Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Improving Translational Accuracy02:07

Improving Translational Accuracy

14.1K
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...
14.1K
Improving Translational Accuracy02:07

Improving Translational Accuracy

3.6K
3.6K
Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

100.2K
Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
100.2K
Accuracy and Precision01:52

Accuracy and Precision

14.3K
Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.  Highly accurate...
14.3K
Accuracy, limits, and approximation01:28

Accuracy, limits, and approximation

1.1K
Accuracy, limits, and approximations are common in many fields, especially in engineering calculations. These concepts are imperative for ensuring that a given value is as close as possible to its true value.
Accuracy is defined as the closeness of the measured value to the true or actual value. In engineering mechanics, repeated measurements are taken during theoretical or experimental analyses to ensure that the result is precise and accurate.
The accuracy of any solution is based on the...
1.1K
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

569
Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
569

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

MotifLeadDB: A Hierarchical Structural Data Set for Congeneric Ligand Binding Activity Change.

Journal of chemical information and modeling·2026
Same author

Protein folding stability estimation with explicit consideration of unfolded states.

Nature communications·2026
Same author

SHARP: Generating Synthesizable Molecules via Fragment-Based Hierarchical Action-Space Reinforcement Learning for Pareto Optimization.

Journal of chemical information and modeling·2025
Same author

Physics-Inspired Accuracy Estimator for Model-Docked Ligand Complexes.

Journal of chemical theory and computation·2025
Same author

An artificial intelligence accelerated virtual screening platform for drug discovery.

Nature communications·2024
Same author

Benchmarking applicability of medium-resolution cryo-EM protein structures for structure-based drug design.

Journal of computational chemistry·2023
Same journal

Engineered HSP90-MP65 Bivalent Fusion Antigen: A Novel Vaccine Candidate Against Invasive Candidiasis.

Proteins·2026
Same journal

Physics-Based Energy Functions for Computational Protein Design.

Proteins·2026
Same journal

Impact of Stabilizing Osmolytes on the Conformational Dynamics of Human and Rat Islet Amyloid Polypeptides.

Proteins·2026
Same journal

Stabilization of Bone Morphogenetic Protein-2 at Physiological pH: Contrasting Roles of CHAPS and Arginine in Aggregation Inhibition.

Proteins·2026
Same journal

Structural Insights Into the Function of Leishmania major Adenylosuccinate Lyase.

Proteins·2026
Same journal

Generalizing the Gaussian Network Model: Spanning-Tree Thermodynamics Shows Entropy-Driven KRAS Activation.

Proteins·2026
See all related articles

Related Experiment Video

Updated: Jan 22, 2026

High-Throughput Metabolic Profiling for Model Refinements of Microalgae
11:07

High-Throughput Metabolic Profiling for Model Refinements of Microalgae

Published on: December 4, 2021

4.2K

High-accuracy refinement using Rosetta in CASP13.

Hahnbeom Park1, Gyu Rie Lee1, David E Kim1,2

  • 1Department of Biochemistry and Institute for Protein Design, University of Washington, Seattle, Washington.

Proteins
|July 21, 2019
PubMed
Summary
This summary is machine-generated.

Energy-guided protein structure refinement improves models by searching for low energy states. This method showed success in CASP13, achieving high accuracy for some targets, but faces challenges with larger protein complexes.

Keywords:
energy functionhomology modelingprotein conformational searchrotein structure prediction

More Related Videos

Importance of Jumping Ability in Handball Throwing Speed and Accuracy
02:43

Importance of Jumping Ability in Handball Throwing Speed and Accuracy

Published on: April 4, 2025

1.3K
Author Spotlight: Training of Laboratory Animals for Gentle and Stress-Free Handling
05:21

Author Spotlight: Training of Laboratory Animals for Gentle and Stress-Free Handling

Published on: February 16, 2024

3.8K

Related Experiment Videos

Last Updated: Jan 22, 2026

High-Throughput Metabolic Profiling for Model Refinements of Microalgae
11:07

High-Throughput Metabolic Profiling for Model Refinements of Microalgae

Published on: December 4, 2021

4.2K
Importance of Jumping Ability in Handball Throwing Speed and Accuracy
02:43

Importance of Jumping Ability in Handball Throwing Speed and Accuracy

Published on: April 4, 2025

1.3K
Author Spotlight: Training of Laboratory Animals for Gentle and Stress-Free Handling
05:21

Author Spotlight: Training of Laboratory Animals for Gentle and Stress-Free Handling

Published on: February 16, 2024

3.8K

Area of Science:

  • Computational Biology
  • Structural Biology
  • Biophysics

Background:

  • Protein structure prediction relies on identifying low free energy states.
  • Current refinement methods are sensitive to energy function errors due to high-dimensional search spaces.
  • Accurate protein models are crucial for understanding biological function.

Purpose of the Study:

  • To systematically improve protein structure models using energy-guided refinement.
  • To explore low energy states near initial models while avoiding excessive deviation.
  • To assess the effectiveness of this approach in the Critical Assessment of techniques for protein Structure Prediction (CASP13).

Main Methods:

  • Utilized energy-guided refinement with restraints to constrain the search space.
  • Focused on thorough exploration of low energy states in the vicinity of starting models.
  • Applied the method to targets in the CASP13 competition.

Main Results:

  • Achieved reasonable improvements for both incorrect and near-native regions of initial models.
  • Generated models with GDT-HA scores exceeding 70 for five targets.
  • Attained a backbone root-mean-square deviation (RMSD) of 0.5 å for one target.

Conclusions:

  • Energy-guided refinement can systematically enhance protein model quality.
  • The approach shows promise but faces challenges in refining oligomers and large proteins.
  • Further development is needed to address the complexity of large protein structure refinement.