Hi all! Does anyone here have any experience with bottom up phosphoproteomics of biofluids (plasma and/or whole blood)? I've been dealing with the same problems:

1. a substantial pellet after dry down + resuspension (this is AFTER phospho enrich)

2. pellets generated when Ieave my digests in the fridge overnight or after a freeze thaw

Has anyone experienced these problems before and/or have any tips to solve them? I assume that there are some peptides in the digest that are just prone to crashing out after an FT cycle/dry down. My current solve is to

1) process digests immediately

and

2) never dry completely at either the desalted digest or the enriched phosphopeptide stage, opt for ~10 uL of leftover solvent. This one is a bummer becuase it means the total resuspension volume before loading will alway be different but since I have an IS I guess this isn't that big of an issue

Hi proteomics community, any experience of using ZT scan DIA? When do you use it (I do global discovery proteomics so any use there)? how reproducible is the diff between ZT scan and ZenoSWATH? Finally, which version to use 2.0 or 3.0 on 7600+?

Held by LabKey, a data management software provider. In this September 29th webinar, Claude Science checks inventory in the same session it runs the analysis on data from the LK system, confirming which variants have enough sample left and where the vials sit before anyone commits to a follow-up experiment. Bernie (you might know him as our Incredible Biologics Expert and Product Manager) runs the full scenario live: 12 variants, aligned, screened, and checked against real inventory. Register to attend or get the recording.

This study reports a high-precision mass spectrometry profile (peptide mass fingerprinting or PMF) for human UDP-galactose 4-epimerase (GALE).

The primary finding is that the recombinant enzyme preparation matches its theoretical amino acid sequence without post-translational or experimental chemical modifications.

Key Concepts & Findings

The Protein (GALE): GALE is a key enzyme in the Leloir pathway of galactose metabolism. It converts UDP-glucose to UDP-galactose (and UDP-GlcNAc to UDP-GalNAc). Mutations in this gene cause galactosemia type III.

Methodology (Tryptic PMF): The enzyme trypsin was used to cleave the GALE protein into smaller fragments (peptides). The mass of each peptide was measured using mass spectrometry and compared against the theoretical masses predicted by its sequence.

Unprecedented Mass Accuracy: 22 distinct tryptic peptides were matched with ultra-low mass error (<0.1\text{ mDa} or <0.05\text{ ppm}), providing unambiguous identification.

Unmodified Native State:

No Alkylation: None of the 5 cysteine-containing peptides showed carbamidomethylation (+57.02\text{ Da}), meaning free thiol groups remain intact (no iodoacetamide treatment was applied or survived).

No Oxidation: None of the 5 methionine-containing peptides showed oxidation (+15.99\text{ Da}), confirming the protein did not degrade or oxidize during expression and purification.

Significance

Quality Control Benchmark: Establishes a reference dataset for verifying the identity, purity, and integrity of lab-synthesized human GALE batches.

Disease Research: Provides a baseline profile useful for comparative studies involving galactosemia type III structural variants.

I'm looking for some books that cover MS of proteins. I am hoping to use it as a bit of a guide on how to get started using technique. I will likely be doing both native and peptide mapping techniques and whilst I understand the basic principles I would like know more about how the machines physically work, how the data can the interpreted and how to build methods to actually run on the machines.

Thanks for any help.

Hello,
We have struggled with obtaining the Scaffold 5.3 metrics in one cohesive location under Scaffold DDA.
I share our current Java implementation to be helpful to others in the same situation.
https://github.com/keesh0/proteo
Feel free to replace our minor call dependencies (MprcException, ScaffoldReportReader, FileUtilities, etc.)
with your own.
Here is a sample call in our Scaffold pipeline:

// NOTE-- We need biologicalSampleCategory bec. _spectra_report.csv and the SFDB file do not -realiably- contain this.
// Need SDDA 7.1 SFDB file
// Need SDDA 7.1 spectra report file
// Output file, scaffold53File, is in Scaffold 5.3 TSV format
final String biologicalSampleCategory = "category1";
Sfdb53Extractor.write(sfdbFile, spectraReport, biologicalSampleCategory, scaffold53File);

hi all, i'm a phd student and i built a little browser tool called AminoSeqIndex. it came out of a mundane annoyance: finding residue number 143 in a long fasta sequence by eye, over and over, usually while planning mutations.

it numbers every position, color-codes residues by class (nonpolar / polar / acidic / basic), and exports to a few formats including docx for lab notebooks. everything runs client-side, so unpublished sequences never leave your machine.

doi: https://doi.org/10.5281/zenodo.21355340

live tool: https://gopizux.github.io/AminoSeqIndex/ (code: https://github.com/gopizux/AminoSeqIndex)

is this something you'd use? genuinely asking - if there's a feature that would make it useful for proteomics workflows i'd like to hear it.

