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

Aggregates Classification01:29

Aggregates Classification

978
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
978
Bonding and Strength of Aggregate01:12

Bonding and Strength of Aggregate

482
The bond between aggregate particles and the cement matrix is significantly influenced by the shape and surface texture of the aggregates. High-strength concretes benefit from a rougher texture, which leads to stronger bonding due to greater adhesion. Angular aggregates with larger surface areas also enhance this bond. The bonding quality, however, is complex to assess as no universally accepted test exists. Good bonding is indicated when a crushed concrete specimen shows some aggregate...
482
Specific Gravity of Aggregate01:19

Specific Gravity of Aggregate

775
Aggregates typically contain pores, which can be either permeable or impermeable. Considering the pores in the aggregates, the specific gravity of aggregates is defined in three different forms, namely, bulk or gross specific gravity, apparent specific gravity, and absolute specific gravity.
Bulk or gross specific gravity is calculated by taking the ratio of the mass of aggregates in the saturated surface-dry state to the total volume that includes both the solids and the voids within the...
775
Bulk Density of Aggregate01:22

Bulk Density of Aggregate

1.1K
Bulk density refers to the mass of aggregate particles that would fill a unit volume. The concept of bulk density originates from the inability to pack aggregate particles in a manner that completely eliminates void spaces. Hence, the term bulk refers to the volume that encompasses both the aggregates and the voids. This measurement is crucial when aggregates are batched by volume and is used to convert quantities by mass to volume.
Most natural mineral aggregates, like sand and gravel,...
1.1K
Design Example: Aggregate Gradation01:24

Design Example: Aggregate Gradation

319
The right type and quality of aggregates are crucial for concrete as they significantly influence its properties, mix proportions, and cost-effectiveness. If different sources are available for sand, the commonly used fine aggregate in concrete, the selection of sand is primarily based on its gradation.
The grading, or particle-size distribution, of sand is determined using sieve analysis, with standard sizes ranging from 150 μm to 10 mm (ASTM No. 100 sieve to 3⁄8 in. sieve). Sand is...
319
Toughness and Hardness of Aggregate01:22

Toughness and Hardness of Aggregate

601
Toughness and hardness are critical properties of aggregate materials used in concrete, particularly on pavement surfaces and industrial flooring subjected to heavy loads. Toughness is defined as the aggregate's resistance to failure by impact and is measured by the aggregate impact value (AIV). For this, the aggregate impact value test is performed, wherein the impact is delivered by a standard hammer, which falls freely under its own weight onto the aggregates. The aggregates fragment in...
601

You might also read

Related Articles

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

Sort by
Same author

Hierarchical multi-timescale structural dynamics of the disordered N-terminal of p53.

Nature communications·2026
Same author

PEG-mCherry interactions beyond classical macromolecular crowding.

Protein science : a publication of the Protein Society·2025
Same author

The structural influence of the oncogenic driver mutation N642H in the STAT5B SH2 domain.

Protein science : a publication of the Protein Society·2024
Same author

The Conformational Space of the SARS-CoV-2 Main Protease Active Site Loops Is Determined by Ligand Binding and Interprotomer Allostery.

Biochemistry·2024
Same author

Secondary Structure Stabilization of Macrocyclic Antimicrobial Peptides via Cross-Link Swapping.

Journal of medicinal chemistry·2024
Same author

Functional protein dynamics in a crystal.

Nature communications·2024

Related Experiment Video

Updated: Jan 21, 2026

Methods to Study Changes in Inherent Protein Aggregation with Age in Caenorhabditis elegans
11:57

Methods to Study Changes in Inherent Protein Aggregation with Age in Caenorhabditis elegans

Published on: November 26, 2017

9.1K

Atomistic Simulation Tools to Study Protein Self-Aggregation.

Deniz Meneksedag-Erol1,2, Sarah Rauscher3,4,5

  • 1Department of Chemical and Physical Sciences, University of Toronto Mississauga, Mississauga, ON, Canada.

Methods in Molecular Biology (Clifton, N.J.)
|July 26, 2019
PubMed
Summary
This summary is machine-generated.

This study uses molecular dynamics simulations to model protein aggregation, a key factor in diseases like Alzheimer's. The research details methods for simulating and analyzing peptide self-aggregation for better disease insight.

Keywords:
Alzheimer’s diseaseAmyloid βIntrinsically disordered proteinsMolecular dynamics simulationsProtein self-aggregation

More Related Videos

Advanced Self-Healing Asphalt Reinforced by Graphene Structures: An Atomistic Insight
08:03

Advanced Self-Healing Asphalt Reinforced by Graphene Structures: An Atomistic Insight

Published on: May 31, 2022

5.6K
In Vitro Assay for Studying the Aggregation of Tau Protein and Drug Screening
09:49

In Vitro Assay for Studying the Aggregation of Tau Protein and Drug Screening

Published on: November 20, 2018

19.7K

Related Experiment Videos

Last Updated: Jan 21, 2026

Methods to Study Changes in Inherent Protein Aggregation with Age in Caenorhabditis elegans
11:57

Methods to Study Changes in Inherent Protein Aggregation with Age in Caenorhabditis elegans

Published on: November 26, 2017

9.1K
Advanced Self-Healing Asphalt Reinforced by Graphene Structures: An Atomistic Insight
08:03

Advanced Self-Healing Asphalt Reinforced by Graphene Structures: An Atomistic Insight

Published on: May 31, 2022

5.6K
In Vitro Assay for Studying the Aggregation of Tau Protein and Drug Screening
09:49

In Vitro Assay for Studying the Aggregation of Tau Protein and Drug Screening

Published on: November 20, 2018

19.7K

Area of Science:

  • Biochemistry and Molecular Biology
  • Computational Biology
  • Neuroscience

Background:

  • Protein misfolding and aggregation are implicated in neurodegenerative diseases such as Alzheimer's and Parkinson's.
  • Intrinsically disordered proteins, common in amyloidogenic processes, present challenges for experimental characterization.
  • Molecular dynamics (MD) simulations offer atomistic detail to study dynamic protein behavior and aggregation mechanisms.

Purpose of the Study:

  • To demonstrate the application of all-atom molecular dynamics simulations for modeling protein self-aggregation.
  • To provide a methodological guide for simulating and analyzing the aggregation of amyloidogenic peptides.
  • To investigate the structural and mechanistic aspects of amyloidogenic peptide self-aggregation.

Main Methods:

  • Utilizing all-atom molecular dynamics simulations to model the self-aggregation of a six-residue amyloidogenic peptide fragment.
  • Detailed instructions for preparing monomer conformations and constructing multichain systems for simulation.
  • Methods for conducting MD simulations and analyzing trajectories to capture aggregation dynamics.

Main Results:

  • Successful modeling of the self-aggregation process of a model amyloidogenic peptide.
  • Generation of detailed insights into the conformational ensemble and aggregation pathways.
  • Demonstration of MD simulations as a powerful tool for studying amyloid formation.

Conclusions:

  • All-atom molecular dynamics simulations are effective for studying amyloidogenic peptide self-aggregation.
  • The provided methodology enables detailed investigation of protein aggregation mechanisms.
  • This approach offers valuable insights into the pathogenesis of protein misfolding diseases.