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

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

Thrilled to see papers coming out using #FragPipe with #diaPASEF data. Attached preprint is one great example in the #chemoproteomics space. I know a lot more on the way, including from this amazing group at Stanford. Just to remind folks, #diaTracer in FragPipe allows deconvolution of diaPASEF .d files, generating pseudo-MS/MS spectra. These DDA-like files can be searched using #MSFragger in any way you want, allowing direct DIA analysis including different #PTMs (biological like #phosphorylation or chemical labels like IA-DBT etc.) or semi-tryptic searches (really recommended for #plasma/#CSF proteomics datasets) or nonspecific (e.g. HLA #immunopeptidome) searches. You can also build #hybrid #libraries (DDA plus direct DIA). Importantly, conversion from .d to Diatracer.mzML files can be done as soon as the mass spec run is finished. So if you run a large-scale analysis with many runs, start the conversion as soon as the files are available, and when the data aquision is finished you already have the files converted (so the rest of the analysis takes less time). As anyone in my lab can tell you, I am the alpha tester of all our tools! When I get a break from paper writing or grants or flying to some foreign lands, I personally run our tools on all sorts of recently published studies I find on ProteomeXchange. Just recently I tested diaPASEF data from studies on Protac degrades, peptidomics, ABPP reactive cystein profiling, everything interesting I can find really. So every workflow in FragPipe has been thoroughly tested by my awsome lab and most by me personally. And while we are on the topic of diaPASEF data, I am also super excited about our FragPipe 23 release coming out around May 1. Pre-release versions are of course always available to our collaborators and early adopters. There will be improved diaTracer (much cleaner data), site-level PTM reports for DIA and diaPASEF data, improved integration with #Skyline, and so much more. Ok, I better stop stressing my finger and my eyes typing this on iPhone in the dark since I need to get some rest before this plane lands in London where I have some busy 10 days including watching a football game at Wembley with my family (hopefully we can still get tickets), scientific advisory board meetings, and the ABPP2025 conference. More about it later. So, finally, here is the link to the paper that used FragPipe to process diaPASEF data - the paper that got me started to write this post - the post that seem to stray away a bit from where it started… https://lnkd.in/guSi9f7H