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© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

As small protein assemblies and even small proteins are becoming more amenable to cryo-Electron Microscopy (EM) structural studies, it is important to consider the complementary dynamic information present in the data. Current computational strategies are limited in their ability to resolve minute differences among low molecular weight entities. Here, we demonstrate a new combinatorial approach to delineate flexible conformations among small proteins using real-space refinement applications. We performed a meta-analysis of structural data for the SARS CoV-2 Nucleocapsid (N) protein using a combination of rigid-body refinement and simulated annealing methods. For the N protein monomer, we determined three new flexible conformers with good stereochemistry and quantitative comparisons provided new evidence of their dynamic properties. A similar analysis performed for the N protein dimer showed only minor structural differences among the flexible models. These results suggested a more stable view of the N protein dimer than the monomer structure. Taken together, the new computational strategies can delineate conformational changes in low molecular weight proteins that may go unnoticed by conventional assessments. The results also suggest that small proteins may be further stabilized for structural studies through the use of solution components that limit the movement of external flexible regions.

Details

Title
Delineating Conformational Variability in Small Protein Structures Using Combinatorial Refinement Strategies
Author
Kelly, Deborah F 1   VIAFID ORCID Logo  ; Jonaid, G M 2 ; Kaylor, Liam 3 ; Solares, Maria J 3 ; Berry, Samantha 1 ; Liza-Anastasia DiCecco 1 ; Dearnaley, William 1 ; Casasanta, Michael 1 

 Department of Biomedical Engineering, Pennsylvania State University, University Park, PA 16802, USA; Center for Structural Oncology, Pennsylvania State University, University Park, PA 16802, USA 
 Department of Biomedical Engineering, Pennsylvania State University, University Park, PA 16802, USA; Center for Structural Oncology, Pennsylvania State University, University Park, PA 16802, USA; Bioinformatics and Genomics Graduate Program, Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, PA 16802, USA 
 Department of Biomedical Engineering, Pennsylvania State University, University Park, PA 16802, USA; Center for Structural Oncology, Pennsylvania State University, University Park, PA 16802, USA; Molecular, Cellular, and Integrative Biosciences Graduate Program, Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, PA 16802, USA 
First page
1869
Publication year
2023
Publication date
2023
Publisher
MDPI AG
e-ISSN
2072666X
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2882812839
Copyright
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.