ReadAb

A package used to read in and work with antibodies from PDBs.

Description

ReadAb is a package used to work with antibodies stored in PDB files. It facilitates the reading of antibodies from PDB files renumbered with any of the Kabat, Chothia, IMGT, or AHo/Honegger schemes to denote the locations of complimentarity determining regions (CDRs) (A.K.A. binding loops.) This toolkit enables researchers to compare binding specificity between multiple antibodies by comparing aligned loop sequences. ReadAb offers tools for visualizing antibodies with color coded components (loops, antigen, other), and for visualizing loop similarity matrices using heatmaps. ReadAb was developed using R version 4.4.1 (2024-06-14), Platform: aarch64-apple-darwin20, and Running under: macOS Sonoma 14.2.1.

The package uses a small subset of the SabDab human dataset (https://opig.stats.ox.ac.uk/webapps/sabdab-sabpred/sabdab) which contains a variety of antibody-antigen-complex structures.

This package was built as an R package, and was developed as a part of a course project for [BCB410H1: Applied Bioinformatics]

More information on antibody structures available here

For information on antibody numbering schemes, see this article

Installation

To install the latest version of the package:

install.packages("devtools")
library("devtools")
devtools::install_github("claregillis/ReadAb", build_vignettes = TRUE)
library("ReadAb")

To run the Shiny app:

RunReadAb()

Overview

ls("package:ReadAb")
data(package = "ReadAb") 
browseVignettes("ReadAb")

ReadAb contains 8 functions.

1. ReadAntibody for reading a PDB file containing an antibody using a selected numbering scheme to identify CDR loops

2. GetLoopSequence to get the sequence of a loop in an antibody

3. SetComponentColor to set the color of a component in an antibody (specific loop, antigen, other) for future visualization

4. AlignLoop to align a given loop for multiple antibodies (ex. H3 loop of 3 antibodies)

5. AssessLoopSimilarity for determining the similarity between the given loop for each pair of antibodies provided

6. AssessOverallLoopSimilarity for determining the similarity of ALL loops (loops may be weighted) for each pair of antibodies provided

7. DisplaySimilarityPlot for displaying the similarity matrices gathered from assessLoopSimilarity and assessOverallLoopSimilarity

8. VisualizeAntibody for displaying the atoms of the antibody with color coded components

The package also contains a dataset of PDB files from the SabDab database. Refer to package vignettes for more details. An overview of the package is illustrated below.

Figure 1. An overview of the ReadAb high level workflow.

Contributions

My name is Clare Gillis and I am the sole author of this package. I am currently working at Fable Therapeutics on a project involved in antibody design. I wanted to create a useful toolkit for manipulating anitbodies in R since there are none that I am aware of.

The bio3d package which reads and stores pdb objects was relied upon in ReadAntibody for reading a pdb and storing the contants and its loops. The plotly package is used in both VisualizeAntibody and DisplaySimilarityMatrix to display plots (an interactive 3D plot in the former, and a heatmap in the latter.) The Biostrings package is used in the alignLoop function to represent the amino acid sequenc of the loop, and the DECIPHER package is used in alignLoops and assessLoopSimilarity to align sequences and calculate a distance matrix between them.

Generative AI was used to help with creating unit tests for each function. Specifically, ChatGPT was prompted with the contents of the function and it generated a series of unit tests that were then edited by the author. Furthermore, ChatGPT was used for assistance during debugging. Specifically, it was prompted with the contents of a function and an error, then identified the location of the bug. More information on ChatGPT is available here

References

• Chang, W., J. Cheng, J. Allaire, C. Sievert, B. Schloerke, Y. Xie, J. Allen, J. McPherson, A. Dipert, B. Borges (2023). shiny: Web Application Framework for R. R package version 1.8.0, https://CRAN.R-project.org/package=shiny

• Chiu, M. L., Goulet, D. R., Teplyakov, A., & Gilliland, G. L. (2019). Antibody structure and function: The basis for engineering therapeutics. Antibodies (Basel), 8(4), Article 55. https://doi.org/10.3390/antib8040055

• Chothia, C., Lesk, A. M., Tramontano, A., Levitt, M., Smith-Gill, S. J., Air, G., et al. (1989). Conformations of immunoglobulin hypervariable regions. Nature, 342:6252, 877–883. Available from: https://www.nature.com/articles/342877a0

• Dondelinger, M., Filée, P., Sauvage, E., Quinting, B., Muyldermans, S., Galleni, M., & Vandevenne, M. S. (2018). Understanding the significance and implications of antibody numbering and antigen-binding surface/residue definition. Frontiers in Immunology, 9, Article 2278. https://doi.org/10.3389/fimmu.2018.02278

• Dunbar, J., Krawczyk, K., Leem, J., Baker, T., Fuchs, A., Georges, G., Shi, J., & Deane, C. M. (2014). SAbDab: The structural antibody database. Nucleic Acids Research, 42(D1), D1140–D1146. https://doi.org/10.1093/nar/gkt1043

• Grant, B. J., Skjaerven, L., & Yao, X. Q. (2021). The Bio3D packages for structural bioinformatics. Protein Science, 30(1), 20–30. https://doi.org/10.1002/pro.3923

• Honegger, A., Plückthun, A. (2001). Yet Another Numbering Scheme for Immunoglobulin Variable Domains: An Automatic Modeling and Analysis Tool. J Mol Biol, 309(3), 657–670.

• Kabat EA, Te Wu T, Bilofsky H, (U.S.) NI of H. Sequences of Immunoglobulin Chains: Tabulation and Analysis of Amino Acid Sequences of Precursors, V-regions, C-regions, J-Chain and BP-Microglobulins, 1979. Department of Health, Education, and Welfare, Public Health Service, National Institutes of Health (1979). Available online at: https://books.google.com/books?id=OpW8-ibqyvcC

• Lefranc, M. P., Lefranc, G. (2020). Immunoglobulins or Antibodies: IMGT® Bridging Genes, Structures and Functions. Biomedicines, 8(9):319. Available from: https://www.mdpi.com/2227-9059/8/9/319/htm

• Pagès, H., Aboyoun, P., Gentleman, R., & DebRoy, S. (2024). Biostrings: Efficient manipulation of biological strings (R package version 2.72.1). https://doi.org/10.18129/B9.bioc.Biostrings

• R Core Team (2023). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/

• Sievert, C. (2020). Interactive Web-Based Data Visualization with R, plotly, and shiny. Chapman and Hall/CRC Florida.

• Wickham, H. (2007). Reshaping data with the reshape package. Journal of Statistical Software, 21(12).

• Wickham H, François R, Henry L, Müller K, Vaughan D (2023). dplyr: A Grammar of Data Manipulation. R package version 1.1.4. https://CRAN.R-project.org/package=dplyr

• Wilson, I. A., & Stanfield, R. L. (2021). 50 years of structural immunology. Journal of Biological Chemistry, 296, Article 100745. https://doi.org/10.1016/j.jbc.2021.100745

• Wright, E. S. (2016). “Using DECIPHER v2.0 to Analyze Big Biological Sequence Data in R.” The R Journal, 8(1), 352-359.

Acknowledgements

This package was developed as part of an assessment for 2024 BCB410H: Applied Bioinformatics course at the University of Toronto, Toronto, CANADA. ReadAb welcomes issues, enhancement requests, and other contributions. To submit an issue, use the GitHub issues. Many thanks to those who provided feedback to improve this package.