DNA unzipping by nanopore translocation has implications in diverse contexts, from polymer physics to single-molecule manipulation to DNA–enzyme interactions in biological systems. Here we use molecular dynamics simulations and a coarse-grained model of DNA to address the nanopore unzipping of DNA filaments that are knotted. This previously unaddressed problem is motivated by the fact that DNA knots inevitably occur in isolated equilibrated filaments and in vivo. We study how different types of tight knots in the DNA segment just outside the pore impact unzipping at different driving forces. We establish three main results. First, knots do not significantly affect the unzipping process at low forces. However, knotted DNAs unzip more slowly and heterogeneously than unknotted ones at high forces. Finally, we observe that the microscopic origin of the hindrance typically involves two concurrent causes: the topological friction of the DNA chain sliding along its knotted contour and the additional friction originating from the entanglement with the newly unzipped DNA. The results reveal a previously unsuspected complexity of the interplay of DNA topology and unzipping, which should be relevant for interpreting nanopore-based single-molecule unzipping experiments and improving the modeling of DNA transactions in vivo.

Human mitochondrial Complex I is one of the largest multi-subunit membrane protein megacomplexes, which plays a critical role in oxidative phosphorylation and ATP production. It is also involved in many neurodegenerative diseases. However, studying its structure and the mechanisms underlying proton translocation remains challenging due to the hydrophobic nature of its transmembrane parts. In this structural bioinformatic study, we used the QTY code to reduce the hydrophobicity of megacomplex I, while preserving its structure and function. We carried out the structural bioinformatics analysis of 20 key enzymes in the integral membrane parts. We compare their native structure, experimentally determined using Cryo-electron microscopy (CryoEM), with their water-soluble QTY analogs predicted using AlphaFold 3. Leveraging AlphaFold 3’s advanced capabilities in predicting protein–protein complex interactions, we further explore whether the QTY-code integral membrane proteins maintain their protein–protein interactions necessary to form the functional megacomplex. Our structural bioinformatics analysis not only demonstrates the feasibility of engineering water-soluble integral membrane proteins using the QTY code, but also highlights the potential to use the water-soluble membrane protein QTY analogs as soluble antigens for discovery of therapeutic monoclonal antibodies, thus offering promising implications for the treatment of various neurodegenerative diseases.

Manipulating matter by strong coupling to the vacuum field has attracted intensive interests over the last decade. In particular, vibrational strong coupling (VSC) has shown great potential for modifying ground state properties in solution chemistry and biochemical processes. In this work, the effect of VSC of water on the melting behaviour of ds-DNA, an important biophysical process, is explored. Several experimental conditions, including the concentration of ds-DNA, cavity profile, solution environment, as well as thermal annealing treatment, were tested. No significant effect of VSC was observed for the melting behaviour of the ds-DNA sequence used. This demonstrates yet again the robustness of ds-DNA to outside perturbations. Our work also provides a general protocol to probe the effects of VSC on biological systems inside microfluid Fabry–Perot cavities and should be beneficial to better understand and harness this phenomenon.

DNA helicases are molecular motors that use the energy from nucleotide hydrolysis to move along DNA, promoting the unwinding or rewinding of the double helix. Here, we use magnetic and optical tweezers to track the motion of three helicases, gp41, RecQ, and RecG, while they unwind or rewind a DNA hairpin. Their activity is characterized by measuring the helicase velocity and diffusivity under different force and ATP conditions. We use a continuous-time random walk framework that allows us to compute the mean helicase displacement and its fluctuations analytically. Fitting the model to the measured helicase velocity and diffusivity allows us to determine the main states and transitions in the helicase mechanochemical cycle. A general feature for all helicases is the need to incorporate an off-pathway pausing state to reproduce the data, raising the question of whether pauses play a regulatory role. Diffusivity measurements also lead to estimations of the thermodynamic uncertainty factor related to the motor efficiency. Assuming a tight mechano-chemical coupling, we find that the RecG helicase reaches a high efficiency when operating uphill, whereas the unwinding gp41 and RecQ helicases display much lower efficiencies. Incorporating the analysis of fluctuations allows for better characterization of the activity of molecular machines, which represents an advance in the field.

Metabolic enzymes are the catalysts that drive the biochemical reactions essential for sustaining life. Many of these enzymes are tightly regulated by feedback mechanisms. To fully understand their roles and modulation, it is crucial to investigate the relationship between their structure, catalytic mechanism, and function. In this perspective, by using three examples from our studies on Mycobacterium tuberculosis (Mtb) isocitrate lyase and related proteins, we highlight how an integrated approach combining structural, activity, and biophysical data provides insights into their biological functions. These examples underscore the importance of employing fast-fail experiments at the early stages of a research project, emphasise the value of complementary techniques in validating findings, and demonstrate how in vitro data combined with chemical, biochemical, and physiological knowledge can lead to a broader understanding of metabolic adaptations in pathogenic bacteria. Finally, we address the unexplored questions in Mtb metabolism and discuss how we expand our approach to include microbiological and bioanalytical techniques to further our understanding. Such an integrated and interdisciplinary strategy has the potential to uncover novel regulatory mechanisms and identify new therapeutic opportunities for the eradication of tuberculosis. The approach can also be broadly applied to investigate other biochemical networks and complex biological systems.

Metabolism is at the core of all functions of living cells as it provides Gibbs free energy and building blocks for synthesis of macromolecules, which are necessary for structures, growth, and proliferation. Metabolism is a complex network composed of thousands of reactions catalyzed by enzymes involving many co-factors and metabolites. Traditionally it has been difficult to study metabolism as a whole network and most traditional efforts were therefore focused on specific metabolic pathways, enzymes, and metabolites. By using engineering principles of mathematical modeling to analyze and study metabolism, as well as engineer it, that is, design and build, new metabolic features, it is possible to gain many new fundamental insights as well as applications in biotechnology. Here, we present the history and basic principles of engineering metabolism, as well as the newest developments in the field. We are using examples of applications in: (1) production of protein pharmaceuticals and chemicals; (2) basic studies of metabolism; and (3) impacting health care. We will end by discussing how engineering metabolism can benefit from advances in artificial intelligence (AI)-based models.

High resolution structures of protein complexes provide a wealth of information on protein structure and function. Databases of these protein structures are also used for artificial-intelligence (AI)-based methods of structural modelling. Despite the wealth of protein structures that have been determined by structural biologists, there are still gaps, or missing pieces in the puzzle of protein structural biology. Highly flexible regions may be missing from protein structures and conformational changes of different protein complex states may not be captured by current databases. In this perspective, I sketch out several ways that cross-linking mass spectrometry can contribute to filling in some of these missing pieces: Identification of cross-linked interactions in highly flexible protein regions not captured by other structural techniques; capturing conformational changes of protein complexes in different functional states; serving as distance constraints in integrative structural modelling and providing structural information of in cellulo proteins. The myriad ways in which cross-linking mass spectrometry contributes to filling in missing pieces in structural biology makes it a powerful technique in structural biology.