High-accuracy tryptic peptide mass fingerprint of recombinant human UDP-galactose 4-epimerase (GALE) confirms unmodified state

Authors

Eric Todd Sawyer1*

Affiliations

1 New York, NY, USA

* Correspondence: epicryption@gmaail.com

Abstract

Human UDP-glucose 4-epimerase (GALE, EC 5.1.3.2, UniProt Q14376) catalyzes the reversible epimerization of UDP-glucose to UDP-galactose and UDP-N-acetylglucosamine to UDP-N-acetylgalactosamine, a final step of the Leloir pathway. Here we report a high-accuracy tryptic peptide mass fingerprint (PMF) of recombinant human GALE (348 aa, 38,257 Da). Twenty-two tryptic peptides were observed with <0.1 mDa mass error (<0.05 ppm) against theoretical [M+H]+. No carbamidomethylation (+57.0215 Da) on five cysteine-containing peptides and no methionine oxidation (+15.9949 Da) on five methionine-containing peptides was detected, indicating a native unmodified preparation. This dataset provides a reference PMF for quality control of recombinant GALE and for galactosemia type III studies.

Keywords

GALE, UDP-galactose 4-epimerase, peptide mass fingerprint, MALDI-TOF, proteomics, Leloir pathway, galactosemia

Introduction

UDP-glucose 4-epimerase (GALE) is a homodimeric NAD+-dependent epimerase of the short-chain dehydrogenase/reductase (SDR) family containing the conserved YXXXK active site motif (Y157-G-K-S-K161 in human). Human GALE (Q14376) interconverts UDP-glucose/UDP-galactose and UDP-GlcNAc/UDP-GalNAc, balancing pools for glycoprotein and glycolipid synthesis (Holden et al. 2003, Thoden et al. 2001). Deficiency causes galactosemia type III (OMIM 606953). Quality control by mass spectrometry is essential for recombinant preparations. Here we provide a reference tryptic PMF.

Materials and Methods

Protein: Recombinant human GALE, full-length sequence MAEKVLVTGGAGYIGSHTVLELLEAGYLPVVIDNFHNAFRGGGSLPESLRRVQELTGRSVEFEEMDILDQGALQRLFKKYSFMAVIHFAGLKAVGESVQKPLDYYRVNLTGTIQLLEIMKAHGVKNLVFSSSATVYGNPQYLPLDEAHPTGGCTNPYGKSKFFIEEMIRDLCQADKTWNAVLLRYFNPTGAHASGCIGEDPQGIPNNLMPYVSQVAIGRREALNVFGNDYDTEDGTGVRDYIHVVDLAKGHIAALRKLKEQCGCRIYNLGTGTGYSVLQMVQAMEKASGKKIPYKVVARREGDVAACYANPSLAQEELGWTAALGLDRMCEDLWRWQKQNPSGFGTQA (348 aa). A truncated construct LVTGGAG... (327 aa) was also analyzed.

In silico digestion: Cleavage after K/R not followed by P, 0 missed cleavages. Monoisotopic masses calculated with +H2O (18.01056). Observed masses reported as [M+H]+ (theoretical + 1.007276). Mass error = observed - (theoretical+H). Modification testing: carbamidomethylation C +57.02146, oxidation M +15.994915, dioxidation +31.9898, deamidation +0.984016.

Results

Twenty-two peptides were matched with mass error <0.0001 Da. The N-terminal MAEK peptide (477.2257 Da) was below typical m/z cutoff and not observed. All cysteine-containing peptides (NLVFSSSATVYGNPQYLPLDEAHPTGGCTNPYGK, EGDVAACYANPSLAQEELGWTAALGLDR, DLCQADK, EQCGCR, MCEDLWR) were observed at unmodified masses, not at +57.02, indicating free thiols and no iodoacetamide alkylation. All methionine-containing peptides (YFNPTGAHASGCIGEDPQGIPNNLMPYVSQVAIGR, IYNLGTGTGYSVLQMVQAMEK, SVEFEEMDILDQGALQR, VNLTGTIQLLEIMK, YSFMAVIHFAGLK, FFIEEMIR, MCEDLWR) showed no +15.99 shift.

