Analysis of Three-Dimensional Protein Images

TL;DR

This paper introduces a computational approach to analyze 3D protein images by segmenting them into critical points and identifying structural subcomponents, aiming to improve protein structure determination.

Contribution

It presents a novel scene analysis methodology for protein structure determination using graph segmentation and Bayesian analysis, reducing reliance on expert interpretation.

Findings

Effective segmentation of low and medium resolution protein images

Identification of alpha-helices and beta-sheets from critical point graphs

Potential to automate parts of protein structure analysis

Abstract

A fundamental goal of research in molecular biology is to understand protein structure. Protein crystallography is currently the most successful method for determining the three-dimensional (3D) conformation of a protein, yet it remains labor intensive and relies on an expert's ability to derive and evaluate a protein scene model. In this paper, the problem of protein structure determination is formulated as an exercise in scene analysis. A computational methodology is presented in which a 3D image of a protein is segmented into a graph of critical points. Bayesian and certainty factor approaches are described and used to analyze critical point graphs and identify meaningful substructures, such as alpha-helices and beta-sheets. Results of applying the methodologies to protein images at low and medium resolution are reported. The research is related to approaches to representation, segmentation and classification in vision, as well as to top-down approaches to protein structure prediction.