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Conservation of Protein Domains Over Different Proteins

Conservation of Protein Domains Over Different Proteins

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.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to...

Conserved Binding Sites

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...

Leaky Scanning

Leaky Scanning

During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA. Marilyn Kozak discovered that the sequence RCCAUGG (where R...

Conservation of Protein Domains

Conservation of Protein Domains

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Related Experiment Video

Updated: Jun 9, 2025

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

Scalable protein design using optimization in a relaxed sequence space.

Christopher Frank^1,2, Ali Khoshouei^1,2, Lara Fuβ^1,2

¹Laboratory for Biomolecular Nanotechnology, Department of Biosciences, School of Natural Sciences Technical University of Munich, 85748 Garching, Germany.

Science (New York, N.Y.)

|October 24, 2024

Summary

This summary is machine-generated.

This study introduces a novel "hallucination"-based protein design method that efficiently creates high-quality protein backbones and interactions without retraining. The approach demonstrates broad applicability and scalability for various protein design challenges.

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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

Area of Science:

Computational biology
Protein engineering

Background:

Machine learning (ML) methods, particularly diffusion models, are advancing de novo protein design.
Existing methods often require retraining for different design tasks.

Purpose of the Study:

To develop a novel, retraining-free protein design approach using "hallucination" in relaxed sequence space.
To enable efficient design of high-quality protein backbones and protein-protein interactions across various scales.

Main Methods:

A "hallucination"-based generative approach operating in relaxed sequence space.
Experimental production and characterization of over 100 designed proteins.
Validation using high-resolution crystal structures and cryo-electron microscopy (cryo-EM).

Main Results:

Successfully designed and validated single-chain proteins up to 1000 amino acids.
Demonstrated accurate design of synthetic protein-protein interactions, including heterodimers.
Achieved high performance in designability, scope, and scalability.

Conclusions:

The "hallucination"-based method offers an efficient and versatile alternative to current protein design pipelines.
Relaxed sequence optimization provides a powerful strategy for de novo protein design and engineering.
The approach is scalable and applicable to diverse protein design problems.