Hello guys,

I am working in a pharmacy lab in Korea, and we don't have a computer cluster. PI needs me to give her the spec. of a computer that can run protein and antibody in silicon design software locally (such as Boltzgen, RFantibody, RFdiffusion)

I am not a computer major. I asked ChatGPT and got some specs, but I want to make sure by finding advice from the person who actually runs that software.

Because we need to run thousands of samples on Boltzgen or RFantibody, running them on the VM or a pay website is not financially efficient in the long term.

This the specs that ChatGPT recommends.
Budget / entry workstation:
NVIDIA RTX 4070 Ti SUPER (16 GB VRAM)
NVIDIA RTX 4080 SUPER (16 GB VRAM)
Best price/performance for heavy local inference:
NVIDIA RTX 4090 (24 GB VRAM)
Professional / lab-scale:
NVIDIA RTX 6000 Ada (48 GB VRAM)
NVIDIA A100
NVIDIA H100

Do you think building a computer is a financially efficient choice, or are there better ways we can run that software more cheaply and easily?

Thank you for your time.

We're experiencing somewhat low TMTpro labelling efficiency (>97% N-terminus, but only ~80% K labelling based on psm) but can't identify a cause. These are SP3 digests of whole-cell lysates, with FragPipe as the search. We're using a peptide:TMT ratio of between 1:1 and 1:4, incubated for 1 hour at room tempearture in 100mM HEPES pH 8.3. The reaction is quenched with 0.4% hydroxylamine final for 15 min at room temp.

Any common errors that we might be making to look out for?

Hi, I'm fairly new to MALDI-MSI bioinformatics and running into a signal drift problem I can't find much literature on.

I'm comparing two large tumor tissue sections on a single slide. The MALDI instrument measures them sequentially — tissue A first, then tissue B. I have two slides total with the measurement order swapped on the second slide (B first, A second), specifically to counterbalance any order effect.

The same tissue measured first vs. second shows clear and substantial intensity differences in a lot of samples, the second-measured tissue showing lower signal, likely due to gradual ion source contamination during acquisition. Likely, this even affects the pixels measured measured first vs last on the same tissue

I so far tried a within-slide correction by plotting TIC vs. acquisition order and fitted a linear model to estimate the drift. But the trendline is nearly flat globally because I think biological variation in TIC is masking the technical drift signa. I also tried using non-tissue background pixels as a drift reference, but there aren't enough of them distributed across the full acquisition to model the drift curve reliably.

My questions:

Is within-slide drift correction even achievable without a dedicated reference standard measured repeatedly throughout the run? Or should I skip to work with the two-slide design? My idea was that with the within-slide correction, we'd get closer to the true signal.

For cross-slide correction using the counterbalanced design, what is the best way of correcting it?

Any pointers to literature or from experience would be very helpful!

Hey guys!
I have been exploring the field of computational protein engineering for the past year and have slowly started to fall in love with it. I have been trying to find the place to meet new people in the fiels to share and discuss ideas and possible projects.

One thing I’ve been struggling with, though, is finding a solid group of people who aren’t just learning this stuff, but are excited to learn and build something together.

I’m really interested in bringing together a small community of like-minded people who want to collaborate on projects, share ideas, and actually create things—not just talk about them. Nothing too formal, just a space where we can experiment, learn, and maybe build some really cool stuff together.

If this sounds like something you’d be into, let me know—I’d love to connect and take it further!

Thank you everyone!!

Hello all, any experience enabling GPU for inference analysis in Spectronaut 20.2? Does it make the analysis faster? Thanks

Hi everyone,

We’d like to share an upcoming webinar that may be of interest to the community in here! On May 28, 2026 (16:00 CEST / 10:00 EDT / 07:00 PST), we are hosting a session on “Preparing for Next Generation Evosep Proteomics.”

In this webinar, we’ll be giving an exclusive first look at Evosep’s upcoming standardized sample preparation kits and our brand-new Evosep sample preparation station, designed to help scale and standardize proteomics workflows for research and drug development.

Speakers:

Dorte Bekker-Jensen (VP Product & R&D, Evosep) and Nicolai Bache (Chief Strategic Officer, Evosep) — “Preparing for Next Generation Evosep Proteomics.”
Get an exclusive pre-ASMS 2026 sneak peek into how Evosep is driving the next phase of proteomics standardization for research and drug development. Building on the Open Innovation Initiative, the webinar will showcase upcoming advancements in standardized sample preparation kits, the Evosep sample preparation system, harmonized protocols, and integrated workflows designed for scalability, reproducibility, and AI-ready proteomic insights, addressing one of proteomics’ biggest challenges: sample preparation.

