Explaining Conformational Diversity in Protein Families through Molecular Motions

Area of Science:

• Structural biology
• Computational biology
• Biophysics

Background:

• Proteins are central to biological processes, and their conformational variability is key to function.
• High-throughput technologies have advanced protein structure knowledge, but predicting conformational states and motions remains difficult.

Purpose of the Study:

• To introduce Dimensionality Analysis for protein Conformational Exploration (DANCE) for systematic analysis of protein families' conformational variability.
• To provide a resource for accessing and exploiting multiple protein conformational states.

Main Methods:

• DANCE accommodates experimental and predicted protein structures, applicable from single proteins to superfamilies.
• Clustered experimentally resolved Protein Data Bank structures into conformational collections.
• Characterized conformational collections as sets of linear motions.
• Assessed dimensionality reduction techniques for sampling unseen protein states.

Main Results:

• Developed DANCE for comprehensive description of protein families' conformational variability.
• Successfully clustered and characterized protein structures from the Protein Data Bank.
• Demonstrated the utility of DANCE in understanding protein deformation and function.

Conclusions:

• DANCE offers a systematic approach to analyzing protein conformational dynamics.
• This work enhances understanding of protein function through conformational analysis.
• Opens avenues for standardized evaluation of protein conformational sampling and generation methods.