Graphical Abstract
![](https://wqketang.oss-cn-beijing.aliyuncs.com/zip-unzip/zip-044b3519-ab7f-483a-94c0-c576782024bf/2021-004-DMQ/files/2021-004-DMQ-toc.jpg?Expires=1725755956&OSSAccessKeyId=STS.NT5eViCkqzTYdbfdqxMkjpyyq&Signature=WzUSenhnGFD5Ozw8NmSi5eiabTA%3D&security-token=CAISywJ1q6Ft5B2yfSjIr5eALuzdrrRQzZayZkTXgHEtQeRGn7zSkzz2IHtKenhsBOsbtfk1mG5W5%2FgZlqJ9SptIAEfJa9d99MzUP%2FpFltCT1fau5Jko1beHewHKeTOZsebWZ%2BLmNqC%2FHt6md1HDkAJq3LL%2Bbk%2FMdle5MJqP%2B%2FUFB5ZtKWveVzddA8pMLQZPsdITMWCrVcygKRn3mGHdfiEK00he8TouufTinpHMskGA1Aell7Mvyt6vcsT%2BXa5FJ4xiVtq55utye5fa3TRYgxowr%2Fwo0v0YpGya5YzHXwcPskvdKZbo78UqLQlla6w%2BGqFJqvPxr%2Fp8t%2Fx5fWJKAezhVgs8cVM8JOjIqKOscIsiJsO71SmqFV55c8Fdm%2BgUooJVgIMhTnduUfAPJAGOxzJitP%2BUVGGphr60TEnBL4rB5MUctfzRp75SXVPUTzDnGoABjuNLcf76FQN6p3t%2BSlCg%2FxR5C72RIk78k%2BUi44L9ZBmFT6F8GV7aXA52D1Ps56knKF6PAnhRivwyTxgsolycBfvccb3t5jGJ24SgY0aYosAECutxAo7sJhSwlwZvGwGDZJsop1vGiZ8eN1MbtZeRRuMeaZ2I%2BQo%2BnVP6Cf%2F0pDsgAA%3D%3D)
Discover the SciOpen Platform and Achieve Your Research Goals with Ease.
High-throughput proteomics based on mass spectrometry (MS) analysis has permeated biomedical science and propelled numerous research projects. pFind 3 is a database search engine for high-speed and in-depth proteomics data analysis. pFind 3 features a swift open search workflow that is adept at uncovering less obvious information such as unexpected modifications or mutations that would have gone unnoticed using a conventional data analysis pipeline. In this protocol, we provide step-by-step instructions to help users mastering various types of data analysis using pFind 3 in conjunction with pParse for data pre-processing and if needed, pQuant for quantitation. This streamlined pParse-pFind-pQuant workflow offers exceptional sensitivity, precision, and speed. It can be easily implemented in any laboratory in need of identifying peptides, proteins, or post-translational modifications, or of quantitation based on 15N-labeling, SILAC-labeling, or TMT/iTRAQ labeling.
Aebersold R, Mann M (2016) Mass-spectrometric exploration of proteome structure and function. Nature 537: 347−355
Chen C, Hou J, TannerJJ, Cheng J (2020) Bioinformatics methods for mass spectrometry-based proteomics data analysis. Int J Mol Sci 21: 2873. https://doi.org/10.3390/ijms21082873
Chi H, Liu C, Yang H, Zeng WF, Wu L, Zhou WJ, Wang RM, Niu XN, Ding YH, Zhang Y, Wang ZW, Chen ZL, Sun RX, Liu T, Tan GM, Dong MQ, Xu P, Zhang PH, He SM (2018) Comprehensive identification of peptides in tandem mass spectra using an efficient open search engine. Nat Biotechnol 36: 1059−1061
Cong Y, Motamedchaboki K, Misal SA, Liang Y, Guise AJ, Truong T, Huguet R, Plowey ED, Zhu Y, Lopez-Ferrer D, Kelly RT (2021) Ultrasensitive single-cell proteomics workflow identifies >1000 protein groups per mammalian cell. Chem Sci 12: 1001−1006
Conrads TP, Alving K, Veenstra TD, Belov ME, Anderson GA, Anderson DJ, Lipton MS, Pasa-Tolic L, Udseth HR, Chrisler WB, Thrall BD, Smith RD (2001) Quantitative analysis of bacterial and mammalian proteomes using a combination of cysteine affinity tags and 15N-metabolic labeling. Anal Chem 73: 2132−2139
Creasy DM, Cottrell JS (2004) Unimod: protein modifications for mass spectrometry. Proteomics 4: 1534−1536
Hoopmann MR, Moritz RL (2013) Current algorithmic solutions for peptide-based proteomics data generation and identification. Curr Opin Biotechnol 24: 31−38
Huesgen PF, Lange PF, Rogers LD, Solis N, Eckhard U, Kleifeld O, Goulas T, Gomis-Ruth FX, Overall CM (2015) LysargiNase mirrors trypsin for protein C-terminal and methylation-site identification. Nat methods 12: 55−58
Ma J, Chen T, Wu S, Yang C, Bai M, Shu K, Li K, Zhang G, Jin Z, He F (2019) iProX: an integrated proteome resource. Nucleic Acids Res 47: D1211−D1217
Meier F, Brunner AD, Frank M, Ha A, Bludau I, Voytik E, Kaspar-Schoenefeld S, Lubeck M, Raether O, Bache N, Aebersold R, Collins BC, Röst HL, Mann M (2020) diaPASEF: parallel accumulation-serial fragmentation combined with data-independent acquisition. Nature methods 17: 1229−1236
Milo R (2013) What is the total number of protein molecules per cell volume? A call to rethink some published values. BioEssays 35: 1050−1055
Muller JB, Geyer PE, Colaco AR, Treit PV, Strauss MT, Oroshi M, Doll S, Virreira Winter S, Bader JM, Kohler N, Theis F, Santos A, Mann M (2020) The proteome landscape of the kingdoms of life. Nature 582: 592−596
Ong SE, Blagoev B, Kratchmarova I, Kristensen DB, Steen H, Pandey A, Mann M (2002) Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol Cell Proteomics 1: 376−386
Thompson A, Schafer J, Kuhn K, Kienle S, Schwarz J, Schmidt G, Neumann T, Johnstone R, Mohammed AK, Hamon C (2003) Tandem mass tags: a novel quantification strategy for comparative analysis of complex protein mixtures by MS/MS. Anal Chem 75: 1895−1904
Valikangas T, Suomi T, Elo LL (2018) A comprehensive evaluation of popular proteomics software workflows for label-free proteome quantification and imputation. Brief Bioinform 19: 1344−1355
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.