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Thermodynamic Potentials

Thermodynamic Potentials

Thermodynamic potentials are state functions that are extremely useful in analyzing a thermodynamic system. They have dimensions of energy. The four important thermodynamic potentials are internal energy, enthalpy, Helmholtz free energy, and Gibbs free energy. These thermodynamic potentials can be expressed using two of the following variables: pressure, volume, temperature, and entropy. These two variables are expressed as the rate of change of the thermodynamic potential with respect to other...

Thermodynamics: Chemical Potential and Activity

Thermodynamics: Chemical Potential and Activity

The effective concentration of a species in a solution can be expressed precisely in terms of its activity. Activity considers the effect of electrolytes present in the vicinity of the species of interest and depends on the ionic strength of the solution. The activity of a species is expressed as the product of molar concentration and the activity coefficient of the species.
The thermodynamic equilibrium constant is more accurately defined in terms of activity rather than concentration.

Mechanical Protein Function

Mechanical Protein Function

Mechanical Protein Functions

Mechanical Protein Functions

Proteins perform many mechanical functions in a cell. These proteins can be classified into two general categories- proteins that generate mechanical forces and proteins that are subjected to mechanical forces. Proteins providing mechanical support to the structure of the cell, such as keratin, are subjected to mechanical force, whereas proteins involved in cell movement and transport of molecules across cell membranes, such as an ion pump, are examples of generating mechanical force.

Graded Potential

Graded Potential

Graded potentials are localized fluctuations in the cell membrane's electrical charge, commonly found in the dendrites of neurons. The magnitude of these potential changes depends on the strength of the initiating stimulus. In a membrane at its resting potential, a graded potential signifies a voltage shift either above -70 mV or below -70 mV.
Graded potentials fall into two categories: depolarizing and hyperpolarizing. Depolarizing graded potentials typically occur when sodium (Na+) or...

Gibbs Free Energy and Thermodynamic Favorability

Gibbs Free Energy and Thermodynamic Favorability

The spontaneity of a process depends upon the temperature of the system. Phase transitions, for example, will proceed spontaneously in one direction or the other depending upon the temperature of the substance in question. Likewise, some chemical reactions can also exhibit temperature-dependent spontaneities. To illustrate this concept, the equation relating free energy change to the enthalpy and entropy changes for the process is considered:

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Related Experiment Video

Updated: Jul 16, 2025

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

Machine learning coarse-grained potentials of protein thermodynamics.

Maciej Majewski^1,2, Adrià Pérez^1,2, Philipp Thölke¹

¹Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), Carrer Dr. Aiguader 88, 08003, Barcelona, Spain.

Nature Communications

|September 15, 2023

Summary

This summary is machine-generated.

Researchers developed machine learning models for protein dynamics simulations. These artificial neural network potentials accelerate simulations by over 1000x, preserving essential thermodynamics and capturing protein behavior.

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Last Updated: Jul 16, 2025

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

Area of Science:

Computational biology
Biophysics
Machine learning in structural biology

Background:

Understanding protein dynamics is crucial for interpreting structure-function relationships in biological processes.
Simulating protein dynamics accurately and efficiently remains a significant scientific challenge.

Purpose of the Study:

To develop a novel approach for simulating protein dynamics using machine learning-based coarse-grained potentials.
To accelerate molecular dynamics simulations while maintaining thermodynamic accuracy.

Main Methods:

Constructed coarse-grained molecular potentials using artificial neural networks grounded in statistical mechanics.
Trained models on a dataset of approximately 9 milliseconds of unbiased all-atom molecular dynamics simulations across twelve diverse proteins.
Validated the coarse-grained models against all-atom simulations and experimental data.

Main Results:

Coarse-grained models achieved over three orders of magnitude acceleration in dynamics simulation speed.
The models preserved the thermodynamics of the simulated systems.
Identified relevant structural states and their energetics, comparable to all-atom simulations.
A single coarse-grained potential successfully integrated all twelve proteins and predicted experimental features of mutated proteins.

Conclusions:

Machine learning-based coarse-grained potentials offer a feasible and efficient method for simulating protein dynamics.
This approach can aid in understanding protein structure-function relationships and essential biological processes.
The developed potentials show promise for integrating diverse protein systems and predicting effects of mutations.