r/proteomics

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!

I am not a biostatician and would love to understand. My project deals with looking at comparing samples from 2 different groups (say one with hot dogs and one without hot dogs). My biostatician sent me the volcano group and I am able to see which proteins are downregulated and those that upregulated. He attached a table with the fold change. However, when I look at the volcano plot, the x axis is log2 fold change, with y axis as pvalue. From my understanding, semantics wise utilizing log2 fold change is usually how represent differential expression. However, when I do the equation for log2 fold change some of the proteins will change to negative values. What does this mean? This does not make sense as in my volcano plots, these proteins are definitely placed in the appropriate side (downregulated vs upregulated).

For example Protein A listed as upregulated; with fold change 0.9, but log2 fold change is -0.11. Does that mean this protein A is actually downregulated? I also have vice versa where protein B is listed as downregulated; with fold change say 1, with log2 fold change as -0.06. Does that mean protein B is actually upregulated?

Thank you for your time!

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!

What if the probability of a site-specific mutation is provided by a language model? Could this requirement be met using current large-scale protein models—such as ESM, ProteinMPNN, or RFdiffusion—instead of relying on traditional computational methods?

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.

Hi everyone,

my PI suggested, mainly to save time, that I could buy individually recombinant proteins and try to reconstitute a heterotrimeric protein complex in vitro for a DSF/thermal shift assay, instead of co-expressing and co-purifying the complex.

I’m a bit skeptical because of potential issues with tags, buffers, stoichiometry, stability, and whether the complex would actually form and be homogeneous enough to give interpretable data. The goal would be to test small-molecule stabilizers.

Has anyone successfully done this with commercial recombinant proteins? Did it work well enough for DSF, SEC, SPR, or similar assays? Any practical advice, experience, or opinions would be very helpful.

Thanks!

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.

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?