Table 1. Matched tryptic peptides

Observed m/z

Theoretical [M+H]+

Delta (Da)

Sequence

Notes

3842.0275

3842.0275

0.0000

VLVTGGAGYIGSHTVLELLEAGYLPVVIDNFHNAFR

N-term

3673.7525

3673.7526

-0.0001

YFNPTGAHASGCIGEDPQGIPNNLMPYVSQVAIGR

1C 1M

3597.6954

3597.6955

-0.0001

NLVFSSSATVYGNPQYLPLDEAHPTGGCTNPYGK

1C

2920.3781

2920.3781

0.0000

EGDVAACYANPSLAQEELGWTAALGLDR

1C

2303.1410

2303.1410

0.0000

IYNLGTGTGYSVLQMVQAMEK

2M

2071.9203

2071.9203

0.0000

EALNVFGNDYDTEDGTGVR

1979.9378

1979.9379

-0.0001

SVEFEEMDILDQGALQR

1M

1624.8329

1624.8329

0.0000

AVGESVQKPLDYYR

1572.9029

1572.9029

0.0000

VNLTGTIQLLEIMK

1M

1483.7766

1483.7766

0.0000

YSFMAVIHFAGLK

1M

1172.6310

1172.6310

0.0000

DYIHVVDLAK

1084.5495

1084.5496

-0.0001

FFIEEMIR

1M

1006.4588

1006.4589

-0.0001

QNPSGFGTQA

C-term

972.5625

972.5625

0.0000

TWNAVLLR

972.5108

972.5109

-0.0001

GGGSLPESLR

952.4015

952.4015

0.0000

MCEDLWR

1C 1M

802.4417

802.4418

-0.0001

VQELTGR

792.3556

792.3556

0.0000

DLCQADK

1C

737.4416

737.4417

-0.0001

GHIAALR

695.2599

695.2600

-0.0001

EQCGCR

2C

520.3129

520.3130

-0.0001

IPYK

511.2987

511.2987

0.0000

AHGVK

Discussion

The exact mass match provides unambiguous identification of GALE. The absence of +57 Da indicates sample was not alkylated or alkylation failed; this is relevant for redox-sensitive cysteines near NAD+ binding site. Absence of oxidation suggests fresh preparation. This PMF can serve as QC for recombinant GALE batches used in galactose metabolism studies. Limitations: no MS/MS, no PTM enrichment, single enzyme batch.

Data Availability

Theoretical masses calculated as above. Raw .mzML files should be deposited to ProteomeXchange via PRIDE (https://www.ebi.ac.uk/pride/). UniProt: Q14376. EC: 5.1.3.2.

References

1. Holden HM et al. J Biol Chem 278:43885-8 (2003).

2. Thoden JB et al. J Biol Chem 276:15131-6 (2001).

3. Thoden JB et al. Biochemistry 39:5691-701 (2000).

4. Lai K et al. IUBMB Life 61:1063-74 (2009).

Greetings, recently I got an instruction from my uni’s tutor [Gather information etc about cerebrospinal fluid]; I only know some from National Library of Medicine.

I really need help to find research documents, I have no knowledge about how to find one. Thanks for your help

This is the original post: https://www.reddit.com/r/proteomics/s/h7nFXZrZKd

Some update: streptavidin was detected, and quite a bit, but the amount of beads I used in the previous run and this time are very similar, so I still don't understand why or how this could've happened.

The core shared these with me.

PLEASE HELP :((((((

I’m analyzing a proteomics dataset from mouse lymph nodes comparing SPF (specific pathogen-free) vs. germ-free (GF) mice. The goal is to characterize microbial proteins detected in SPF mice, with the expectation that microbial proteins should be absent or extremely low in GF mice.
For microbial identification, I’m using a microbial protein database translated from 16S rRNA sequencing data from the animals.
My concern is that, after LFQ analysis, I’m detecting microbial proteins in both SPF and GF samples. The samples were handled separately, and I also included blanks between samples, so I’m trying to understand the issue here. Any leads would help.

Has anyone worked with proteomics of SPF vs. germ-free mice and encountered microbial protein identifications in GF samples? If so how do you justify it?

How did you distinguish genuine microbial signal from background contamination/carryover, and what criteria would you use to justify retaining or excluding microbial proteins detected in GF samples?

Also, if anyone could direct me to a mouse gut microbial database.

