Wolfram Language Paclet Repository

Community-contributed installable additions to the Wolfram Language

WolframChemistry/ ProteinVisualization

Functions for protein visualizations

Contributed by: Soutick Saha, Wolfram Alpha

Explore the fascinating world of biomolecular structures with our state-of-the-art Wolfram paclet! Designed for researchers, educators, and structural biology enthusiasts alike, this powerful tool offers an immersive experience for visualizing the intricate structures of biomolecules, including proteins, nucleic acids, and their complexes. Explore the elegant geometry of molecular backbones, amide planes, and more with captivating 3D graphics. Delve deeper into structural biology by computing and visualizing key features such as contact maps and dihedral angles. Analyze the effect of mutations on human health and predict the structure of proteins from custom sequences. Whether you are analyzing protein structures, studying nucleic acid interactions, or simply marveling at the beauty of nature's molecular machines, our paclet empowers you to visualize biomolecules in a new light. Experience the wonder of biomolecular visualization—get started with our paclet today!

Installation Instructions

To install this paclet in your Wolfram Language environment, evaluate this code:
PacletInstall["WolframChemistry/ProteinVisualization"]


To load the code after installation, evaluate this code:
Needs["WolframChemistry`ProteinVisualization`"]

Paclet Guide

Examples

Basic Examples (13) 

Visualize the ribbon diagram of human Apolipoprotein A-I:

In[1]:=
protein = ExternalIdentifier["PDBStructureID", "1AV1"];
In[2]:=
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Out[2]=

Visualize the backbone atoms of the same structure:

In[3]:=
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Out[3]=

Visualize the α Carbon trace of the protein:

In[4]:=
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Out[4]=

Visualize the amide planes of protein, highlighting the planar geometry of peptide bonds:

In[5]:=
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Out[5]=

Visualize protein backbones (with residue and dihedral angle information) with relevant planes that give rise to dihedral angles ϕ and ψ:

In[6]:=
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Out[6]=

Visualize the contact map of distances between residues in a protein, nucleic acid or protein nucleic acid complexes:

In[7]:=
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Out[7]=

Visualize the contact graph of the protein which is an alternative way of representing the contact map of biomolecule structure:

In[8]:=
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Out[8]=

Visualize the Ramachandran plot of the protein:

In[9]:=
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Out[9]=

Fold a BioSequence using ESMFold API:

In[10]:=
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Out[10]=

Mutations are changes in the amino acid sequence of proteins that can lead to a wide range of human diseases. AlphaMissense by Google DeepMind is an AI model that has categorized most of the mutations in human proteins and has predicted which ones have a high likelihood of causing disease based on their pathogenicity score. Using their publicly available data we can visualize the effect of single point mutations in human proteins.

MissenseVariantPlot3D gives the 3D structure of a protein where the residues are color coded by its mean or median predicted pathogenicity (by AlphaMissense) due to point mutation. Here is the plot of Human KRAS protein, one the most frequently mutated proteins in cancer:

In[11]:=
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Out[11]=

MissenseVariantListPlot gives the ListPlot of the predicted pathogenicity score of a human protein:

In[12]:=
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Out[12]=

MissenseVariantArrayPlot gives the ArrayPlot of the predicted pathogenicity score of a human protein:

In[13]:=
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Out[13]=

MissenseVariantSubstitutionMatrixPlot gives the plot of the average pathogenicity score of a given pair of amino acid mutation in a protein:

In[14]:=
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Out[14]=

Publisher

WolframChemistry

Disclosures

Compatibility

Wolfram Language Version 13.0

Version History

  • 1.2.0 – 11 November 2024
  • 1.1.2 – 01 March 2024
  • 1.1.1 – 20 December 2023
  • 1.1.0 – 25 October 2023
  • 1.0.1 – 30 May 2023
  • 1.0.0 – 26 May 2023

License Information

MIT License

Paclet Source

Source Metadata

  • Citation:
    • G. N. Ramachandran, C. Ramakrishnan, and V. Sasisekharan "Stereochemistry of polypeptide chain configurations", J Mol Biol., 1963 Jul, 7:95-9
    • Jumper, J et al. Highly accurate protein structure prediction with AlphFold. Nature (2021).
    • Lin, Zeming, et al. "Evolutionary-scale prediction of atomic-level protein structure with a language model." Science 379.6637 (2023): 1123-1130.
    • Cheng, Jun, et al. "Accurate proteome-wide missense variant effect prediction with AlphaMissense." Science 381.6664 (2023): eadg7492.

See Also