Proteomics is not a word we generally use in our usual conversations. Proteomics is the study of proteomes on a large-scale. A proteome is a set of proteins produced in an organism or system in a biological context. It deals with the analysis of the protein content within a cell /tissue under a defined set of conditions. It is mainly used to detect protein expression patterns when subjected to a particular stimulus and hence determine the functional protein networks at a cell/tissue level. Proteomics plays a major role in medicine and drug development.
With time, Proteomics has become the major method used for identifying and characterizing proteins. The developments in mass spectrometry, protein fractionation techniques, bioinformatics, etc. have pushed Proteomics to the next level.
Proteomics is used to investigate:
· expression of proteins – when and where
· protein production and degradation rates, an abundance of steady-state
· possibilities of protein modification
· protein movements
· protein interactions
The role of Mass Spectrometry
In proteomics, protein profiling through biochemical mass spectrometric methods can identify and classify different types of proteins. Mass spectrometry, a method to characterize biological samples, is a highly preferred method in proteomics due to its characteristic properties and unique abilities. It generates datasets requiring the intervention of informatics approaches such as machine learning techniques, to analyze and interpret discrete data. Machine learning techniques can be applied to classify proteins and for biomarker identification in postgenomics biology, mass spectrometry and machine learning techniques are used to detect and study a huge number of proteins and have been pivotal in biomarker discovery for different diseases.
Use Of AI/ML In Proteomics today
Mass spectrometry conventionally poses some challenges in recognizing protein patterns correctly. The technique analyses smaller parts consisting of amino acid sequences up to 30 building blocks. The measured spectra of these sequences are then compared with the database and assigned to specific proteins, which is, however, not always accurate.
AI is also useful in optimizing mass spectrometry for proteomics and also in speeding up the analysis of massive datasets. Conventional methods such as fluorescence resonance energy transfer (FRET) techniques require excellent expertise.
Proteomics is crucial for early diagnosis, prognosis, and monitoring of diseases as well as for drug development. One major challenge is that proteome or the set of proteins in a cell/tissue/organism fluctuates from time to time. In such cases, AI and ML techniques can prove helpful for accurate protein pattern recognition and classification.
Follow and connect with us on Facebook, Linkedin & Twitter
