Calculation of the Protein Turnover Rate Using the Number of Incorporated 2H Atoms and Proteomics Analysis of a Single Labeled Sample

Rate constant estimation with heavy water requires a long-term experiment with data collection at multiple time points (3–4 weeks for mitochondrial proteome dynamics in mice and much longer in other species). When tissue proteins are analyzed, this approach requires euthanizing animals at each time point or multiple tissue biopsies in humans. Although short-term protocols are available, they require knowledge of the maximum number of isotope labels (N) and accurate quantification of observed 2H-enrichment in the peptide. The high-resolution accurate mass spectrometers used for proteome dynamics studies are characterized by a systematic spectral error that compromises these measurements. To circumvent these issues, we developed a simple algorithm for the rate constant calculation based on a single labeled sample and comparable unlabeled (time 0) sample. The algorithm determines N for all proteogenic amino acids from a long-term experiment to calculate the predicted plateau 2H-labeling of peptides for a short-term protocol and estimates the rate constant based on the measured baseline and the predicted plateau 2H-labeling of peptides. The method was validated based on the rate constant estimation in a long-term experiment in mice and dogs. The improved 2 time-point method enables the rate constant calculation with less than 10% relative error compared to the bench-marked multi-point method in mice and dogs and allows us to detect diet-induced subtle changes in ApoAI turnover in mice. In conclusion, we have developed and validated a new algorithm for protein rate constant calculation based on 2-time point measurements that could also be applied to other biomolecules.

采用重水开展速率常数估算，需实施长期实验并于多个时间点采集数据（小鼠线粒体蛋白质组动力学研究需3~4周，其他物种所需周期更长）。若对组织蛋白质进行分析，该方法需在每个时间点处死实验动物，或对人类受试者实施多次组织活检。尽管已有可用的短期实验方案，但此类方案需明确同位素标记的最大数量（N），并精准定量肽段中观测到的氘（²H）富集水平。用于蛋白质组动力学研究的高分辨精准质谱仪存在系统性光谱误差，会对上述测量造成干扰。为规避上述问题，本研究开发了一种简易算法，基于单标记样本与匹配的未标记（时间0）样本完成速率常数计算。该算法先通过长期实验确定所有蛋白源性氨基酸的N值，以此计算短期实验方案下肽段的预测平台期氘标记水平，再结合实测基线与肽段预测平台期氘标记水平估算速率常数。本方法已通过小鼠与犬的长期实验速率常数估算得到验证。经优化的双时间点方法与基准多点法相比，在小鼠与犬的实验中可实现相对误差低于10%的速率常数计算，且能够检测到小鼠体内由饮食诱导的载脂蛋白AI（ApoAI）周转的细微变化。综上，本研究开发并验证了一种基于双时间点测量的蛋白质速率常数计算新算法，该算法同样可应用于其他生物分子的相关研究。