Machine learning to estimate the local quality of protein crystal structures

Autor: Ikuko Miyaguchi, Miwa Sato, Akiko Kashima, Hiroyuki Nakagawa, Yuichi Kokabu, Biao Ma, Shigeyuki Matsumoto, Atsushi Tokuhisa, Masateru Ohta, Mitsunori Ikeguchi
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Scientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
Druh dokumentu: article
ISSN: 2045-2322
DOI: 10.1038/s41598-021-02948-y
Popis: Abstract Low-resolution electron density maps can pose a major obstacle in the determination and use of protein structures. Herein, we describe a novel method, called quality assessment based on an electron density map (QAEmap), which evaluates local protein structures determined by X-ray crystallography and could be applied to correct structural errors using low-resolution maps. QAEmap uses a three-dimensional deep convolutional neural network with electron density maps and their corresponding coordinates as input and predicts the correlation between the local structure and putative high-resolution experimental electron density map. This correlation could be used as a metric to modify the structure. Further, we propose that this method may be applied to evaluate ligand binding, which can be difficult to determine at low resolution.
Databáze: Directory of Open Access Journals