Protein SEC Analysis Workflow

step_sec_protein() creates a specification of a recipe step that provides a streamlined workflow for protein SEC analysis, combining baseline correction, aggregate quantitation, and optionally oligomer analysis in a single step.

Usage

step_sec_protein(
  recipe,
  measures = NULL,
  type = c("native", "denaturing"),
  monomer_mw = NULL,
  monomer_start = NULL,
  monomer_end = NULL,
  extinction_coef = NULL,
  aggregate_threshold = 0.001,
  baseline_method = c("linear", "median", "spline"),
  baseline_left_frac = 0.05,
  baseline_right_frac = 0.05,
  include_oligomer = NULL,
  output_prefix = "protein_",
  role = NA,
  trained = FALSE,
  skip = FALSE,
  id = recipes::rand_id("sec_protein")
)

Arguments

recipe

A recipe object.

measures

Character vector of measure columns to analyze. If NULL, analyzes all measure columns.

type

Analysis type:

• "native" (default): Native conditions (aqueous buffer, non-denaturing)

• "denaturing": Denaturing conditions (e.g., with SDS or guanidine)

monomer_mw

Expected monomer molecular weight in Da. Required for oligomer analysis if include_oligomer = TRUE.

monomer_start

Start of the monomer peak region (in location units). If NULL, automatically determined.

monomer_end

End of the monomer peak region. If NULL, automatically determined.

extinction_coef

Extinction coefficient for UV-based concentration. If NULL, signal remains in raw units.

aggregate_threshold

Minimum fraction of signal to report as aggregate/fragment. Default is 0.001 (0.1%).

baseline_method

Method for baseline correction. One of "linear" (default), "median", or "spline".

baseline_left_frac

Fraction of chromatogram start for baseline. Default is 0.05.

baseline_right_frac

Fraction of chromatogram end for baseline. Default is 0.05.

include_oligomer

Logical. Include detailed oligomer analysis? Default is TRUE if monomer_mw is provided.

output_prefix

Prefix for output columns. Default is "protein_".

role

Role for generated columns.

trained

Logical indicating if the step has been trained.

skip

Logical. Should the step be skipped when baking?

id

Unique step identifier.

Value

An updated recipe with new columns:

protein_hmws_pct

Percent high molecular weight species

protein_monomer_pct

Percent monomer

protein_lmws_pct

Percent low molecular weight species

protein_main_start

Start of main peak region

protein_main_end

End of main peak region

If include_oligomer = TRUE and monomer_mw is provided:

protein_monomer_oligo_pct

Percent monomer (from oligomer analysis)

protein_dimer_pct

Percent dimer

protein_trimer_pct

Percent trimer

protein_hmw_oligo_pct

Percent HMW oligomers

protein_lmw_oligo_pct

Percent fragments

protein_species_count

Number of detected species

Details

This step provides a convenient "one-stop" workflow for protein SEC analysis, suitable for biopharmaceutical characterization. It combines:

1. Baseline correction: SEC-optimized linear or median baseline

2. Aggregate quantitation: HMWS/monomer/LMWS percentages

3. Oligomer analysis (optional): Detailed species identification

Native vs Denaturing:

• Native: Preserves quaternary structure; use for oligomer analysis

• Denaturing: Disrupts non-covalent interactions; use for covalent aggregate detection

Regulatory Context: Aggregate analysis is critical for biopharmaceutical characterization:

• ICH Q6B requires aggregate content specification

• USP <129> provides guidance on aggregate testing

• Typical specifications: HMWS < 5%, Monomer > 95%

For More Control: For advanced analysis or custom workflows, use the individual steps:

• step_sec_baseline() for baseline correction

• step_sec_uv() for UV signal processing

• step_sec_aggregates() for HMWS/monomer/LMWS

• step_sec_oligomer() for detailed species analysis

See also

step_sec_aggregates(), step_sec_oligomer()

Other sec-protein: step_sec_aggregates(), step_sec_oligomer()

Examples

if (FALSE) { # \dontrun{
library(recipes)
library(measure)

# Basic protein SEC workflow
rec <- recipe(~., data = mab_data) |>
  step_measure_input_long(uv280, location = vars(time), col_name = "uv") |>
  step_sec_protein(monomer_mw = 150000) |>
  prep()

# Native mAb analysis with oligomer detection
rec <- recipe(~., data = mab_data) |>
  step_measure_input_long(uv280, location = vars(time), col_name = "uv") |>
  step_sec_protein(
    type = "native",
    monomer_mw = 150000,
    extinction_coef = 1.4,
    include_oligomer = TRUE
  ) |>
  prep()

# Denaturing conditions (SDS-SEC)
rec <- recipe(~., data = sds_sec_data) |>
  step_measure_input_long(uv280, location = vars(time), col_name = "uv") |>
  step_sec_protein(type = "denaturing", monomer_mw = 150000) |>
  prep()
} # }