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New method for testing protein interactions reduces large data sets from a new angle

A cluster map showing the profiles of beta proteins (rows) that associate with human DNA repair and epigenetic proteins (columns) based on high topological scores (TopS ) values. Yellow (high TopS score) indicates a higher protein interaction preference. Credit: Washburn Lab, Stowers Institute for Medical Research.Researchers from the Stowers Institute for Medical Research have created a new way of defining individual protein compounds in a fast, efficient and informative way. These results, published in March 1 9, 2019, edition of Nature Communications show how the topological scoring (TopS) algorithm created by Stowers researchers can combine the dataset-identifying proteins that come together. The approach is similar to looking at the activities and interactions of all individuals in a community and then selecting the most meaningful interactions, some of which may be very rare. The researchers are looking for the biological equivalent of two individuals who may be the only two in the whole community who participate in an important interaction. This not only helps researchers to identify how proteins perform biological functions or carry out biological processes, the algorithm can be applied to previously generated biological data and possibly other fields of science to obtain new information. "It is a form of large data analysis that we apply to proteomic data to identify and understand protein interaction networks," said Michael Washburn, Ph.D., Head of Stowers Proteomics Center. "It complements many techniques already in use, so it can be used to ask and answer new questions." Protein dataset can be challenging to…



A cluster map showing the profiles of beta proteins (rows) that associate with human DNA repair and epigenetic proteins (columns) based on high topological scores (TopS ) values. Yellow (high TopS score) indicates a higher protein interaction preference. Credit: Washburn Lab, Stowers Institute for Medical Research.

Researchers from the Stowers Institute for Medical Research have created a new way of defining individual protein compounds in a fast, efficient and informative way. These results, published in March 1

9, 2019, edition of Nature Communications show how the topological scoring (TopS) algorithm created by Stowers researchers can combine the dataset-identifying proteins that come together.

The approach is similar to looking at the activities and interactions of all individuals in a community and then selecting the most meaningful interactions, some of which may be very rare. The researchers are looking for the biological equivalent of two individuals who may be the only two in the whole community who participate in an important interaction.

This not only helps researchers to identify how proteins perform biological functions or carry out biological processes, the algorithm can be applied to previously generated biological data and possibly other fields of science to obtain new information.

“It is a form of large data analysis that we apply to proteomic data to identify and understand protein interaction networks,” said Michael Washburn, Ph.D., Head of Stowers Proteomics Center. “It complements many techniques already in use, so it can be used to ask and answer new questions.”

Protein dataset can be challenging to investigate for meaningful information because they are so large. “You have thousands of proteins to look at,” says Mihaela Sardiu, Ph.D., a senior research specialist at Stowers. Understanding how a wide variety of proteins come together to do something, such as repairing DNA, is a difficult problem. “We wanted to simplify the problem.”

Instead, it meant having an overall view of everything, hunting for less common events. Researchers did this by looking for bait (proteins that are already known to be involved in processes of interest) and switching (proteins that can interact with beta proteins) to see how they interacted in DNA repair and yeast chromin remodeling complexes. Through TopS, data is analyzed in a parallel manner, which means that data from several biologically related baits are considered simultaneously. An important feature of TopS is the ability to evaluate the preference of a prey protein for a bait relative to other baits. “Instead of calculating a score by just concentrating information on a single bait, we now sum up information from the entire dataset,” Sardiu explains.

Washburn and Sardiu believe that TopS can be applied to a wide range of data sets beyond proteomics, both basic and beyond. Sardiu sees the potential to use it for care data, where doctors may be able to compare the patient’s health with others, as being able to tell if the patient’s illness is “very advanced compared to others or not,” she says.

Team has also published these findings on Github, a computer code store, because they want to offer other researchers the opportunity to test the algorithm and see how they can apply it to their own projects.

“We are happy to see how far this can go. It is a potentially powerful tool and we want to see what other creative and innovative people can come up with,” says Washburn. “We think this is a very valuable potential tool for many people out there who are struggling with the challenge of sorting by very large-scale data. “

Other contributors from the Stowers Institute included Joshua M. Gilmore, Ph.D., Brad D. Groppe, Arnob Dutta, Ph.D. ., and Laurence Florence, Ph.D. Dutta is currently assistant professor at the University of Rhode Island, Groppe is now working with Thermo Fisher Scientific, and Gilmore is a scientist with Boehringer Ingelheim.

This research was funded by the Stowers Institute and one contributions from the National Institute of General Medical Sciences of the National Institute of Health under price number R01GM112639.The content is solely the responsibility of the authors and does not represent necessary show the official views of the National Institute of Health.

Lay Summary of Findings

Researchers from the Stowers Institute for Medical Research have created a topological scoring (TopS) algorithm, which enables researchers to look at large amounts of data in new ways to help them discover more details about how proteins interact and understand more precisely how certain activities at the mobile level happen. The results appear from March 8, 2019, edition of Nature Communications . Study leader and head of Stowers Proteomics Center Michael Washburn, Ph.D., sees potential in applying this algorithm to large datasets in other areas of scientific research and beyond.


Explore further:
Uncover new relationships and organizational principles in protein interaction networks

More information:
Mihaela E. Sardiu et al., Topological scoring of protein interaction networks, Nature Communications (2019). DOI: 10.1038 / s41467-019-09123-y

Journal reference:
nature Communications

Provided by:
Stowers Institute for Medical Research

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