Enabling More Sophisticated Proteomic Profile Analysis

Participants: Wilson Wen Bin Goh, Yie Hou Lee, Limsoon Wong


Mass spectrometry (MS)-based proteomics is a powerful tool for profiling systems-wide protein expression changes. It can be applied for various purposes, e.g., biomarker discovery in diseases and study of drug responses. However, MS-based proteomics tend to have consistency (poor reproducibility and inter-sample agreement) and coverage (inability to detect the entire proteome) issues that need to be urgently addressed. The former implies that multiple analytical runs of the same sample under constant experimental conditions will result in the detection of different but overlapping sets of proteins. Intuitively, this means more LC-MS/MS runs are required to identify a sufficiently large portion of any proteome and is intricately linked to the second issue of inadequate proteome coverage.

Experimental methods to overcome these issues are technically challenging, resource heavy or place an unreasonable heavy dependency on the quality of the initial data set. These include exhaustive fractionation of samples, repeated MS runs of the same sample to reach saturation and compilation of MS data specific to a sample type generated and archived from different laboratories. The problems are particularly exemplified in a large-scale collaborative study to assess the extent of reproducibility across different laboratories. The results were striking: only 7 out of 27 laboratories correctly reported all 20 proteins, and only 1 laboratory successfully reported all 22 unique peptides. Therefore, alternative approaches are needed to complement existing experimental approaches to circumvent the stochastic sampling of peptides by MS and increase the comprehensiveness of proteome coverage.


In this project, we aim to deal with the two challenges above by proposing approaches that analyze proteomic profiles in the context of biological networks. Our two main goals are thus:

In addition, the following two secondary goals are complementary to and support the two main goals:

Selected Publications


Selected Presentations


This project is supported in part by NRF CRP grant NRF-G-CRP-2997-04-082(d), A*STAR PSF grant SERC 102 101 0030, MOE Tier-2 grant MOE2012-T2-1-061, and MOE Tier-2 grant MOE2019-T2-1-042.

Last updated: 29/8/2019, Limsoon Wong.