Here's ChatGPT's description of ADJNSAF which is a metric exported by the Abacus spectral count tool.

ADJNSAF is a label-free, spectral-count–based protein quantitation metric used in shotgun proteomics to estimate relative protein abundance. It extends the Normalized Spectral Abundance Factor (NSAF) by applying additional adjustments that improve comparability across samples, particularly in affinity-purification mass spectrometry (AP-MS) experiments.

It builds on the original NSAF concept by adding practical adjustments that improve comparability, especially in affinity-purification (AP-MS) or interactomics experiments.

Why ADJNSAF Is Needed

Raw spectral counts are biased because:

• Longer proteins generate more tryptic peptides and therefore more spectra
• Total numbers of MS/MS spectra differ between runs
• Highly abundant proteins can dominate the signal and obscure lower-abundance proteins

NSAF corrects for protein length and total spectral depth. ADJNSAF further corrects for run-to-run compositional effects and background dominance.

Core Concept

ADJNSAF represents spectral evidence for a protein, normalized by protein length and total signal, with additional scaling to improve cross-sample comparison.

ADJNSAF provides relative, not absolute, protein quantitation.

Overview of the Calculation

1. Spectral Counts

For each protein i, count the number of MS/MS spectra assigned to that protein:

Spectral counts typically include unique and shared peptides and are filtered at a fixed false discovery rate (e.g., 1% FDR).

2. Length Normalization (SAF)

To correct for protein length:

where Li is the protein length in amino acids.

3. Run-Level Normalization (NSAF)

SAF values are normalized across all proteins in the run:

NSAF values sum to 1 for each run and allow comparison of proteins within the same sample.

4. Adjustment Step (ADJNSAF)

The adjustment step implemented by Abacus:

Abacus improves spectral count quantitation by explicitly accounting for shared peptides. Instead of assigning the full spectral count of a shared peptide to every protein it maps to, Abacus distributes those spectra across proteins based on the amount of unique spectral evidence supporting each protein.

Step 1: Unique Spectral Evidence

For each protein i, Abacus first computes:

These unique spectra form the basis for weighting shared peptides.

Step 2: Weighting Shared Peptides

For a peptide p shared among N proteins, Abacus computes an adjustment factor ap,i for each protein i:

This factor represents the proportion of the shared peptide’s spectral evidence that should be attributed to protein i. Proteins with little or no unique evidence receive little or no contribution.

Step 3: Adjusted Spectral Counts

The adjusted spectral count for protein i is computed by summing:

• All spectra from peptides unique to protein i
• Weighted contributions from shared peptides using ap,i

This produces a more realistic estimate of protein abundance, especially for homologous proteins and protein families.

Step 4: NSAF Calculation Using Adjusted Counts

Abacus then computes NSAF using the adjusted spectral counts:

where Li is the protein length in amino acids.

When NSAF is calculated using adjusted spectral counts, it is commonly referred to as ADJNSAF.

Interpretation

• Higher ADJNSAF indicates higher relative protein abundance
• Values are meaningful for comparisons within and across samples
• ADJNSAF does not represent absolute protein concentration

Strengths and Limitations

Strengths

• No isotope labeling required
• Robust to missing MS1 intensity values
• Well-suited for interactomics and large protein complexes

Limitations

• Lower dynamic range than MS1 intensity–based methods
• Reduced accuracy for very low spectral counts
• Dependent on digestion efficiency and peptide detectability

Common Applications

• Affinity purification–mass spectrometry (AP-MS)
• Protein–protein interaction studies
• Semi-quantitative analysis of large proteomics datasets