Efficient Enumeration and Visualization of Helix-coil Ensembles.

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2023-09-17

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Abstract

Helix-coil models are routinely used to interpret CD data of helical peptides or predict the helicity of naturally-occurring and designed polypeptides. However, a helix-coil model contains significantly more information than mean helicity alone, as it defines the entire ensemble - the equilibrium population of every possible helix-coil configuration - for a given sequence. Many desirable quantities of this ensemble are either not obtained as ensemble averages, or are not available using standard helicity-averaging calculations. Enumeration of the entire ensemble can allow calculation of a wider set of ensemble properties, but the exponential size of the configuration space typically renders this intractable. We present an algorithm that efficiently approximates the helix-coil ensemble to arbitrary accuracy, by sequentially generating a list of the M highest populated configurations in descending order of population. Truncating this list of (configuration, population) pairs at a desired accuracy provides an approximating sub-ensemble. We demonstrate several uses of this approach for providing insight into helix-coil ensembles and folding mechanisms, including landscape visualization.

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Published Version (Please cite this version)

10.1101/2023.09.16.558052

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Schmidler, Scott C, Roy Gene Hughes, Terrence G Oas and Shiwen Zhao (2023). Efficient Enumeration and Visualization of Helix-coil Ensembles. bioRxiv. 10.1101/2023.09.16.558052 Retrieved from https://hdl.handle.net/10161/29320.

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Scholars@Duke

Schmidler

Scott C. Schmidler

Associate Professor of Statistical Science

Research Interests:

  • Monte Carlo methods; high-dimensional sampling algorithms; Mixing times of Markov chains; MCMC; Sequential Monte Carlo; probabilistic graphical models; Bayesian computation; Computational complexity of statistical inference.

  • Computational biology; Protein structure and folding; computational immunology; computational biophysics; statistical physics; computational statistical mechanics; molecular evolution.
Oas

Terrence Gilbert Oas

Professor of Biochemistry

Our laboratory is primarily interested in the mechanisms of protein folding. We use nuclear magnetic resonance (NMR) and other types of spectroscopy to study the solution structure, stability and folding reactions of small protein models. These include monomeric λ repressor, the B domain of protein A (BdpA) and various regulator of G-protein signalling (RGS) domains. Our biophysical studies are used to inform our investigations of the role of folding mechanism in the function of proteins in the cell. For example, a naturally occuring cancer-causing mutation in the RGS domain of axin appears to lower the thermodynamic stability of the domain. We are developing methods to compensate for such destabilizing mutations, thereby restoring normal function to the protein.We are also developing computational models of protein folding as a way to better understand the mechanisms and as a tool in the design of new experiments.


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