Jove
Visualize
Contact Us

Related Concept Videos

Lattice Centering and Coordination Number02:33

Lattice Centering and Coordination Number

10.4K
The structure of a crystalline solid, whether a metal or not, is best described by considering its simplest repeating unit, which is referred to as its unit cell. The unit cell consists of lattice points that represent the locations of atoms or ions. The entire structure then consists of this unit cell repeating in three dimensions. The three different types of unit cells present in the cubic lattice are illustrated in Figure 1.
Types of Unit Cells
Imagine taking a large number of identical...
10.4K
Bewley Lattice Diagram01:12

Bewley Lattice Diagram

959
The Bewley lattice diagram, developed by L. V. Bewley, effectively organizes the reflections occurring during transmission-line transients. It visually represents how voltage waves propagate and reflect within a transmission line, making it easier to understand the complex interactions that occur.
959
Protein-protein Interfaces02:04

Protein-protein Interfaces

14.1K
Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
14.1K
Protein-Protein Interfaces02:04

Protein-Protein Interfaces

4.0K
4.0K
Conserved Binding Sites01:49

Conserved Binding Sites

4.7K
Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
4.7K
Behavioral Genetics and Its Designs01:23

Behavioral Genetics and Its Designs

665
Behavior genetics explores how genetic inheritance influences human behavior. It focuses on how genes, passed from parents to offspring, contribute to the development of behavioral traits and tendencies. This branch of genetics seeks to understand the complex interplay between inherited genetic factors and environmental influences in shaping our behaviors.
The primary methodologies used in behavior genetics include family studies, twin studies, and adoption studies, each providing unique...
665

You might also read

Related Articles

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

Sort by
Same author

RINAMI: Residue-attributed interpretable neural network for predicting absolute folding free energy by merging structure and sequence information.

Protein science : a publication of the Protein Society·2026
Same author

Crystallization and initial X-ray crystallographic analysis of a de novo-designed protein with left-handed βαβ units.

Acta crystallographica. Section F, Structural biology communications·2025
Same author

Exploring hydrophilic sequence space to search for uncharted foldable proteins by AlphaFold2.

Biophysics and physicobiology·2025
Same author

Statistical Analysis of Walker-A Motif-Containing β-α-β Supersecondary Structures in the Protein Data Bank.

Methods in molecular biology (Clifton, N.J.)·2024
Same author

Protein superfolds are characterised as frustration-free topologies: A case study of pure parallel β-sheet topologies.

PLoS computational biology·2024
Same author

Exploration of novel αβ-protein folds through de novo design.

Nature structural & molecular biology·2023
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 Experiment Video

Updated: Oct 23, 2025

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

17.2K

Lattice protein design using Bayesian learning.

Tomoei Takahashi1, George Chikenji2, Kei Tokita1

  • 1Graduate School of Informatics, Nagoya University, Nagoya 464-8601, Japan.

Physical Review. E
|August 20, 2021
PubMed
Summary

This study introduces a novel Bayesian learning method for protein design, significantly reducing computation time by eliminating exhaustive conformational searches. The approach successfully designs lattice proteins with unique ground states, offering insights into protein structure and water interactions.

More Related Videos

Ligand Nano-cluster Arrays in a Supported Lipid Bilayer
10:34

Ligand Nano-cluster Arrays in a Supported Lipid Bilayer

Published on: April 23, 2017

7.1K
Automated Robotic Liquid Handling Assembly of Modular DNA Devices
11:22

Automated Robotic Liquid Handling Assembly of Modular DNA Devices

Published on: December 1, 2017

12.6K

Related Experiment Videos

Last Updated: Oct 23, 2025

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

17.2K
Ligand Nano-cluster Arrays in a Supported Lipid Bilayer
10:34

Ligand Nano-cluster Arrays in a Supported Lipid Bilayer

Published on: April 23, 2017

7.1K
Automated Robotic Liquid Handling Assembly of Modular DNA Devices
11:22

Automated Robotic Liquid Handling Assembly of Modular DNA Devices

Published on: December 1, 2017

12.6K

Area of Science:

  • Computational biology
  • Protein engineering
  • Statistical mechanics

Background:

  • Protein design aims to determine amino acid sequences for specific 3D structures.
  • Traditional methods involve computationally intensive double-loop searches.
  • Understanding the sequence-structure relationship is crucial for protein function.

Purpose of the Study:

  • To develop a novel, computationally efficient protein design method.
  • To integrate Bayesian learning and statistical mechanics for lattice protein design.
  • To investigate the influence of water effects on protein structure.

Main Methods:

  • A statistical mechanical design method using Bayesian learning.
  • Incorporation of a thermodynamic hypothesis for protein evolution.
  • Application of the grand canonical picture to account for water effects.
  • Utilized 2D and 3D lattice hydrophobic-polar (HP) models.

Main Results:

  • Successfully designed lattice proteins with unique ground states using the 2D HP model.
  • Observed improved performance in 3D HP models with a 20-letter alphabet.
  • Identified a linear relationship between water chemical potential and surface residue count.
  • Demonstrated significant reduction in computation time compared to exhaustive search methods.

Conclusions:

  • The proposed Bayesian learning method offers a computationally efficient alternative for protein design.
  • The study provides insights into the role of water molecules in protein structure.
  • The generalizable framework can inform heuristics in existing protein design strategies.