PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 11690459

How many conformations can a protein remember?

T M Fink·R C Ball
View original source ↗Export metadata ↓

Abstract

We show that a protein can be trained to recognize multiple conformations, analogous to an associative memory, and provide capacity calculations based on energy fluctuations and information theory. Unlike the linear capacity of a Hopfield network, the number of conformations which can be remembered by a protein sequence depends on the size of the amino acid alphabet as lnA, independent of protein length. This admits the possibility of certain proteins, such as prions, evolving to fold to independent stable conformations, as well as novel possibilities for protein and heteropolymer design.

Explore related subjects

Protein ConformationProteinsThermodynamicsJournal Article

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

T M Fink, R C Ball. 2001-10-23. How many conformations can a protein remember?. https://doi.org/10.1103/physrevlett.87.198103

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2006

Toward absolute density of states calculations for proteins.

The density of states (DOS), which gives the number of conformations with a particular energy E, is a prerequisite in computing most thermodynamic quantities and in elucidating important biological processes such as the mechanism of protein folding. However, current methods for computing DOS of large systems such as proteins generally yield only the ratios of microstate counts for different energies, which could yield absolute conformation counts if the total number of conformations in phase space is known, thus motivating this work. Here, the total number of energy minima of 50-mer polyalanine, whose size corresponds to naturally occurring small proteins, was estimated under an all-atom potential energy function based on the cumulative distribution function (CDF) of conformational differences to be approximately 10(38). This estimate can place any DOS function, such as the Gaussian DOS distribution in the random energy model, on an absolute scale. Comparing the absolute conformational counts from a Gaussian DOS function with those from the CDF derived from quenched molecular dynamics ensembles shows that the former are far greater than the latter, indicating far fewer low-energy minima actually exist. In addition to showing how CDF and relative DOS calculations can yield absolute DOS for a discrete system, we also show how they can yield absolute DOS for continuous variable systems to a specified atomic variance. In the context of protein folding, knowing this phase-space "volume" of conformations in a DOS function, as well as characteristic transition times, constrains the set of possible folding mechanisms.

Protein Conformation↗
PubMed2006

Design of protein conformational switches.

Protein conformational switches are ubiquitous in nature and often regulate key biological processes. To design new proteins that can switch conformation, protein designers have focused on the two key components of protein switches: the amino acid sequence must be compatible with the multiple target states and there must be a mechanism for perturbing the relative stability of these states. Proteins have been designed that can switch between folded and disordered states, between distinct folded states and between different aggregation states. A variety of trigger mechanisms have been used, including pH shifts, post-translational modification and ligand binding. Recently, computational protein design methods have been applied to switch design. These include algorithms for designing novel ligand-binding sites and simultaneously optimizing a sequence for multiple target structures.

Protein Conformation↗
PubMed2006

QBES: predicting real values of solvent accessibility from sequences by efficient, constrained energy optimization.

Solvent accessibility, one of the key properties of amino acid residues in proteins, can be used to assist protein structure prediction. Various approaches such as neural network, support vector machines, probability profiles, information theory, Bayesian theory, logistic function, and multiple linear regression have been developed for solvent accessibility prediction. In this article, a much simpler quadratic programming method based on the buriability parameter set of amino acid residues is developed. The new method, called QBES (Quadratic programming and Buriability Energy function for Solvent accessibility prediction), is reasonably accurate for predicting the real value of solvent accessibility. By using a dataset of 30 proteins to optimize three parameters, the average correlation coefficients between the predicted and actual solvent accessibility are about 0.5 for all four independent test sets ranging from 126 to 513 proteins. The method is efficient. It takes only 20 min for a regular PC to obtain results of 30 proteins with an average length of 263 amino acids. Although the proposed method is less accurate than a few more sophisticated methods based on neural network or support vector machines, this is the first attempt to predict solvent accessibility by energy optimization with constraints. Possible improvements and other applications of the method are discussed.

Protein Conformation↗