Distributions in protein conformation space: implications for structure prediction and entropy

Area of Science:

• Computational biology
• Polymer physics
• Structural bioinformatics

Background:

• Accurate prediction of polymer and protein structures is essential for understanding biological function.
• Quantifying the difficulty of structure prediction and measuring progress in computational methods remains a challenge.

Purpose of the Study:

• To develop a method for quantifying the difficulty of polymer and protein structure prediction.
• To establish a metric for assessing the progress of prediction calculations.
• To relate conformational distributions to protein folding thermodynamics.

Main Methods:

• Analysis of polymer structure distribution in conformation space.
• Construction of a cumulative distribution function (CDF) based on limiting behavior and numerical simulations of polyalanine chains.
• Estimation of conformational entropy and native state's conformational span.

Main Results:

• The CDF allows estimation of prediction accuracy against random chance.
• The probability of random native backbone prediction for a 150-amino acid protein is 10(-14) at 6 Å resolution and 10(-57) at 2 Å resolution.
• Conformational entropy and native state's conformational span can be derived from the CDF and folding entropy changes.

Conclusions:

• The developed method provides a quantitative measure of structure prediction difficulty.
• This approach offers insights into the thermodynamics of protein folding.
• The findings have implications for advancing computational protein design and structure prediction algorithms.