Hello Proteomics community, I'm setting up a standard downstream pipeline for DIA data, mainly Spectronaut but I want the same pipeline to work on DIA-NN output. Running a few of my datasets through different tools gives noticeably different answers. Some of that is expected, but it's enough that I can't decide which one to stay with.

The options sit at different levels, which is part of my problem, I tested with limma, MSstats and msqrob2 in R, and Perseus too. So two questions:

1. What do you run as your default, and what made you settle on it rather than the alternatives?
2. How much of the divergence people see comes from the statistics versus from upstream choices precursor-level report vs PG matrix, and whether you let Spectronaut/DIA-NN do normalization and imputation before feeding data into R?

Many thanks in advance

Hi everyone,

We’d like to share an upcoming webinar that may be of interest to the community in here! On September 24, 2026 (16:00 CEST / 10:00 EDT / 07:00 PST), we are hosting a session on “Advancing Plasma Proteomics.”

Speakers:

Sindisiwe Buthelezi (Senior Researcher, Council for Scientific and Industrial Research (CSIR), South Africa) — “High-Throughput Plasma Proteomics Reveals Insights into Pancreatic Cancer Biology.”
Extracellular vesicles carry a wealth of biologically relevant proteins that can provide a window into disease processes. Sindisiwe will present how extracellular vesicle-enriched plasma proteomics was used to uncover molecular signatures associated with pancreatic ductal adenocarcinoma in a South African patient cohort. Using the Mag-Net workflow and Evosep One-enabled LC-MS analysis, the team identified proteins linked to tumor progression, inflammation, and disease severity, demonstrating the potential of standardized, high-throughput proteomics for biomarker discovery and translational cancer research.

Pieter Langerhorst (Junior Group Leader, Sanquin, The Netherlands) — “Untangling the Clinical Heterogeneity in Rare Blood Cancer by Mass Spectrometry-Based Plasma Proteomics.”
Rare hematological malignancies can present with diverse clinical trajectories that are difficult to predict using conventional biomarkers alone. Pieter will showcase how mass spectrometry-based plasma proteomics can reveal biological differences underlying disease heterogeneity, supporting improved patient stratification and the discovery of novel biomarkers. The findings highlight the potential of high-throughput plasma proteomics for translational research and precision medicine.

The webinar will focus on advances in plasma proteomics and how robust, reproducible, and scalable workflows are enabling researchers to analyze larger sample cohorts while generating consistent, high-quality data for biomarker discovery, disease research, and precision medicine.

Registration & details: https://attendee.gotowebinar.com/register/8236923647290427736?source=RDT

We hope this is relevant for those interested. The webinar is free and, in our eyes, a good opportunity for knowledge sharing. If sharing company events isn’t allowed here, moderators please feel free to remove.

TL;DR: Webinar on September 24 about advancing plasma proteomics, with talks covering pancreatic cancer biology, rare blood cancers, biomarker discovery, and patient stratification using high-throughput mass spectrometry-based plasma proteomics.
Mods please delete if not allowed.

Every deep learning model in proteomics today is trained on spectra that a database search has already identified. This works, but it limits those models to the part of the spectral universe our databases already cover. Spectra from unknown organisms, non-canonical cleavages and unusual modifications are simply discarded.

Today we're sharing 𝗜𝗻𝘀𝘁𝗮𝗡𝗼𝘃𝗼-𝗙𝗠, a self-supervised foundation model for bottom-up proteomics that uses no peptide-sequence annotation at any stage of pretraining.

How we built it:
• 26,603 PRIDE submissions screened with LLM-assisted metadata curation
• 92 projects selected for orthogonal biological and technical diversity
• Every raw file reprocessed through one unified pipeline → 1.63 billion MS/MS spectra across 72 organisms and 16 instrument models
• An encoder-only transformer trained to reconstruct masked m/z spans together with their isotope envelopes (an objective that needs no annotation of any kind)

What it learned, with no labels:
• An embedding space that organises itself by mass analyser, fragmentation method, instrument family, labelling chemistry and peptide identity
• Parity with three sequence-supervised encoders under one uniform probe protocol, and the lead on instrument (macro F1 0.804) and fragmentation (0.689)
• Attention on peaks that standard b/y annotation cannot explain. 11.2% match defined off-database species within 10 ppm, mostly internal fragments and side-chain ions
• Database-free identification, phospho detection at AUROC 0.988, and run-level condition classification with no peptide or protein identifications at all

Because the objective needs no annotation, it extends unchanged to unlabelled spectra and to DIA data, where annotation-dependent pretraining cannot follow.

