Biology subjects

Postoenko, V. I.

Publications and source records attributed to Postoenko, V. I..

3 recordsLinked to original sources

bioRxiv2026

msallsearchpy: Universal Search Engine with Enhanced Performance for High-Flow Quantitative LC-MS Proteomics

Recent studies have demonstrated that the flow rates of a few ul/min can be a more efficient alternative to nanoLC-based proteome analysis. We present a novel search engine, msallsearchpy, applicable to data acquired using all existing proteome-wide acquisition methods including DIA, DDA and MS1-only, with enhanced efficiency for higher flow rate analysis. For the data acquired using Orbitrap FTMS at the flow rates of up to 400 ul/min, msallsearchpy demonstrates significantly improved identification and quantification performance compared with the state-of-the-art DDA and DIA alternatives. As one of the striking examples, msallsearchpy provides 2000 protein identifications for the DDA data acquired for 20 ng proteomic samples and flow rate of 50 uL/min, compared with the none by popular search engines. The results provide further support to the currently floating ideas in quantitative proteomics regarding the use of high-flow rates as a more efficient alternative to nanoLC.

bioinformatics↗

bioRxiv2024

A modified decision tree improves generalization across multiple brains proteomic data sets and reveals the role of ferroptosis in Alzheimer disease

Low generalization to the patient cohort and variety of experimental conditions in the proteomic search for disease biomarkers are among the main reasons for the bumpy road of quantitative proteomics from discovery stage to clinical validation. Only a small fraction of biomarkers discovered so far by proteomic analysis reaches clinical trials. Here, we presented a machine learning-based workflow for proteomics data analysis, which partially solves some of these issues. In particular, we used a customized decision tree model, which was regulated using a newly introduced parameter, min_cohorts_leaf, that resulted in better generalization of trained models. Further, we analyzed the trend of feature importances curve as a function of min_cohorts_leaf parameter and found that it could be used for accurate feature selection to obtain a list of proteins with significantly improved generalization. Finally, we demonstrated that the recently introduced DirectMS1 search algorithm for protein identification and quantitation provides a simple, yet, a highly efficient solution for the problem of combining multiple data sets obtained using different experimental settings. The developed workflow was tested using five published LC-MS/MS data sets obtained in the large consortia studies of Alzheimers disease brain samples. The selected data sets consist of 535 files in total analyzed using label-free single-shot data-dependent or data-independent acquisitions. Using the proposed modified ExtraTrees model we found that the expressions of two proteins involved in ferroptosis Serotransferrin TRFE and DNA repair nuclease/redox regulator APEX1, are important for explaining a lack of dementia for patients with the presence of neuritic plaques and neurofibrillary tangles.

molecular biology↗

bioRxiv2023

IQMMA: an efficient MS1 intensity extraction using multiple feature detection algorithms for DDA proteomics

One of the key steps in data dependent acquisition (DDA) proteomics is detection of peptide isotopic clusters, also called < >, in MS1 spectra and matching them to MS/MS-based peptide identifications. A number of peptide feature detection tools became available in recent years each relying on its own matching algorithm. Here, we provide an integrated solution, Intensity-based Quantitative Mix and Match Approach (IQMMA), which integrates a number of peptide feature detection algorithms and returns the most probable intensity values for the MS/MS-based identifications. IQMMA was tested using available proteomic data acquired for both well-characterized (ground truth) and real-world biological samples, including a mix of E. Coli digest spiked at different concentrations into the HeLa digest used as a background and a set of glioblastoma cell lines. Three open-source feature detection algorithms were integrated: Dinosaur, biosaur2 and OpenMS FeatureFinder. Neither of them was found optimal when applied individually to all the datasets employed in this work, however, their combined use in IQMMA improved efficiency of subsequent protein quantitation. The software implementing IQMMA is freely available at https://github.com/PostoenkoVI/IQMMA under Apache 2.0 license.

bioinformatics↗