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Protein Organization

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Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
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Exploring Caspase Mutations and Post-Translational Modification by Molecular Modeling Approaches

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Published on: October 13, 2022

Protein structure modeling and refinement by global optimization in CASP12.

Seung Hwan Hong^1,2, InSuk Joung^1,2, Jose C Flores-Canales^1,2

¹Center for In Silico Protein Science, Korea Institute for Advanced Study, Seoul, South Korea.

|November 22, 2017

Summary

This summary is machine-generated.

We improved protein structure modeling using conformational space annealing (CSA) and updated quality assessment (QA) methods. New energy terms and a modified approach for difficult targets (LEEab) enhanced accuracy for free modeling (FM) targets.

Keywords:

CASP global optimization homology modeling protein structure modeling protein structure refinement

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Area of Science:

Computational biology
Structural bioinformatics
Protein structure prediction

Background:

Protein structure modeling is crucial for understanding biological function.
Previous methods like CASP11 provided a foundation for improvement.
Accurate modeling requires robust alignment, chain building, and refinement strategies.

Purpose of the Study:

To develop an enhanced protein structure modeling protocol for the CASP12 experiment.
To improve template and model selection using updated quality assessment methods.
To refine existing energy functions and introduce new terms for better accuracy.

Main Methods:

Applied conformational space annealing (CSA) for global optimization across three modeling stages.
Updated the support vector machine for quality assessment (SVMQA) method.
Incorporated predicted residue-residue contacts, secondary structure, and solvent accessible surface area into the energy function.
Developed a new method (LEEab) for difficult targets, reducing reliance on template information.
Modified molecular dynamics (MD) based refinement using explicit solvents and an augmented statistical energy term.
Utilized the Lorentzian function to bound penalties from inaccurate predicted data.

Main Results:

The updated SVMQA method improved template and model selection.
New energy terms enhanced 3D chain building accuracy.
LEEab outperformed the standard LEE method for free modeling (FM) targets.
Modified MD refinement yielded promising results with the new energy term.
The Lorentzian function effectively managed inaccuracies in predicted contacts.

Conclusions:

The developed protocol shows significant improvements in protein structure modeling, particularly for FM targets.
Integration of predicted data with appropriate functions enhances model accuracy.
The updated QA and refinement methods contribute to more reliable protein structure predictions.