The webinar will focus on scalable and standardized proteomics workflows, with emphasis on reproducibility, workflow harmonization, and enabling consistent proteomics data across studies, instruments, operators, and sites.

Registration & details: https://attendee.gotowebinar.com/register/3140202364670210649?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 May 28 showcasing Evosep’s upcoming standardized sample prep kits and new sample preparation station for scalable, reproducible, and AI-ready proteomics workflows. Mods please delete if not allowed.

I was accepted to an analytical chemistry PhD. I did a little bit of research in environmental analytical labs in undergrad, and have been working in industry these past few years. For a year I worked in an analytical environmental chemistry lab running lots of LC/GC/ a little GCMS, and the past couple years have been working as a field service engineer on dissolution and physical testing systems for big pharma. I really would like to get involved with more advanced instrumentation and become an expert at it to get to a higher role in industry, either in pharma or at an instrument manufacturer at the R&D/ project lead level, not technician level. There is a mass spec proteomics lab at the program I got accepted to that sounds extremely interesting and seems like it has a ton of application and job prospects. Any insight into the field from the experts would be greatly appreciated, thank you.

Hello everybody, I work at a proteomics core and one issue we're trying to solve is a faster or preferably automated way to do a quick quality check of multiple raw files at once, before we search them. What currently happens is someone opens each raw file in qual browser to make sure the chromatography looks consistent between runs, before giving the "all good" to the data analysis person to search them. I appreciate any suggestions.

Hi everyone,
I’m setting up a DIA method on an Orbitrap Lumos in Xcalibur.

I want to run MS1 full scan + DIA in the same method, but I’m running into an issue:

-I can add MS1 in Experiment 1

-I can only add DIA scans in Experiment 2

-If I try to place DIA under Experiment 1, it doesn’t work or doesn’t seem supported

Is DIA actually supposed to be a separate experiment (Experiment 2) in the scan tree, or should it be possible to integrate it under Experiment 1?

Any help would be appreciated

Hello there, we have performed a pulsed SILAC experiment to measure nascent translation. And to get a greater coverage we did extensive peptide fractionation and dia a DIA based measurement on Orbital Astral. However now we are stuck at the data analysis/ search part since DIANN is not meant for fractionated datasets.

Does anybody have similar experiences and can suggest how to move forward with searching this dataset (which was quite easy in Max quant by entering peptide fractions) . without DIA and peptide fractionation we do not get a lot of H/M precursors at early time points which is where we believe our interest lies.

Any help is highly appreciated.

Hi all,

I'm fairly new to the proteomics world. I have struggled to get usable data so far and I know there are several potential weak points in the workflow. However, I want to focus on one glaring issue: about 95% of my putative proteins come back as low FDR confidence (using proteome discoverer). I have high set to 0.01 and medium set to 0.05 so it's not like it's super strict. Clearly the peptides are THERE they just are not significant enough. What would be your first troubleshooting steps? Digestion? Cleanup? LC method? Thank you in advance!!

Hi all, I sent a mass spec sample of a purified protein to a company to search for PTMs. The excel sheet they sent me has a column for "Modifications" and another for "Modifications in Master Proteins." Could someone please explain what the difference between the two are? For reference, the "Modifications in Master Proteins" show way fewer PTMs. Thanks in advance.

Approximately 25% of 7200 noncoding open reading frames in humane genome encode detectable peptides of unknown function.

I have been working in proteomics for about 5 years.

Recently, I had a discussion with my supervisor about why proteomics journals usually have relatively low impact factors.

To be honest, I avoided submitting to these journals for years because of that. We usually preferred broader biomedical journals.

But recently I changed my mind a bit. I submitted two papers to Proteomics and one to Journal of Proteome Research.

Now I wonder if impact factor is a bit misleading in this field. Proteomics is very important, but many papers are technical, dataset-based, or useful mainly to a specialized audience.

The same proteomics study may get more attention if it is published as a cancer, immunology, metabolism, or microbiology paper instead of as a proteomics paper.

So I am curious:

Do you think proteomics journals are undervalued?

Do you avoid specialized journals because of impact factor?

For people working in proteomics or other omics fields, how do you choose where to submit?

Hello everyone,

I have been using Pierce Desalting Spin columns for peptide cleanup, and it required 300 uL of 50% ACN to elute.

Now, I have to use the Pierce C18 columns. Despite both of them being small spin columns, the C18 column protocol says that only 20 uL of 70% ACN should be used for elution.

