Poznan University of Technology, Poznan, 60-965, Poland

3. Center for Biocrystallographic Research, Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, 61-704, Poland

4. College of Inter-Faculty Individual Studies in Mathematics and Natural Sciences, University of Warsaw, Warsaw, 02-097, Poland

5. Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw, Warsaw, 02-089, Poland

Abstract

Abstract 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.

Funder

National Institute of General Medical Sciences

Polish National Agency for Academic Exchange

European Social Fund

Publisher

Oxford University Press (OUP)

Subject

Genetics

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