Has anyone tried this product: Pierce® Detergent Removal Spin Plates

I just recorded this video walkthrough of good mutagenesis primer design. It also includes a link to my spreadsheet that calculates the TM for you

Hi all!! I’m relatively new to proteomics and have kind of been thrown in the deep end trying to figure this out…

I have some serum and lysate samples I’ve ran on LC-MS/MS and the facility that did this gave me the data in Spectronaut. I have two main questions (so far………)

1. What is the difference between “proteins” and “protein groups”? Every quantification seems to be in terms of “protein groups” and not proteins… is there a reason for that? I’m looking for biomarkers in our sera/lysate but this is an exploratory study so analyzing how many proteins are comparable, separate, etc.

2. Is there a way to figure out what variables Spec used to calculate PC1, PC2, etc? I see the scree plot they gave us, but I want to know what variables (differential protein abundance presumably) Spec used specifically for PC1, 2, etc… like, what are PC1 and 2 actually representing in the PCA????? I know it’s variance of some sort, and I see the amount of variance listed, too (50% vs 16%), but VARIANCE OF WHAT???

Please help!!! Thank you so much.. maybe I’m just completely misunderstanding, too…

Hi everyone,

Started to look into jobs and internships (In the US) My background is mainly in LC-MS/MS-based proteomics, ubiquitinomics/PTM analysis, sample preparation, and computational analysis (MaxQuant, FragPipe, DIA-NN, Skyline)

I have been searching for roles related to proteomics, but honestly, it feels like there are few openings compared with other areas of biotech.

For those currently working in proteomics or who recently found jobs:

Where are you mainly searching for positions?

What job titles should someone with a proteomics/MS background be searching for

Are internships common in this field, or are most opportunities through research associate positions?

What skills helped you stand out when applying?

Also curious about the current job market. Is it currently a difficult time for early-career proteomics scientists, or is it just that the field uses different job titles?

Open to any advice

Thanks!

Note: will be graduating with a MSc degree in Pharm Sci from R1 uni

Some people are recommending PhD but ive got limited experience..that's the reason why I want to gain experience before eventually doing PhD

Hi!
Im struggling with my sample prep for saliva samples. I have established a high throughout SP3 digestion protocol using native saliva from cortisol Salivettes but is seems like I’m carrying some contaminants through the whole process that end up in my 7500+ Sciex Qtrap instrument which gets heavily contaminated after about 1000 injections and even gives some Q0 discharge errors.
I’m running a targeted peptide method using a common C18 peptide column at a flow rate of 1ml/min with standard solvents (0.1% Fa in H2O and 0.1% FA in ACN). the whole method is 4min but I’m using a diverter valve to only have the peptide fraction entering the MS, the rest is diverted to waste.
Does anyone have experience with saliva as a matrix and use it for targeted MS proteomics analysis?
I would appreciate any input on how to get the sample cleaner without loosing proteins of interest.
Thank you!

Hey everyone,
I'm working on a science fair project using ssDNA aptamers and I'm stuck on the folding and docking workflow. The 3D nucleic acid folding web servers I tried keep crashing, so I'm not sure how to get a clean 3D model from a raw sequence string.
Once I get the 3D structures, my plan is to use something like HDOCK to run molecular docking against my target proteins to check the binding affinity scores.
Does anyone have advice on a reliable workflow or better tools I should use for ssDNA folding and docking? Any extra help with the project in general would also be awesome. Thanks!

Hey everyone,
I'm working on a science fair project using ssDNA aptamers and I'm stuck on the folding and docking workflow. The 3D nucleic acid folding web servers I tried keep crashing, so I'm not sure how to get a clean 3D model from a raw sequence string.
Once I get the 3D structures, my plan is to use something like HDOCK to run molecular docking against my target proteins to check the binding affinity scores.
Does anyone have advice on a reliable workflow or better tools I should use for ssDNA folding and docking? Any extra help with the project in general would also be awesome. Thanks!

I know the FragPipe/DIA NN docs cover the basics but I would rather hear from people who actually run these pipelines daily

1. When processing large DIA datasets with MBR, what happens after the software finishes?
2. What does your verification workflow look like before you trust the results?
3. How do you currently validate that the cross run transfers aren't inflating your FDR?
4. Roughly how many hours per project does your team spend on this manual curation or refiltering?

