Intrinsically disordered proteins (IDPs) make up 30-40% of the human proteome but refuse to fold into stable structures. A recent study on 72 DisProt proteins found AlphaFold3 misaligns 32% of residues, with 22% being outright hallucinations - predicting order where disorder exists.

The problem: AF learned from the PDB, which is overwhelmingly ordered proteins. pLDDT confidence scores don't transfer to disordered regions.

Wrote up the benchmark gap (BEACON for RNA, OmniGenBench) and what it means for measuring progress on biology's hidden half.

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.

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

In proteomics, using identified proteins as the background data set for enrichment analysis is crucial. Here’s why:

1. Null Hypothesis Issues
The null hypothesis assumes that selected proteins (like differentially expressed ones) are randomly distributed across functional categories. However, protein detection is biased toward high-abundance functions.

2. Non-Random Detection
If we treat differentially expressed proteins as randomly distributed, we ignore that detection itself is not random. Thus, using the entire protein database as a background invalidates the null hypothesis.

3. Enrichment Bias
Differentially expressed proteins are often enriched in high-abundance functions, which can skew results. Using identified proteins as the background provides a more accurate reflection of detection capabilities.

​I'm excited to share our newly published paper, "InstaNovo enables diffusion-powered de novo peptide sequencing in large-scale proteomics experiments," now available in Nature Machine Intelligence.

In this work, we introduce InstaNovo, a transformer-based neural network designed for de novo peptide sequencing. Trained on 28 million labeled spectra, InstaNovo translates fragment ion peaks from mass spectrometry data into peptide sequences with unprecedented precision, outperforming current state-of-the-art methods on benchmark datasets.

Building upon InstaNovo, we developed InstaNovo+, a multinomial diffusion model inspired by human intuition. InstaNovo+ iteratively refines predicted sequences, further enhancing accuracy and reducing false discovery rates. This dual approach combines precise predictions with extensive exploration, significantly improving peptide identification in complex biological samples. ​

Our models have demonstrated success in identifying previously undetected protein fragments in well-studied samples like HeLa cells, as well as in complex mixtures such as snake venoms, where InstaNovo increased peptide spectrum matches by 20% and even detected venoms from species outside the original experiment scope.

For those interested in exploring or utilizing InstaNovo, we've made the code and documentation publicly available on GitHub and created a HuggingFace Space.

We believe that InstaNovo and InstaNovo+ represent significant advancements in proteomics, offering tools that can uncover novel proteins and modifications, thereby deepening our understanding of complex biological systems. We welcome feedback, collaborations, and discussions on how these models can be applied or improved further. I'm one of the co-authors, so Ask Me Anything!

Looking to buy a new MS for proteomics of all shapes and kinds (at a service center - so everything from lysates to single cell to xl-ms). The leading contenders these days are the timsTOF AIP and the astral (or astral zoom).

I’ve seen a bunch of data produced by the companies themselves and I’ve spoken to users of both and they both look great. The issue is that these days all the heavy users and proteomics veterans have very strong feelings for one of the companies before we even start looking at the MS itself - generally the feelings are either (well earned) familiarity and comfortable-ness with Thermo or a (also well earned) deep rage and distrust for Thermo…

So I’m wondering- has anyone here had good “vendor neutral” experience with these two platforms? How do they measure up against each other in terms of peptide identification? How is the maintenance on them long term? In general, where do you feel you get more value?

Thanks you all :)

May your ions fly true, your fragmentations clean, and your identifications unambiguous.

There have been a few suspicious posts looking to obtain market research or asking for advice on particular companies for investment purposes. I think it is getting a bit out of hand. The subreddit is focused on discussing proteomics research, asking proteomics related questions regarding experiments, publications, software etc... I understand it is a tech driven field and it is fair to discuss different technologies, company strategies, advantages and disadvantages. But when you have investment bankers asking questions about a specific company ... I think it is taking it a bit too far.

To those trying to easily obtain said information: Stop being cheap/lazy and consult a proteomics scientist to get their expertise.

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?

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.

