How SciChart Interactive Visualization Unlocks Protein Structures to Advance Protein Research

Thanks to advancements in protein structure prediction technologies like AlphaFold2, structural biology is undergoing a transformation. These tools have generated vast repositories of high-quality 3D protein models, offering an unprecedented opportunity to explore the structural and functional relationships of proteins in real time. 

However, with the increasing complexity of datasets, researchers face new challenges in how to efficiently navigate, visualize, and interpret this data.

In response, a team of researchers has developed an innovative web server to integrate and visualize protein structures from multiple large databases. 

At the heart of this platform is SciChart, a powerful charting tool that provides the interactive capabilities necessary for researchers to explore protein structure and function relationships dynamically.

Why Is Protein Structure Prediction Important?

Recent breakthroughs in protein structure prediction have revolutionized how researchers understand biological systems. Databases like the AlphaFold Protein Structure Database (AFDB), ESMAtlas, and the Microbiome Immunity Project (MIP) collectively hold hundreds of millions of protein structures. These resources have immense potential for discovering new biological insights, but they also present a key challenge: how to dynamically explore and analyze this data in a way that helps connect protein structures to their functions.

Predicting the structure of proteins baffled scientists for years, but thanks to AI deep-learning systems, a protein’s three-dimensional structure can now be detected with atomic-level accuracy, with just the amino acid sequence. Before, it could take months or even years to figure out just a single protein. 

There are billions of known protein sequences, and scientists have only scratched the surface. To unlock more impactful insights, fast real-time data processing is required. This has applications for the design of drugs, protein design, and the prediction of protein function. 

The research team’s goal was to provide an interactive and intuitive way for scientists to investigate proteins from multiple databases, not just as static entities, but as dynamic structures that reveal functional potential. 

The solution they developed—an open-access web server—allows users to explore protein structures in real time, with the help of powerful visualization software like SciChart. To simplify and increase accessibility, protein structure was displayed in two dimensions using PaCMAP (a dimensionality reduction technique), offering a low-dimension representation of the structure/function landscape for easier navigation and exploration.

Pain Points in the Sector: How to Predict Protein Structure with Real-Time Data

Despite the availability of large protein databases, researchers in structural biology often face significant challenges with real-time data exploration, including:

1. Interactive filtering and toggling: Scientists need to filter proteins based on functional categories, structure quality, or database origin and toggle between different datasets quickly without delays.
2. Dynamic exploration of structure-function relationships: To understand the biological implications of a protein’s structure, researchers require tools that let them click on structures and instantly access detailed annotations or links to related proteins.
3. Real-time annotations: Exploring how a protein’s structural features link to its function requires real-time annotation tools that can handle complex datasets and allow users to drill down into specific details.

The sheer complexity of these datasets, combined with the need for real-time interaction, means that traditional visualization tools are often inadequate. Researchers need a scientific charts platform that not only handles live data updates but also offers intuitive features like toggling, on-click actions, and immediate functional annotations.

Figure 1: Overview of the pipeline and Protein Structure Space Visualization
(A) The two-stage pipeline used to cluster and analyze protein structures from the AlphaFold Protein Structure Database (AFDB), ESMAtlas, and the Microbiome Immunity Project (MIP). Clusters are created first within each dataset and then merged across datasets, with structural and functional annotations applied.
(B) The 2D visualization of the protein structure space using PaCMAP, showing the distribution of CATH structural classes, protein lengths, and AlphaFold confidence scores (pLDDT) later to be visualised by SciChart.
(C) Visualization of the overlap and complementarity between AFDB, ESMAtlas, and MIP, illustrating how each database contributes unique and shared regions to the protein structure space

Pain Points in Software: The Need for Interactive, Real-Time Charting

Traditional charting solutions often struggle with real-time interactivity and live data handling, especially when applied to large and complex biological datasets. 