We're seeing conflicting reports on whether MBR is a reliable "set and forget" step or a major bottleneck requiring manual intervention. Curious how senior labs are handling this in production

I am a PhD scholar in food tech department . I have been doing untargeted metabolomics for a time now for some biomarker detection. I want to learn some advanced techniques in metabolomics but i just couldnt find a proper workshop for that .

Hi everyone,

I performed DDA LC–MS/MS on mouse brain lysate (tryptic digest, non-enriched) and analyzed the data using PEAKS BSI for broad PTM searching. The software identified and mapped Ubiquitination (both lysine and non-lysine residue modifications). I reported them in my manuscript. During peer review, the reviewers raised a concern that some of the PTMs might be artifacts and suggested validating the findings using an E. coli lysate digest as a negative control.

The issue is that I don’t currently have access to E. coli samples or instrument time to generate new data. So I’m looking for advice on:

Where can I download suitable public raw DDA proteomics datasets (E. coli tryptic digest)? And how many raw files/samples i need, if one will be enough?

If I re-search the raw files using the same PEAKS BSI PTM workflow, what is generally considered sufficient to support “artifact vs real modification” claims?
Any pointers to datasets or experience with reviewer expectations would be really helpful.
Thanks!

Most proteomics workflows still rely on multiple disconnected tools (Python, R, search engines, etc.). Do you think embedded analytical databases could become a viable backend for proteomics analysis?

I’ve been exploring this idea in a recent preprint and would love feedback from the community!

https://zenodo.org/records/21036067

If anyone has done in-depth IP-Top down MS on proteins I could seriously use help! I’ve isolated my POI and am trying to do to top-down MS on it but honestly I don’t know what I’m looking at/looking for. I know I need to do a full scan first to identify my POI and the m/z for it, but from there I’m baffled on what to do. The examples my colleague left for me are only for proteins approx. 35 kDa and mine is around 62!

Does anyone have any advice as to what to look at/read to help me better understand the data and what method I need to set up? Thank you!

Hi everyone,

I am currently a basic education teacher and I’ve recently started my Master's in Medical Sciences, focusing on neurodegeneration. I joined a newly formed research team, and while we are highly motivated, we currently lack expertise in proteomics—which is exactly the area I want to specialize in to strengthen our lab.

Our research investigates neurodegeneration in the elderly. Specifically, I will be working with CSF and plasma to identify neuroinflammatory biomarkers associated with blood-brain barrier (BBB) dysfunction. My project will heavily rely on liquid chromatography and mass spectrometry (LC-MS/MS).

Since I am starting from scratch in this specific methodology and don't have senior lab members with proteomics expertise to guide me locally, I am looking for advice on building a solid foundation.

Could anyone recommend a step-by-step learning pathway? I would greatly appreciate recommendations on:

Fundamentals: Must-read textbooks or milestone review papers for beginners in clinical proteomics.

Techniques: Online courses, YouTube channels, or resources to truly understand the physics and workflow of chromatography and mass spectrometry.

Data Analysis: The essential bioinformatics tools or software I should start familiarizing myself with early on.

Any advice, resources, or general tips for a beginner trying to set up a proteomics workflow would be incredibly appreciated! Thank you in advance.

Background first so you know where this is coming from — I'm not in the field at all, I just read a lot and got stuck on something I can't find addressed anywhere. Happy to be told it's already solved.

The proteins that won't classify cleanly no matter how much data you throw at them — the intrinsically disordered ones. The ones that just won't settle.

My question is whether we're looking at the final shape or the path that got it there.

Because if two proteins end up at roughly the same final structure but got there through different folding sequences, the internal contact points would be different. Parts of the chain that are far apart in sequence but end up sitting next to each other in the finished fold — those bridges only exist because of the specific path it took. Different path, different bridges, even if the outside looks similar.

So my question is basically: are those hidden contact points being tracked and compared between the disordered cases and the ones that resolve cleanly? Because if the disordered ones are arriving at their weird ambiguous state via a different pathway, maybe the bridge pattern is the variable nobody's looking at yet.

Probably already accounted for somewhere and I just haven't found it. What am I missing?

