The team from Stowers Proteomics Center used normalised spectral counts derived from a series of affinity purifications analysed by mass spectrometry (APMS) to generate a probabilistic measure of the preference of proteins to associate with one another. The work is published in the Proceedings of the National Academy of Sciences (PNAS).
At a time when productivity in the pharma industry is low, computational methods are being increasingly utilised to predict experimental results and thus save time and money. One of the problems these computational scientists face is that the number of possible interactions between the thousands of proteins in a single human cell is incredibly large.
Large-scale APMS studies have played important roles in the assembly and analysis of comprehensive protein interaction networks for lower eukaryotes, such as yeast. However, the computing power required to calculate such networks in humans has been prohibitively large.
Now, with this latest model, the researchers believe that not only will significant advances in proteomic analysis be possible, but it could speed up the development of disease treatments.
"By having insight regarding the most probable contacts within a multiprotein complex, we can devise targeted strategies to disrupt specific interactions," said Dr Mihaela Sardiu, lead author on the paper. "This could be useful for developing new drugs for disrupting protein complexes involved in disease."
The technique can not only assign probabilities to interactions between proteins but also between protein complexes. The chance that two proteins will come together is calculated only from their so called 'bait-to-prey' relationship. This is an improvement over previous methods, which required requiring systematic reciprocal bait-prey interactions or co-purification of preys by a third bait.
"Previous protein interaction networks built using protein mass spectrometry data were largely based on binary 'yes/no' data, where a protein is present in a sample or it is not," explained Dr Michael Washburn, director of proteomics and senior author on the paper.
"We were interested in quantitative proteomics approaches. We were able to develop a method to generate more information-rich networks, where the preference of two proteins to associate within a defined complex or within a larger network assembly can be estimated using Baysian probabilities. The new approach adds more information to the analysis of protein complexes and networks, since not all proteins interact in the same way."
The bottom line is that the idiom 'knowledge is power' is no less true in the pharma industry and drug developers now have access to increased knowledge in their fight to find better drugs.