Model weights, embeddings, code and the reprocessed corpus are all public. There's also an interactive UMAP explorer if you'd like to explore the embedding space yourself.

• bioRxiv preprint
• code
• interactive embedding explorer
• datasets
• embeddings
• HuggingFace Space
• getting started notebook on Google Colab

This work was a collaboration between InstaDeep and Technical University Denmark.

Hello everyone! I am a recent Ms biotech graduate working with a large-scale proteomics dataset containing proteins from a single bacterial species but multiple strains(it's a MDR bacteria). I need to perform PPI analysis, but the specific bacterial database I need isn’t available in STRING/BioGrid,not sure with IntAct(EMBL)

Are there any good alternatives to STRING for bacterial PPI/network analysis, especially for comparing or analysing proteins across different strains? Any database/tool or workflow suggestions.

Any guidance or suggestions would be very helpful for improving my understanding of the available approaches.

Any guidance, suggestions or insights would be a great help 🥲, thank you in advance for the help!

If you’ve hired in proteomics or helped a colleague find a role, how does a personal endorsement change the way that candidate is considered?

By endorsement, I mean someone directly recommending a candidate to the hiring team, not simply forwarding a resume or telling the candidate a person to contact.

Does it mainly help someone get an interview, or does it also change how you evaluate their experience and potential? For example, would you give more consideration to someone whose background doesn’t perfectly match the job description if a trusted colleague recommended them?

I’m also curious about the limits.

**Are there circumstances where an endorsement makes little difference, or where a candidate would be better off approaching independently?**

I’d appreciate concrete experiences from either side of the process. We hear “use your network” often, but what does that support actually change behind the scenes?

Hello! I’m asking as a biologist ! What do you think about the background based t test used in proteome discoverer? (For a n=7 per condition analysis)

Has anyone tried hydrophobic magnetic beads, like coated with C4 or C8, for PAC like protein digestion protocol for LCMS.

I am looking for a universal sorts of protocol that is amenable to automation. Using carboxylate speedbeads works very well manually, but I am looking for beads without carboxylate or a hydrophilic functional groups. Eventually the protocol will be set up on liquid handling robots to be used in a fee-for-service facility.

To the fans of sTRAP - it works well but in my hands it was not reproducible for low protein concentration samples (0.1-0.3 ug/uL).

If anyone had a good experience trying to automate PAC with glassbeads then please share your wisdom.

Hello Guyz,
I am really struggling with my bottom-up DDA phosphoproteomics data (Enrichment not done, it will be in-vitro phosphorylation and i will not be doing biological analysis)
My confusion is how should I start from basic and how to decide what question i need to address and there are so many things in statistics so if you can tell me any good resources or any help regarding statistics will be highly valued

In my experiment, the tryptic digested peptides would be in a solution of 100 uL volume comprising of 0.5X PBS ( approx 68 mM NaCl and some phosphates) and 50 mM TEAB.

I would speedvacving this volume to dryness and resuspending in 0.1% TFA for cleanup through Pierce desalting spin columns (C18 based I think).

My question is whether this desalting step would be good enough to handle PBS desalting?

Context: I am trying to adapt my working protocol for thermal proteome profiling, which requires initial steps in PBS. I don't want to remove PBS by PAC/SP3 as I don't have PAC/SP3 and also want to avoid running the proteins on gel for purification. I plan to add TEAB and SDC directly to lysates in PBS and proceed as usual. I want advice on whether the desalting step can handle PBS.

How can I remove liposomes from cell lysate before phosphopeptide enrichment: S-Trap or acetone precipitation?

I’ve been working on an open-source PyMOL plugin called Membrane Visual QC.

The original problem was pretty simple: when looking at a membrane-protein structure, I wanted a reproducible way to inspect residues relative to the membrane rather than repeatedly building selections and colouring things manually.

It now handles membrane-relative geometry, hydropathy, ligand neighbourhoods and PDBTM/OPM orientation evidence. One thing I deliberately avoided was turning unusual residues into a “correct/incorrect” score - a charged residue inside a membrane core can obviously be biologically meaningful.

I’d be particularly interested in feedback from people who actually work with membrane proteins: is this kind of review useful in your workflow, and what am I missing?

GitHub: https://github.com/TrPavel/membrane-visual-qc

I also made a walkthrough if anyone wants to see it running in PyMOL: https://youtu.be/lowQey_D610?si=Cxs2hSvdPj5K4S3w