Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.
School of Biological Sciences, Nanyang Technological University, Singapore, Singapore.
PLoS Comput Biol. 2023 Mar 17;19(3):e1010961. doi: 10.1371/journal.pcbi.1010961. eCollection 2023 Mar.
In mass spectrometry (MS)-based proteomics, protein inference from identified peptides (protein fragments) is a critical step. We present ProInfer (Protein Inference), a novel protein assembly method that takes advantage of information in biological networks. ProInfer assists recovery of proteins supported only by ambiguous peptides (a peptide which maps to more than one candidate protein) and enhances the statistical confidence for proteins supported by both unique and ambiguous peptides. Consequently, ProInfer rescues weakly supported proteins thereby improving proteome coverage. Evaluated across THP1 cell line, lung cancer and RAW267.4 datasets, ProInfer always infers the most numbers of true positives, in comparison to mainstream protein inference tools Fido, EPIFANY and PIA. ProInfer is also adept at retrieving differentially expressed proteins, signifying its usefulness for functional analysis and phenotype profiling. Source codes of ProInfer are available at https://github.com/PennHui2016/ProInfer.
在基于质谱（MS）的蛋白质组学中，从鉴定的肽（蛋白质片段）推断蛋白质是一个关键步骤。我们提出了 ProInfer（蛋白质推断），这是一种新颖的蛋白质组装方法，利用了生物网络中的信息。ProInfer 有助于恢复仅由模糊肽（一种映射到多个候选蛋白质的肽）支持的蛋白质，并增强了由独特肽和模糊肽共同支持的蛋白质的统计置信度。因此，ProInfer 挽救了弱支持的蛋白质，从而提高了蛋白质组的覆盖率。在 THP1 细胞系、肺癌和 RAW267.4 数据集上进行评估时，与主流蛋白质推断工具 Fido、EPIFANY 和 PIA 相比，ProInfer 总是推断出最多的真阳性。ProInfer 还擅长检索差异表达的蛋白质，这表明它对功能分析和表型分析很有用。ProInfer 的源代码可在 https://github.com/PennHui2016/ProInfer 上获得。
