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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.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to...
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Conservation of Protein Domains02:26

Conservation of Protein Domains

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Leaky Scanning02:28

Leaky Scanning

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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...
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Proteins: From Genes to Degradation02:11

Proteins: From Genes to Degradation

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Within a biological system, the DNA encodes the RNA, and the nucleotide sequence in the RNA further defines the amino acid sequence in the protein. This is referred to as “The Central Dogma of Molecular Biology” - a term coined by Francis Crick.  Central dogma is a firm principle in biology that defines the flow of genetic information within any life form. The two fundamental steps in central dogma are - transcription and translation.
Transcription is the synthesis of RNA...
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Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

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The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
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From DNA to Protein03:06

From DNA to Protein

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The flow of genetic information in cells from DNA to mRNA to protein is described by the central dogma, which states that genes specify the sequence of mRNAs, which in turn specify the sequence of amino acids making up all proteins. The decoding of one molecule to another is performed by specific proteins and RNAs. Because the information stored in DNA is so central to cellular function, it makes intuitive sense that the cell would make mRNA copies of this information for protein synthesis...
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Related Experiment Video

Updated: Jun 18, 2025

An Integrated Approach for Microprotein Identification and Sequence Analysis
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An Integrated Approach for Microprotein Identification and Sequence Analysis

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Rapid protein evolution by few-shot learning with a protein language model.

Kaiyi Jiang1,2,3,4, Zhaoqing Yan1,2,3, Matteo Di Bernardo4

  • 1Department of Medicine Division of Engineering in Medicine Brigham and Women's Hospital Harvard Medical School Boston, 02115 MA, USA.

Biorxiv : the Preprint Server for Biology
|July 29, 2024
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Summary

EVOLVEpro, a new AI framework, accelerates protein engineering by efficiently optimizing multiple protein properties. This method uses few-shot active learning to achieve significant improvements in protein function with minimal experimental data.

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Area of Science:

  • Protein engineering
  • Computational biology
  • Artificial intelligence in life sciences

Background:

  • Traditional directed evolution is labor-intensive and struggles with multi-property optimization.
  • Current in silico methods using protein language models (PLMs) lack generalizability across protein families.
  • Efficient protein engineering is crucial for advancements in research, medicine, and biotechnology.

Purpose of the Study:

  • To introduce EVOLVEpro, a novel few-shot active learning framework for rapid protein engineering.
  • To enhance the efficiency and effectiveness of in silico protein evolution.
  • To demonstrate the broad applicability of AI-guided protein engineering.

Main Methods:

  • Integration of protein language models (PLMs) with protein activity predictors.
  • Implementation of a few-shot active learning strategy.
  • Validation across diverse protein engineering applications.

Main Results:

  • Achieved significant protein activity improvements within as few as four rounds of evolution.
  • Demonstrated up to 100-fold improvement in desired protein properties.
  • Showcased successful application in engineering RNA polymerase, CRISPR nucleases, prime editors, integrases, and antibodies.

Conclusions:

  • EVOLVEpro significantly outperforms current state-of-the-art in silico protein evolution methods.
  • Few-shot active learning with minimal experimental data is superior to zero-shot predictions for protein engineering.
  • EVOLVEpro enables broader applications of AI-guided protein engineering in biology and medicine.