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

Antibody Structure01:10

Antibody Structure

62.1K
Overview
Antibodies, also known as immunoglobulins (Ig), are essential players of the adaptive immune system. These antigen-binding proteins are produced by B cells and make up 20 percent of the total blood plasma by weight. In mammals, antibodies fall into five different classes, which each elicits a different biological response upon antigen binding.
The Y-Shaped Structure of Antibodies Consists of Four Polypeptide Chains
Antibodies consist of four polypeptide chains: two identical heavy...
62.1K
Amyloid Fibrils03:03

Amyloid Fibrils

10.9K
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,...
10.9K
Amyloid Fibrils03:03

Amyloid Fibrils

6.0K
6.0K
Antibody Structure and Classes01:25

Antibody Structure and Classes

6.9K
Antibodies, also known as immunoglobulins, are produced by B cells in response to foreign substances, such as bacteria and viruses. These proteins are critical for recognizing and neutralizing these substances, protecting the body from potential harm.
The basic structure of an antibody consists of four protein chains: two identical heavy chains and two identical light chains. These chains are held together by disulfide bonds and other non-covalent interactions, forming a Y-shaped structure.
6.9K

You might also read

Related Articles

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

Sort by
Same author

PDA-MutPred: Reliable prediction of binding affinity change upon mutation in protein-DNA complexes.

International journal of biological macromolecules·2026
Same author

Quantifying uncertainty in protein representations across models and tasks.

Nature methods·2026
Same author

Classification of driver and passenger mutations in different cancer types using deep neural networks.

Bioinformatics advances·2026
Same author

Identification of a type 1 diabetes-associated T cell receptor repertoire signature from the human peripheral blood.

Science advances·2026
Same author

Investigating protein aggregation in protein-carbohydrate interfaces using sequence and structural features.

Biochimica et biophysica acta. Proteins and proteomics·2026
Same author

TCR2HLA: Calibrated inference of HLA genotypes from TCR repertoires enables identification of immunologically relevant metaclonotypes.

PLoS computational biology·2026

Related Experiment Video

Updated: Oct 30, 2025

Novel Atomic Force Microscopy Based Biopanning for Isolation of Morphology Specific Reagents against TDP-43 Variants in Amyotrophic Lateral Sclerosis
13:31

Novel Atomic Force Microscopy Based Biopanning for Isolation of Morphology Specific Reagents against TDP-43 Variants in Amyotrophic Lateral Sclerosis

Published on: February 12, 2015

9.0K

Exploring the sequence features determining amyloidosis in human antibody light chains.

Puneet Rawat1, R Prabakaran1, Sandeep Kumar2

  • 1Protein Bioinformatics Lab, Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, 600036, Tamil Nadu, India.

Scientific Reports
|July 3, 2021
PubMed
Summary

We developed VLAmY-Pred, a machine learning tool to predict the amyloidogenic potential of antibody light chains. This aids in diagnosing light chain amyloidosis and engineering safer antibody therapeutics.

More Related Videos

Imaging Amyloid Tissues Stained with Luminescent Conjugated Oligothiophenes by Hyperspectral Confocal Microscopy and Fluorescence Lifetime Imaging
10:04

Imaging Amyloid Tissues Stained with Luminescent Conjugated Oligothiophenes by Hyperspectral Confocal Microscopy and Fluorescence Lifetime Imaging

Published on: October 20, 2017

13.6K
Detecting Amyloid-β Accumulation via Immunofluorescent Staining in a Mouse Model of Alzheimer's Disease
08:25

Detecting Amyloid-β Accumulation via Immunofluorescent Staining in a Mouse Model of Alzheimer's Disease

Published on: April 19, 2021

3.6K

Related Experiment Videos

Last Updated: Oct 30, 2025

Novel Atomic Force Microscopy Based Biopanning for Isolation of Morphology Specific Reagents against TDP-43 Variants in Amyotrophic Lateral Sclerosis
13:31

Novel Atomic Force Microscopy Based Biopanning for Isolation of Morphology Specific Reagents against TDP-43 Variants in Amyotrophic Lateral Sclerosis

Published on: February 12, 2015

9.0K
Imaging Amyloid Tissues Stained with Luminescent Conjugated Oligothiophenes by Hyperspectral Confocal Microscopy and Fluorescence Lifetime Imaging
10:04

Imaging Amyloid Tissues Stained with Luminescent Conjugated Oligothiophenes by Hyperspectral Confocal Microscopy and Fluorescence Lifetime Imaging

Published on: October 20, 2017

13.6K
Detecting Amyloid-β Accumulation via Immunofluorescent Staining in a Mouse Model of Alzheimer's Disease
08:25

Detecting Amyloid-β Accumulation via Immunofluorescent Staining in a Mouse Model of Alzheimer's Disease

Published on: April 19, 2021

3.6K

Area of Science:

  • Biochemistry
  • Computational Biology
  • Immunology

Background:

  • Light chain (AL) amyloidosis results from antibody light chain aggregation into amyloid fibrils.
  • Predicting whole protein amyloidogenicity from sequence/structure is challenging.
  • Antibody light chain architecture and binding sites offer clues to amyloid formation.

Purpose of the Study:

  • To compare sequence-based, aggregation-related features in amyloidogenic and non-amyloidogenic antibody light chains.
  • To develop a machine learning model for predicting antibody light chain amyloidogenicity.
  • To create a webserver, VLAmY-Pred, for practical application of the model.

Main Methods:

  • Calculated sequence-based features (hydrophobicity, gatekeeper residues, disorderness, β-propensity) for CDR, FR, and VL regions.
  • Trained a machine learning model using these features on a dataset of 1828 antibody light chain variable regions.
  • Implemented the model into the VLAmY-Pred webserver (https://web.iitm.ac.in/bioinfo2/vlamy-pred/).

Main Results:

  • The VLAmY-Pred model achieved 79.7% prediction accuracy (78.7% sensitivity, 79.9% specificity).
  • The model demonstrated a ROC value of 0.88.
  • Analysis focused on distinguishing amyloidogenic from non-amyloidogenic antibody light chains.

Conclusions:

  • VLAmY-Pred offers a valuable tool for predicting light chain amyloidosis risk.
  • The tool aids in understanding antibody aggregation and designing aggregation-resistant antibodies.
  • Applications include improved patient prognosis and analysis of next-generation sequencing data.