Introduction to RBMRB

Overview

RBMRB provides a comprehensive R interface to the Biological Magnetic Resonance Data Bank (BMRB), mirroring all functionality of the PyBMRB 3.0 Python package.

Four main modules are available:

Module	Key functions
ChemicalShift	`cs_from_bmrb()`, `cs_from_file()`
ChemicalShiftStatistics	`get_cs_data()`, `get_2d_cs_data()`
Spectra	`n15hsqc()`, `c13hsqc()`, `tocsy()`, `generic_2d()`
Histogram	`cs_hist()`, `cs_hist2d()`, `conditional_cs_hist()`

Verify connectivity

bmrb_test()

Fetch chemical shifts from BMRB

# Fetch entry 15060 (returns nested list: dataset -> chain -> seq_id -> atom -> ppm)
cs <- cs_from_bmrb(15060)

# Preview first few residues
cs_peek(cs)

# Convert to a flat data.frame for dplyr/ggplot2 workflows
df <- cs_to_df(cs)
head(df)

Read a local NMR-STAR file

cs_file <- cs_from_file("my_protein.str")
cs_peek(cs_file)

Simulate NMR spectra

All spectrum functions return interactive Plotly plots that open in your browser.

# 1H-15N HSQC coloured by residue type
n15hsqc(bmrb_ids = 15060, legend = "residue")

# Overlay two entries with shift-tracking lines
n15hsqc(bmrb_ids = c(17076, 17077),
        legend     = "dataset",
        draw_trace = TRUE)

# 1H-13C HSQC
c13hsqc(bmrb_ids = 15060, legend = "residue")

# 1H-1H TOCSY
tocsy(bmrb_ids = 15060)

# Generic 2D: N vs CA with sequence walk
generic_2d("N", "CA", bmrb_ids = 15060, seq_walk = TRUE)

Export peak lists

pl <- create_n15hsqc_peaklist(bmrb_ids = 15060)

# CSV
export_peak_list(pl, "peaks.csv")

# Sparky format
export_peak_list(pl, "peaks.sparky", output_format = "sparky")

Chemical shift histograms

# 1D histogram for ALA CA
cs_hist("ALA", "CA")

# Compare multiple residue-atom pairs
cs_hist(list_of_atoms = c("ALA-CA", "GLY-CA", "SER-CA"))

# 2D correlation heatmap
cs_hist2d("ALA", "CA", "CB")

# Conditional distribution: N shifts of SER given CA ~ 55.5 ppm
conditional_cs_hist("SER", "N",
  filtering_rules = list(CA = 55.5),
  c_tolerance = 2.0)

Database-wide statistics

# All CA shifts for alanine (returns data.frame)
df <- get_cs_data("ALA", "CA")
summary(df$Val)

# Filter by pH and temperature
df_filtered <- get_cs_data("ALA", "CA",
  ph_min = 6.5, ph_max = 7.5,
  t_min  = 293, t_max  = 303)

# Paired CA-CB shifts (for scatter plots)
paired <- get_2d_cs_data("ALA", "CA", "CB")
plot(paired$atom1, paired$atom2,
     xlab = "CA (ppm)", ylab = "CB (ppm)")

API utilities

# Search by keyword
bmrb_search("ubiquitin human")

# Cross-reference with PDB
bmrb_pdb_ids(15000)
bmrb_from_pdb("2JM0")

# Get BibTeX citation
cat(bmrb_citation(15060, format = "text"))