ESM (Evolutionary Scale Modeling) is a family of large-scale protein language models developed by Meta AI. They’re trained on massive protein sequence databases, learning contextual representations of amino acids purely from sequence data. These representations—often called embeddings—capture both structural and functional clues.
In practice, you feed a protein sequence into ESM to obtain per-residue embeddings, which you can then use for downstream tasks like structure prediction, function annotation, or variant effect prediction. If you batch multiple sequences together, ESM aligns them by adding special start/end tokens and padding shorter sequences to match the longest one. You then slice out the valid embedding region for each protein, ignoring any padding.

Render Your Protein in Blender with Molecular Nodes

Protein Dock Tools and algorithm Overview

In summary, extracting information from PDB files and visualizing it with PyVista enables a deep and interactive exploration of biomolecular structures, which is vital for various scientific and medical research purposes. This approach harnesses the power of computational tools to augment our understanding of complex biological systems.

The main idea of this work is to use PyMOL for calculating the surface information/mesh structure, and then employ Python to read that information for visualization and other complex/advanced calculations. In other words, it seamlessly bridges PyMOL and Python.

Pymol: the best protein structure viewer