Journal News

From the Journals: MCP

Indumathi Sridharan
Aug. 30, 2024

A deep learning approach to phosphoproteomics. Untangling complex proteomics mass spec data. Read about papers on these topics recently published in the journal Molecular & Cellular Proteomics.

 

A deep learning approach to phosphoproteomics

An example of a phosphorylated protein; X-ray structure of an active ERK2 kinase. The phosphorylated threonine and tyrosine residues are highlighted in red.
Bubus, CC BY-SA 3.0 / Wikimedia Commons
An example of a phosphorylated protein; X-ray structure of an active ERK2 kinase. The phosphorylated threonine and tyrosine residues are highlighted in red.

The post-translational modification known as phosphorylation regulates numerous cellular functions, including cell growth, movement and metabolism. Phosphoproteomics, the study of phosphorylated proteins, offers valuable insights into dynamic changes in signaling pathways mediated by phosphorylation. Phosphoproteomics relies on mass spectrometry and computational methods to detect phosphorylated sites within a protein sequence. However, accurate detection is challenging due to the transient nature and low abundance of phosphorylated peptides relative to the proteome. Moreover, different computational methods yield varying outcomes, thus undermining confidence in the results.

In a recent study published in Molecular & Cellular Proteomics, Xinpei Yi and colleagues from Baylor College of Medicine and the Liver Cancer Institute at Fudan University employed deep learning to enhance phosphoproteomics accuracy. Deep learning is an artificial intelligence method that discerns patterns from vast amounts of unstructured data using neural networks.

Yi and collaborators used a multifaceted approach that included deep-learning algorithms for predicting peptide retention time and fragment ion intensity, a statistical scoring algorithm that integrates these deep-learning predictions for determining the probability of a site being phosphorylated, and a machine learning algorithm that also integrates the deep learning predictions for identifying peptides based on theoretical spectra. Compared to standalone algorithms, this integrative approach, named DeepRescore2, identified more phosphorylated peptides in synthetic and biological data sets from normal and liver cancer tissues.  

Accurately identifying phosphopeptides can aid in precise inference of the activity of kinases, which drive phosphorylation. The authors used DeepRescore2 to infer activity of epidermal growth factor receptor kinase, a biomarker of liver cancer. DeepRescore2 correctly inferred high kinase activity in liver cancer samples from patients with poor prognoses. The combinatorial deep learning approach used in the study enables accurate profiling of phosphoproteins and may serve as a prognostic tool. Combining DeepRescore2 with other algorithms enables more precise detection of phosphorylation activity, thus paving the way for biomarker-guided cancer therapies.

Untangling complex proteomics mass spec data

Data-independent acquisition, or DIA, is a popular technique used to analyze proteomes by mass spectrometry, or MS. In DIA, all detectable ions from a proteomics sample are iteratively analyzed, much like observing a crowded street one angle at a time. Despite the broad coverage and sensitivity DIA provides, its application is limited due to the difficulty in discerning similar molecules with overlapping signals in a complex sample.

In a recent study published in the journal Molecular & Cellular Proteomics, Sophia Steigerwald at the Max Planck Institute of Biochemistry and colleagues combined DIA with an advanced signal processing method, the phase–constrained spectrum deconvolution method, or ΦSDM, to tackle the challenge of complex spectra. The ΦSDM method imposes mathematical constraints based on the phase information of peptide ions to untangle overlapping signals within a mass spectrum. The authors tested the ΦSDM–DIA approach using HeLa cells.

By implementing ΦSDM signal processing on additional graphics processing units, the researchers quickly achieved a higher signal-to-noise ratio and 15% more peptide coverage compared to conventional methods, particularly in samples with short gradient times. Thus, advanced analytical techniques such as ΦSDM–DIA may help unravel unknown cellular pathways and events driven by complex proteomes or transient proteins without compromising on speed, quality and accuracy.

Enjoy reading ASBMB Today?

Become a member to receive the print edition four times a year and the digital edition monthly.

Learn more
Indumathi Sridharan

Indumathi (Indu) Sridharan holds a Ph.D. in molecular biochemistry and currently works as a product lead at Medidata AI. She is an ASBMB Today volunteer contributor.

Get the latest from ASBMB Today

Enter your email address, and we’ll send you a weekly email with recent articles, interviews and more.

Latest in Science

Science highlights or most popular articles

Glow-based assay sheds light on disease-causing mutations
Journal News

Glow-based assay sheds light on disease-causing mutations

Sept. 2, 2025

University of Michigan researchers create a way to screen protein structure changes caused by mutations that may lead to new rare disease therapeutics.

How signals shape DNA via gene regulation
Journal News

How signals shape DNA via gene regulation

Aug. 19, 2025

A new chromatin isolation technique reveals how signaling pathways reshape DNA-bound proteins, offering insight into potential targets for precision therapies. Read more about this recent MCP paper.

A game changer in cancer kinase target profiling
Journal News

A game changer in cancer kinase target profiling

Aug. 19, 2025

A new phosphonate-tagging method improves kinase inhibitor profiling, revealing off-target effects and paving the way for safer, more precise cancer therapies tailored to individual patients. Read more about this recent MCP paper.

How scientists identified a new neuromuscular disease
Feature

How scientists identified a new neuromuscular disease

Aug. 14, 2025

NIH researchers discover Morimoto–Ryu–Malicdan syndrome, after finding shared symptoms and RFC4 gene variants in nine patients, offering hope for faster diagnosis and future treatments.

Unraveling cancer’s spaghetti proteins
Profile

Unraveling cancer’s spaghetti proteins

Aug. 13, 2025

MOSAIC scholar Katie Dunleavy investigates how Aurora kinase A shields oncogene c-MYC from degradation, using cutting-edge techniques to uncover new strategies targeting “undruggable” molecules.

How HCMV hijacks host cells — and beyond
Profile

How HCMV hijacks host cells — and beyond

Aug. 12, 2025

Ileana Cristea, an ASBMB Breakthroughs webinar speaker, presented her research on how viruses reprogram cell structure and metabolism to enhance infection and how these mechanisms might link viral infections to cancer and other diseases.