MBG — Match-Between-Glycans

MBG is a lightweight, modular tool that expands glycopeptide identification in mass spectrometry-based glycoproteomics by inferring additional glycoforms at the MS1 level. It is fully integrated into FragPipe and designed to work seamlessly after an initial MSFragger-Glyco database search.

---

Overview

Protein glycosylation is one of the most important and complex post-translational modifications, regulating protein function, stability, and localization. Because glycosylation is a non-template-driven biosynthetic process, a single glycosite typically carries many co-occurring glycoforms that differ by the sequential addition or removal of monosaccharide units. This dilutes the signal of individual glycopeptides, leading low-abundance glycoforms to be missed in standard DDA database searches.

MBG addresses this by exploiting a key property of reverse-phase LC separation: glycopeptides sharing the same peptide backbone elute at nearly the same retention time (RT), regardless of their glycan. After an initial MSFragger-Glyco search provides a set of high-confidence glycopeptide identifications, MBG:

1. Generates candidate glycoforms — for each identified glycopeptide, it predicts neighboring glycoforms differing by one or more monosaccharide units (e.g., +Hex, +HexNAc, +NeuAc, +Fuc) or user-defined glycan modifications (e.g., NH₄⁺ or Fe³⁺ adducts).
2. Searches for MS1 evidence — using IonQuant, it looks for precursor signals at the expected m/z and within a narrow RT/IM window anchored to the parent glycopeptide's observed RT/IM plus a learned monosaccharide-specific shift.
3. Scores and filters candidates — a linear discriminant analysis (LDA) model with 7 features (RT/IM shift, mass error, precursor intensity, Y0/Y1 ion relative intensities, isotope envelope KL divergence, glycan shift frequency) separates targets from decoys (decoys use a +11 Da mass offset). FDR is controlled at the precursor level.

The result is an expanded PSM table containing both the original MSFragger-Glyco identifications and the new MBG-inferred glycopeptides, providing a more complete quantitative profile of glycosylation at each glycosite.

---

Key Features

• MS1-based inference — identifies glycopeptides that lack MS2 spectra or have low-quality MS2, using only precursor-level evidence.
• Learned RT/IM shifts — monosaccharide-specific RT and ion-mobility (IM) shifts are estimated from the data itself, making MBG robust across different LC gradients and instrument platforms.
• Adduct and modification recovery — can recover glycoforms bearing adducts (NH₄⁺, Fe³⁺, Na⁺) or modifications (e.g., phosphorylation for M6P glycans) without expanding the original database search space.
• Target-decoy FDR control — rigorous statistical filtering at a user-defined FDR threshold.
• FragPipe integration — runs as a one-click step inside the FragPipe glycoproteomics workflow, compatible with label-free and TMT-labeled experiments, DDA and PASEF data.

---

Workflow

Raw files
   │
   ▼
MSFragger-Glyco (database search)
   │
   ▼
PTM-Shepherd + Philosopher (PSM validation, FDR filtering)
   │
   ▼
IonQuant (quantification of identified glycopeptides)
   │
   ▼
MBG  ◄─── this tool
   │   1. Group PSMs by glycosite
   │   2. Estimate per-monosaccharide RT/IM shifts
   │   3. Generate candidate glycoforms (+/- monosaccharides, adducts)
   │   4. Search MS1 via IonQuant within RT/IM windows
   │   5. Score with LDA; apply FDR filter
   │
   ▼
Expanded PSM table (original + inferred glycopeptides)
   │
   ▼
Downstream analysis / FragPipe-Analyst

---

Performance (from the manuscript)

Dataset	Instrument	Increase in glycopeptide IDs
Fission yeast	Orbitrap Fusion	+7.5% at 1% FDR; +23.6% at 5% FDR
Human plasma (PASEF)	timsTOF HT	+14.6%
GBM (CPTAC, TMT-11)	Orbitrap Fusion Lumos	740 exclusive MBG IDs (4.6% of total)
Mouse liver (adducts)	Orbitrap Fusion	+1,234 glycopeptides incl. NH₄⁺ and Fe³⁺ adducts

Entrapment analysis on the yeast dataset showed an estimated false inference rate of 0.63%.

---

Requirements

• Java 11 or later
• FragPipe (for integrated use) — MBG is bundled as a tool within FragPipe
• IonQuant (bundled as a dependency) — used internally for MS1 feature detection
• BatMassIO (bundled) — used for reading raw spectrum files (Thermo .raw, Bruker .d, mzML, etc.)

Building from source

./gradlew jar

The output JAR will be at build/libs/MBG-<version>.jar.

---

Usage

MBG is typically run automatically within FragPipe. For standalone use:

java -jar mbg-<version>.jar --match [args]

Required arguments

Argument	Description
--psm <path>	Path to the input PSM file (FragPipe psm.tsv format)

Optional arguments

Argument	Default	Description
--manifest <path>	—	Path to the FragPipe .fp-manifest file mapping raw file names to full paths
--residuedb <path>	built-in	Path to a custom glycan residue definitions file
--glycanmoddb <path>	built-in	Path to a custom glycan modification definitions file (for adducts, etc.)
--maxq <float>	0.01	Maximum glycan q-value to accept as a high-confidence input glycoPSM
--minpsms <int>	2	Minimum number of PSMs required for a glycan to be used for RT/IM shift estimation
--minglycans <int>	2	Minimum number of distinct glycans observed per peptide to enable inference
--rttol <float>	0.4	RT tolerance (minutes) for matching inferred glycoforms
--imtol <float>	0.05	Ion mobility tolerance (V·s·cm⁻²) for PASEF data
--mztol <int>	5	Mass tolerance (ppm) for MS1 matching
--fdr <float>	0.05	FDR threshold for accepting inferred glycopeptides
--nopasef <bool>	false	Set to true for non-PASEF (Orbitrap) data
--runtmt <bool>	false	Set to true for TMT-labeled experiments
--toaddresiduals <str>	—	Comma-separated list of monosaccharides or glycan compositions to add as candidate shifts (e.g., HexNAc,Fuc,HexNAc(1)Hex(1))
--expanddb <int>	0	Number of additional rounds of iterative inference on newly found peaks
--maxskips <int>	0	Number of missed peaks allowed during iterative expansion
--numthreads <int>	4	Number of threads for parallel processing
--allowchimeric <bool>	false	Allow chimeric spectra when searching for supporting MS2

Example

java -jar mbg-0.3.7.jar --match \
    --psm /data/experiment/psm.tsv \
    --manifest /data/experiment/fragpipe.fp-manifest \
    --maxq 0.01 \
    --minpsms 1 \
    --rttol 0.4 \
    --fdr 0.05 \
    --nopasef true

---

Output

MBG appends inferred glycopeptide PSMs to the input PSM table and writes:

• Updated psm.tsv — original PSMs plus MBG-inferred entries (marked with an MBG flag in the Hyperscore or source columns)
• rt_shifts.csv / im_shifts.csv — per-monosaccharide RT and IM shift distributions (useful for QC and plotting)
• Skyline modifications file — definitions for any novel glycan compositions inferred by MBG, for import into Skyline

---

Citation

Shen J, Polasky DA, Jager S, Yu F, Heck AJR, Reiding KR, Nesvizhskii AI. Expanding Glycopeptide Identification with Match-Between-Glycans in FragPipe. Manuscript in preparation, 2026. Data: PRIDE PXD074575.

---

License

See LICENSE for details.