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Related Concept Videos

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...
Ribosome Profiling02:24

Ribosome Profiling

Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique helps...

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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

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Published on: July 25, 2013

Improvements to robotics-inspired conformational sampling in rosetta.

Amelie Stein1, Tanja Kortemme

  • 1California Institute for Quantitative Biomedical Research and Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, United States of America. amelie.stein@ucsf.edu

Plos One
|May 25, 2013
PubMed
Summary
This summary is machine-generated.

A new computational method, next-generation KIC, significantly improves protein structure prediction by enhancing sampling strategies. This advancement leads to a four-fold increase in generating accurate atomic models for proteins.

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Area of Science:

  • Computational biology
  • Structural bioinformatics
  • Protein modeling

Background:

  • Accurate protein structure prediction requires efficient sampling of conformational space.
  • Vast conformational possibilities and rugged energy landscapes pose significant challenges for all-atom sampling.
  • Existing methods struggle with local conformational sampling, especially for segments lacking regular secondary structure.

Purpose of the Study:

  • To evaluate the effectiveness of three sampling strategies for protein conformational prediction.
  • To develop an improved computational method for accurate, all-atom protein structure sampling.
  • To enhance the kinematic closure (KIC) method for local conformational sampling in Rosetta.

Main Methods:

  • Testing conformational diversification, torsion/omega-angle intensification, and parameter annealing strategies.
  • Evaluating strategies using the robotics-based kinematic closure (KIC) method in Rosetta.
  • Assessing performance on a benchmark of 45 12-residue protein segments without regular secondary structure.

Main Results:

  • Individual sampling strategies showed modest improvements in generating sub-Angstrom models.
  • The combined "next-generation KIC" method achieved a four-fold increase in median sub-Angstrom models compared to standard KIC.
  • Next-generation KIC successfully remodeled longer protein segments previously intractable for accurate remodeling.

Conclusions:

  • Next-generation KIC significantly enhances protein conformational sampling and prediction accuracy.
  • The improved method facilitates progress in complex protein modeling tasks.
  • This advancement holds promise for applications like simultaneous multi-segment remodeling and protein design.