In maialab/agvgd: An R Implementation of the 'Align-GVGD' Method

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(agvgd)

Introduction

One of the greatest challenges in the study of cancer risk is the assessment of unclassified missense variants.

Besides using biochemical and cell-based transcriptional assays to assess the structural and functional defects associated with missense variants, one can also use bioinformatics analysis based on multiple sequence alignment data and protein structure prediction to approach this problem.

Align-GVGD is one such method that uses protein multiple sequence alignment (PMSA) data to provide cancer risk estimates.

In this vignette we show you how you can use {agvgd} to reproduce the results obtained by Lee et al. (2010) [@Lee.CR.2010] on the study of missense variations in the BRCT Domain of BRCA1 gene.

In Lee's paper, Align-GVGD prediction scores are part of their cross-validation of structural and functional assays, as indicated in the column labeled AG in Figure 3 of said paper.

knitr::include_graphics('../man/figures/lee2010_fig03.png') 

Reproducing the AGVGD scores

Data sets

To reproduce the AGVGD prediction scores, we need two data sets:

• The protein sequence alignment of BRCA1 that included the BRCT Domain
• The list of the 117 BRCA1 missense variants studied [@Lee.CR.2010]

Both these data sets are already bundled with {agvgd}.

To read in the alignment of BRCA1 use the function read_alignment() and the name of the gene "BRCA1":

brca1_alignment <- read_alignment("BRCA1")
print(brca1_alignment, line_width = 200)

To read in the 117 BRCA1 missense variants, i.e., the 117 substitutions, we use the function read_substitutions():

path <- system.file("extdata/lee2010_sub.txt", package = 'agvgd', mustWork = TRUE)
missense_variants <- read_substitutions(path)
missense_variants

The list of missense variants comes with the indication of the residue number in the protein sequence, i.e. column res in missense_variants. As we'll see next, the function agvgd() uses the alignment positions (column poi in missense_variants) instead to refer to those positions in the alignment.

The difference between res and poi is that res counts the residues in the protein primary sequence reference, while poi refers to the positions in the alignment, accounting for gaps.

So, before we proceed, we will update the missense_variants tibble and replace the NA values with the corresponding poi values. For that we use the function res_to_poi():

missense_variants$poi <- res_to_poi(brca1_alignment, missense_variants$res)
missense_variants

Running agvgd()

Run Align-GVGD with the function agvgd():

scores <- agvgd(alignment = brca1_alignment,
      poi = missense_variants$poi,
      sub = missense_variants$sub)

print(scores, n = Inf)

References

maialab/agvgd documentation built on Jan. 10, 2024, 6:08 p.m.