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Conservation of Protein Domains Over Different Proteins

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Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

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Construct a variable-length fragment library for de novo protein structure prediction.

Qiongqiong Feng¹, Minghua Hou¹, Jun Liu¹

¹College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China.

Briefings in Bioinformatics

|March 14, 2022

Summary

We developed VFlib, a novel variable-length fragment library for de novo protein structure prediction. VFlib enhances protein structure prediction accuracy and explores protein folding mechanisms more effectively than existing methods.

Keywords:

de novo protein structure prediction fragment library hidden Markov model secondary structure

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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:

Computational biology
Structural bioinformatics
Protein structure prediction

Background:

End-to-end protein structure prediction has advanced significantly, yet fragment libraries are crucial for de novo prediction and understanding protein folding.
Existing methods may not fully capture the diversity needed for comprehensive exploration of protein folding mechanisms.

Purpose of the Study:

To develop a variable-length fragment library (VFlib) for improved de novo protein structure prediction.
To enhance the exploration of protein-folding mechanisms by generating diverse and accurate fragment libraries.

Main Methods:

Constructed a master structure database from the Protein Data Bank using sequence clustering.
Generated Hidden Markov Model (HMM) profiles and secondary structures using HHsuite and DSSP.
Retrieved variable-length fragments via dynamic profile-profile comparison and HMM-profile chopping for diversity.
Screened fragments using secondary structure information to create query-specific libraries.

Main Results:

VFlib achieved 55.04% global precision and 94.95% coverage at 1.5 Å RMSD on 120 nonredundant proteins.
VFlib demonstrated a 62.89% increase in global precision compared to NNMake with equivalent coverage.
Protein structure models predicted using VFlib fragments showed a 16.00% higher average TM-score than those from NNMake.

Conclusions:

VFlib provides a significant improvement in fragment library generation for de novo protein structure prediction.
The enhanced precision and coverage of VFlib contribute to more accurate protein structure modeling.
VFlib offers a promising approach for advancing the understanding of protein-folding mechanisms.