Methods to model large scale sequence-dependent conformational dynamics have been reviewed.

Apart from conventional real-time coarse-grained and all-atom investigation of DNA shape dynamics; there exist methods trying to encode them in dictionaries of smaller fragments- a term coined in this work.

A sequence-to-shape DNA dictionary is defined as any reported method that models intrinsic DNA-shape properties from a set of specific DNA sequences.

Concatenation of fragment-based mapping from these dictionaries can be used for studying DNA-dynamics at genomic scales.

This review provides a novel compilation of various dictionary-like methods in literature and their successful applications to studying transcription factor binding and other DNA shape-dependent events.

Sequence-dependent intrinsic conformational dynamics confer specificity to transcriptional factor recognition of genomic DNA. Their genome-scale investigation using all-atom simulations is challenging, and alternative approaches by coarse-graining DNA into beads-and-sticks or polymer models have their own limitations. One parallel approach is what we describe here as a dictionary-based approach. This has been shown to explain several transcriptional events in biological systems but has been inadequately reviewed. These approaches represent studies based on a finite number of DNA fragments and their corresponding conformational properties, scaled up to genomes by pooling nearby fragments and machine learning models. This article aims to organize efforts made in generating these models and their recent successful applications to stimulate further development of this approach.

No data was used for the research described in the article.

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