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Related Concept Videos

SBAR II: Application of SBAR01:14

SBAR II: Application of SBAR

SBAR is an effective communication tool used by healthcare professionals to communicate patient information accurately. SBAR stands for Situation, Background, Assessment, and Recommendation. For a better understanding, an example is given below.
SBAR Report from a Nurse to a Health Care Provider
S: "Hello, Dr. Smith. This is Jane, RN, from the Med Surg unit. I am calling to tell you about Ms. White in Room 210, who is experiencing increased pain and redness at her incision site. Her recent...
Introduction to z Scores01:05

Introduction to z Scores

A z score (or standardized value) is measured in units of the standard deviation. It indicates how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a zero z score. It is important to note that the mean of the z scores is zero, and the standard deviation is one.
z scores help...
Introduction to z Scores01:06

Introduction to z Scores

A z score (or standardized value) is measured in units of the standard deviation. It tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a zero z score. It is important to note that the mean of the z scores is zero, and the standard deviation is one.
z scores help...
z Scores and Area Under the Curve01:17

z Scores and Area Under the Curve

z scores are the standardized values obtained after converting a normal distribution into a standard normal distribution. A z score is measured in units of the standard deviation. The z score tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a z score of zero.
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...

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Related Experiment Videos

Scoring and lessons learned with the CSAR benchmark using an improved iterative knowledge-based scoring function.

Sheng-You Huang1, Xiaoqin Zou

  • 1Department of Physics and Astronomy, University of Missouri, Columbia, Missouri 65211, United States.

Journal of Chemical Information and Modeling
|August 12, 2011
PubMed
Summary

A new scoring function, ITScore 2.0, was developed using an iterative method to predict protein-ligand binding affinity. It shows improved performance compared to existing methods, aiding drug discovery efforts.

Related Experiment Videos

Area of Science:

  • Computational chemistry
  • Structural biology
  • Drug discovery

Background:

  • Accurate prediction of protein-ligand binding affinity is crucial for drug discovery.
  • Knowledge-based scoring functions often face challenges with reference state definitions.

Purpose of the Study:

  • To develop and validate a novel all-atom pairwise potential scoring function for protein-ligand interactions.
  • To address the reference state problem in knowledge-based scoring functions.

Main Methods:

  • A statistical mechanics-based iterative method was employed.
  • Extracted distance-dependent potentials from 1300 protein-ligand crystal structures.
  • Tested the ITScore 2.0 function on the CSAR 2009 benchmark dataset.

Main Results:

  • ITScore 2.0 achieved a Pearson correlation of R² = 0.54 in binding affinity prediction.
  • Comparative analysis showed ITScore 2.0's performance against VDW and DOCK's FF scoring functions.
  • Identified key factors influencing scoring function performance.

Conclusions:

  • The developed ITScore 2.0 offers improved protein-ligand binding affinity prediction.
  • The iterative method successfully circumvents the reference state problem.
  • Findings provide insights for future scoring function development.