Next Article in Journal
The Effects of a Combination of Ion Channel Inhibitors in Female Rats Following Repeated Mild Traumatic Brain Injury
Next Article in Special Issue
Modeling of Protein Structural Flexibility and Large-Scale Dynamics: Coarse-Grained Simulations and Elastic Network Models
Previous Article in Journal
Effect of Roasting Levels and Drying Process of Coffea canephora on the Quality of Bioactive Compounds and Cytotoxicity
Previous Article in Special Issue
Role of Computational Methods in Going beyond X-ray Crystallography to Explore Protein Structure and Dynamics
Article Menu
Issue 11 (November) cover image

Export Article

Open AccessArticle
Int. J. Mol. Sci. 2018, 19(11), 3406; https://doi.org/10.3390/ijms19113406

Revealing Unknown Protein Structures Using Computational Conformational Sampling Guided by Experimental Hydrogen-Exchange Data

Department of Computer Science, Rice University, 6100 Main St, Houston, TX 77005, USA
*
Author to whom correspondence should be addressed.
Received: 24 September 2018 / Revised: 19 October 2018 / Accepted: 25 October 2018 / Published: 31 October 2018
(This article belongs to the Special Issue Protein Structural Dynamics)
Full-Text   |   PDF [3278 KB, uploaded 31 October 2018]   |  

Abstract

Both experimental and computational methods are available to gather information about a protein’s conformational space and interpret changes in protein structure. However, experimentally observing and computationally modeling large proteins remain critical challenges for structural biology. Our work aims at addressing these challenges by combining computational and experimental techniques relying on each other to overcome their respective limitations. Indeed, despite its advantages, an experimental technique such as hydrogen-exchange monitoring cannot produce structural models because of its low resolution. Additionally, the computational methods that can generate such models suffer from the curse of dimensionality when applied to large proteins. Adopting a common solution to this issue, we have recently proposed a framework in which our computational method for protein conformational sampling is biased by experimental hydrogen-exchange data. In this paper, we present our latest application of this computational framework: generating an atomic-resolution structural model for an unknown protein state. For that, starting from an available protein structure, we explore the conformational space of this protein, using hydrogen-exchange data on this unknown state as a guide. We have successfully used our computational framework to generate models for three proteins of increasing size, the biggest one undergoing large-scale conformational changes. View Full-Text
Keywords: protein structure; protein conformational sampling; hydrogen exchange; mass spectrometry; nuclear magnetic resonance protein structure; protein conformational sampling; hydrogen exchange; mass spectrometry; nuclear magnetic resonance
Figures

Graphical abstract

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).

Supplementary material

SciFeed

Share & Cite This Article

MDPI and ACS Style

Devaurs, D.; Antunes, D.A.; Kavraki, L.E. Revealing Unknown Protein Structures Using Computational Conformational Sampling Guided by Experimental Hydrogen-Exchange Data. Int. J. Mol. Sci. 2018, 19, 3406.

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]
Int. J. Mol. Sci. EISSN 1422-0067 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top