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Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

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Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
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Groups of proteins may form a complex where each protein in this complex has a different role in the overall execution of the complex’s function. Often some of the proteins in the complex can be replaced by a closely related variant to give a complex that contains many of the same components yet is functionally distinct.
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Proteins perform many mechanical functions in a cell. These proteins can be classified into two general categories- proteins that generate mechanical forces and proteins that are subjected to mechanical forces. Proteins providing mechanical support to the structure of the cell, such as keratin, are subjected to mechanical force, whereas proteins involved in cell movement and transport of molecules across cell membranes, such as an ion pump, are examples of generating mechanical force. 
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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
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ProT-VAE: Protein Transformer Variational AutoEncoder for functional protein design.

Emre Sevgen1, Joshua Moller1, Adrian Lange1

  • 1Evozyne, Inc., Chicago, IL 60614.

Proceedings of the National Academy of Sciences of the United States of America
|October 6, 2025
PubMed
Summary
This summary is machine-generated.

We developed the Protein Transformer Variational AutoEncoder (ProT-VAE) for data-driven protein design. This model accelerates the discovery of synthetic proteins with enhanced functions, including enzymes with improved catalytic activity and thermostability for industrial applications.

Keywords:
generative modelingprotein designprotein language modelstransformersvariational autoencoders

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Area of Science:

  • Biotechnology
  • Computational Biology
  • Protein Engineering

Background:

  • Deep generative models excel at predicting protein sequence-function relationships.
  • Designing synthetic proteins with novel functionalities requires advanced computational tools.

Purpose of the Study:

  • Introduce the Protein Transformer Variational AutoEncoder (ProT-VAE) for data-driven protein design.
  • Leverage variational autoencoders and transformer-based models for enhanced protein sequence design.
  • Validate ProT-VAE's performance in functional prediction and design.

Main Methods:

  • Implemented ProT-VAE using NVIDIA's BioNeMo framework.
  • Combined variational autoencoders for latent embeddings with transformer models for featurization.
  • Validated the model through retrospective functional prediction and prospective functional design.

Main Results:

  • Identified a phenylalanine hydroxylase with 2.5x wild-type catalytic activity.
  • Engineered a gamma-carbonic anhydrase with a +61°C melting temperature elevation.
  • Demonstrated enzyme activity under industrially relevant conditions for carbon capture.

Conclusions:

  • ProT-VAE is an accurate, fast, and transferable model for protein design.
  • The model enables the discovery of synthetic proteins with engineered functions.
  • ProT-VAE serves as a powerful platform for machine learning-guided directed evolution.