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

Amyloid Fibrils03:03

Amyloid Fibrils

9.5K
Amyloid fibrils are aggregates of misfolded proteins.  Under most circumstances, misfolded proteins are either refolded by chaperone proteins or degraded by the proteasome. However, in the case of a mutation or a disease, these proteins can accumulate to form large clusters and often further assemble to form elongated fibers, called fibrils. 
Amyloid deposits were observed as early as 1639 in the liver and the spleen.   In 1854, Rudolph Virchow performed iodine staining,...
9.5K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

54
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...
54

You might also read

Related Articles

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

Sort by
Same author

The Effects of Phosphorylation on the Structure and Function of Motif A, an Intrinsically Disordered Region within SIRT1.

bioRxiv : the preprint server for biology·2026
Same author

The first word in accessibility is "access".

Augmentative and alternative communication (Baltimore, Md. : 1985)·2025
Same author

Neural Upscaling from Residue-Level Protein Structure Networks to Atomistic Structures.

Biomolecules·2021
Same author

Network-Based Classification and Modeling of Amyloid Fibrils.

The journal of physical chemistry. B·2019
Same author

Predicting Reaction Products and Automating Reactive Trajectory Characterization in Molecular Simulations with Support Vector Machines.

Journal of chemical information and modeling·2019
Same author

Rapid detection of enriched uranium in food.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine·2017

Related Experiment Video

Updated: Jul 3, 2025

Author Spotlight: In Silico Creation and Impact of Carbonylated Amino Acids on Protein Structure and Function
05:57

Author Spotlight: In Silico Creation and Impact of Carbonylated Amino Acids on Protein Structure and Function

Published on: April 26, 2024

388

Genetic Algorithm for Automated Parameterization of Network Hamiltonian Models of Amyloid Fibril Formation.

Gianmarc Grazioli1, Andy Tao1, Inika Bhatia1

  • 1Department of Chemistry, San José State University, San Jose, California 95192, United States.

The Journal of Physical Chemistry. B
|February 15, 2024
PubMed
Summary

This study introduces a novel computational method using network Hamiltonians and genetic algorithms to simulate protein aggregation, overcoming time scale challenges in studying diseases like Alzheimer's. The AI-driven approach successfully optimizes models, achieving higher fibril fractions than previous methods.

More Related Videos

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

17.1K
Rapid Generation of Amyloid from Native Proteins In vitro
05:48

Rapid Generation of Amyloid from Native Proteins In vitro

Published on: December 5, 2013

6.2K

Related Experiment Videos

Last Updated: Jul 3, 2025

Author Spotlight: In Silico Creation and Impact of Carbonylated Amino Acids on Protein Structure and Function
05:57

Author Spotlight: In Silico Creation and Impact of Carbonylated Amino Acids on Protein Structure and Function

Published on: April 26, 2024

388
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

17.1K
Rapid Generation of Amyloid from Native Proteins In vitro
05:48

Rapid Generation of Amyloid from Native Proteins In vitro

Published on: December 5, 2013

6.2K

Area of Science:

  • Computational biology
  • Biophysics
  • Materials science

Background:

  • Atomistic simulations face time scale limitations (microseconds) for protein aggregation, hindering study of disease-related fibril formation (minutes/hours).
  • Coarse-grained simulations and network Hamiltonian models offer a computationally feasible approach to investigate molecular mechanisms of protein aggregation.
  • Determining parameters for network Hamiltonian models, representing proteins as nodes and bonds as edges, is a significant technical challenge.

Purpose of the Study:

  • To develop and demonstrate a computational methodology for simulating protein aggregation using network Hamiltonian models.
  • To address the significant time scale gap between atomistic simulations and experimental observations of amyloid fibril formation.
  • To optimize network Hamiltonian models for predicting amyloid fibril formation and structure.

Main Methods:

  • Employed a genetic algorithm to evolve network Hamiltonian models from low to high fibril fractions (>70%).
  • Applied the methodology to optimize existing network Hamiltonian models for five key amyloid fibril topologies from the Protein Data Bank (PDB).
  • Utilized a graph-based representation where proteins are nodes and non-covalent bonds are edges to define system energy.

Main Results:

  • The AI-generated models successfully evolved from low (<5%) to high (>70%) fibril fractions.
  • Optimized models surpassed previously published fibril fractions in 3 out of 5 tested amyloid fibril topologies.
  • Achieved superior performance for the 1,2 2-ribbon topology, a common structure featuring a steric zipper.

Conclusions:

  • The developed genetic algorithm-based methodology effectively optimizes network Hamiltonian models for protein self-assembly.
  • This approach offers a powerful tool for studying the molecular mechanisms of amyloid formation implicated in neurodegenerative diseases.
  • The open-source release of the genetic algorithm aims to promote wider adoption for various self-assembling systems.