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Related Experiment Video

Updated: May 22, 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

Optimal simultaneous superpositioning of multiple structures with missing data.

Douglas L Theobald¹, Phillip A Steindel

¹Department of Biochemistry, Brandeis University, MS009, Waltham, MA 02454, USA. dtheobald@brandeis.edu

Bioinformatics (Oxford, England)

|May 1, 2012

Summary

This summary is machine-generated.

This study introduces a new method to optimally superimpose macromolecular structures even with missing data points. The expectation-maximization algorithm provides a robust solution for structural biology comparisons.

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Correlative Microscopy for 3D Structural Analysis of Dynamic Interactions

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Published on: June 24, 2013

Area of Science:

Structural Biology
Computational Biology
Bioinformatics

Background:

Superpositioning is crucial for comparing conformational changes in similar structures.
Existing methods struggle with missing data points, leading to inefficient analysis and ignoring valuable information.
Current approaches fail to meet the least-squares criterion when data is incomplete.

Purpose of the Study:

To develop a general solution for optimal superpositioning with incomplete structural data.
To address the limitations of current methods in handling missing atomic coordinates.
To provide a statistically robust approach for macromolecular structure comparison.

Main Methods:

Utilized the expectation-maximization algorithm, a statistical technique for handling incomplete datasets.
Implemented the method in THESEUS 2.0 software for macromolecular structure superposition.
The algorithm finds both maximum-likelihood and optimal least-squares solutions.

Main Results:

A general solution for optimal superpositioning with missing data has been developed.
The expectation-maximization algorithm effectively handles incomplete structural information.
The approach provides accurate least-squares solutions even with gaps in the data.

Conclusions:

The novel method enables more accurate and complete structural comparisons in the presence of missing data.
THESEUS 2.0 offers a practical implementation for researchers in structural biology.
This advancement improves the analysis of conformational differences in macromolecular structures.