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Enzyme–enzyme interactions are fundamental to the function of cells. Their atomistic mechanisms remain elusive mainly due to limitations of in-cell measurements. We address this challenge by atomistically modeling, for a total of ≈80 μs, a slice of the human cell cytoplasm that includes three successive enzymes along the glycolytic pathway: glyceraldehyde-3-phosphate dehydrogenase (GAPDH), phosphoglycerate kinase (PGK), and phosphoglycerate mutase (PGM). We tested the model for nonspecific protein stickiness, an artifact of current atomistic force fields in crowded environments. The simulations reveal that the human enzymes co-organize in-cell into transient submetabolon complexes, consistent with previous experimental results. Our data both reiterate known specificity between GAPDH and PGK and reveal extensive direct interactions between GAPDH and PGM. Our simulations further reveal, through force field benchmarking, the critical role of protein solvation in facilitating these enzyme–enzyme interactions. Transient interenzyme interactions with μs lifetime occur repeatedly in our simulations via specific sticky protein surface patches, with interactions often mediated by charged patch residues. Some of the residues that interact frequently with one another lie in or near the active site of the enzymes. We show that some of these patches correspond to a general mode to interact with several partners for promiscuous enzymes like GAPDH. We further show that the non-native yeast PGK is stickier than human PGK in our human cytoplasm model, supporting the idea of evolutionary pressure to reduce sticking. Our cytoplasm modeling paves the way toward capturing the atomistic dynamics of an entire enzymatic pathway in-cell. 

For carbon dots, careful purification and electronic structure calculations facilitate learning about the origin of optical properties. 

Nanomaterials acquire a biomolecular corona upon introduction to biological media, leading to biological transformations such as changes in protein function, unmasking of epitopes, and protein fibrilization. Ex vivo studies to investigate the effect of nanoparticles on protein–protein interactions are typically performed in buffer and are rarely measured quantitatively in live cells. Here, we measure the differential effect of silica nanoparticles on protein association in vitro vs. in mammalian cells. BtubA and BtubB are a pair of bacterial tubulin proteins identified inProsthecobacterstrains that self-assemble like eukaryotic tubulin, first into dimers and then into microtubules in vitro or in vivo. Förster resonance energy transfer labeling of each of the Btub monomers with a donor (mEGFP) and acceptor (mRuby3) fluorescent protein provides a quantitative tool to measure their binding interactions in the presence of unfunctionalized silica nanoparticles in buffer and in cells using fluorescence spectroscopy and microscopy. We show that silica nanoparticles enhance BtubAB dimerization in buffer due to protein corona formation. However, these nanoparticles have little effect on bacterial tubulin self-assembly in the complex mammalian cellular environment. Thus, the effect of nanomaterials on protein–protein interactions may not be readily translated from the test tube to the cell in the absence of particle surface functionalization that can enable targeted protein–nanoparticle interactions to withstand competitive binding in the nanoparticle corona from other biomolecules. 

Intrinsically disordered proteins (IDPs) that lie close to the empirical boundary separating IDPs and folded proteins in Uversky’s charge–hydropathy plot may behave as “marginal IDPs” and sensitively switch conformation upon changes in environment (temperature, crowding, and charge screening), sequence, or both. In our search for such a marginal IDP, we selected Huntingtin-interacting protein K (HYPK) near that boundary as a candidate; PKIα, also near that boundary, has lower secondary structure propensity; and Crk1, just across the boundary on the folded side, has higher secondary structure propensity. We used a qualitative Förster resonance energy transfer-based assay together with circular dichroism to simultaneously probe global and local conformation. HYPK shows several unique features indicating marginality: a cooperative transition in end-to-end distance with temperature, like Crk1 and folded proteins, but unlike PKIα; enhanced secondary structure upon crowding, in contrast to Crk1 and PKIα; and a cross-over from salt-induced expansion to compaction at high temperature, likely due to a structure-to-disorder transition not seen in Crk1 and PKIα. We then tested HYPK’s sensitivity to charge patterning by designing charge-flipped variants including two specific sequences with identical amino acid composition that markedly differ in their predicted size and response to salt. The experimentally observed trends, also including mutants of PKIα, verify the predictions from sequence charge decoration metrics. Marginal proteins like HYPK show features of both folded and disordered proteins that make them sensitive to physicochemical perturbations and structural control by charge patterning. 

