Fold assembly of small proteins using monte carlo simulations driven by restraints derived from multiple sequence...

Area of Science:

• Computational Biology
• Protein Structure Prediction
• Bioinformatics

Background:

• Accurate prediction of small protein global folds is crucial for understanding their function.
• Existing methods face challenges in predicting complex protein topologies.

Purpose of the Study:

• To demonstrate the feasibility of predicting small protein global folds using predicted restraints in ab initio folding simulations.
• To evaluate the accuracy and requirements of secondary and tertiary structure prediction for successful fold assembly.

Main Methods:

• Incorporated predicted secondary structure restraints from the PHD algorithm.
• Derived predicted tertiary restraints using correlated mutation analysis and threading-based algorithms from multiple sequence alignments.
• Employed a lattice-based reduced protein model and a folding algorithm to assemble topologies.

Main Results:

• Achieved native-like topologies with coordinate root-mean-square deviation from native between 3.0 Å and 6.5 Å.
• Demonstrated that approximately 25% accuracy in side-chain contact prediction is sufficient with specific positional accuracy.
• Showed that only about 25% of total tertiary contacts are needed for successful topology assembly.

Conclusions:

• The integration of predicted restraints from multiple sequence alignments with fold assembly algorithms shows significant promise for predicting small protein global topology.
• Precision in tertiary contact prediction is more critical than absolute accuracy for successful fold assembly.