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Protein Folding

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Related Experiment Video

Updated: May 13, 2026

Assessment of Immunologically Relevant Dynamic Tertiary Structural Features of the HIV-1 V3 Loop Crown R2 Sequence by ab initio Folding

Assessment of Immunologically Relevant Dynamic Tertiary Structural Features of the HIV-1 V3 Loop Crown R2 Sequence by ab initio Folding

Published on: September 15, 2010

A Probabilistic Graphical Model for Ab Initio Folding.

Feng Zhao¹, Jian Peng, Joe Debartolo

¹Toyota Technological Institute at Chicago, Chicago, IL 60637.

Research in Computational Molecular Biology : ... Annual International Conference, RECOMB ... : Proceedings. RECOMB (Conference : 2005- )

|March 6, 2013

Summary

This summary is machine-generated.

This study introduces a novel probabilistic graphical model for ab initio protein folding, offering a new approach to predict 3D protein structures. The model explores conformations in continuous space, outperforming existing methods.

Keywords:

ab initio folding conditional random fields (CRFs)directional statistics fragment assembly lattice model protein structure prediction

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A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Area of Science:

Structural Biology
Computational Biology
Biophysics

Background:

Ab initio protein folding remains a significant challenge in structural biology despite recent advancements.
Existing methods like fragment assembly and lattice models have limitations in exploring protein conformations.

Purpose of the Study:

To present a novel probabilistic graphical model for ab initio protein folding.
To model the relationship between a protein's primary sequence and its 3D structure using Conditional Random Fields (CRFs) and directional statistics.
To explore protein conformations in a continuous space based on their probability.

Main Methods:

Utilizes Conditional Random Fields (CRFs) and directional statistics.
Models protein conformations in a continuous space, unlike fragment assembly or lattice models.
Estimates conformation probability using PSI-BLAST sequence profiles and predicted secondary structure.

Main Results:

The proposed graphical model explores protein conformations in a continuous probability space.
Conformation stability is directly reflected in its probability.
Experimental results show favorable comparisons against fragment assembly and lattice models.

Conclusions:

The developed probabilistic graphical model presents a promising advancement in ab initio protein folding.
This method offers a more nuanced exploration of protein conformations compared to traditional approaches.
The model's ability to predict structure based on sequence and secondary structure holds significant potential for structural biology research.