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

Predicting Products: Substitution vs. Elimination02:52

Predicting Products: Substitution vs. Elimination

14.8K
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:
14.8K
G-protein Coupled Receptors01:21

G-protein Coupled Receptors

132.0K
G-protein coupled receptors are ligand binding receptors that indirectly affect changes in the cell. The actual receptor is a single polypeptide that transverses the cell membrane seven times creating intracellular and extracellular loops. The extracellular loops create a ligand specific pocket which binds to neurotransmitters or hormones. The intracellular loops holds onto the G-protein.
132.0K
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

45.8K
VSEPR Theory for Determination of Electron Pair Geometries
45.8K
Types of Receptors: Cell Surface Receptors01:28

Types of Receptors: Cell Surface Receptors

27.4K
Cell-surface receptors, also known as transmembrane receptors, are cell surface, membrane-anchored (integral) proteins that bind to external ligand molecules. This type of receptor spans the plasma membrane and performs signal transduction, converting an extracellular signal into an intracellular signal. Ligands that interact with cell-surface receptors do not have to enter the cell that they affect. Cell-surface receptors are also called cell-specific proteins or markers because they are...
27.4K
Trigonometric Substitution01:23

Trigonometric Substitution

61
Trigonometric substitution is a technique used to simplify integrals that contain square root expressions involving quadratic forms. It is particularly effective when the integrand includes terms resembling those found in standard geometric equations, such as circles or ellipses.Molniya satellites follow highly elliptical orbits, repeatedly sweeping out the same regions of space as they revolve around Earth. To estimate the area enclosed by such an orbit, the path is modeled as an ellipse...
61
Rationalizing Substitutions01:29

Rationalizing Substitutions

59
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...
59

You might also read

Related Articles

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

Sort by
Same author

Entrenchment of germline amino-acid differences in antibody affinity maturation.

bioRxiv : the preprint server for biology·2026
Same author

Succinate dehydrogenase loss suppresses pyrimidine biosynthesis via succinate-mediated inhibition of aspartate transcarbamylase.

Nature metabolism·2026
Same author

Inference of germinal center evolutionary dynamics via simulation-based deep learning.

eLife·2026
Same author

Low-Dose Versus Standard-Dose Abiraterone in Patients With Metastatic Castration-Resistant Prostate Cancer: A Multicenter Randomized Phase III Trial.

JCO global oncology·2026
Same author

Separating selection from mutation in antibody language models.

eLife·2026
Same author

Excess cysteine drives conjugate formation and impairs proliferation of NRF2-activated cancer cells.

Nature metabolism·2026

Related Experiment Video

Updated: Feb 3, 2026

T and B Cell Receptor Immune Repertoire Analysis using Next-generation Sequencing
08:59

T and B Cell Receptor Immune Repertoire Analysis using Next-generation Sequencing

Published on: January 12, 2021

8.8K

Predicting B cell receptor substitution profiles using public repertoire data.

Amrit Dhar1,2, Kristian Davidsen2, Frederick A Matsen2

  • 1Department of Statistics, University of Washington, Seattle, Washington, United States of America.

Plos Computational Biology
|October 18, 2018
PubMed
Summary

Predicting B cell substitution profiles is now possible using the SPURF method. This approach leverages related B cell families to reconstruct amino acid frequency profiles for antibody engineering and studying B cell maturation.

More Related Videos

Identification of Mouse and Human Antibody Repertoires by Next-Generation Sequencing
08:51

Identification of Mouse and Human Antibody Repertoires by Next-Generation Sequencing

Published on: March 15, 2019

13.1K
Profiling of Estrogen-regulated MicroRNAs in Breast Cancer Cells
16:24

Profiling of Estrogen-regulated MicroRNAs in Breast Cancer Cells

Published on: February 21, 2014

20.7K

Related Experiment Videos

Last Updated: Feb 3, 2026

T and B Cell Receptor Immune Repertoire Analysis using Next-generation Sequencing
08:59

T and B Cell Receptor Immune Repertoire Analysis using Next-generation Sequencing

Published on: January 12, 2021

8.8K
Identification of Mouse and Human Antibody Repertoires by Next-Generation Sequencing
08:51

Identification of Mouse and Human Antibody Repertoires by Next-Generation Sequencing

Published on: March 15, 2019

13.1K
Profiling of Estrogen-regulated MicroRNAs in Breast Cancer Cells
16:24

Profiling of Estrogen-regulated MicroRNAs in Breast Cancer Cells

Published on: February 21, 2014

20.7K

Area of Science:

  • Immunology
  • Computational Biology
  • Bioinformatics

Background:

  • B cells mature their receptors through mutation and selection, forming clonal families.
  • Amino acid substitution profiles within these families reveal allowed mutations but are hard to obtain from single sequences.
  • Existing B cell receptor datasets offer potential for predictive modeling.

Purpose of the Study:

  • To develop a method for predicting clonal-family-specific substitution profiles from single B cell receptor sequences.
  • To enable the study of B cell affinity maturation and facilitate antibody engineering.

Main Methods:

  • Introduced the Substitution Profiles Using Related Families (SPURF) method, a penalized tensor regression framework.
  • Integrated information from multiple B cell receptor datasets, including simulated profiles and germline gene sequences.
  • Trained and validated the SPURF model on large public and external datasets.

Main Results:

  • SPURF successfully predicts clonal-family-specific substitution profiles using related families.
  • Leveraging similar clonal families, simulated data, and germline information improves prediction accuracy.
  • The model demonstrates robustness across multiple datasets.

Conclusions:

  • SPURF enables accurate prediction of B cell substitution profiles from limited sequence data.
  • This method enhances the study of B cell affinity maturation and aids antibody engineering.
  • An open-source tool is available for practical application of the SPURF method.