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

Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to form...
Per-Unit Sequence Models01:26

Per-Unit Sequence Models

An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
Conservation of Protein Domains02:26

Conservation of Protein Domains

Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to form...
Physiological Pharmacokinetic Models: Assumption with Protein Binding01:13

Physiological Pharmacokinetic Models: Assumption with Protein Binding

Physiological models with protein binding in pharmacokinetics offer a sophisticated approach to understanding drug disposition. These models consider drug-protein interactions, enabling them to effectively predict drug concentrations in different organs and tissues. This precision aids in accurate drug dosing, providing a significant advantage over conventional models. A key process within these models is equilibration, which ensures that drug concentrations achieve a steady state within the...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
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)...

You might also read

Related Articles

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

Sort by
Same author

IQ-TREE 3: phylogenomic inference software using complex evolutionary models.

Molecular biology and evolution·2026
Same author

Rate variation and recurrent sequence errors in pandemic-scale phylogenetics.

Nature methods·2026
Same author

Assessing phylogenetic confidence at pandemic scales.

Nature·2025
Same author

Highly Recurrent Multinucleotide Mutations in SARS-CoV-2.

Molecular biology and evolution·2025
Same author

Raman identification of single nucleotides flowing through permeable plasmonic films.

Nature communications·2025
Same author

Phylogenetic Methods Meet Deep Learning.

Genome biology and evolution·2025

Related Experiment Video

Updated: May 31, 2026

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
07:08

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

Markovian and non-Markovian protein sequence evolution: aggregated Markov process models.

Carolin Kosiol1, Nick Goldman

  • 1Institut für Populationsgenetik, Vetmeduni Vienna, Veterinärplatz 1, A-1210 Wien, Austria. carolin.kosiol@vetmeduni.ac.at

Journal of Molecular Biology
|July 2, 2011
PubMed
Summary

Protein evolution appears non-Markovian due to aggregated Markov processes (AMPs) when viewed at the amino acid level. Modeling protein evolution at the codon level, not the amino acid level, resolves this apparent timescale dependency.

More Related Videos

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
09:17

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

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

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

Published on: July 25, 2013

Related Experiment Videos

Last Updated: May 31, 2026

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
07:08

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
09:17

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

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

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

Published on: July 25, 2013

Area of Science:

  • Evolutionary biology
  • Molecular evolution
  • Bioinformatics

Background:

  • Protein evolution studies often assume Markov models, but apparent non-Markovian behavior has been observed.
  • This time-dependent behavior is often attributed to the genetic code at short timescales and amino acid physicochemical properties at long timescales.
  • The discrepancy between timescales in evolutionary models remains unexplained.

Purpose of the Study:

  • To explain the observed time-dependent behavior in protein evolution.
  • To investigate the relationship between amino acid-level and codon-level models of sequence evolution.
  • To determine the appropriate level for modeling protein sequence evolution.

Main Methods:

  • Modeling protein sequence evolution as an aggregated Markov process (AMP).
  • Analyzing protein evolution at the amino acid level as an observation of an underlying codon-level process.
  • Comparing amino acid substitution models with codon-level evolutionary models.

Main Results:

  • Observed time-dependent behavior in protein evolution can be explained by aggregated Markov processes (AMPs).
  • AMPs reconcile apparent non-Markovian behavior at the amino acid level with time-homogeneous Markovian processes at the codon level.
  • The genetic code and amino acid properties influence the aggregated amino acid substitution patterns.

Conclusions:

  • Protein evolution should be modeled at the codon level, not the amino acid level, to accurately capture its dynamics.
  • Aggregated Markov processes provide a framework for understanding timescale-dependent phenomena in molecular evolution.
  • This research offers new insights into the relationship between DNA sequence evolution and protein sequence evolution.