Proteomics Software Applications

𝐅𝐢𝐫𝐬𝐭 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧 𝐌𝐨𝐝𝐞𝐥 𝐟𝐨𝐫 𝐒𝐩𝐚𝐭𝐢𝐚𝐥 𝐏𝐫𝐨𝐭𝐞𝐨𝐦𝐢𝐜𝐬 Understanding where proteins are located within tissues is crucial for cancer diagnosis, drug development, and precision medicine. But analyzing these complex spatial patterns has remained largely manual and inconsistent across laboratories. Muhammad Shaban et al. developed KRONOS, a foundation model specifically designed for analyzing spatial proteomics data - imaging that maps protein expression at single-cell resolution within tissues. 𝗧𝗵𝗲 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲: Current spatial proteomics analysis typically relies on cell segmentation followed by rule-based classification. While effective for well-defined cell types, this approach struggles with complex tissue regions and treats each protein marker independently, potentially missing important spatial relationships. 𝗧𝗵𝗲 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵: KRONOS was trained using self-supervised learning on 47 million image patches from 175 protein markers across 16 tissue types and 8 imaging platforms. The model uses a Vision Transformer architecture adapted for the variable number of protein channels in multiplex imaging. 𝗞𝗲𝘆 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗙𝗶𝗻𝗱𝗶𝗻𝗴𝘀: The research identified several important architectural choices: • 𝗠𝗮𝗿𝗸𝗲𝗿 𝗲𝗻𝗰𝗼𝗱𝗶𝗻𝗴 𝗶𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁𝘀: Adding dedicated sinusoidal encoding for different protein markers yielded a large increase in balanced accuracy on Hodgkin lymphoma data • 𝗧𝗼𝗸𝗲𝗻 𝘀𝗶𝘇𝗲 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Using smaller 4×4 pixel tokens improved accuracy compared to standard 16×16 tokens, though overlapping tokens with 50% overlap achieved similar performance • 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆: Replacing image-level (CLS token) embeddings with marker-specific embeddings led to substantial performance gains 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗗𝗲𝗺𝗼𝗻𝘀𝘁𝗿𝗮𝘁𝗲𝗱: - Cell phenotyping without requiring cell segmentation - Cross-dataset generalization across different imaging platforms - Few-shot learning with limited labeled examples - Patient stratification for treatment response prediction - Tissue region classification and artifact detection This work represents a step toward more automated and scalable analysis of spatial proteomics data, which could be valuable for biomarker discovery and understanding tissue architecture in disease. paper: https://lnkd.in/eDebvrXy blog: https://lnkd.in/ePAxM7zZ code: https://lnkd.in/e7f95fZK model: https://lnkd.in/eFnJFYYy #SpatialProteomics #ComputationalBiology #MachineLearning #Biomedical #Research

Bayesian network modeling for analyzing protein dynamics Proteins are constantly moving, and these structural shifts help determine their roles in biology. Capturing the shifting conformations is critical for applications like drug development, yet the sheer amount of data produced from molecular simulations can be overwhelming. New strategies are needed to identify which interactions matter most and how they shape a protein’s overall behavior. Mukhaleva et al. introduce BaNDyT, a specialized software that employs Bayesian network modeling, an interpretable machine learning method designed to uncover probabilistic relationships in high-dimensional data. In this framework, each residue or residue pair is modeled as a node, and edges represent direct dependencies rather than mere correlations. The approach involves converting continuous simulation output into data bins, systematically searching for the best-fitting network structure, and then measuring each node’s weighted degree to highlight particularly influential contacts or regions. By filtering out redundant connections, the software effectively pinpoints functionally significant interactions buried in large-scale simulation datasets. Using this method on G protein-coupled receptor systems, the authors discovered both local and long-range interactions that drive protein dynamics. The researchers showed how BaNDyT can identify critical residues and communication pathways, even in distant parts of the structure, offering fresh insights into protein allostery. This interpretable machine learning approach lays a foundation for more nuanced studies of molecular interactions, broadening possibilities for research and therapeutic innovation. Paper: https://lnkd.in/dw6ypcaK #MachineLearning #BayesianNetworks #DataScience #ProteinDynamics #StructuralBiology #ComputationalBiology #Bioinformatics #DrugDiscovery #ComputationalChemistry #Proteomics #Pharmacology #ProteinFunction #MolecularModeling #AIforScience #Biotech

