Principal component analysis (PCA) of molecular dynamics simulations of the PCSK9–LDLR complex and of PCSK9 missense variants.
Proprotein convertase subtilisin/kexin type 9 (PCSK9) binds the low-density lipoprotein receptor (LDLR) and induces its degradation. Gain-of-function (GOF) PCSK9 variants increase LDLR degradation and cause familial hypercholesterolemia (FH).
This analysis supports a study of PCSK9 missense variants identified by exon-targeted sequencing in 113 Brazilian FH patients (annotated with ANNOVAR). Eight variants in the pro-domain (p.E32K, p.E57K), catalytic domain (p.R237W, p.P279T, p.A443T) and C-terminal histidine-cysteine-rich (CHR) domain (p.R469W, p.Q619P, p.R680Q) were modeled. MD showed that the catalytic- and CHR-domain variants lie more than 22.5 Å from the LDLR-EGF-A interface, and that p.A443T, p.R469W and p.Q619P perturb internal interactions, mainly with the pro-domain. In cells, the GOF variants p.E32K and p.R469W reduced surface LDLR by less than 11%.
| File | Description |
|---|---|
PCA-PCSK9-gmf-maestro.ipynb |
PCA conformer plots for the PCSK9 pro-domain (chain P), catalytic + CHR domains (chain A) and LDLR-EGF-A (chain E) |
Backbone atoms of each MD frame were extracted and aligned with trj_selection_dl.py and
trj_align.py (Schrödinger Suite 2019v.4). Individual simulations were merged into a single
trajectory and CMS file with trj_merge.py, and principal components were computed with
trj_essential_dynamics.py. PCA conformer plots (PC1 × PC2, states and motions) are
generated in the notebook with Matplotlib, Seaborn, NumPy and pandas.
Python 3 with Jupyter and the libraries listed below. Trajectories and Schrödinger Suite outputs are not included in this repository.
pip install jupyter numpy pandas matplotlib seabornGlaucio Monteiro Ferreira — Research Associate, Institute of Chemistry, University of São Paulo (IQ-USP) · LIBNM · Ferreira Lab · ORCID · FAPESP profile · gmf@usp.br