Search research articles

Search research articles

Related Experiment Videos

Protein loop structure prediction with flexible stem geometries.

M Mönnigmann¹, C A Floudas

¹Department of Chemical Engineering, Princeton University, Princeton, New Jersey 08544-5263, USA.

|October 14, 2005

Summary

This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

High-throughput proteomic analysis of candidate biomarker changes in gingival crevicular fluid after treatment of chronic periodontitis.

Journal of periodontal research·2018

Same author

ASTRO-FOLD 2.0: an Enhanced Framework for Protein Structure Prediction.

AIChE journal. American Institute of Chemical Engineers·2012

Same author

Structure prediction of loops with fixed and flexible stems.

The journal of physical chemistry. B·2012

Same author

Enhanced Inter-helical Residue Contact Prediction in Transmembrane Proteins.

Chemical engineering science·2011

Same author

Discovery of entry inhibitors for HIV-1 via a new de novo protein design framework.

Biophysical journal·2010

Same author

New compstatin variants through two de novo protein design frameworks.

Biophysical journal·2010

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

This study presents a novel computational method for predicting protein loop structures with flexible stems, even when the surrounding protein geometry is unknown. The approach improves accuracy by iteratively clustering and discarding inaccurate models, enhancing protein structure prediction.

Area of Science:

Computational Biology
Structural Bioinformatics
Protein Structure Prediction

Background:

Predicting protein loop structures is crucial for understanding protein function and dynamics.
Existing methods often assume known surrounding protein geometry, limiting their applicability.
Flexible stem residues in loops present a significant challenge in structure prediction.

Purpose of the Study:

To develop and assess a computational methodology for predicting protein loop structures with flexible stems.
To evaluate the precision of loop structure prediction when surrounding protein geometry is unknown.
To compare different strategies for selecting accurate loop structures from predicted ensembles.

Main Methods:

Dihedral angle sampling and energy minimization using a physically based energy function.

Related Experiment Videos

A novel iterative clustering approach to identify both near-native and far-from-native decoys.

Comparative analysis of loop selection criteria including energy, colony energy, cluster density, and a hybrid criterion.

Main Results:

The proposed method was tested on a large dataset (3215 loops) and a smaller set (179 loops) from the Casp6 experiment.
Iterative clustering effectively improved the quality of the predicted loop structure ensemble by removing decoys.
The study compared various selection strategies, introducing a novel hybrid criterion.

Conclusions:

The developed methodology provides a robust approach for protein loop structure prediction with flexible stems and unknown protein geometry.
Iterative clustering is a key innovation for enhancing prediction accuracy by actively identifying and discarding decoys.
The comparative analysis of selection criteria offers insights into optimizing the final loop structure selection process.