State Key Laboratory for Diagnosis and Treatment of Infectious Disease, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310000, China. Electronic address: https://twitter.com/zhou36847275.
College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China. Electronic address: https://twitter.com/ZYintao.
J Mol Biol. 2023 Jul 15;435(14):167944. doi: 10.1016/j.jmb.2022.167944. Epub 2023 Jan 10.
Spatial proteomics aims for a global description of organelle-specific protein distribution and dynamics, which is essential for understanding the molecular functions and cellular processes in health and disease. However, the application of this technique is seriously restricted by the neglect of robustness among proteomic signatures identified using standard statistical frameworks. Moreover, it is still a major bottleneck to automatically interpretate the identified proteomic signatures due to lack of integration of subcellular information. Herein, an online-tool SISPRO was constructed to (a) identify proteomic signatures with good robustness and accuracy via collectively evaluating relative weighted consistency (CWrel) & area under the curve (AUC) and (b) interpretate the identified signature based on comprehensive subcellular information from 9 organelles and 22 subcellular structures. All in all, SISPRO provides the endeavor to realize the simultaneous improvement of robustness and accuracy in signature identification and the unique capacity in biological annotation lies in its wide coverage of proteins and comprehensive spatial information. SISPRO is expected to be critical in spatial proteomic studies, which can be freely accessed without any login requirement at https://idrblab.org/sispro/.
空间蛋白质组学旨在全面描述细胞器特异性蛋白质分布和动态，这对于理解健康和疾病中的分子功能和细胞过程至关重要。然而，由于标准统计框架中鉴定的蛋白质组学特征缺乏稳健性，该技术的应用受到严重限制。此外，由于缺乏对亚细胞信息的整合，自动解释鉴定的蛋白质组学特征仍然是一个主要瓶颈。本文构建了一个在线工具 SISPRO，用于 (a) 通过集体评估相对加权一致性 (CWrel) 和曲线下面积 (AUC) 来识别具有良好稳健性和准确性的蛋白质组学特征，(b) 基于来自 9 个细胞器和 22 个亚细胞结构的综合亚细胞信息来解释鉴定的特征。总的来说，SISPRO 提供了一种努力，实现了在特征识别中稳健性和准确性的同时提高，并且其在生物注释方面的独特能力在于其广泛的蛋白质覆盖范围和全面的空间信息。SISPRO 有望在空间蛋白质组学研究中发挥关键作用，可在无需登录的情况下免费访问，网址为 https://idrblab.org/sispro/。
