Name
Title | ||
|---|---|---|
Application of Coarse-Grained (CG) Models to Explore Conformational Pathway of Large-Scale Protein Machines |
Abstract | ||
|---|---|---|
Protein machines are clusters of protein assemblies that function in order to control the transfer of matter and energy in cells. For a specific protein machine, its working mechanisms are not only determined by the static crystal structures, but also related to the conformational transition dynamics and the corresponding energy profiles. With the rapid development of crystallographic techniques, the spatial scale of resolved structures is reaching up to thousands of residues, and the concomitant conformational changes become more and more complicated, posing a great challenge for computational biology research. Previously, a coarse-grained (CG) model aiming at conformational free energy evaluation was developed and showed excellent ability to reproduce the energy profiles by accurate electrostatic interaction calculations. In this study, we extended the application of the CG model to a series of large-scale protein machine systems. The spike protein trimer of SARS-CoV-2, ATP citrate lyase (ACLY) tetramer, and P4-ATPases systems were carefully studied and discussed as examples. It is indicated that the CG model is effective to depict the energy profiles of the conformational pathway between two endpoint structures, especially for large-scale systems. Both the energy change and energy barrier between endpoint structures provide reasonable mechanism explanations for the associated biological processes, including the opening of receptor binding domain (RBD) of spike protein, the phospholipid transportation of P4-ATPase, and the loop translocation of ACLY. Taken together, the CG model provides a suitable alternative in mechanistic studies related to conformational change in large-scale protein machines. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.3390/e24050620 | ENTROPY |
Keywords | DocType | Volume |
protein machines, coarse-grained model, conformational pathway | Journal | 24 |
Issue | ISSN | Citations |
5 | 1099-4300 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Danfeng Shi | 1 | 0 | 0.34 |
| An Ke | 2 | 0 | 1.01 |
| Honghui Zhang | 3 | 0 | 0.34 |
| Peiyi Xu | 4 | 0 | 0.34 |
| Chen Bai | 5 | 0 | 0.34 |