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Improving Translational Accuracy02:07

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...
Improving Translational Accuracy02:07

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...
Initiation of Translation02:33

Initiation of Translation

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.
First, the initiator tRNA must be selected from the pool of elongator tRNAs by eukaryotic initiation factor 2 (eIF2). The initiator tRNA (Met-tRNAi) has conserved sequence elements including modified bases at...
Initiation of Translation02:33

Initiation of Translation

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.
First, the initiator tRNA must be selected from the pool of elongator tRNAs by eukaryotic initiation factor 2 (eIF2). The initiator tRNA (Met-tRNAi) has conserved sequence elements including modified bases at...
Translation01:31

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 Life
Proteins are called the...
Translation01:31

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 Life

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Related Experiment Videos

Steps toward knowledge-based machine translation.

J G Carbonell1, R E Cullingford, A V Gershman

  • 1Artificial Intelligence Project, Department of Computer Science, Yale University, New Haven, CT 06520; Department of Computer Science, Carnegie-Mellon University, Pittsburgh, PA 15.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
PubMed
Summary
This summary is machine-generated.

Knowledge-based machine translation uses artificial intelligence to understand source text context for accurate translation. This approach enhances translation quality by incorporating world knowledge into a language-free representation before generating target text.

Related Experiment Videos

Area of Science:

  • Artificial Intelligence
  • Computational Linguistics
  • Natural Language Processing

Background:

  • Traditional machine translation often struggles with nuanced understanding.
  • Recent AI advancements offer new possibilities for knowledge-based approaches.

Purpose of the Study:

  • To explore knowledge-based automatic text translation using AI.
  • To propose a novel machine translation paradigm incorporating contextual understanding.

Main Methods:

  • Analyzing source text into a language-free conceptual representation.
  • Employing inference mechanisms with contextual world knowledge to augment representations.
  • Utilizing a natural-language generator for target language output.

Main Results:

  • Demonstrated a method for enhancing machine translation through deep understanding.
  • Illustrated solutions for challenging translation problems (e.g., English-to-Spanish, English-to-Russian).

Conclusions:

  • Competent translation necessitates deep source text comprehension and contextual information.
  • The proposed paradigm, exemplified by the SAM system, shows promise for improved machine translation quality.