If you have an Astral MS, #FragPipe is a great proteomics data analysis platform to consider. To illustrate this, I downloaded the data from the recent Josh Coon Lab manuscript "One hour human proteome". https://lnkd.in/gjN7UaAr. The data is on ProteomeXchange: PXD049028. I first downloaded the Astral files (they also posted Ascend data), and processed using a pre-release of FragPipe 22 (it should be similar to what is currently available in v. 21). I took 6 .raw files (2Th DIA runs) for the WT vs MEGE1 gene knock-out experiment. I also downloaded 8 fractionated runs (4Th DIA) they used in the paper to build the spectral library. However, even without using these library runs I got good results (see Figure). FragPipe is easy to use. For this analysis, I 1) selected DIA_SpecLib_Quant workflow (which uses #MSFragger-DIA to identify peptides directly from DIA runs). 1) Uploaded mzML files (after converting .raw to mzML with Proteowizard) and annotated files to experiments (WT or MGME). 3) In Database Tab, I downloaded the full UniProt database (what authors used). 4) In Run tab, I clicked Run. Once the analysis was finished, I opened FragPipe-Analyst link, and uploaded the protein quantification matrix (protein_pg_matrix.tsv file) generated by DIA-NN (FragPipe uses DIA-NN for extracting quantification) and the experiment_annotation.tsv file. So, with our tools, you can really go from raw MS data to differential expression/pathway results with just a few clicks. In this dataset, I identified ~10,500 proteins (9750 genes) per file. Over 550 proteins showed differential expression in the knockout vs WT cells. Importantly, FragPipe is fast. It took me 200 min (~ 33 min per file) to process the data from mzML to quantification tables. This is faster than the "mass spec time" (i.e. what it took the authors to run the mass spec; they used a 30 min LC gradient, with a 40 min total LC time per file). I used a good but rather standard Dell Windows desktop (i9 processor with 20 cores, 120 RAM). Note I had the data on an SSD drive, which really helps (compared to using an HDD spinning drive) since the files are big and the SSD makes reading the files a lot faster. I also repeated the analysis using their Ascend data generated in parallel with the Astral data (but using a longer LC gradient). Although Ascend identified less proteins, it was still a very impressive number, and the downstream results were also very good. So, if you do not have an Astral, but have an Ascend, Exploris or another Thermo system, do not depressed. You can get great data on older instruments too. Finally, I want to state that I am not promoting Thermo MS instruments here. In FragPipe, we support Sciex and Bruker data as well. For example, we get really good results with the #diaPASEF Bruker data (and we have a new workflow for diaPASEF data available in the next FragPipe release). I am happy to see good data from any MS platform.

Unification of the analysis of bottom-up proteomics data across all major mass spectrometry acquisition methods. Proteomic data analysis has long been fragmented across different software for data-dependent acquisition (DDA), data-independent acquisition (DIA), and parallel reaction monitoring (PRM). 𝗖𝗛𝗜𝗠𝗘𝗥𝗬𝗦 𝗶𝘀 𝗮 𝘀𝗽𝗲𝗰𝘁𝗿𝘂𝗺-𝗰𝗲𝗻𝘁𝗿𝗶𝗰 𝘀𝗲𝗮𝗿𝗰𝗵 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺 𝘁𝗵𝗮𝘁 𝗱𝗲𝗰𝗼𝗻𝘃𝗼𝗹𝘂𝘁𝗲𝘀 𝗰𝗵𝗶𝗺𝗲𝗿𝗶𝗰 𝘀𝗽𝗲𝗰𝘁𝗿𝗮 𝗮𝗻𝗱 𝘂𝗻𝗶𝗳𝗶𝗲𝘀 𝗽𝗲𝗽𝘁𝗶𝗱𝗲 𝗶𝗱𝗲𝗻𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗾𝘂𝗮𝗻𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗮𝗰𝗿𝗼𝘀𝘀 𝗗𝗗𝗔, 𝗗𝗜𝗔, 𝗮𝗻𝗱 𝗣𝗥𝗠.  1. Identified over 238,000 peptide-spectrum matches (PSMs) in a 2-hour HeLa DDA dataset, exceeding the identification rate of eight leading search engines while completing analysis faster than data acquisition time.  2. Increased peptide group identifications in complex biological samples by up to 98% (acetylation-enriched samples) compared to traditional tools like Sequest HT and MSFragger.  3. Demonstrated robust quantification, achieving a Pearson correlation of 0.99 with manually curated Skyline data across five orders of magnitude of protein abundance in PRM assays.  4. Unified DDA and DIA analysis under one framework, revealing DIA quantified up to 98.7% of peptide groups across replicates, while DDA quantified 61.7% under the same conditions. Couple thoughts:  • The use of entrapment experiments across isolation window widths was cool. They confirm that CHIMERYS’ q-values closely match empirical FDR. This ensures trustworthy identifications even in highly chimeric spectra  • to broaden applicability and accommodate non-Thermo instruments, the deep-learning fragmentation models could be expanded to cover rare post-translational modifications and adopt open formats (mzML)  • could introducing a lightweight, neural-based pre-scoring step to filter unlikely peptide candidates before regression? i'm thinking benefits include shrinking problem size and improving scalability for proteome-wide libraries. Here's the awesome work: https://lnkd.in/g94Earve Congrats to Martin Frejno, Michelle Tamara Berger, Johanna Tueshaus, Daniel P. Zolg, Mathias Wilhelm, and co! I post my takes on the latest developments in health AI – 𝗰𝗼𝗻𝗻𝗲𝗰𝘁 𝘄𝗶𝘁𝗵 𝗺𝗲 𝘁𝗼 𝘀𝘁𝗮𝘆 𝘂𝗽𝗱𝗮𝘁𝗲𝗱! Also, check out my health AI blog here: https://lnkd.in/g3nrQFxW

