Automating Deep Characterization of Biotherapeutic Proteins
News Jun 19, 2014
As protein-based drugs become more important in treating major diseases, producers face the critical challenge of deeply characterizing these highly-complex molecules for efficacy and safety, as well as to meet quality requirements. Now there’s a new software tool designed to increase the speed, quality and confidence in comprehensive biotherapeutic protein characterization useful for drug development and production quality control.
Thermo Scientific PepFinder software is designed to streamline relative and absolute quantitation, and identification of proteins from biological samples using liquid chromatography-mass spectrometry.
Therapeutic proteins such as monoclonal antibodies, interferons, and insulin must be produced in biologically-active forms that have proper folding and have specific post-translational modificiations (PTM). Traditional analysis of these complex biological molecules is highly complex and time-consuming. PepFinder software uses multidimensional dynamic search capabilities to automatically integrate complex data into concise reports.
“This is a new paradigm for peptide mapping data analysis,” said Ken Miller, vice president, marketing mass spectrometry for Thermo Fisher Scientific. “Customers can expect to reduce analysis time from weeks to hours in some cases, while generating high-confidence, comprehensive characterization reports, including known and unknown modifications.”
“Using PepFinder, a complex protein was analyzed with great details (including PTMs), and with great ease and speed!” said Shiaw-Lin (Billy) Wu, Head of Analytical Sciences at BioAnalytix and Research Associate Professor at Northeastern University.
PepFinder software, based on Mass Analyzer software licensed from Amgen, is compatible with Thermo Scientific LC-MS instruments, including the industry-leading high resolution, accurate-mass Orbitrap-based systems. This software is designed for fast, high-quality PTM profiles and peptide identifications based on a novel prediction algorithm. The software displays sequence coverage maps, automates relative changes in site specific PTMs for rapid monitoring of modifications in a single sample or across sample comparison. The software is written to provide an automated workflow for glycopeptides ID, disulfide bond mapping and other common PTM’s, including oxidation, deamidation and phosphorylation. This can eliminate time consuming and error-prone manual PTM mapping.
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