Protein fold refinement: building models from idealized folds using motif constraints and multiple sequence data

Area of Science:

• Computational biology
• Structural bioinformatics
• Protein modeling

Background:

• Multiple sequence alignments (MSAs) offer valuable evolutionary data for protein structure prediction.
• Directly incorporating MSA data into molecular model construction remains a challenge.

Purpose of the Study:

• To develop a general method for integrating multiple sequence alignment data into protein molecular model construction.
• To enable rapid generation of protein models suitable for further analysis.

Main Methods:

• Calculated estimated pairwise distances based on conserved hydrophobicity from MSAs.
• Developed a scaling method to ensure bulk geometric properties of distances mimic globular proteins.
• Used regularization towards an ideal form to induce specific structures like secondary structures and motifs.
• Refined an initial structure derived from secondary structure axes.

Main Results:

• The method successfully generated protein-like structures from abstract representations.
• Estimated distances, despite individual inaccuracies, were compatible with native structures and weighted highly.
• The process allowed for rapid conversion of rough folds into models ready for analysis.

Conclusions:

• This approach provides a general solution for incorporating MSA data into molecular modeling.
• The method facilitates the rapid generation of refined protein models from sequence information.
• The generated models are suitable for analysis by existing molecular modeling assessment tools.