Biophysical journal最新文献

The adhesin FimH is expressed by commensal Escherichia coli and is implicated in urinary tract infections, where it mediates adhesion to mannosylated glycoproteins on urinary and intestinal epithelial cells in the presence of a high-shear fluid environment. The FimH-mannose bond exhibits catch behavior in which bond lifetime increases with force, because tensile force induces a transition in FimH from a compact native to an elongated activated conformation with a higher affinity to mannose. However, the lifetime of the activated state of FimH has not been measured under force. Here we apply multiplexed magnetic tweezers to apply a preload force to activate FimH bonds with yeast mannan, then we measure the lifetime of these activated bonds under a wide range of forces above and below the preload force. A higher fraction of FimH-mannan bonds were activated above than below a critical preload force, confirming the FimH catch bond behavior. Once activated, FimH detached from mannose with multi-state kinetics, suggesting the existence of two bound states with a 20-fold difference in dissociation rates. The average lifetime of activated FimH-mannose bonds was 1000 to 10,000 s at forces of 30-70 pN. Structural explanations of the two bound states and the high force resistance provide insights into structural mechanisms for long-lived, force-resistant biomolecular interactions.

Guanine radical cations are precursors to oxidatively induced DNA lesions, and the determination of oxidative DNA hot spots beyond oligonucleotides remains a current challenge. In order to rationalize the finetuned ionization properties of the ∼60 guanines in a nucleosome core particle, we report a robust molecular dynamics-then-FO-DFTB/MM (fragment-orbital tight-binding density functional theory/molecular mechanics) simulation protocol spanning 20 μs. Our work allows us to identify several factors governing guanine ionization potential and map oxidative hotspots. Our results highlight the predominant role of the proximity of positively charged histone residues in the modulation of the guanine ionization potential up to 0.6 eV. Consequently, fast, long-range hole transfer in nucleosomal DNA could be tuned by the proximity of histone tails, which differs, from a biological point of view, on the chromatin state.

Many biological systems exhibit precise timing of events, and one of the most known examples is cell lysis, which is a process of breaking bacterial host cells in the virus infection cycle. However, the underlying microscopic picture of precise timing remains not well understood. We present a novel theoretical approach to explain the molecular mechanisms of effectively deterministic dynamics in biological systems. Our hypothesis is based on the idea of stochastic coupling between relevant underlying biophysical and biochemical processes that lead to noise cancellation. To test this hypothesis, we introduced a minimal discrete-state stochastic model to investigate how holin proteins produced by bacteriophages break the inner membranes of gram-negative bacteria. By explicitly solving this model, the dynamic properties of cell lysis are fully evaluated, and theoretical predictions quantitatively agree with available experimental data for both wild-type and holin mutants. It is found that the observed threshold-like behavior is a result of the balance between holin proteins entering the membrane and leaving the membrane during the lysis. Theoretical analysis suggests that the cell lysis achieves precise timing for wild-type species by maximizing the number of holins in the membrane and narrowing their spatial distribution. In contrast, for mutated species, these conditions are not satisfied. Our theoretical approach presents a possible molecular picture of precise dynamic regulation in intrinsically random biological processes.

The significant effects of lipid binding on the functionality of potassium channel KcsA have been validated by brilliant studies. However, the specific interactions between lipids and KcsA, such as binding parameters for each binding event, have not been fully elucidated. In this study, we employed native mass spectrometry to investigate the binding of lipids to KcsA and their effects on the channel. The tetrameric structure of KcsA remains intact even in the absence of lipid binding. However, the subunit architecture of the E71A mutant, which is constantly open at low pH, relies on tightly associated copurified lipids. Furthermore, we observed that lipids exhibit weak binding to KcsA at high pH when the channel is at a closed/inactivation state in the absence of permeant cation K⁺. This feeble interaction potentially facilitates the association of K⁺ ions, leading to the transition of the channel to a resting closed/open state. Interestingly, both anionic and zwitterionic lipids strongly bind to KcsA at low pH when the channel is in an open/inactivation state. We also investigated the binding patterns of KcsA with natural lipids derived from E. coli and Streptomyces lividans. Interestingly, lipids from E. coli exhibited much stronger binding affinity compared to the lipids from S. lividans. Among the natural lipids from S. lividans, free fatty acids and triacylglycerols demonstrated the tightest binding to KcsA, whereas no detectable binding events were observed with natural phosphatidic acid lipids. These findings suggest that the lipid association pattern in S. lividans, the natural host for KcsA, warrants further investigation. In conclusion, our study sheds light on the role of lipids in stabilizing KcsA and highlights the importance of specific lipid-protein interactions in modulating its conformational states.

Protein-DNA interactions and protein-mediated DNA compaction play key roles in a range of biological processes. The length scales typically involved in DNA bending, bridging, looping, and compaction (≥1 kbp) are challenging to address experimentally or by all-atom molecular dynamics simulations, making coarse-grained simulations a natural approach. Here, we present a simple and generic coarse-grained model for DNA-protein and protein-protein interactions and investigate the role of the latter in the protein-induced compaction of DNA. Our approach models the DNA as a discrete worm-like chain. The proteins are treated in the grand canonical ensemble, and the protein-DNA binding strength is taken from experimental measurements. Protein-DNA interactions are modeled as an isotropic binding potential with an imposed binding valency without specific assumptions about the binding geometry. To systematically and quantitatively classify DNA-protein complexes, we present an unsupervised machine learning pipeline that receives a large set of structural order parameters as input, reduces the dimensionality via principal-component analysis, and groups the results using a Gaussian mixture model. We apply our method to recent data on the compaction of viral genome-length DNA by HIV integrase and find that protein-protein interactions are critical to the formation of looped intermediate structures seen experimentally. Our methodology is broadly applicable to DNA-binding proteins and protein-induced DNA compaction and provides a systematic and semi-quantitative approach for analyzing their mesoscale complexes.