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Thermodynamic state ensemble models of cis-regulation.

ABSTRACT: A major goal in computational biology is to develop models that accurately predict a gene's expression from its surrounding regulatory DNA. Here we present one class of such models, thermodynamic state ensemble models. We describe the biochemical derivation of the thermodynamic framework in simple terms, and lay out the mathematical components that comprise each model. These components include (1) the possible states of a promoter, where a state is defined as a particular arrangement of transcription factors bound to a DNA promoter, (2) the binding constants that describe the affinity of the protein-protein and protein-DNA interactions that occur in each state, and (3) whether each state is capable of transcribing. Using these components, we demonstrate how to compute a cis-regulatory function that encodes the probability of a promoter being active. Our intention is to provide enough detail so that readers with little background in thermodynamics can compose their own cis-regulatory functions. To facilitate this goal, we also describe a matrix form of the model that can be easily coded in any programming language. This formalism has great flexibility, which we show by illustrating how phenomena such as competition between transcription factors and cooperativity are readily incorporated into these models. Using this framework, we also demonstrate that Michaelis-like functions, another class of cis-regulatory models, are a subset of the thermodynamic framework with specific assumptions. By recasting Michaelis-like functions as thermodynamic functions, we emphasize the relationship between these models and delineate the specific circumstances representable by each approach. Application of thermodynamic state ensemble models is likely to be an important tool in unraveling the physical basis of combinatorial cis-regulation and in generating formalisms that accurately predict gene expression from DNA sequence.

SUBMITTER: Sherman MS

PROVIDER: S-EPMC3315449 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Thermodynamic state ensemble models of cis-regulation.

Sherman Marc S MS Cohen Barak A BA

PLoS computational biology 20120329 3

A major goal in computational biology is to develop models that accurately predict a gene's expression from its surrounding regulatory DNA. Here we present one class of such models, thermodynamic state ensemble models. We describe the biochemical derivation of the thermodynamic framework in simple terms, and lay out the mathematical components that comprise each model. These components include (1) the possible states of a promoter, where a state is defined as a particular arrangement of transcri ...[more]

PMID: 22479169

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Project description:An amino acid sequence, in the context of the solvent environment, contains all of the thermodynamic information necessary to encode a three-dimensional protein structure. To investigate the relationship between an amino acid sequence and its corresponding protein fold, a database of thermodynamic stability information was assembled that spanned 2951 residues from 44 nonhomologous proteins. This information was obtained using the COREX algorithm, which computes an ensemble-based description of the native state of a protein. It was observed that amino acid types partitioned unequally into high, medium, and low thermodynamic stability environments. Furthermore, these distributions were reproducible and were significantly different than those expected from random partitioning. To assess the structural importance of the distributions, simple fold-recognition experiments were performed based on a 3D-1D scoring matrix containing only COREX residue stability information. This procedure was able to recover amino acid sequences corresponding to correct target structures more effectively than scoring matrices derived from randomized data. High-scoring sequences were often aligned correctly with their corresponding target profiles, suggesting that calculated thermodynamic stability profiles have the potential to encode sequence information. As a control, identical fold-recognition experiments were performed on the same database of proteins using DSSP secondary structure information in the scoring matrix, instead of COREX residue stability information. The comparable performance of both approaches suggested that COREX residue stability information and secondary structure information could be of equivalent utility in more sophisticated fold-recognition techniques. The results of this work are a consequence of the idea that amino acid sequences fold not into single, rigidly stable structures but rather into thermodynamic ensembles best represented by a time-averaged structure.

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Thermodynamic state ensemble models of cis-regulation.

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Thermodynamic state ensemble models of cis-regulation.

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