🚀 New Preprint Alert! 🧬💻 “Rapid Development of Omics Data Analysis Applications through Vibe Coding” 👉 arXiv:2510.09804 https://lnkd.in/gUapVFH7 Building scientific software has traditionally required months or years of manual coding. In this paper, I show that modern LLMs and coding agents can build a complete proteomics data analysis app in under 10 minutes — for less than $2 — using only a few natural language prompts. ⚡ 🔹 What’s “Vibe Coding”? A conversational, iterative way of creating software by describing your goals — and letting AI do the coding, debugging, and refining in real time. 🔹 What I built: A fully functional Streamlit app for proteomics data analysis (normalization, t-tests, volcano plots, PCA, etc.) ✅ No manual coding ✅ Reproducible results across datasets ✅ Open-source and runnable locally 🧠 Beyond proteomics, vibe coding points to a future where any scientist can build domain-specific analytical tools in minutes — without needing to become a software engineer. Check out the paper ⬇️ 📄 arXiv:2510.09804 💾 Example app + data: 🔗 https://lnkd.in/gNS6Dkx4 🔗 https://lnkd.in/gY_K7bNA #AI #Bioinformatics #Proteomics #VibeCoding #LLMs #Streamlit #OpenScience #ComputationalBiology

🔬 𝗠𝗮𝘀𝘀 𝘀𝗽𝗲𝗰𝘁𝗿𝗼𝗺𝗲𝘁𝗿𝘆 𝗶𝘀 𝗻𝗼 𝗹𝗼𝗻𝗴𝗲𝗿 𝘁𝗵𝗲 𝗯𝗼𝘁𝘁𝗹𝗲𝗻𝗲𝗰𝗸 𝗶𝗻 𝗽𝗿𝗼𝘁𝗲𝗼𝗳𝗼𝗿𝗺 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵. 𝗢𝘂𝗿 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗶𝘀 🛎 Overview Proteoforms capture the true complexity of proteins from genetic variation to post-translational modifications offering a far more precise view of biology than traditional peptide centric methods. While bottom-up workflows are now routine, they struggle to map peptides back to intact proteoforms with certainty. Top-down and middle-down approaches solve this but introduce significant experimental and, more critically, analytical complexity. 📌 The Challenge Advances in mass spectrometry have outpaced the software needed to interpret the data. Unlike peptides, intact proteins bring structural variability that demands careful tuning and multi-method integration. Today’s tools remain fragmented some handle deconvolution, others identification forcing researchers into complex, multi-step workflows. This fragmentation increases expertise requirements, slows validation, and limits scalability. Even when aggregation is possible, it often relies heavily on manual intervention, impacting reproducibility. Here Carfagno et al presents Proteoform Studio comes in. Designed as an end-to-end platform, it integrates method setup, deconvolution, identification, and fragmentation data aggregation into a single automated workflow. By combining complementary fragmentation strategies and optimizing ion matching, it enables high-confidence characterization with minimal manual input achieving >80% sequence coverage for antibody subunits in middle-down workflows 🎯 Summary: By consolidating the analytical pipeline into a unified, automated environment, Proteoform Studio addresses a critical gap. Its ability to integrate complementary data, apply sliding window deconvolution, and align automated outputs with validation standards reduces both analysis time and technical barriers. The impact goes beyond efficiency. It moves proteoform research toward routine, high-throughput intact protein characterization. For biopharma, that means more precise mapping of therapeutic proteoforms and ultimately, better control and understanding of protein-based therapies. #Proteomics #MassSpectrometry #Biopharma #LifeSciences #Innovation