Systematic Exploration of Protein Conformational Space Using a Distance Geometry Approach.

Area of Science:

• Computational Biology
• Structural Biology
• Bioinformatics

Background:

• Classical protein structure determination methods struggle with large conformational spaces and convergence criteria.
• The interval branch-and-prune (iBP) approach offers a theoretical framework for protein conformation generation but is sensitive to distance measurement errors.

Purpose of the Study:

• To improve the efficiency and accuracy of protein structure determination using an enhanced interval branch-and-prune method.
• To alleviate the combinatorial explosion of traditional iBP by incorporating threading and self-organizing maps.

Main Methods:

• Developed the threading-augmented interval branch-and-prune (TAiBP) approach.
• Partitioned protein instances into peptide fragments and utilized self-organizing maps (SOMs) for solution clustering.
• Validated TAiBP using covalent geometry, dihedral angle error intervals, and long-range distances.

Main Results:

• TAiBP successfully generated protein conformations with RMSD < 3 Å for proteins < 50 residues and interval widths of 20°.
• The method addresses the limitations of the original iBP approach concerning error propagation.
• Efficiency for larger proteins may require non-uniform covalent geometry and advanced force fields.

Conclusions:

• TAiBP presents a significant advancement in systematically sampling protein conformational space.
• The approach demonstrates practical utility for determining the structure of small to medium-sized proteins.
• Future work should focus on optimizing TAiBP for larger proteins and exploring advanced force-field applications.