DeePNAP: A Deep Learning Method to Predict Protein-Nucleic Acid Binding Affinity from Their Sequences.

Predicting the protein-nucleic acid (PNA) binding affinity solely from their sequences is of paramount importance for the experimental design and analysis of PNA interactions (PNAIs). A large number of currently developed models for binding affinity prediction are limited to specific PNAIs while also relying on the sequence and structural information of the PNA complexes for both training and testing, and also as inputs. As the PNA complex structures available are scarce, this significantly limits the diversity and generalizability due to the small training data set.

TetR and OmpR family regulators in natural product biosynthesis and resistance.

This article provides a comprehensive review and sequence-structure analysis of transcription regulator (TR) families, TetR and OmpR/PhoB, involved in specialized secondary metabolite (SSM) biosynthesis and resistance. Transcription regulation is a fundamental process, playing a crucial role in orchestrating gene expression to confer a survival advantage in response to frequent environmental stress conditions. This process, coupled with signal sensing, enables bacteria to respond to a diverse range of intra and extracellular signals.