Mapping of Protein–Protein Interaction: Identification, Affinity Purification, Tandem Affinity Purification and Quality of Protein Interaction Data

Abstract

The utilization of mass spectrometry for protein identification has brought about a sea change in the proteomics discipline. Mass spectrometry, in conjunction with a number of affinity purification methods, can detect interactions between proteins. Protein complexes can be purified via tandem affinity purification or any of a number of other tags. Another popular tag is the FLAG tag; it is tiny and usually doesn't get in the way of the protein's activity. To prepare proteins for further identification using ESI-MS or MALDI-MS, various affinity purification techniques are employed. Rapid progress in the creation of new treatment strategies depends on our ability to better comprehend the biological pathways underpinning disease. Protein interactions are a common mediator of disease processes. We can learn more about the causes, development, and pathophysiology of diseases and find possible druggable targets if we can deduce how protein-protein interactions physically change in reaction to mutations, pathological circumstances, or pathogen infection. Recent developments in quantitative mass spectrometry (MS)-based methods have made it possible to map these alterations in protein-protein interactions produced by diseases on a worldwide scale in an unbiased manner. In this article, we take a look back at magnetic resonance imaging (MS) methods that have helped pinpoint system-level protein-protein interactions, and we talk about the problems with these approaches, as well as new developments in MS that try to fix them. Nevertheless, disease networks should not be considered independently. The importance of the mechanisms suggested by an interactome must be assessed, however, in the same way as with any systems biology approach. Because of its scalability, immortalised cell lines are a common tool for PPI research. Though simple to work with, these cell lines may miss the mark when it comes to capturing relationships that matter in more complicated tissues and creatures. More functionally relevant and physiologically correct disease models for studying interactions can be developed with the use of newer genetic techniques, such as CRISPR/Cas9-based genome engineering of primary cells. We are getting closer to incorporating these technologies into personalised medical applications as technology keeps getting better and these methods become more widely available and have higher throughput. In addition to helping doctors understand how the body works, they may also pinpoint exactly where a patient's network is most vulnerable and advise them on the best courses of treatment. Proteomics has progressed greatly since MS was used for protein identification. This area will see further advancements with the introduction of novel affinity purification methods and MS machines. The rate of false positives and negatives can be reduced through more stringent experimental design and data processing. At long last, everything is in place to go on with the human interactome identification. On a grand scale, scientists will one day be able to examine how protein interactions change in response to various stimuli and compare the interactome of cells in various disease states or after treatment with various cues.