Next Article in Journal
Robot-Controlled Acupuncture—An Innovative Step towards Modernization of the Ancient Traditional Medical Treatment Method
Previous Article in Journal
A Short Review of Iron Metabolism and Pathophysiology of Iron Disorders
Previous Article in Special Issue
Synthetic Lethality in Lung Cancer—From the Perspective of Cancer Genomics
Open AccessArticle

A Visualization Tool for Cryo-EM Protein Validation with an Unsupervised Machine Learning Model in Chimera Platform

1
Department of Computer Science, Valdosta State University, Valdosta, GA 31693, USA
2
Department of Natural Science, Elizabeth City State University, Elizabeth City, NC 27909, USA
3
Department of Mathematics & Computer Science, Elizabeth City State University, Elizabeth City, NC 27909, USA
*
Author to whom correspondence should be addressed.
Medicines 2019, 6(3), 86; https://doi.org/10.3390/medicines6030086
Received: 1 July 2019 / Revised: 31 July 2019 / Accepted: 2 August 2019 / Published: 6 August 2019
(This article belongs to the Special Issue Novel Therapeutic and Preventive Approaches for Cancer)
  |  
PDF [2837 KB, uploaded 6 August 2019]
  |  

Abstract

Background: Cryo-electron microscopy (cryo-EM) has become a major technique for protein structure determination. However, due to the low quality of cryo-EM density maps, many protein structures derived from cryo-EM contain outliers introduced during the modeling process. The current protein model validation system lacks identification features for cryo-EM proteins making it not enough to identify outliers in cryo-EM proteins. Methods: This study introduces an efficient unsupervised outlier detection model for validating protein models built from cryo-EM technique. The current model uses a high-resolution X-ray dataset (<1.5 Å) as the reference dataset. The distal block distance, side-chain length, phi, psi, and first chi angle of the residues in the reference dataset are collected and saved as a database of the histogram-based outlier score (HBOS). The HBOS value of the residues in target cryo-EM proteins can be read from this HBOS database. Results: Protein residues with a HBOS value greater than ten are labeled as outliers by default. Four datasets containing proteins derived from cryo-EM density maps were tested with this probabilistic anomaly detection model. Conclusions: According to the proposed model, a visualization assistant tool was designed for Chimera, a protein visualization platform. View Full-Text
Keywords: protein; cryo-electron microscopy; validation; machine learning; Chimera; statistics protein; cryo-electron microscopy; validation; machine learning; Chimera; statistics
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Chen, L.; Baker, B.; Santos, E.; Sheep, M.; Daftarian, D. A Visualization Tool for Cryo-EM Protein Validation with an Unsupervised Machine Learning Model in Chimera Platform. Medicines 2019, 6, 86.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Medicines EISSN 2305-6320 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top