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

Surface Tension and Surface Energy01:16

Surface Tension and Surface Energy

3.3K
When a paint brush is immersed in water, the bristles wave freely inside the water. When it is taken out, the bristles stick together. The reason behind this effect is surface tension.
Consider a beaker filled with liquid. The bulk molecules in the liquid experience equal attractive forces on all sides with the surrounding molecules. However, the surface molecules experience a net attractive force downward due to the bulk molecules. The surface of the liquid behaves like a stretched membrane,...
3.3K
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

1.1K
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
1.1K
Peptide Bonds02:43

Peptide Bonds

83.5K
A peptide bond covalently attaches amino acids through a dehydration reaction. One amino acid's carboxyl group and another amino acid's amino group combine, releasing a water molecule. The resulting bond is the peptide bond. The products that such linkages form are peptides. As more amino acids join this growing chain, the resulting chain is a polypeptide. Each polypeptide has a free amino group at one end. This end has the N-terminal, or the amino-terminal, and the other end has a free...
83.5K
What is Energy?04:10

What is Energy?

59.6K
The universe is composed of matter in different forms, and all forms of matter contain energy.  The different forms of energy on Earth originate from the Sun — the ultimate energy source. Plants capture light energy from the Sun, and, via the process of photosynthesis, convert it into chemical energy. This stored energy from plants can be harnessed in many ways. For example, eating plant products as food provides energy for our body to function, and burning wood or coal (fossilized...
59.6K
Gibbs Free Energy02:39

Gibbs Free Energy

39.3K
One of the challenges of using the second law of thermodynamics to determine if a process is spontaneous is that it requires measurements of the entropy change for the system and the entropy change for the surroundings. An alternative approach involving a new thermodynamic property defined in terms of system properties only was introduced in the late nineteenth century by American mathematician Josiah Willard Gibbs. This new property is called the Gibbs free energy (G) (or simply the free...
39.3K
Free Energy01:21

Free Energy

52.2K
Free energy—abbreviated as G for the scientist Gibbs who discovered it—is a measurement of useful energy that can be extracted from a reaction to do work. It is the energy in a chemical reaction that is available after entropy is accounted for. Reactions that take in energy are considered endergonic and reactions that release energy are exergonic. Plants carry out endergonic reactions by taking in sunlight and carbon dioxide to produce glucose and oxygen. Animals, in turn, break...
52.2K

You might also read

Related Articles

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

Sort by
Same author

ProtAff: Protein Binding Affinity Prediction via LoRA-Finetuned ESM-2.

bioRxiv : the preprint server for biology·2026
Same author

Predictions from deep learning propose substantial protein-carbohydrate interplay.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

The Open Molecular Software Foundation (OMSF) and the Growing Role of Open Source Software in Molecular Modeling.

Journal of chemical information and modeling·2026
Same author

Fitness Landscape for Antibodies 2: Benchmarking Reveals That Protein AI Models Cannot Yet Consistently Predict Developability Properties.

bioRxiv : the preprint server for biology·2026
Same author

Can We Extract Physics-like Energies from Generative Protein Diffusion Models?

bioRxiv : the preprint server for biology·2025
Same author

Adapting Co-Folding Models for Structure-Based Protein-Protein Docking Through Flow Matching.

bioRxiv : the preprint server for biology·2025

Related Experiment Video

Updated: Feb 12, 2026

Quantification of Global Diastolic Function by Kinematic Modeling-based Analysis of Transmitral Flow via the Parametrized Diastolic Filling Formalism
11:04

Quantification of Global Diastolic Function by Kinematic Modeling-based Analysis of Transmitral Flow via the Parametrized Diastolic Filling Formalism

Published on: September 1, 2014

11.6K

A Parametric Rosetta Energy Function Analysis with LK Peptides on SAM Surfaces.

Joseph H Lubin, Michael S Pacella, Jeffrey J Gray

    Langmuir : the ACS Journal of Surfaces and Colloids
    |April 10, 2018
    PubMed
    Summary
    This summary is machine-generated.

    Molecular simulations like Rosetta can model protein-surface interactions. Optimizing Rosetta

    More Related Videos

    Peptide-based Identification of Functional Motifs and their Binding Partners
    14:28

    Peptide-based Identification of Functional Motifs and their Binding Partners

    Published on: June 30, 2013

    13.0K
    Precision Measurements and Parametric Models of Vertebral Endplates
    10:35

    Precision Measurements and Parametric Models of Vertebral Endplates

    Published on: September 17, 2019

    6.9K

    Related Experiment Videos

    Last Updated: Feb 12, 2026

    Quantification of Global Diastolic Function by Kinematic Modeling-based Analysis of Transmitral Flow via the Parametrized Diastolic Filling Formalism
    11:04

    Quantification of Global Diastolic Function by Kinematic Modeling-based Analysis of Transmitral Flow via the Parametrized Diastolic Filling Formalism

    Published on: September 1, 2014

    11.6K
    Peptide-based Identification of Functional Motifs and their Binding Partners
    14:28

    Peptide-based Identification of Functional Motifs and their Binding Partners

    Published on: June 30, 2013

    13.0K
    Precision Measurements and Parametric Models of Vertebral Endplates
    10:35

    Precision Measurements and Parametric Models of Vertebral Endplates

    Published on: September 17, 2019

    6.9K

    Area of Science:

    • Computational biology
    • Biophysics
    • Materials science

    Background:

    • Experimental protein structure determination is challenging for protein-solid surface complexes.
    • Molecular simulation offers an economical alternative for modeling these interactions.
    • The Rosetta software's energy function for surface interactions is not well-validated.

    Purpose of the Study:

    • To assess the RosettaSurface algorithm's performance in modeling protein-surface interactions.
    • To test the accuracy of Rosetta's energy function for protein adsorption on self-assembled monolayers (SAMs).
    • To identify parameters that improve Rosetta's predictive accuracy for these systems.

    Main Methods:

    • Modeling the adsorption of leucine/lysine (LK)-repeat peptides on methyl- and carboxy-terminated SAMs using RosettaSurface.
    • Comparing computational predictions with experimental results for peptide structures (helical vs. extended).
    • Performing a parametric analysis of Rosetta's Talaris energy function, focusing on solvation parameters.

    Main Results:

    • RosettaSurface predictions were compared against experimental data showing LK-α peptides forming helices and LK-β peptides adopting extended structures on both surfaces.
    • Adjusting solvation parameters in Rosetta's Talaris energy function improved predictive accuracy.
    • Simultaneously increasing lysine carbon hydrophilicity and surface methyl group hydrophobicity yielded the best agreement with experimental findings.

    Conclusions:

    • The study validates and refines the RosettaSurface algorithm for modeling protein-surface interactions.
    • Optimizing specific energy function parameters, particularly solvation, enhances computational prediction accuracy.
    • Integrative approaches combining computational modeling with experimental data are crucial for reliable de novo protein structure modeling.