The ultimate regularity of quantum mechanics creates a tension with the assumption of classical chaos used in many of our pictures of chemical reaction dynamics. Out-of-time-order correlators (OTOCs) provide a quantum analog to the Lyapunov exponents that characterize classical chaotic motion. Maldacena, Shenker, and Stanford have suggested a fundamental quantum bound for the rate of information scrambling, which resembles a limit suggested by Herzfeld for chemical reaction rates. Here, we use OTOCs to study model reactions based on a double-well reaction coordinate coupled to anharmonic oscillators or to a continuum oscillator bath. Upon cooling, as one enters the tunneling regime where the reaction rate does not strongly depend on temperature, the quantum Lyapunov exponent can approach the scrambling bound and the effective reaction rate obtained from a population correlation function can approach the Herzfeld limit on reaction rates: Tunneling increases scrambling by expanding the state space available to the system. The coupling of a dissipative continuum bath to the reaction coordinate reduces the scrambling rate obtained from the early-time OTOC, thus making the scrambling bound harder to reach, in the same way that friction is known to lower the temperature at which thermally activated barrier crossing goes over to the low-temperature activationless tunneling regime. Thus, chemical reactions entering the tunneling regime can be information scramblers as powerful as the black holes to which the quantum Lyapunov exponent bound has usually been applied. 

Protein–protein and protein–water hydrogen bonding interactions play essential roles in the way a protein passes through the transition state during folding or unfolding, but the large number of these interactions in molecular dynamics (MD) simulations makes them difficult to analyze. Here, we introduce a state space representation and associated “rarity” measure to identify and quantify transition state passage (transit) events. Applying this representation to a long MD simulation trajectory that captured multiple folding and unfolding events of the GTT WW domain, a small protein often used as a model for the folding process, we identified three transition categories: Highway (faster), Meander (slower), and Ambiguous (intermediate). We developed data sonification and visualization tools to analyze hydrogen bond dynamics before, during, and after these transition events. By means of these tools, we were able to identify characteristic hydrogen bonding patterns associated with “Highway” versus “Meander” versus “Ambiguous” transitions and to design algorithms that can identify these same folding pathways and critical protein–water interactions directly from the data. Highly cooperative hydrogen bonding can either slow down or speed up transit. Furthermore, an analysis of protein–water hydrogen bond dynamics at the surface of WW domain shows an increase in hydrogen bond lifetime from folded to unfolded conformations with Ambiguous transitions as an outlier. In summary, hydrogen bond dynamics provide a direct window into the heterogeneity of transits, which can vary widely in duration (by a factor of 10) due to a complex energy landscape. 

Abstract Despite a vaccine, hepatitis B virus (HBV) remains a world-wide source of infections and deaths. We develop a whole-cell computational platform combining spatial and kinetic models describing the infection cycle of HBV in a hepatocyte host. We simulate key parts of the infection cycle with this whole-cell platform for 10 min of biological time, to predict infection progression, map out virus-host and virus-drug interactions. We find that starting from an established infection, decreasing the copy number of the viral envelope proteins shifts the dominant infection pathway from capsid secretion to re-importing the capsids into the nucleus, resulting in more nuclear-localized viral covalently closed circular DNA (cccDNA) and boosting transcription. This scenario can mimic the consequence of drugs designed to manipulate viral gene expression. Mutating capsid proteins facilitates capsid destabilization and disassembly at nuclear pore complexes, resulting in an increase in cccDNA copy number. However, excessive destabilization leads to premature cytoplasmic disassembly and does not increase the cccDNA counts. Finally, our simulations can predict the best drug dosage and its administration timing to reduce the cccDNA counts. Our adaptable computational platform can be parameterized to study other viruses and identify the most central viral pathways that can be targeted by drugs.