Interfacial water confers transcription factors with dinucleotide specificity.

Transcription factors (TFs) recognize specific bases within their DNA-binding motifs, with each base contributing nearly independently to total binding energy. However, the energetic contributions of particular dinucleotides can deviate strongly from the additive approximation, indicating that some TFs can specifically recognize DNA dinucleotides. Here we solved high-resolution (<1 Å) structures of MYF5 and BARHL2 bound to DNAs containing sets of dinucleotides that have different affinities to the proteins.

Special issue: Multiscale simulations of DNA from electrons to nucleosomes.

This editorial for Volume 16, Issue 3 of highlights the three-dimensional structural and dynamic information encoded in DNA sequences and introduces the topics covered in this special issue of the journal on Multiscale Simulations of DNA from Electrons to Nucleosomes. is the official journal of the International Union for Pure and Applied Biophysics (IUPAB 2024). The international scope of the articles in the issue exemplifies the goals of IUPAB to organize worldwide advancements, co-operation, communication, and education in biophysics.

Protein Oxidative Modifications in Neurodegenerative Diseases: From Advances in Detection and Modelling to Their Use as Disease Biomarkers.

Oxidation-reduction post-translational modifications (redox-PTMs) are chemical alterations to amino acids of proteins. Redox-PTMs participate in the regulation of protein conformation, localization and function, acting as signalling effectors that impact many essential biochemical processes in the cells. Crucially, the dysregulation of redox-PTMs of proteins has been implicated in the pathophysiology of numerous human diseases, including neurodegenerative diseases such as Alzheimer's disease and Parkinson's disease.

CGeNArate: a sequence-dependent coarse-grained model of DNA for accurate atomistic MD simulations of kb-long duplexes.

We present CGeNArate, a new model for molecular dynamics simulations of very long segments of B-DNA in the context of biotechnological or chromatin studies. The developed method uses a coarse-grained Hamiltonian with trajectories that are back-mapped to the atomistic resolution level with extreme accuracy by means of Machine Learning Approaches. The method is sequence-dependent and reproduces very well not only local, but also global physical properties of DNA.