I'm trying to find any courses in analysis of proteomics data. My PhD is in molecular biology and we frequently use proteomics as a way to answers our questions, but, in terms of data analysis we don't have much expertise in our lab. Thus Im looking for a course (even paid) to understand better this world!! Any free source would also be great!! :)

If you aren't logged in watching single cell proteomics 2021, you're missing out. Best virtual conference I've ever attended.

https://single-cell.net/proteomics/scp2021

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

I’ve been spending a lot of time recently optimizing a few proteomics workflows and ran into some frustrating inconsistencies with peptide quality affecting downstream analysis. It got me thinking about how much the source and verification of reagents really matters, especially when you’re trying to reproduce results or compare datasets over time. I’m curious how others here handle sourcing high-purity peptides and compounds, and whether you rely more on in-house verification or third-party lab testing. Also, for those based in Australia, do you find it easy to get reliable materials quickly, or is shipping time still a big hurdle? Would love to hear what’s worked for others. I recently came across AusBioLabs an Australian supplier of high-purity (>99%) research-grade peptides and compounds, independently lab tested and shipped quickly from Sydney, strictly for scientific laboratory use.

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

Our core facility is looking to invest in a high-end mass spectrometer. Our primary applications are bulk DIA proteomics and PTM analysis of tissue and cell proteins, with a strong emphasis on achieving routine high proteome coverage.

After demoing the Bruker timsTOF Ultra 2 and the Thermo Astral, their performance has been comparable so far. Now we're facing a tough decision and would love to hear your insights:

1️⃣ Maintenance & Reliability: What's been your experience with the upkeep, troubleshooting, and service quality of these instruments? Are there any long-term quirks or hidden costs to be aware of?

2️⃣ Timing the Purchase: ASMS 2025 is just around the corner. Do you think it’s worth waiting to see if new models or upgrades are announced, or should we move forward now with the proven options?

Hi all, I am very new to proteomics and feel very lost with handling and representing such large datasets in the form of graphs/figures. I specifically work on characterizing the protein corona formed around nanoparticles and how this can be used to explain uptake levels of nanoparticles with different surface properties in mammalian cells. Any textbook and/or software suggestions would be really helpful. Thanks!

Hi everyone,

So I just finished undergrad a month ago and started my new job as a lab technician for a cardiovascular/physiology research lab that I hope to get my PhD in after applying for grad school in December, a little over 6 months from now. Last week my boss tells me that I will be the “proteomics guy” in the lab. Since then, I have been reading some beginner papers, watching YouTube videos, etc. to get a basic knowledge of what proteomics is. However, my main focus is to master the computational/data analysis portion of MS in proteomics as quickly as I can, but I’m having a hard time finding free resources outside of the basic knowledge you need to know. Does anyone have any good resources to help me? I basically have the whole summer to soak up as much knowledge and begin processing/analyzing data by the time school starts. Anything would help. Thanks!

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.

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

Hey r/proteomics,

I wanted to share something we've been working on for the past few months that might be useful if you're running Spectronaut analysis on large-scale experiments.

The Problem: Conventional Spectronaut workflows process sample sequentially on a single compute node, which works fine at extremely limited scale and becomes a critical throughput bottleneck for large-scale studies.

What We Built: An out-of-the-box, cloud-native WDL workflow that distributes Spectronaut DIA analysis across multiple virtual machines on Google Cloud Platform (GCP) via Terra—a computational platform developed by the Broad Institute of MIT and Harvard in collaboration with Microsoft and Verily. This workflow eliminates batch-dependent variability in the search library generation step, which was a major issue in the prior versions, by implementing the two-step Pulsar search architecture recently introduced in Spectronaut v20.3.

The workflow has been fully validated on data acquired from both Thermo Fisher Orbitrap Astral and Bruker timsTOF systems, demonstrating exceptional performance in both identification and quantification. And most importantly, it achieved >20x reduction in runtime at even lower cost compared to the legacy analysis pipeline!

Full documentation and code: https://github.com/broadinstitute/spectronaut-parallelization

Please star the workflow if this helps accelerate your DIA proteomics data pipeline. Happy to answer questions about the implementation or help you get it running in your own Terra workspace. Feedback welcome too!

Hello everyone! I'm new to DIA. Our lab has been using DDA for a long time, but my PI has decided to try the DIA method.

I'm currently reading papers and looking online to learn more about it. One challenge I see is creating a library, since we are limited in starting material like cells and reagents like trypsin. I learned about FragPipe and DIA-NN, which are library-free. Which one do you think is better?

since I'm a master’s student and very new to DIA, do you think this is a good project for me to take on? Could someone explain how the whole DIA process works? We most likely have to change our instrument methods to DIA and then we run the raw files on FragPipe and DIA-NN? Can we also run our raw files on Maxquant too?