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Related Concept Videos

Improving Translational Accuracy02:07

Improving Translational Accuracy

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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...
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Initiating translation is complex because it involves multiple molecules. Initiator tRNA, ribosomal subunits, and eukaryotic initiation factors (eIFs) are all required to assemble on the initiation codon of mRNA. This process consists of several steps that are mediated by different eIFs.
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Translation01:31

Translation

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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.
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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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Termination of Translation01:44

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The large ribosomal subunit has several important structures essential to translation. These include the peptidyl transferase center (PTC) - which is the site where the peptide bond is formed - and a large, internal, water-filled tube through which the nascent polypeptide moves. This latter structure is called the Peptide Exit Tunnel, and it begins at the PTC and spans the body of the large ribosomal subunit. During translation, as the nascent polypeptide chain is synthesized, it passes through...
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Prokaryote translation is a complex, highly coordinated process that converts genetic information from mRNA into functional proteins. It involves three stages: initiation, elongation, and termination, each facilitated by specific molecular components.Initiation of TranslationThe process begins with the assembly of the ribosomal subunits and initiation factors on the mRNA. In bacteria, the 30S ribosomal subunit recognizes the Shine-Dalgarno sequence in the mRNA, a conserved region upstream of...
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Back translation for molecule generation.

Yang Fan1, Yingce Xia2, Jinhua Zhu1

  • 1University of Science and Technology of China, Hefei, Anhui 230027, China.

Bioinformatics (Oxford, England)
|December 7, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces back translation for molecule generation, a semi-supervised method that leverages unlabeled molecules to improve performance. The approach achieves state-of-the-art results in molecular property improvement and retrosynthesis tasks.

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Area of Science:

  • Bioinformatics
  • Computational Chemistry
  • Machine Learning

Background:

  • Molecule generation is crucial in bioinformatics for tasks like property improvement and retrosynthesis.
  • Deep learning methods are gaining traction, but labeled bioinformatics data is scarce, unlike abundant unlabeled molecules.

Purpose of the Study:

  • To explore an effective method for molecule generation using unlabeled data.
  • To develop a semi-supervised approach inspired by natural language processing techniques.

Main Methods:

  • Propose a novel 'back translation' method for molecule generation.
  • Train a reversed model (Y to X mapping) using available data.
  • Utilize the reversed model to generate synthetic data from unlabeled molecules (Y).
  • Combine synthetic and labeled data to train the main task model (X to Y mapping).

Main Results:

  • Achieved state-of-the-art results on four molecule generation tasks.
  • Demonstrated effectiveness in molecular property improvement.
  • Showcased strong performance on the USPTO-50k retrosynthesis benchmark.

Conclusions:

  • Back translation is a simple yet effective semi-supervised method for molecule generation.
  • The approach successfully leverages unlabeled molecules to enhance performance.
  • The method shows promise for various molecule generation applications.