πDMD simulation as a strategy for refinement of AlphaFold2 modeled fuzzy protein complex structures

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Abstract

Disordered proteins are of great interest due to their structural features, as they do not have well-defined three-dimensional structures. These proteins, often called intrinsically disordered proteins or regions, play critical roles in various cellular processes and are associated with the development of a number of diseases. Our in silico research focused on the investigation of protein complexes that include both the ordered protein, such as 14-3-3γ, and proteins with intrinsically disordered regions, such as nucleocapsid (N) of SARS-CoV-2 and p53. Our findings demonstrate that complexes modeled by AlphaFold2 and refined using discrete molecular dynamics simulations acquire assembled structures in disordered regions. After refinement, the modeled complexes exhibit a degree of structural assembly that addresses a key challenge in studying disordered proteins – their propensity to evade stable conformations.

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About the authors

N. G. Muradyan

Institute of Molecular Biology, National Academy of Sciences of the Republic of Armenia (NAS RA)

Email: g_arakelov@mb.sci.am

Laboratory of Computational Modeling of Biological Processes

Armenia, Yerevan

A. A. Sargsyan

Institute of Molecular Biology, National Academy of Sciences of the Republic of Armenia (NAS RA); Russian–Armenian University

Email: g_arakelov@mb.sci.am

Laboratory of Computational Modeling of Biological Processes

Armenia, Yerevan; Yerevan

V. G. Arakelov

Institute of Molecular Biology, National Academy of Sciences of the Republic of Armenia (NAS RA)

Email: g_arakelov@mb.sci.am

Laboratory of Computational Modeling of Biological Processes

Armenia, Yerevan

A. K. Paronyan

Institute of Molecular Biology, National Academy of Sciences of the Republic of Armenia (NAS RA); Russian–Armenian University

Email: g_arakelov@mb.sci.am

Laboratory of Computational Modeling of Biological Processes

Armenia, Yerevan; Yerevan

G. G. Arakelov

Institute of Molecular Biology, National Academy of Sciences of the Republic of Armenia (NAS RA); Russian–Armenian University

Author for correspondence.
Email: g_arakelov@mb.sci.am

Laboratory of Computational Modeling of Biological Processes

Armenia, Yerevan; Yerevan

K. B. Nazaryan

Institute of Molecular Biology, National Academy of Sciences of the Republic of Armenia (NAS RA); Russian–Armenian University

Email: g_arakelov@mb.sci.am

Laboratory of Computational Modeling of Biological Processes

Armenia, Yerevan; Yerevan

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Supplementary files

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2. Fig. 1. Modeled AlphaFold2 complexes of 14-3-3γ/N and 14-3-3γ/p53. Comparison with domains available for X-ray structural analysis for the SARS-CoV-2 N protein [PDB ID: 6WZQ (CTD) and 7N0R (NTD)] and p53 protein [PDB ID: 2XWR (DBD) and 1AIE (TD)]. RMSD – root mean square deviation; X-ray – X-ray structural analysis.

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3. Fig. 2. Final structures simulated by the AF2 algorithm, colored according to the plDDT scale.

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4. Fig. 3. Results of πDMD simulation. a – Complex-1 114-3-3γ/N: initial structure and frame 800; b – Complex-2 14-3-3γ/N: initial structure and frame 1500; c – Complex-1 14-3-3γ/p53: initial structure and frame 500; d – Complex-2 14-3-3γ/p53: initial structure and frame 1500. All structures are shown on the side.

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5. Fig. 4. Results of cluster analysis of 14-3-3γ/N complexes (TTClust program). a – Clusters of 14-3-3γ/N complex-1 with 4 representative centroids; b – Clusters of 14-3-3γ/N complex-2 with two representative centroids.

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6. Fig. 5. Binding site distances for 14-3-3γ/N complexes. The interaction involves S197 and T205 of N protein N and K50, R57, K125, and R132 of 14-3-3γ protein. Distances are given in angstroms (Å).

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7. Fig. 6. Binding site distances for 14-3-3γ/p53 complexes. The interaction involves S366, S378, and T387 of p53 and K50, R57, K125, and R132 of 14-3-3γ. Distances are given in angstroms (Å).

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