Proteins are the key players in many cellular processes. Their composition, trafficking, and interactions underlie the dynamic processes of life. Furthermore, diseases are frequently accompanied by malfunction of prot...Proteins are the key players in many cellular processes. Their composition, trafficking, and interactions underlie the dynamic processes of life. Furthermore, diseases are frequently accompanied by malfunction of proteins at multiple levels. Understanding how biological processes are regulated at the protein level is critically important to understanding the molecular basis for diseases and often shed light on disease prevention, diagnosis, and treatment. With rapid advances in mass spectrometry(MS)instruments and experimental methodologies, MS-based proteomics has become a reliable and essential tool for elucidating biological processes at the protein level. Over the past decade, we have witnessed great expansion of knowledge of human diseases with the application of MS-based proteomic technologies, which has led to many exciting discoveries. Herein we review the recent progress in MS-based proteomics in biomedical research, including that in establishing disease-related proteomes and interactomes. We also discuss how this progress will benefit biomedical research and clinical diagnosis and treatment of disease.展开更多
Proteome-wide Amino aCid and Elemental composition （PACE） analysis is a novel and informative way of interrogating the proteome. The PACE approach consists of in silico decompo- sition of proteins detected and quant...Proteome-wide Amino aCid and Elemental composition （PACE） analysis is a novel and informative way of interrogating the proteome. The PACE approach consists of in silico decompo- sition of proteins detected and quantified in a proteomics experiment into 20 amino acids and five elements （C, H, N, O and S）, with protein abundances converted to relative abundances of amino acids and elements. The method is robust and very sensitive; it provides statistically reliable differ- entiation between very similar proteomes. In addition, PACE provides novel insights into prote- ome-wide metabolic processes, occurring, e.g., during cell starvation. For instance, both Escherichia coli and Synechocystis down-regulate sulfur-rich proteins upon sulfur deprivation, but E. coli preferentially down-regulates cysteine-rich proteins while Synechocystis mainly down- regulates methionine-rich proteins. Due to its relative simplicity, flexibility, generality and wide applicability, PACE analysis has the potential of becoming a standard analytical tool in proteomics.展开更多
目的通过所检测到的肽段丰度计算蛋白质丰度是蛋白质组学的一个重要部分。由于退化肽段的丰度可能由多个蛋白质提供,简单剔除退化肽段可消除这种不确定性并简化问题。但是由于退化肽段的信息没有被充分利用,蛋白质定量的准确性会受到影...目的通过所检测到的肽段丰度计算蛋白质丰度是蛋白质组学的一个重要部分。由于退化肽段的丰度可能由多个蛋白质提供,简单剔除退化肽段可消除这种不确定性并简化问题。但是由于退化肽段的信息没有被充分利用,蛋白质定量的准确性会受到影响,还可能显著降低可以定量的蛋白质规模。如何充分利用退化肽段的信息,提高蛋白定量的准确性和全面性,并且不会导致问题规模更为复杂,成为一个重要的问题。方法为了在不引入更多误差的情况下充分利用退化肽段来进行更准确的定量,本文提出一个基于误差最小化的方法(error-minimization-based quantification for protein,EMQ)。不同于以往的大多数算法,EMQ利用退化肽段中的信息,最大限度地将肽段层面信息还原到蛋白质层面,得到了更多的蛋白质定量结果并提高了结果的精度。结果多个实验数据上的表现证明本方法可以在较小的时间代价下获得更高的精度,并提高结果的规模。结论本文提出的基于误差最小化的方法可以快速准确地对大规模蛋白质组学问题进行定量。展开更多
文摘Proteins are the key players in many cellular processes. Their composition, trafficking, and interactions underlie the dynamic processes of life. Furthermore, diseases are frequently accompanied by malfunction of proteins at multiple levels. Understanding how biological processes are regulated at the protein level is critically important to understanding the molecular basis for diseases and often shed light on disease prevention, diagnosis, and treatment. With rapid advances in mass spectrometry(MS)instruments and experimental methodologies, MS-based proteomics has become a reliable and essential tool for elucidating biological processes at the protein level. Over the past decade, we have witnessed great expansion of knowledge of human diseases with the application of MS-based proteomic technologies, which has led to many exciting discoveries. Herein we review the recent progress in MS-based proteomics in biomedical research, including that in establishing disease-related proteomes and interactomes. We also discuss how this progress will benefit biomedical research and clinical diagnosis and treatment of disease.
基金the Swedish Research Council(Grant No.2009-4103)
文摘Proteome-wide Amino aCid and Elemental composition （PACE） analysis is a novel and informative way of interrogating the proteome. The PACE approach consists of in silico decompo- sition of proteins detected and quantified in a proteomics experiment into 20 amino acids and five elements （C, H, N, O and S）, with protein abundances converted to relative abundances of amino acids and elements. The method is robust and very sensitive; it provides statistically reliable differ- entiation between very similar proteomes. In addition, PACE provides novel insights into prote- ome-wide metabolic processes, occurring, e.g., during cell starvation. For instance, both Escherichia coli and Synechocystis down-regulate sulfur-rich proteins upon sulfur deprivation, but E. coli preferentially down-regulates cysteine-rich proteins while Synechocystis mainly down- regulates methionine-rich proteins. Due to its relative simplicity, flexibility, generality and wide applicability, PACE analysis has the potential of becoming a standard analytical tool in proteomics.
文摘目的通过所检测到的肽段丰度计算蛋白质丰度是蛋白质组学的一个重要部分。由于退化肽段的丰度可能由多个蛋白质提供,简单剔除退化肽段可消除这种不确定性并简化问题。但是由于退化肽段的信息没有被充分利用,蛋白质定量的准确性会受到影响,还可能显著降低可以定量的蛋白质规模。如何充分利用退化肽段的信息,提高蛋白定量的准确性和全面性,并且不会导致问题规模更为复杂,成为一个重要的问题。方法为了在不引入更多误差的情况下充分利用退化肽段来进行更准确的定量,本文提出一个基于误差最小化的方法(error-minimization-based quantification for protein,EMQ)。不同于以往的大多数算法,EMQ利用退化肽段中的信息,最大限度地将肽段层面信息还原到蛋白质层面,得到了更多的蛋白质定量结果并提高了结果的精度。结果多个实验数据上的表现证明本方法可以在较小的时间代价下获得更高的精度,并提高结果的规模。结论本文提出的基于误差最小化的方法可以快速准确地对大规模蛋白质组学问题进行定量。
