Title: Accelerating Site-specific Characterization of Protein Therapeutics with Novel Machine Learning Methods The project seeks to improve reliability and speed up the development of life-saving and life-enhancing, precision, protein therapeutics and magnify the positive impact of biomedical research and education worldwide, leading to a quantum leap in our understanding of the molecular and cellular pathways and mechanisms involved in healthy and diseased biological systems. The development of biologics is bottlenecked across the entire drug development process, from discovery to early stage candidate selection, process development and manufacturing, due to manual intervention in the mass spectrometry data analysis pipeline. Similarly, the proteomics research community is hindered as it moves from analysis of complex mixtures to more in-depth characterization of proteins and their modifications. Novel machine learning methods will be added to MassMatrix?s (LC-MS/MS software) proven analytical engine and visualization platform to minimize the loss of true positive peptide spectral matches. An innovative approach for efficient accountability of experimental data at the chromatogram level will also be researched, developed and added. The latter providing for easy traceability of each peak?s status in the chromatogram as soon as possible, thus providing convenient high-level assessment. Together, these aims are expected to improve the reliability and accuracy of results as well as to significantly reduce the mass spec bottleneck for the pharmaceutical industry and the research community. Deeper understanding and better decision making will follow, both having a potentially dramatic positive impact on downstream processes and resource deployment, including improved drug safety and efficacy.
Title: Accelerating Site-specific Characterization of Protein Therapeutics with Novel Machine Learning Methods The project seeks to speed up and improve reliability in the development of life-saving and life-enhancing, precision, protein therapeutics and magnify the positive impact of biomedical research and life science education worldwide. Due to manual intervention in a critical, mass spectrometry data analysis pipeline, progress in drug development and protein research is bottlenecked and compromised. Through novel machine learning methods and an innovative visualization approach for validating experimental data, the project is expected to improve the accuracy and reliability of results and alleviate the bottleneck, thereby deepening understanding of proteins and biological systems and improving resource allocation through better decision making, including improved drug safety and efficacy. 1