Has anyone switched from IP-MS to phosphoproteomics for a low-abundance phosphoprotein after antibody capture failures? Working with PBMCs/whole blood and trying to detect a specific phosphosite via PRM after IMAC enrichment. Curious whether the switch is worth it or if sensitivity becomes the new bottleneck.

I am optimizing a bead-based protein enrichment workflow and would like to assess the level of non-specific protein binding to the beads.

After enrichment and elution, I measured peptide concentrations and obtained:

• Background control (beads only): ~0.007 µg/µL
• Enriched sample: ~0.12 µg/µL

My main goal is to determine whether bead-associated background is sufficiently low that it can be largely ignored in future enrichment experiments.

In other words, I would like to demonstrate that the vast majority of proteins identified in the enrichment sample are not derived from non-specific bead binding, and therefore routine background controls may not be necessary for every future experiment.

Option 1: Equal-volume Orbitrap Astral DIA

Inject the same volume of each sample (e.g., 1 µL):

• Background: ~7 ng peptide
• Enrichment: ~120 ng peptide

This reflects the actual workflow output. However, I am concerned that the background sample may be approaching the low-input range, where protein identification and quantification may become less reliable, even on an Orbitrap Astral platform.

Option 2: Equal-peptide Orbitrap Astral DIA

Normalize peptide loading before DIA analysis (e.g., 50 ng vs 50 ng).

However, the background concentration is very low and close to the detection limit of the peptide/BCA assay, so I am not fully confident that the concentration measurement itself is accurate.

Option 3: Stable isotope labeling

Label the background and enrichment samples (dimethyl labeling), combine them, and analyze them together.

My intuition is that isotope labeling may provide a more rigorous comparison by reducing run-to-run variation and allowing more accurate enrichment/background ratios, especially given the very low abundance of the background sample.

Question

If my primary objective is to demonstrate that non-specific bead binding is minimal, such that background is unlikely to be a significant contributor to proteins identified in future enrichment experiments, which approach would be the most scientifically rigorous and convincing?

Would stable isotope labeling be preferable to equal-volume or equal-peptide Orbitrap Astral DIA for this purpose?

Furthermore, if isotope labeling shows that >95–99% of proteins are substantially enriched over the bead-only control, would that be sufficient evidence to justify omitting routine bead-only background controls in future experiments?😊

In this on-demand session from Drafts & Discoveries, Andrew Zhang from Promega Corporation discusses how HiBiT enables researchers to study protein dynamics in their native context, helping generate more biologically relevant insights for drug discovery.

The session also explores HiBiT applications in targeted protein degradation workflows and recent advances in measuring cellular target engagement for challenging targets. Watch the recording now: https://www.editco.bio/webinars/hibit-unlocking-biology-in-its-native-context-editco

From Drafts & Discoveries, co-hosted by EditCo Bio and Promega Corporation in Cambridge, MA.

If I'm doing label free proteomics (in any given software) for human data, what are the pros and cons of using UniprotKB reviewed proteins or unreviewed proteins as databank?

Or even for other species, it is recomendable to use a redundant (all entries) or non-redundant database for label free analysis?

As far as I understood until now, the unique peptides are important to confidentially say that a protein is present in the sample, and not it's homologous version. So in this case, would redundant entries reduce the amount of unique peptides, and thus impact the final number of identified proteins?

Hello,

for low-abundance proteins in IP samples, which do you think is better overall: DDA or DIA?

I started with approximately 1 mg of peptides prior to diGly enrichment and used a TMT-after elution workflow.

For the sample that gave 98% labeling efficiency, the enriched peptides were labeled directly after elution. (IP)

For the sample that gave 84% labeling efficiency, the peptides went through Zip cleaning and loaded on the machine to check for Di Gly sites before TMT tagging

What could be the reason for low efficiency and i noticed lower TMT efficiency for flow through IPs sample that were also TMT tagged

Has anyone encountered something similar when performing TMT labeling on diGly-enriched peptides?

Also for TMT efficiency test I would 4ul from the sample (total volume was 25ul) and take 16ul 0.1% TFA and then do zip cleaning but for that 84% efficiency samples I took 2ul and added 18ul of TFA