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Conservation of Protein Domains Over Different Proteins

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Updated: May 7, 2026

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

Multiscale Differential Geometry Learning for Protein Flexibility Analysis.

Hongsong Feng¹, Jeffrey Y Zhao², Guo-Wei Wei^1,3,4

¹Department of Mathematics, Michigan State University, East Lansing, Michigan, USA.

Journal of Computational Chemistry

|March 12, 2025

Summary

This summary is machine-generated.

This study introduces a novel multiscale differential geometry (mDG) model for predicting protein B-factors, enhancing accuracy by 27% over existing methods. The approach leverages protein structural properties on low-dimensional manifolds for improved flexibility and function insights.

Keywords:

blind prediction multiscale differential geometry protein flexibility

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Last Updated: May 7, 2026

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Analyzing Protein Architectures and Protein-Ligand Complexes by Integrative Structural Mass Spectrometry

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Published on: October 15, 2018

Area of Science:

Structural Biology
Computational Biology
Biophysics

Background:

Protein B-factors quantify structural fluctuations, correlating with flexibility and function.
Existing theoretical models predict B-factors but can be improved.
Understanding protein flexibility is crucial for biological function.

Purpose of the Study:

To develop a novel and accurate method for predicting protein B-factors.
To explore the application of differential geometry in modeling protein structures.
To enhance the understanding of protein flexibility and its relation to function.

Main Methods:

Proposed a novel approach using differential geometry theory.
Analyzed mean and Gaussian curvatures of low-dimensional manifolds.
Developed multiscale differential geometry (mDG) models.
Constructed a machine-learning model incorporating global and local protein features.

Main Results:

The mDG model achieved a 27% increase in accuracy for B-factor prediction compared to the Gaussian network model (GNM).
The machine-learning model demonstrated high effectiveness in blind B-factor prediction.
Validated the mDG approach through least-squares approximations and machine learning.

Conclusions:

The mDG approach provides an effective and robust method for B-factor prediction.
Integrating differential geometry offers new insights into protein structural properties.
The developed models advance the prediction of protein flexibility and function.