Journal of Molecular Biology最新文献

Malate dehydrogenase (MalDH) (EC.1.1.1.37) is an enzyme engaged in the central metabolism of cells, catalyzing the interconversion between oxaloacetate and malate using NADH or NADPH as coenzyme. These enzymes are particularly interesting models for studying how proteins adapt to physical and chemical environmental constraints. In this study, we investigated the molecular mechanisms that have enabled MalDHs to adapt to changes in temperature, using Methanococcales archaea as a model organism. We solved the crystal structure of ancestral MalDHs in these archaea. Structural comparison with present-day MalDHs such as those from Methanocaldococcus infernus (M. inf) and Methanocaldococcus jannaschii (M. jan), highlights the role salt-bridges in thermal adaptation. We also found that present-day MalDHs from M. inf and M. jan, show structural features that resemble the extended or compact states typical of allosteric lactate dehydrogenases. To test hypotheses about a possible link between thermal adaptation and the emergence of allosteric regulation, we characterized structurally two M. jan MalDH mesophilic-like mutants. Molecular dynamics simulations using the Wt M. jan and mutant MalDHs were used to rationalize the experimental data. The results indicate that uncompetent and competent catalytic site configurations are in an equilibrium that depends on temperature conditions. At low temperature the Wt M. jan MalDH select non-competent conformers, whereas high temperature favors active conformers. In contrast, the M. jan MalDH mutants explore competent conformers for catalysis at a lowest temperature, a phenomenon that fits well with their biochemical behavior. Our work reveals that thermal adaptation and evolution of allostery are strongly linked via the modulation of the protein conformational landscape.

Proteins and nucleic acids function by interacting with each other and with other molecules. Protein interactions with DNA are the basis of key cellular processes, such as replication, transcription, packaging, and DNA repair. To understand the mechanisms of these processes, it is important to know the structure of the molecular components and their interactions. Structures of protein complexes with nucleic acids are especially difficult to solve experimentally due to their greater instability. Thus, relatively few experimentally determined structures of such complexes are available. Computational modeling is essential for understanding biomolecular mechanisms and for the development of our ability to modulate them. Advances in the protein structure prediction field have shown that protein models can routinely reach unprecedented levels of near-experimental accuracy. In this context, modeling macromolecular interactions has become a focal point in structural biology. The Dockground project increases our knowledge of macromolecular interfaces and provides data resources for the development of techniques for structure-based modeling of macromolecular interactions. The resource, which contains protein-protein and protein-RNA complexes, has now been expanded to protein-DNA interactions. To advance the scope and functionality of the resource, the expansion includes new automatic weekly update procedures and user-friendly web interface for search, analysis and download of the protein-DNA complexes. The utility of the new release of the resource is illustrated by an example of benchmarking AlphaFold3 predictions. Dockground is publicly available at https://dockground.compbio.ku.edu.