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Chin. Phys. B, 2020, Vol. 29(10): 108710    DOI: 10.1088/1674-1056/aba9ba
Special Issue: SPECIAL TOPIC — Modeling and simulations for the structures and functions of proteins and nucleic acids
Topical Review—Modeling and simulations for the structures and functions of proteins and nucleic acids Prev   Next  

Structural and dynamical mechanisms of a naturally occurring variant of the human prion protein in preventing prion conversion

Yiming Tang(唐一鸣), Yifei Yao(姚逸飞), and Guanghong Wei(韦广红)†
1 Department of Physics, State Key Laboratory of Surface Physics and Key Laboratory for Computational Physical Science (Ministry of Education), and Multiscale Research Institute of Complex Systems, Fudan University, Shanghai 200433, China

Prion diseases are associated with the misfolding of the normal helical cellular form of prion protein (PrPC) into the β-sheet-rich scrapie form (PrPSc) and the subsequent aggregation of PrPSc into amyloid fibrils. Recent studies demonstrated that a naturally occurring variant V127 of human PrPC is intrinsically resistant to prion conversion and aggregation, and can completely prevent prion diseases. However, the underlying molecular mechanism remains elusive. Herein we perform multiple microsecond molecular dynamics simulations on both wildtype (WT) and V127 variant of human PrPC to understand at atomic level the protective effect of V127 variant. Our simulations show that G127V mutation not only increases the rigidity of the S2–H2 loop between strand-2 (S2) and helix-2 (H2), but also allosterically enhances the stability of the H2 C-terminal region. Interestingly, previous studies reported that animals with rigid S2–H2 loop usually do not develop prion diseases, and the increase in H2 C-terminal stability can prevent misfolding and oligomerization of prion protein. The allosteric paths from G/V127 to H2 C-terminal region are identified using dynamical network analyses. Moreover, community network analyses illustrate that G127V mutation enhances the global correlations and intra-molecular interactions of PrP, thus stabilizing the overall PrPC structure and inhibiting its conversion into PrPSc. This study provides mechanistic understanding of human V127 variant in preventing prion conversion which may be helpful for the rational design of potent anti-prion compounds.

Keywords:  prion protein      V127 variant      molecular dynamics simulations      dynamic network analysis  
Received:  24 June 2020      Revised:  19 July 2020      Published:  05 October 2020
PACS:  87.15.-v (Biomolecules: structure and physical properties)  
  87.14.E- (Proteins)  
  87.15.ap (Molecular dynamics simulation)  
Corresponding Authors:  Corresponding author. E-mail:   
About author: 
†Corresponding author. E-mail:
* Project supported by the Key Program of the National Key Research and Development Program of China (Grant No. 2016YFA0501702) and the National Natural Science Foundation of China (Grant No. 11674065).

Cite this article: 

Yiming Tang(唐一鸣), Yifei Yao(姚逸飞), and Guanghong Wei(韦广红)† Structural and dynamical mechanisms of a naturally occurring variant of the human prion protein in preventing prion conversion 2020 Chin. Phys. B 29 108710

Fig. 1.  

Stability and rigidity of WT PrP and its V127 variant. (a) A snapshot of the human PrP structure. (b)–(c) PDF of RMSD values in four consecutive time windows of (b) WT systems and (c) G127V systems. (d) RMSF of each residue on WT and G127V averaging over the three individual simulations for each system. The error bars are calculated by computing independent values from each individual simulation and taking the maximums and minimums of those values. The two regions where RMSF of G127V is remarkably lower than that of WT are labeled (1) and (2).

Fig. 2.  

Conformational characteristics of the S2–H2 loop. (a) Snapshots of the S2–H2 loop (residue 165–174) in three prion-resistant PrPs. The red and blue dashed boxes correspond to the two structural characteristics: a helix-like structure and a turn-like loop. (b) The Ramachandran plot of residue D167 in bank vole, elk, and horse PrPs. (c)–(f) Snapshots of the representative conformations of (c) the top four S2–H2 loop clusters in WT-1 MD run and of (c)–(e) the top one cluster in each G127V MD run. (g)–(h) PMF of D167 plotted as a function of the (Φ, Ψ) values in (g) WT and (h) G127V PrPs.

Fig. 3.  

Influence of G127V mutation on the interactions in the vicinity of residue 127 and S2–H2 loop. (a), (c), (f) Time evolution of (a) contact number between G/V127 and P165, (c) number of H-bonds between residues 125–129 and residues 162–169, and (f) centroid distance between residue R164 and D178 charged sidechain groups, in WT-1 (blue line) and in G127V-1 (red line). (b), (d), (g) Statistical analysis using a combined trajectory of the last 1.0 μs in the three simulations of WT and G127V systems. (e) Time evolution of H-bond numbers between residues 125–129 and residues 162–169. Only residue pairs forming H-bonds in more than 1/4 of simulation time are shown. (h)–(i) Representative snapshots of the N-terminal region and the S2–H2 loop region of (h) WT system, and (i) G127V system. (j)–(k) Snapshots of the G127V showing (j) the E168-involved H-bonds, and (k) the R164-D178 salt bridge.

Fig. 4.  

Allosteric paths from the mutation site (G/V127) to the C-terminal of H2 in WT and G127V systems. (a), (d) Optimal path from residue G/V127 to G195 in (a) WT PrP and (d) G127V. (b), (e) The correlation values of residue pairs forming the edges along (b) the G127–G195, and (e) the V127–G195 optimal paths. (c), (f) Percentage of optimal path length increase upon removal of each node of (c) WT and (f) G127V optimal paths.

Fig. 5.  

Correlation and community network analysis of WT and V127 variant. (a)–(b) Inter-residue correlation matrices of (a) WT and (b) G127V systems. (c)–(d) Community networks of (c) WT and (d) G127V systems. Left panels: snapshots of the proteins colored by communities. Right panels: schematic diagrams of the community networks. Each circle represents a single community. The size of the circle and the width of the edges correspond respectively to the size of the community and the connectivity strength between two communities.

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