For molecular modelers, efficiently identifying and manipulating molecules is often a critical challenge. When working with complex molecular structures, how can you classify molecules, identify specific molecular components, or check their visibility and states? This is where Backbone Attributes in SAMSON’s Node Specification Language (NSL) come into play. Let’s explore how these attributes can make your workflow smoother.
What Are Backbone Attributes?
The backbone attribute space (short name: s) specifically caters to structural backbones in molecular modeling, providing a way to enhance selectivity during searches or analyses. If you’re modeling a protein, for example, you often need to isolate individual nodes or analyze their visibility state. Backbone attributes, derived from the broader node or structuralGroup attribute spaces, enable focused control over these elements.
Key Backbone Attributes You Should Know
Below is an overview of some of the most useful backbone attributes that can boost your molecular modeling process.
| Attribute Name | Short Name | Possible Values | Examples |
|---|---|---|---|
| hasMaterial | hm |
true, false |
bb.hmnot bb.hm |
| hidden | h |
true, false |
bb.hnot bb.h |
| name | n |
Strings in quotes | bb.n "A"bb.n "L*" |
| ownsMaterial | om |
true, false |
bb.om |
| selectionFlag | sf |
true, false |
bb.sf false |
| visibilityFlag | vf |
true, false |
bb.vf |
Use Case: Making Data Visible
One essential attribute of backbones is visible (v), which determines whether a node (or backbone) is displayed in molecular visualization. Ensuring visibility is key when preparing publications or presentations. For instance, entering bb.v ensures visibility, while not bb.v excludes those that are invisible.
Why It Matters
The ability to differentiate attributes like selected or hasMaterial helps streamline workflows in computational research. For example, by querying bb.sf (selection flags), you can filter a large dataset to analyze only specific backbone subsets.
Another standout attribute is numberOfAtoms (nat), which allows you to focus on backbones meeting numerical criteria. For example, bb.nat > 100 isolates backbones with more than 100 atoms—a capability particularly useful when dealing with coarse-grained simulations or complex proteins.
Getting Started
Understanding and applying backbone attributes in NSL is a game-changer for many aspects of molecular modeling. By combining attributes like visibility and node counts, you can effectively classify, assess, and present molecular data based on your project’s objectives.
Discover the full list of Backbone Attributes in the official documentation to explore more possibilities.
SAMSON and all SAMSON Extensions are free for non-commercial use. If you haven’t tried them already, you can download SAMSON from SAMSON Connect.