We've updated our Privacy Policy to make it clearer how we use your personal data. We use cookies to provide you with a better experience. You can read our Cookie Policy here.


Thermo Fisher Scientific Announces Collaboration to Provide Access to Deep Learning Tools for Discovery and Targeted Proteomics

Thermo Fisher Scientific Announces Collaboration to Provide Access to Deep Learning Tools for Discovery and Targeted Proteomics content piece image
Listen with
Register for free to listen to this article
Thank you. Listen to this article using the player above.

Want to listen to this article for FREE?

Complete the form below to unlock access to ALL audio articles.

Read time: 1 minute

Thermo Fisher Scientific and MSAID GmbH, a software company transforming proteomics with deep learning, have announced an exclusive license agreement to develop and commercialize deep learning tools for proteomics, making MSAID’s Prosit-derived framework widely accessible to proteomics laboratories. The availability of deep learning tools will enable improved confidence in proteomics research results, primarily in the areas of protein profiling using label-free or tandem mass tag (TMT)-based quantification, and a variety of new applications.

The new algorithm aims to allow gains in confidence and reproducibility and will be released as part of Thermo Fisher’s newest Thermo Scientific Proteome Discoverer 2.5 software release. Users can now access deep-learning-based prediction of tandem mass spectra, allowing for the formation of entire spectral libraries on demand and facilitating the identification of peptides with up to 10 times higher confidence and the extraction of more identifications from proteomics datasets via intensity-based rescoring. In combination with Thermo Scientific Orbitrap technology, the new algorithm enables emerging applications, such as immunopeptidomics and metaproteomics, for which traditional database search and statistical approaches are often ineffective.

"Increasing the confidence of protein and peptide identifications is a growing need, given that a false discovery rate of even 1% means that 1,000 out of every 100,000 peptides might be incorrectly assigned," said Mark Sanders, director of life science mass spectrometry software, Thermo Fisher Scientific. "Applying deep learning tools enables data-independent analysis of proteomics samples with higher confidence and reproducibility, and, when used with Orbitrap technology, reduces the false discovery rate 10-fold, to merely 100 out of every 100,000 peptides."

Martin Frejno, chief executive officer, MSAID GmbH, said, "At MSAID, we reinvent the way proteomic data is acquired and analyzed by using state-of-the-art deep learning. Through our collaboration with Thermo Fisher Scientific, we can bring this technological revolution to laboratories around the world and empower the scientific community to gain exceptional insight into new and existing data."