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ProteinWeaver: A New Tool for Biological Network Visualization

ProteinWeaver helps researchers visualize protein interactions and their roles in biological functions.

― 5 min read


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Table of Contents

Biological networks are useful for studying how different parts of living things interact with each other. One kind of network that scientists focus on is called the protein-protein interaction (PPI) network. This type of network shows how proteins, which are important molecules in cells, interact with one another. By understanding these interactions, scientists can learn more about how living systems work and what happens when something goes wrong, like during diseases.

PPI networks have been created using various methods, including some that examine proteins in a lab, in living organisms, or through computer simulations. These networks help researchers see how proteins work together, which can provide insights into both healthy and unhealthy states in living organisms.

Gene Regulatory Networks

Another type of network that researchers study is called the gene regulatory network (GRN). This network focuses on how genes are controlled, including how they are turned on or off. In a GRN, the connections show which proteins, known as transcription factors, influence the activity of other genes. While both PPI and GRN networks are important, they represent different kinds of interactions. PPI networks show physical links between proteins, while GRNS show how proteins can regulate gene activity.

Both networks provide valuable information, but many existing tools for visualizing these networks only focus on one type at a time. Moreover, these interactions do not happen in isolation; they often interact with each other. For example, physical interactions among proteins can also play a role in how genes are regulated.

The Need for Better Tools

Even though there are many online tools available for visualizing molecular networks, researchers often find it difficult to use them for generating new ideas or hypotheses. Sometimes, researchers are interested in understanding how a specific protein fits into a biological process or pathway, but current tools do not adequately address these questions. Many tools require users to input their own data, making them a bit daunting for those who just want to explore possibilities without having all the details.

Because of this gap, there is a need for a new tool that can help researchers visualize these interactions and generate new ideas.

Introducing ProteinWeaver

ProteinWeaver is a new tool designed to help researchers visualize molecular interaction networks involving proteins and genes. It specifically looks at how proteins connect to biological functions in different organisms. Currently, ProteinWeaver supports five different types of organisms: a bacterium, a yeast, two types of insects (fruit flies and worms), and a fish.

This tool uses a classification system called Gene Ontology (GO) that groups genes based on their functions. ProteinWeaver helps create a visual representation that links a specific protein to other proteins that are associated with a particular biological function. The interface is designed to be user-friendly, so anyone can use it, even if they don’t have technical expertise.

Features of ProteinWeaver

  1. Subnetwork Generation: Users can enter a protein they are interested in and a biological function they want to explore. The tool generates a "subnetwork", which shows how the chosen protein is connected to other proteins associated with the specified function. Users can choose how large they want this subnetwork to be.

  2. Graphical Interface: The display is intuitive and fast, allowing users to see the connections clearly. Information about different proteins, their interactions, and statistics about the network is easily accessible.

  3. Mixed Motif Identification: ProteinWeaver counts and identifies different types of interaction patterns within the network, known as Motifs. These motifs can provide valuable insights into how proteins might work together in biological processes.

  4. Contextual Information: The tool provides additional context for each protein, including how close it is to proteins associated with a particular biological function. This helps researchers generate hypotheses about what these proteins might do together.

  5. GO Term Prediction: ProteinWeaver scores how likely a specific protein is to be related to a certain function based on its connections in the network. It uses a method that considers how often a protein interacts with others that are linked to that function.

Case Studies

To show how ProteinWeaver can be used in research, let’s look at two examples.

In the first case, researchers were interested in a protein called Eb1, which is involved in the growth of structures called microtubules. Microtubules help cells change shape and divide. Even though Eb1 has not been specifically linked to "microtubule bundle formation" in scientific databases, using ProteinWeaver, they found connections between Eb1 and other important proteins involved in this process. This analysis might help researchers better understand how Eb1 functions in the context of microtubules.

The second case involves a protein known for regulating certain processes during the development of embryos. Researchers looked at a protein called gdf6a, which is part of a larger group involved in various developmental processes. By querying this protein using ProteinWeaver, they discovered connections to other proteins linked to dorsal/ventral patterning in zebrafish. This finding could shed light on how these proteins work together to control embryo development.

Future Directions

ProteinWeaver is continually being developed to improve its features and expand its capabilities. Initially focusing on five organisms, the creators plan to add more species, including various bacteria and plants.

The development team is also working on providing more detailed information about local structures in the network, beyond what is currently available. This includes enhancing statistics that detail how connected different proteins are within the network.

In addition, they aim to make it easier for users to search for multiple proteins or functions at once, which would allow for more in-depth exploration of how proteins interact in different biological situations.

Conclusion

ProteinWeaver serves as a bridge for researchers looking to understand molecular interactions and their roles in various biological contexts. Its user-friendly interface and integration with biological classification systems help address challenges faced by those trying to situate proteins within their biological environments. The tool has the potential to assist in generating hypotheses and exploring interactions in non-human model organisms, paving the way for new discoveries and insights in biological research.

Original Source

Title: ProteinWeaver: A Webtool to Visualize Ontology-Annotated Protein Networks

Abstract: Molecular interaction networks are a vital tool for studying biological systems. While many tools exist that visualize a protein or a pathway within a network, no tool provides the ability for a researcher to consider a proteins position in a network in the context of a specific biological process or pathway. We developed ProteinWeaver, a web-based tool designed to visualize and analyze non-human protein interaction networks by integrating known biological functions. ProteinWeaver provides users with an intuitive interface to situate a user-specified protein in a user-provided biological context (as a Gene Ontology term) in five model organisms. Protein-Weaver also reports the presence of physical and regulatory network motifs within the queried subnetwork and statistics about the proteins distance to the biological process or pathway within the network. These insights can help researchers generate testable hypotheses about the proteins potential role in the process or pathway under study. Two cell biology case studies demonstrate ProteinWeavers potential to generate hypotheses from the queried subnetworks. ProteinWeaver is available at https://proteinweaver.reedcompbio.org/.

Authors: Anna Ritz, O. F. Anderson, A. A. Barelvi, A. O'Brien, A. Norman, I. Jan

Last Update: Nov 1, 2024

Language: English

Source URL: https://www.biorxiv.org/content/10.1101/2024.10.24.620032

Source PDF: https://www.biorxiv.org/content/10.1101/2024.10.24.620032.full.pdf

Licence: https://creativecommons.org/licenses/by/4.0/

Changes: This summary was created with assistance from AI and may have inaccuracies. For accurate information, please refer to the original source documents linked here.

Thank you to biorxiv for use of its open access interoperability.

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