Accelerating Site-specific Characterization of Protein Therapeutics with Novel Machine Learning Methods

PROJECT SUMMARYABSTRACT TitleAccelerating 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 enhancingprecisionprotein therapeutics and magnify the positive impact of biomedical research and education worldwideleading to a quantum leap in our understanding of the molecular and cellular pathways and mechanisms involved in healthy and diseased biological systemsThe development of biologics is bottlenecked across the entire drug development processfrom discovery to early stage candidate selectionprocess development and manufacturingdue to manual intervention in the mass spectrometry data analysis pipelineSimilarlythe proteomics research community is hindered as it moves from analysis of complex mixtures to more in depth characterization of proteins and their modificationsNovel machine learning methods will be added to MassMatrix sLC MS MS softwareproven analytical engine and visualization platform to minimize the loss of true positive peptide spectral matchesAn innovative approach for efficient accountability of experimental data at the chromatogram level will also be researcheddeveloped and addedThe latter providing for easy traceability of each peak s status in the chromatogram as soon as possiblethus providing convenient high level assessmentTogetherthese 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 communityDeeper understanding and better decision making will followboth having a potentially dramatic positive impact on downstream processes and resource deploymentincluding improved drug safety and efficacy PROJECT NARRATIVE TitleAccelerating 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 enhancingprecisionprotein therapeutics and magnify the positive impact of biomedical research and life science education worldwideDue to manual intervention in a criticalmass spectrometry data analysis pipelineprogress in drug development and protein research is bottlenecked and compromisedThrough novel machine learning methods and an innovative visualization approach for validating experimental datathe project is expected to improve the accuracy and reliability of results and alleviate the bottleneckthereby deepening understanding of proteins and biological systems and improving resource allocation through better decision makingincluding improved drug safety and efficacy