Using AI to Improve Proteomic Research

Using artificial intelligence, researchers at the Technical University of Munich have succeeded in making the mass analysis of proteins from any organism significantly faster and more accurate than before. This new approach is set to provoke a considerable change in the field of proteomics, as it can be applied in both basic and clinical research. The study was published this week in Nature Methods.

The genome of an organism contains the blueprints for thousands of proteins, which control almost all the functions of life. Defective proteins lead to serious diseases, such as cancer, diabetes, or dementia. Therefore, proteins are important targets for drugs.

At present, scientists use mass spectrometry to determine the type and quantity of proteins in a biological system. However, the current methods of data analysis continue to produce many mistakes. The team has now succeeded in using proteomic data to train a neural network in such a way that it is able to recognize proteins much more quickly and with almost no errors.

Mass spectrometers do not measure proteins directly. They analyze smaller parts consisting of amino acid sequences with up to 30 building blocks. The measured spectra of these chains are compared with databases in order to assign them to a specific protein. However, the evaluation software can only use part of the information that the spectra contain. Therefore, certain proteins are not recognized or are recognized incorrectly. However, the neural network developed by the researchers is able to use all the information of the spectra for the process of identification.

“We miss fewer proteins and make 100 times fewer mistakes,” says coauthor Bernhard Küster.

With the help of 100 million mass spectra, the algorithm has been so extensively trained that it can be used for all common mass spectrometers without any additional training. And according to senior author Mathias Wilhelm, the software is “applicable to all organisms in the world, even if their proteomes have never been examined before.”

Along with its potential for use in the clinic, the new technique has high promise for fundamental research. “The method can be used to track down new regulatory mechanisms in cells,” Küster notes. “We hope to gain a considerable amount of knowledge here, which, in the medium and long term, will be reflected in the treatment of diseases suffered by humans, animals, and plants.”