Deep DNAshape is a deep learning method that accurately predicts DNA shape features for any DNA sequence, overcoming the limitations of traditional k-mer-based methods. Unlike previous approaches that rely on pentamer query tables and only consider nearby nucleotides, Deep DNAshape accounts for extended flanking regions, enabling high-throughput prediction of DNA shape features without extensive molecular simulations or structural biology experiments. This method provides insights into how flanking regions influence DNA structure and improves the accuracy of machine learning models used to predict protein-DNA binding. DNA shape features describe the three-dimensional structure of DNA, including the minor groove width (MGW), propeller twist (ProT), roll, and helix twist (HelT). These features are crucial for understanding how proteins recognize and bind to DNA, as they influence binding affinity and specificity. Deep DNAshape can predict these features for any DNA sequence, considering longer-range effects, and has been validated against experimental data and simulations. The method was tested on various datasets, including TF-DNA binding assays, and showed improved performance compared to traditional methods. It was able to predict DNA shape features with higher accuracy, particularly for sequences with extended flanking regions. Deep DNAshape also demonstrated the ability to predict DNA shape fluctuations, which are important for understanding DNA conformational flexibility. The model was trained on data from Monte Carlo simulations, X-ray crystallography, and molecular dynamics simulations, and was able to predict DNA shape features for sequences of varying lengths. It was also tested on genomic data, revealing conserved DNA shape features in transcription start sites across different Drosophila species. Deep DNAshape provides a powerful tool for studying DNA structure and function, offering a more accurate and comprehensive understanding of DNA shape features and their influence on protein-DNA interactions. The method is versatile and can be applied to a wide range of DNA structure-related studies, making it a valuable resource for researchers in the field of molecular biology and genomics.Deep DNAshape is a deep learning method that accurately predicts DNA shape features for any DNA sequence, overcoming the limitations of traditional k-mer-based methods. Unlike previous approaches that rely on pentamer query tables and only consider nearby nucleotides, Deep DNAshape accounts for extended flanking regions, enabling high-throughput prediction of DNA shape features without extensive molecular simulations or structural biology experiments. This method provides insights into how flanking regions influence DNA structure and improves the accuracy of machine learning models used to predict protein-DNA binding. DNA shape features describe the three-dimensional structure of DNA, including the minor groove width (MGW), propeller twist (ProT), roll, and helix twist (HelT). These features are crucial for understanding how proteins recognize and bind to DNA, as they influence binding affinity and specificity. Deep DNAshape can predict these features for any DNA sequence, considering longer-range effects, and has been validated against experimental data and simulations. The method was tested on various datasets, including TF-DNA binding assays, and showed improved performance compared to traditional methods. It was able to predict DNA shape features with higher accuracy, particularly for sequences with extended flanking regions. Deep DNAshape also demonstrated the ability to predict DNA shape fluctuations, which are important for understanding DNA conformational flexibility. The model was trained on data from Monte Carlo simulations, X-ray crystallography, and molecular dynamics simulations, and was able to predict DNA shape features for sequences of varying lengths. It was also tested on genomic data, revealing conserved DNA shape features in transcription start sites across different Drosophila species. Deep DNAshape provides a powerful tool for studying DNA structure and function, offering a more accurate and comprehensive understanding of DNA shape features and their influence on protein-DNA interactions. The method is versatile and can be applied to a wide range of DNA structure-related studies, making it a valuable resource for researchers in the field of molecular biology and genomics.