Common challenges include:

• Limited interactivity: Many charting tools fail to offer the responsive toggling, filtering, and on-click functionality needed for users to explore multiple databases in real time.
• Delayed updates: When filtering or selecting specific proteins, traditional tools often suffer from slow rendering times, reducing the fluidity of the research process.
• Difficulty integrating multiple data sources: The need to pull in data from multiple databases (like AFDB, ESMAtlas, and MIP) in real time adds another layer of complexity that many tools struggle to manage.

 How SciChart Solved These Problems

SciChart, a high-performance scientific chart visualization tool, provided the real-time interactivity and dynamic data handling needed to make this research platform a success. 

Here’s how SciChart addressed the specific software challenges:

1. Real-Time Filtering and Toggling: SciChart’s live data handling capabilities allowed users to toggle between different protein datasets effortlessly. Researchers can filter structures based on properties like length, origin, or quality, and see updates in real time without lag.
2. Super Fast Real-time Updates: SciChart can process >100,000 updates to a DataSeries per second without lag, even when using lower memory hardware. However, to get the most out of the performance, it’s better to update more data less often. With a 64-bit library, SciChart makes it possible to process 1 billion data points with WPF and 1 million points with JavaScript, which supports hundreds of data series within a single dashboard.
3. On-Click Actions and Annotations: One of the key features of SciChart is its ability to handle on-click actions, enabling researchers to click on a specific protein structure and instantly pull up detailed annotations, including its functional potential and links to related proteins in the database. This feature is crucial for exploring the structure-function relationships that are the core of this research.
4. Responsive Interaction: SciChart ensures smooth, real-time interaction, allowing users to pan, zoom, and focus on different protein clusters without delays. This level of responsiveness is essential for scientists who need to explore the data deeply, investigating structural variations across multiple proteins.
5. Integration of Multiple Data Sources: SciChart seamlessly integrates data from AFDB, ESMAtlas, and MIP, enabling researchers to explore different structural and functional datasets in a unified platform. The ability to switch between datasets without disrupting the user experience is a significant achievement, given the size and complexity of the underlying data.
6. Customizable Functional Views: The platform powered by SciChart allows customizable views, where users can adjust filters to explore specific regions of the protein structure space or focus on certain functional categories. Researchers can also visualize how different proteins from various databases share structural similarities or differences.

Figure 2: Interactive Protein Structure Space Visualization powered by SciChart

This screenshot of the web server showcases the real-time, interactive exploration of the protein structure space, enabled by SciChart. Users can dynamically filter and toggle between datasets, click on individual protein structures to reveal detailed annotations, and zoom in on specific regions of the structure-function landscape. The intuitive interface allows seamless navigation and immediate insights into structural relationships across multiple databases (AFDB, ESMAtlas, and MIP). View here.

The Outcomes: Enabling Real-Time Insights into Protein Function

Why do scientists use computer programs to model? With SciChart, the research platform provided a fully interactive, real-time environment for exploring protein structures and their potential functions. This tool has already transformed the way scientists navigate and analyze large protein datasets, delivering several key outcomes:

• Real-time structure-function exploration: Researchers can now dynamically explore how protein structures relate to their biological functions, gaining new insights on the fly without relying on static datasets.
• Improved data interpretation: The ability to filter and annotate in real time allows scientists to make more informed hypotheses about previously uncharacterized proteins.
• Enhanced collaboration: By offering an open-access platform powered by SciChart, the research team made it possible for scientists worldwide to collaborate, share findings, and generate new ideas based on live data.

Conclusion: Why Tools Like SciChart Matter for Scientific Research

The success of this project highlights the critical role of real-time, interactive visualization tools like SciChart in driving scientific research forward. As datasets in structural biology continue to grow in size and complexity, the ability to dynamically explore these data in real time—complete with on-click annotations and live toggling between datasets—will become essential for researchers looking to unlock new biological insights.

SciChart’s JavaScript Chart Library and React Charts offer a combination of performance, interactivity, and flexibility making it an invaluable tool for R&D scientists working in bioinformatics, computational biology, and structural biology. By enabling real-time structure-function investigations, SciChart helps scientists make sense of the biological complexity and drive new discoveries in protein modelling and research.

Read the research paper below.

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