AIMC Topic: Proteomics

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Prognostic accuracy of MALDI-TOF mass spectrometric analysis of plasma in COVID-19.

Life science alliance
SARS-CoV-2 infection poses a global health crisis. In parallel with the ongoing world effort to identify therapeutic solutions, there is a critical need for improvement in the prognosis of COVID-19. Here, we report plasma proteome fingerprinting that...

Investigation and Highly Accurate Prediction of Missed Tryptic Cleavages by Deep Learning.

Journal of proteome research
Trypsin is one of the most important and widely used proteolytic enzymes in mass spectrometry (MS)-based proteomic research. It exclusively cleaves peptide bonds at the C-terminus of lysine and arginine. However, the cleavage is also affected by seve...

Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics.

Nature communications
Characterizing the human leukocyte antigen (HLA) bound ligandome by mass spectrometry (MS) holds great promise for developing vaccines and drugs for immune-oncology. Still, the identification of non-tryptic peptides presents substantial computational...

Retention time prediction using neural networks increases identifications in crosslinking mass spectrometry.

Nature communications
Crosslinking mass spectrometry has developed into a robust technique that is increasingly used to investigate the interactomes of organelles and cells. However, the incomplete and noisy information in the mass spectra of crosslinked peptides limits t...

Permutation-based identification of important biomarkers for complex diseases via machine learning models.

Nature communications
Study of human disease remains challenging due to convoluted disease etiologies and complex molecular mechanisms at genetic, genomic, and proteomic levels. Many machine learning-based methods have been developed and widely used to alleviate some anal...

Deep learning neural network tools for proteomics.

Cell reports methods
Mass-spectrometry-based proteomics enables quantitative analysis of thousands of human proteins. However, experimental and computational challenges restrict progress in the field. This review summarizes the recent flurry of machine-learning strategie...

Improved Discrimination of Disease States Using Proteomics Data with the Updated Aristotle Classifier.

Journal of proteome research
Mass spectrometry data sets from omics studies are an optimal information source for discriminating patients with disease and identifying biomarkers. Thousands of proteins or endogenous metabolites can be queried in each analysis, spanning several or...

PCprophet: a framework for protein complex prediction and differential analysis using proteomic data.

Nature methods
Despite the availability of methods for analyzing protein complexes, systematic analysis of complexes under multiple conditions remains challenging. Approaches based on biochemical fractionation of intact, native complexes and correlation of protein ...

DeepDigest: Prediction of Protein Proteolytic Digestion with Deep Learning.

Analytical chemistry
Proteolytic digestion of proteins by one or more proteases is a key step in shotgun proteomics, in which the proteolytic products, i.e., peptides, are taken as the surrogates of their parent proteins for further qualitative or quantitative analysis. ...

Using machine learning approaches for multi-omics data analysis: A review.

Biotechnology advances
With the development of modern high-throughput omic measurement platforms, it has become essential for biomedical studies to undertake an integrative (combined) approach to fully utilise these data to gain insights into biological systems. Data from ...