Find Studio

Proteins are vital parts of living organisms, as they are the main components of the physiological metabolic pathways of cells. While proteomics generally refers to the large-scale experimental analysis of proteins, it is often specifically used for protein purification and mass spectrometry which is an analytical technique that measures the mass-to-charge ratio of charged particles.

pFind Studio is a computational solution for such mass spectrometry-based proteomics. It germinated in 2002 in Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China. We call ourselves "pFinders". Our goal is to study bioinformatics algorithms and to develop easy-to-use software tools to help answer biological questions.
pFind团队招收2022年度入学学生!The pFind team is recruiting new members for 2022!

What's New

Sep 20, 2021 - pFinders celebrated the Mid-Autumn Festival together.

Sep 19, 2021 - The CXMS training of CNCP-2021, co-hosted by the Meng-Qiu Dong (董梦秋) Lab of NIBS and pFind Team, has been successfully completed!

Aug 26, 2021 - The 6th China Workshop on Computational Proteomics (CNCP-2021) successfully completed. More than 400 people registered and participated online. We give thanks to all experts for their good presentations and to all participants for their support.

June 30, 2021 - A protocol for Open-pFind has been published on Biophysics Reports.


pFind is a search engine for peptide and protein identification via tandem mass spectrometry.[download...]

pLink is a tool dedicated for the analysis of chemically cross-linked proteins or protein complexes using mass spectrometry.

pNovo+ is a de novo peptide sequencing algorithm using complementary HCD and ETD tandem mass spectra. [download...]


We participated in the ABRF iPRG study with pFind developed by our group in the past few years.

"The mission of the ABRF iPRG (formerly the Bioinformatics Committee) is to educate ABRF members and the scientific community on best application and practice of bioinformatics toward accurate and comprehensive analysis of proteomics data."

We believe it is advantageous to improve our algorithms, software tools and strategies for proteome informatics.