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Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

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Published on: July 16, 2017

Three-Dimensional Krawtchouk Descriptors for Protein Local Surface Shape Comparison.

Atilla Sit¹, Woong-Hee Shin², Daisuke Kihara^2,3,4

¹Department of Mathematics and Statistics, Eastern Kentucky University, Richmond, KY, 40475 USA.

Pattern Recognition

|February 12, 2020

Summary

This summary is machine-generated.

This study introduces 3D Krawtchouk descriptors (3DKDs) for efficient 3D object comparison. These descriptors enable precise local shape analysis of 3D surfaces, aiding in predicting protein-ligand binding.

Keywords:

3D Krawtchouk moments 3D image retrieval Krawtchouk polynomials discrete orthogonal functions ligand binding site local image comparison pocket comparison protein surface region of interest structure-based function prediction weighted Krawtchouk polynomials

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Area of Science:

Computer Vision
Computational Biology
Geometric Modeling

Background:

Direct comparison of 3D objects is computationally intensive.
Identifying local features in 3D objects is crucial for many applications.
Existing methods for local image patch comparison using 2D Krawtchouk polynomials have shown promise.

Purpose of the Study:

To extend 2D Krawtchouk polynomials to 3D for creating invariant descriptors.
To develop 3D Krawtchouk descriptors (3DKDs) capable of local feature extraction from 3D surfaces.
To apply 3DKDs for local shape comparison of protein surfaces to predict ligand binding.

Main Methods:

Formulation of 3D Krawtchouk descriptors (3DKDs) invariant under translation, rotation, and scaling.
Development of a method for extracting local features from any region-of-interest on a 3D surface.
Application of 3DKDs to compare local shapes of protein surfaces.

Main Results:

Successful construction of 3D Krawtchouk descriptors (3DKDs).
Demonstrated ability of 3DKDs to extract local 3D surface features.
Validation of 3DKDs for local shape comparison in the context of protein-ligand interaction prediction.

Conclusions:

3DKDs offer an efficient approach for comparing local regions of 3D objects.
The developed descriptors are invariant to translation, rotation, and scaling.
3DKDs show potential for advancing computational biology, particularly in predicting protein-ligand binding.