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Related Concept Videos

Predicting Products: SN1 vs. SN202:27

Predicting Products: SN1 vs. SN2

Nucleophilic substitution reactions of alkyl halides can proceed via an SN1 or an SN2 mechanism. While in SN2 reactions, the nucleophile attacks the substrate simultaneously as the leaving group departs, in SN1 reactions, the substrate first dissociates to give the carbocation intermediate. Various factors such as the structure of the substrate, the strength of the nucleophile, and the nature of the solvent promote one mechanism over the other.
With increased substitution on the alkyl halide,...
Predicting Products: Substitution vs. Elimination02:52

Predicting Products: Substitution vs. Elimination

When a nucleophile and an alkyl halide react, nucleophilic substitution and β-elimination reactions compete to generate products.
The following factors can influence the mechanisms competing against each other:
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Rationalizing Substitutions01:29

Rationalizing Substitutions

Integrals involving non-rational functions are often difficult to evaluate using standard techniques, especially when radicals appear in the integrand. Rationalizing substitution provides a systematic method for simplifying such integrals by converting them into rational forms that are easier to handle.Consider a rod whose linear mass density depends on a constant linear density, a characteristic length, and the distance from the left end of the rod. Determining the total mass requires...

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Related Experiment Video

Updated: Jun 28, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

An expectation maximization algorithm for training hidden substitution models.

I Holmes1, G M Rubin

  • 1Howard Hughes Medical Institute, University of California, Berkeley, CA 94720, USA. ihh@fruitfly.org

Journal of Molecular Biology
|April 17, 2002
PubMed
Summary
This summary is machine-generated.

We developed a new algorithm to train substitution rate matrices from protein sequence alignments. This method improves the accuracy of multiple sequence alignment algorithms compared to existing methods like the PAM series.

Related Experiment Videos

Last Updated: Jun 28, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Evolutionary Biology

Background:

  • Substitution rate matrices are crucial for understanding protein evolution and for tasks like multiple sequence alignment.
  • Existing models, such as the PAM series, have limitations in capturing the complexity of substitution processes.

Purpose of the Study:

  • To develop a novel expectation maximization algorithm for training substitution rate matrices.
  • To incorporate hidden variables representing residue structural context into substitution models.
  • To evaluate the performance of the trained matrices in multiple sequence alignment.

Main Methods:

  • Derivation of an expectation maximization algorithm for maximum-likelihood training of substitution rate matrices.
  • Training of hidden substitution models using protein alignments from the Pfam database.
  • Evaluation of multiple alignment accuracy using BAliBASE reference alignments.

Main Results:

  • The trained hidden substitution matrices consistently outperformed the PAM series in accuracy.
  • Performance improvement increased with the addition of up to four hidden site classes.
  • The algorithm demonstrates robust training on large-scale protein alignment data.

Conclusions:

  • The developed algorithm provides a more accurate method for training substitution rate matrices.
  • Incorporating hidden variables significantly enhances the predictive power of substitution models.
  • This approach has broad applications in various bioinformatics tasks, including multiple sequence alignment.