Ab initio protein structure prediction using a combined hierarchical approach.

Area of Science:

• Computational Biology
• Structural Bioinformatics
• Protein Structure Prediction

Background:

• Accurate prediction of three-dimensional protein structures from amino acid sequences is a fundamental challenge in biology.
• The Critical Assessment of Structure Prediction (CASP) initiative benchmarks computational methods for protein structure prediction.
• Previous ab initio methods have shown limited success in predicting global protein topology.

Purpose of the Study:

• To develop and evaluate a hierarchical computational approach for ab initio protein structure prediction.
• To assess the performance of the developed method in the CASP3 experiment.
• To improve the accuracy of predicting the global topology and shape of proteins.

Main Methods:

• A hierarchical strategy combining lattice modeling, secondary structure incorporation, and knowledge-based scoring functions.
• Enumeration of compact conformations using a simplified tetrahedral lattice model.
• Selection of candidate structures using lattice-based and all-atom knowledge-based scoring functions, followed by distance geometry refinement.

Main Results:

• The hierarchical method successfully predicted the global topology or shape for a significant portion of the sequence in 6 out of 13 proteins.
• Accurate topology/shape predictions were achieved for shorter fragments in 2 additional proteins.
• This performance marked a substantial advancement in ab initio protein structure prediction capabilities since the inception of CASP.

Conclusions:

• The developed hierarchical approach demonstrates improved efficacy in predicting protein tertiary structures, particularly global topology.
• The combination of lattice modeling, secondary structure prediction, and knowledge-based scoring offers a robust framework for ab initio structure prediction.
• The results highlight the potential of integrated computational strategies to address the protein folding problem.