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Mutations

Mutations

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...

Covalently Linked Protein Regulators

Covalently Linked Protein Regulators

Proteins can undergo many types of post-translational modifications, often in response to changes in their environment. These modifications play an important role in the function and stability of these proteins. Covalently linked molecules include functional groups, such as methyl, acetyl, and phosphate groups, and also small proteins, such as ubiquitin. There are around 200 different types of covalent regulators that have been identified.
These groups modify specific amino acids in a protein....

Improving Translational Accuracy

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

Translation

Translation

Lesson: Translation
Translation is the process of synthesizing proteins from the genetic information carried by messenger RNA (mRNA). Following transcription, it constitutes the final step in the expression of genes. This process is carried out by ribosomes, complexes of protein and specialized RNA molecules. Ribosomes, transfer RNA (tRNA), and other proteins produce a chain of amino acids—the polypeptide—as the end product of translation.
Translation Produces the Building Blocks of...

Mismatch Repair

Mismatch Repair

Organisms are capable of detecting and fixing nucleotide mismatches that occur during DNA replication. This sophisticated process requires identifying the new strand and replacing the erroneous bases with correct nucleotides. Mismatch repair is coordinated by many proteins in both prokaryotes and eukaryotes.
The Mutator Protein Family Plays a Key Role in DNA Mismatch Repair
The human genome has more than 3 billion base pairs of DNA per cell. Prior to cell division, that vast amount of genetic...

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Related Experiment Video

Updated: May 16, 2025

In Vivo Modeling of the Morbid Human Genome using Danio rerio

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

[Research progress in mutation effect prediction based on protein language models].

Liang Zhang¹, Pan Tan^2,3, Liang Hong^1,2,3,4

¹School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai 200240, China.

Sheng Wu Gong Cheng Xue Bao = Chinese Journal of Biotechnology

|April 2, 2025

Summary

This summary is machine-generated.

Protein language models (PLMs) are revolutionizing bioinformatics by improving predictions of protein mutation effects. This review covers sequence, structure, and combined models, discussing challenges and future directions.

Keywords:

deep learning multimodal fusion mutation effect prediction protein language models sequence models structure models supervised learning unsupervised learning

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Area of Science:

Bioinformatics
Computational Biology
Protein Engineering

Background:

Predicting protein mutation effects is crucial for bioinformatics and protein engineering.
Deep learning, especially protein language models (PLMs), offers new approaches.

Purpose of the Study:

To review the application of PLMs in predicting protein mutation effects.
To analyze different PLM types, their principles, and limitations.

Main Methods:

Focus on sequence-based, structure-based, and hybrid PLMs.
Discuss unsupervised and supervised learning in PLM training.
Analyze challenges like dataset quality and data noise.

Main Results:

PLMs show significant promise in predicting protein mutation effects.
Different model architectures offer varying advantages and limitations.
Current challenges include data acquisition and noise handling.

Conclusions:

PLMs are a powerful tool for predicting protein mutation effects.
Future research should explore multimodal fusion and few-shot learning.
This review provides a comprehensive perspective for advancing the field.