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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...
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Related Experiment Video

Updated: May 31, 2026

RNA Secondary Structure Prediction Using High-throughput SHAPE
13:42

RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

Improving the performance of β-turn prediction using predicted shape strings and a two-layer support vector machine

Zehui Tang1, Tonghua Li, Rida Liu

  • 1Department of Chemistry, Tongji University, Shanghai, 200092, China.

BMC Bioinformatics
|July 14, 2011
PubMed
Summary

Accurate prediction of protein beta-turns (β-turns) is crucial. A novel two-layer model using predicted structures and PSSM features significantly improves β-turn prediction accuracy over existing methods.

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RNA Secondary Structure Prediction Using High-throughput SHAPE
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Published on: May 31, 2013

The ITS2 Database
16:17

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Published on: March 12, 2012

Area of Science:

  • Protein structure analysis
  • Bioinformatics
  • Computational biology

Background:

  • Beta-turns (β-turns) are vital secondary protein structures influencing protein configuration and function.
  • Accurate prediction of β-turns in protein sequences remains a challenge, with existing methods offering room for improvement.

Purpose of the Study:

  • To develop an improved method for predicting β-turns in protein sequences.
  • To enhance prediction accuracy by exploring novel features and architectural models.

Main Methods:

  • A novel two-layer model was proposed for β-turn prediction.
  • Input features included predicted secondary structures, predicted shape strings, and Position-Specific Scoring Matrix (PSSM).

Main Results:

  • The proposed two-layer model achieved high performance on the BT426 dataset: Q(total) = 87.2%, MCC = 0.66, Q(observed) = 75.9%, and Q(predicted) = 73.8%.
  • The model demonstrated superior discrimination between β-turns and non-β-turns compared to single-layer models, evidenced by higher Q(predicted) values.
  • Predicted shape strings significantly enhanced prediction performance across multiple datasets (BT426, BT547, BT823).

Conclusions:

  • The developed method represents a significant advancement in β-turn prediction.
  • The proposed approach offers a substantial improvement over existing prediction methods.