For molecular modelers, understanding the most visited regions in complex coordinate spaces can be a challenging task. The 2D density map feature in SAMSON’s Path Analyzer offers a powerful way to uncover patterns in your data by analyzing the density of molecular paths in a two-dimensional observable space.
Imagine working with a molecular simulation where you track key observables such as distance or energy. The challenge comes when you have two critical variables and need to identify preferred regions of your molecular paths in the reduced coordinate space. This is where the 2D density map can provide clarity.
What is a 2D Density Map?
A 2D density map allows you to combine two scalar analyses into a single, two-dimensional density estimate. This type of visualization generates a heatmap showing areas where the molecular path spends most of its time. The result? A practical way to detect regions of high sampling density based on the variables that are most meaningful to you.
This tool is useful for a variety of applications, from understanding conformational states to evaluating sampling efficiency in simulations. For example, you might want to see how systems behave in terms of radius of gyration compared to energy, or distance from a reference point vs RMSD. The 2D density map helps you focus on these relationships in a visually intuitive manner.
How to Add a 2D Density Map?
Here’s how you can create your own 2D density map in SAMSON:
- Open the Path Analyzer module in SAMSON.
- Choose or prepare two saved scalar analyses in the Analysis Tray. Make sure these analyses are frame-wise scalar analyses derived from the same path.
- In the Observable settings, select 2D density map.
- Highlight two scalar analyses in the tray.
- Click on Add Density Map to generate the visualization.
The resulting heatmap reveals where the system prefers to reside in the space defined by your two chosen observables. Areas of higher density indicate longer dwell times, providing insights into molecular behavior.
Why Use Density Maps?
- Simplify analysis: By summarizing sampling density in reduced coordinate spaces, these maps make your data more interpretable.
- Focus on what matters: Choose observables that reflect orthogonal (non-overlapping) aspects of a system’s dynamics. Examples include kinetic energy and potential energy.
- Gain clarity: Detect clustering regions or validate hypotheses on molecular behavior.
Pro Tips
- Pick observables that capture different aspects of motion, like RMSD vs distance, for complementary insights.
- Remember that these maps illustrate sampling density and not energy landscapes. If you’d like a projection additionally colored by energy, explore the Energy Landscape feature in Path Analyzer.
To learn more about how to leverage 2D density maps for your molecular analyses, check out the full documentation page.
SAMSON and all SAMSON Extensions are free for non-commercial use. You can download SAMSON here.