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. Additionally, a majority of the tools predict a single parameter, such as binding affinity or free energy changes upon mutations, rendering a model less versatile for usage. Hence, we propose DeePNAP, a machine learning-based model built from a vast and heterogeneous data set with 14,401 entries (from both eukaryotes and prokaryotes) from the ProNAB database, consisting of wild-type and mutant PNA complex binding parameters. Our model precisely predicts the binding affinity and free energy changes due to the mutation(s) of PNAIs exclusively from their sequences. While other similar tools extract features from both sequence and structure information, DeePNAP employs sequence-based features to yield high correlation coefficients between the predicted and experimental values with low root mean squared errors for PNA complexes in predicting KD and ΔΔG, implying the generalizability of DeePNAP. Additionally, we have also developed a web interface hosting DeePNAP that can serve as a powerful tool to rapidly predict binding affinities for a myriad of PNAIs with high precision toward developing a deeper understanding of their implications in various biological systems. Web interface: http://14.139.174.41:8080/.

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. Thus, major bacterial signaling systems use a receptor domain to sense chemical stimuli along with an output domain responsible for transcription regulation through DNA-binding. Sensory and output domains on a single polypeptide chain (one component system, OCS) allow response to stimuli by allostery, that is, DNA-binding affinity modulation upon signal presence/absence. On the other hand, two component systems (TCSs) allow cross-talk between the sensory and output domains as they are disjoint and transmit information by phosphorelay to mount a response. In both cases, however, TRs play a central role. Biosynthesis of SSMs, which includes antibiotics, is heavily regulated by TRs as it diverts the cell's resources towards the production of these expendable compounds, which also have clinical applications. These TRs have evolved to relay information across specific signals and target genes, thus providing a rich source of unique mechanisms to explore towards addressing the rapid escalation in antimicrobial resistance (AMR). Here, we focus on the TetR and OmpR family TRs, which belong to OCS and TCS, respectively. These TR families are well-known examples of regulators in secondary metabolism and are ubiquitous across different bacteria, as they also participate in a myriad of cellular processes apart from SSM biosynthesis and resistance. As a result, these families exhibit higher sequence divergence, which is also evident from our bioinformatic analysis of 158 389 and 77 437 sequences from TetR and OmpR family TRs, respectively. The analysis of both sequence and structure allowed us to identify novel motifs in addition to the known motifs responsible for TR function and its structural integrity. Understanding the diverse mechanisms employed by these TRs is essential for unraveling the biosynthesis of SSMs. This can also help exploit their regulatory role in biosynthesis for significant pharmaceutical, agricultural, and industrial applications.