Atomistic modeling of protein–DNA interaction specificity: progress and applications

Abstract

An accurate, predictive understanding of protein-DNA binding specificity is crucial for the successful design and engineering of novel protein-DNA binding complexes. In this review, we summarize recent studies that use atomistic representations of interfaces to predict protein-DNA binding specificity computationally. Although methods with limited structural flexibility have proven successful at recapitulating consensus binding sequences from wild-type complex structures, conformational flexibility is likely important for design and template-based modeling, where non-native conformations need to be sampled and accurately scored. A successful application of such computational modeling techniques in the construction of the TAL-DNA complex structure is discussed. With continued improvements in energy functions, solvation models, and conformational sampling, we are optimistic that reliable and large-scale protein-DNA binding prediction and engineering is a goal within reach.

Absence of a simple code: how transcription factors read the genome

“…Similar to probabilistic models, some energy-based models assume independent contributions among positions in the TFBS [81–83], whereas others incorporate nonindependent contributions [73]. Structure-based atomistic models of DNA binding specificity have also been developed [84–90]. However, these models are not yet widely used, likely because they require knowledge of the structure of the protein (or one of its homologs) when bound to the target DNA site.…”

Specificity and nonspecificity in RNA–protein interactions

“…An array of approaches to accomplish this goal have been developed for DNA binding proteins, including higher order Hidden Markov models 84 , neural network analysis 85 , decision tree guided approaches 86 , higher order Bayesian networks 87 , and approaches that incorporate structural information about the protein 88 . Neural network analysis has been applied to RNA–protein interactions measured in vitro with the HiTS-Kin approach 67 .…”

Mapping specificity landscapes of RNA-protein interactions by high throughput sequencing

“…Rate and equilibrium constants are readily converted into free energy values that can be correlated with free energy values calculated from structures of RNA-protein complexes (14). Models that comprehensively explain structure function relationships that govern inherent RBP specificity can thus be developed, analogous to efforts modeling transcription factor specificity (15). …”