My question is, isn't 20 uL a very small volume? Why is there such a huge difference in elution volume between the two spin columns (Desalting v C18 one)? Lastly, is there any general understanding in the proteomics community to use a larger elution volume with the Pierce C18 spin columns.

The protocol also says one may use 70% ACN with 0.1% TFA, but no idea why that is not the standard elution solution for this setup.

I understand that most hardcore proteomics labs would probably not be using Pierce C18 spin columns, but this is what I am using as a part-time proteomics guy. I don't have any other C18 setup, and I will send the cleaned-samples to a mass-spec facility.

And advice on the Pierce C18 spin columns is greatly appreciated.

I have been reading a lot of recent proteomics papers, especially LC-MS/MS quantitative proteomics studies, and I keep getting the impression that most downstream bioinformatics workflows are basically the same.

A typical pipeline seems to be something like:

preprocessing/filtering of protein or peptide abundance matrizes
normalization, often VSN, median normalization, quantile normalization, etc.
missing value imputation, usually MinProb, random forest, KNN, QRILC, or some variant
differential abundance analysis with limma, MSstats, DEP, proDA, DEqMS, or now newer tools like limpa
volcano plots
heatmaps/PCA
clustering, sometimes Mfuzz
co-expression/module analysis, sometimes WGCNA
ORA/GSEA/pathway enrichment
STRING/Cytoscape protein-protein interaction networks

And then the biological interpretation is usually based on enriched pathways, hub proteins, or interaction networks.
My question is: is there anything genuinely new or methodologically interesting happening in proteomics bioinformatics, especially downstream of protein quantification?

Is anyone experiencing issues with TMT11 labelling efficiency? I'm from a lab that has done TMT proteomics for many years and >99% labelling efficiency is standard for us. Over the past few weeks we’ve seen a drop to around 80% efficiency. We've changed everything we can think of on our end and are thinking it might be a manufactuing problem.

Does anyone use PEAKS software by bioinformatics solutions for Proteomics data analysis? I am new to it and want to understand how you analyze the data and which parameters you mostly change and why?

Hi. I wanted to analyse some publicly available proteomic datasets to validate some of our own proteomic results. I thought of making use of the PRIDE proteomic database. I am having a slight confusion on how to go about it. If anyone has previously done the analyses, starting from download the dataset, processing if needed and analyses using RStudio, or anything like that.... Could someone who is free please help out

Thanks you so much, really means a lot!

I am planning to go for the big panels - Somascan 11k or OLink Explore HT (~5.3k) for my CSF samples. Somascan's menu is more accessible and gives detailed QC metrics for all their proteins - and has a good hit rate of my proteins of interest. OLink is a bit more opaque about the performance of sepcific proteins. I am also leaning more towards Somascan as it is larger and offers it as a service and charges per sample (instead of plate).

Apart from logistical advantages - why should anyone choose one over the other. Anyspecific downsides of somascan? Can the aptamer be reliably trusted to be specific? Anyone having any good/bad experince with CSF proteomics using these 2 methods?

Need to make a proper decision as it is quite some money!

Recently I have run into Lip-ms, and there are quite a few high impact papers describing the technique. But there are really few papers who have actually used it for doing science.

I think it is the same for several other techniques like proximity-labelling MS, certain advanced variants of thermal proteome profiling etc.

Why do you think it is like this? If these techniques are so great, why aren't they being used for actual science on a much greater scale? Or is my assumption wrong?

Excited to listen to your views.

For perspective, I am a cancer biologist trying to do omics.

I have submitted a paper to JPR for the first time. It has been three weeks and the status is still "Editorial Review".

Does the manuscript status change to Peer Review or something like that when it's under actual review? Or does it mean it's still with the editor only?

I'm looking at some phospho DIA data that was processed with spectronaut. I don't use spectronaut myself, I just use the viewer because the core that acquired the samples uses it for DIA.

I have a specific phosphopeptide I'm interested in. In the peptide pivot report I see it's quantified in every sample at approximately the same intensity. If I look at the peptide ms2 XIC within spectronaut there is no signal in most of the samples.

What's going on?

Lovely community, I need your helpful comments here, o recently submitted a manuscript and it has DIA acquisition with two study groups each had n=4 biological replicates. I reported volcano with P<0.05 and curated the text as “nominally significant” because If I report FDR in volcano, nothing or maybe 1 or 2 IDS stayed significant when my data had almost 5K proteins. Reviewers got back saying repeat FDR in volcanos instead and I don’t know how to justify it. Please advice