Biology subjects

Garibova, L. A.

Publications and source records attributed to Garibova, L. A..

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

Study on the mechanism of action of the Pt(IV) complex with lonidamine ligands by ultrafast chemical proteomics

Platinum (II) complexes such as cisplatin among the few others are well-known and approved for clinical use as anticancer metal-based drugs. In spite of their successful and wide acceptance, the respective chemotherapy is associated with severe side effects and the ability of tumors to quickly develop resistance. To overcome these drawbacks the novel strategy is considered, which is based on the use of platinum complexes with bioactive ligands attached to act in synergy with platinum and further improve its pharmacological properties. Among the recently introduced such multi-action prodrugs is Pt(IV) complex with two lonidamine ligands, the latter selectively inhibiting hexokinase and, thus, the glycolysis in cancer cells. While platinum based multi-action prodrugs are exhibiting increased levels of activity towards cancer cells and, thus, considered as potent to overcome the resistance to cisplatin, there is a crucial need to uncover their mechanism of action by revealing all possibly affected processes and targets across the whole cellular proteomes. These are the challenging tasks in proteomics requiring high-throughput analysis of hundreds of samples for just a single drug-to-proteome system. In this work we performed these analyses for 8-azaguanine and experimental Pt(IV)-lonidamine complex applied to ovarian cancer cell line A2780, using both mechanism- and compound-centric chemical proteomics approaches based on ultrafast expression proteomics and thermal proteome profiling, respectively. Analysis of data obtained for Pt(IV)-lonidamine complex revealed regulation of proteins involved in glucose metabolic process associated with lonidamine further supporting the multi-action mechanism of this prodrug action.

pharmacology and toxicology↗

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↗