A combinatorial distance-constraint approach to predicting protein tertiary models from known secondary structure

Area of Science:

• Computational biology
• Structural bioinformatics
• Protein structure prediction

Background:

• Distance geometry methods construct protein structures using experimental distance constraints (e.g., NMR).
• Predicting distance constraints from sequence data (via multiple sequence alignments) enables structure prediction without direct experimental data.
• This study integrates these approaches with a novel combinatorial procedure.

Purpose of the Study:

• To develop and evaluate a novel combinatorial procedure for protein structure prediction.
• To assess the method's accuracy in constructing native-like models for small beta-proteins.
• To determine the impact of known sheet topology and disulfide connectivities on prediction accuracy.

Main Methods:

• Utilized distance geometry methods with predicted distance constraints from sequence information.
• Developed a novel combinatorial procedure to explore possible sheet topologies and disulfide formations.
• Employed a geometric evaluation scheme to rank predicted protein models.

Main Results:

• Successfully constructed native-like Calpha models for eight small beta-protein structures when sheet topology and disulfide formations were correct.
• Explored all possible sheet topologies when topology was unknown but disulfide connectivities were provided, ranking correct topologies highly.
• Identified correct topologies within the top five folds for half of the cases when neither topology nor disulfide information was initially known.

Conclusions:

• The combinatorial procedure is effective for identifying low-resolution candidate folds of small, disulfide-rich, beta-protein structures.
• Accurate disulfide connectivities significantly improve the prediction results.
• The method is applicable when a limited number of finite connectivities are available.