The length of glycine-rich linker in DNA-binding domain is critical for optimal functioning of quorum-sensing master regulatory protein HapR

Arginine 37 of Glycine Linker Dictates Regulatory Function of HapR

HapR is designated as a high cell density quorum sensing master regulatory protein of Vibrio cholerae. It is a member of the TetR family protein and functions both as an activator and a repressor by directly communicating with cognate promoters, thus controlling the expression of a plethora of genes in a density-dependent manner. Molecular insights reveal the domain architecture and further unveil the significance of a cross talk between the DNA binding domain and the dimerization domain for the functionality of the wild-type protein. The DNA binding domain is made up of three α-helices, where a helix-turn-helix motif spans between the helices α2 and α3. The essentiality of the glycine-rich linker linking helices α1 and α2 came into prominence while unraveling the molecular basis of a natural non-functional variant of HapR. Subsequently, the importance of linker length was demonstrated. The present study, involving a series of biochemical analyses coupled with molecular dynamics simulation, has illustrated the indispensability of a critical arginine within the linker at position 37 contributing to HapR–DNA binding activity.

Functional Site Discovery From Incomplete Training Data: A Case Study With Nucleic Acid-Binding Proteins

Function annotation efforts provide a foundation to our understanding of cellular processes and the functioning of the living cell. This motivates high-throughput computational methods to characterize new protein members of a particular function. Research work has focused on discriminative machine-learning methods, which promise to make efficient, de novo predictions of protein function. Furthermore, available function annotation exists predominantly for individual proteins rather than residues of which only a subset is necessary for the conveyance of a particular function. This limits discriminative approaches to predicting functions for which there is sufficient residue-level annotation, e.g., identification of DNA-binding proteins or where an excellent global representation can be divined. Complete understanding of the various functions of proteins requires discovery and functional annotation at the residue level. Herein, we cast this problem into the setting of multiple-instance learning, which only requires knowledge of the protein’s function yet identifies functionally relevant residues and need not rely on homology. We developed a new multiple-instance leaning algorithm derived from AdaBoost and benchmarked this algorithm against two well-studied protein function prediction tasks: annotating proteins that bind DNA and RNA. This algorithm outperforms certain previous approaches in annotating protein function while identifying functionally relevant residues involved in binding both DNA and RNA, and on one protein-DNA benchmark, it achieves near perfect classification.

Insights into the effects of mutations on Cren7-DNA binding using molecular dynamics simulations and free energy calculations

A novel, highly conserved chromatin protein, Cren7 is involved in regulating essential cellular processes such as transcription, replication and repair. Although mutations in the DNA-binding loop of Cren7 destabilize the structure and reduce DNA-binding activity, the details are not very clear. Focusing on the specific Cren7-dsDNA complex (PDB code ), we applied molecular dynamics (MD) simulations and the molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) free energy calculations to explore the structural and dynamic effects of W26A, L28A, and K53A mutations in comparison to the wild-type protein. The energetic analysis indicated that the intermolecular van der Waals interaction and nonpolar solvation energy play an important role in the binding process of Cren7 and dsDNA. Compared with the wild type Cren7, all the studied mutants W26A, L28A, and K53A have obviously reduced binding free energies with dsDNA in the reduction of the polar and/or nonpolar interactions. These results further elucidated the previous experiments to understand the Cren7-DNA interaction comprehensively. Our work also would provide support for an understanding of the interactions of proteins with nucleic acids.