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

项目摘要/摘要 标题：用新的机器学习方法加速蛋白质治疗的位点特异性表征 该项目旨在提高可靠性，加快救生和增强生命的发展， 精准，蛋白质疗法，放大生物医学研究和教育的积极影响 在世界范围内，导致了我们对分子和细胞途径和 涉及健康和疾病生物系统的机制。生物制品的发展是 整个药物开发过程的瓶颈，从发现到早期候选人选择， 过程开发和制造，由于人工干预了质谱学数据分析 输油管道。同样，蛋白质组学研究社区也受到阻碍，因为它从复杂的分析转向 对蛋白质及其修饰进行更深入的表征。 新的机器学习方法将添加到MassMatrix(LC-MS/MS软件)经过验证的分析引擎中 和可视化平台，最大限度地减少真阳性肽谱匹配的损失。创新的 还将研究在色谱层对实验数据进行有效问责的方法， 开发和添加。后者提供了对色谱图中每个峰的状态的容易追踪性 尽快，从而提供方便的高水平评估。这些目标加在一起，预计将 提高结果的可靠性和准确性，并显著减少质量分析的瓶颈 制药业和研究界。更深入的理解和更好的决策 都将对下游流程和资源产生潜在的积极影响 部署，包括提高药物安全性和有效性。

PROJECT SUMMARY / ABSTRACT 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.