Chronic infection with the hepatitis B virus (HBV) remains a major global health burden and depends on efficient viral replication, including the assembly of the viral capsid during the HBV life cycle. Capsid assembly proceeds through transient oligomeric intermediates, among which the formation of hexameric units is thought to underlie the energetic bottleneck associated with nucleation during capsid assembly. Despite extensive experimental and computational work, the structural and energetic determinants of hexamer closure remain incompletely understood at the molecular level. Here, we employ a multistage computational approach to investigate the open-to-closed transition of the HBV capsid hexamer in the apo system, in which targeted molecular dynamics is employed to generate diverse open-to-closed transition pathways. These pathways are subsequently refined and sampled by using path-based free-energy methods to construct multidimensional free-energy landscapes of hexamer closure. Across independently sampled pathways, we observe diverse transition routes, while a conserved steric rearrangement in the gate region emerges as the dominant rate-limiting feature. These results provide a qualitative characterization of hexamer closure energetics and establish a general framework for studying complex conformational transitions in large, flexible biomolecular assemblies.

Prokaryotic Argonaute proteins (pAgos) are nucleic acid-guided endonucleases with diverse functions. Mucilaginibacter paludis Argonaute (MbpAgo) is unusual in using guide DNA (gDNA) to cleave target RNA (tgRNA), but the structural basis for this activity has been unclear. Here we present cryo-electron microscopy structures of MbpAgo in apo, binary, and ternary states at up to 2.55 Å resolution. The apo structure reveals a conserved bilobal scaffold with unique insertions in the PIWI and MID domains that stabilize the catalytic conformation. Upon gDNA binding, MbpAgo forms a dimer stabilized by multiple protein-protein interfaces and an auxiliary nucleic acid-like density bridging the PAZ-MID lobes. The auxiliary nucleic acid interactions coordinate gDNA binding and dimer stabilization to support MbpAgo activity, with dimerization becoming particularly important for efficient cleavage with double-stranded DNA (dsDNA) guides. Binding of tgRNA induces a DNA-RNA hybrid duplex and conformational changes that destabilize the dimer, reverting MbpAgo to an active monomer capable of cleaving structured viral RNAs such as the SARS-CoV-2 5'UTR and HIV-1 CES. These findings suggest a dynamic monomer-dimer transition as both the regulatory mechanism of MbpAgo and an evolutionary adaptation for processing dsDNA-derived guides, providing a structural framework for programmable RNA targeting.

Outer mitochondrial membrane fusion is mediated by the mitofusin paralogs Mfn1 and Mfn2. Nucleotide-driven self-assembly and conformational changes are required for regulated membrane fusion activity, but the allosteric mechanisms remain enigmatic due to incomplete structural information. In this study, we investigate the GTP-coupled conformational dynamics of Mfn1 using time-resolved transition metal ion fluorescence resonance energy transfer (tmFRET). Using the minimal Mfn1 construct with the GTPase domain and helical bundle 1 (HB1) connected by Hinge 2, we engineered FRET pairs by incorporating a fluorescent noncanonical amino acid donor and a metal ion acceptor. For each state of the catalytic cycle, we measured tmFRET with fluorescence lifetimes and determined distance distributions, which can capture complex structural heterogeneity. Our distance measurements for the GDP-bound state matched predictions from the atomic resolution structure, establishing that the same open state, with GTPase and HB1 domains far apart, exists in solution. Our data reveal that the transition state is not a single closed state with HB1 stably contacting the GTPase domain. Rather, the distance distributions indicate that the presence of GDP + Pi results in an equilibrium between the open and closed states. We also captured the GTP-bound and nucleotide-free states of Mfn1. GTP binding favors the open state, and the conformation of the apo state is distinct from any nucleotide-bound state. Together, these findings redefine our understanding of GTP-driven conformational dynamics in Mfn1, demonstrating an unexpected conformational reversal in a single catalytic cycle and a heterogeneous transition state ensemble with implications for the mechanism and regulation of mitochondrial membrane fusion.

Eukaryotic cap-dependent translation initiation is regulated by binding of the predominantly folded eukaryotic initiation factor 4E (eIF4E) to the intrinsically disordered eIF4E binding proteins (4E-BPs). Here, we report full-length atomistic conformational ensembles generated by IDPConformerGenerator and optimized by X-EISDv2 workflow for both apo 4E-BP2, the neuronal 4E-BP, and 4E-BP2 in complex with eIF4E, using data from single-molecule fluorescence and nuclear magnetic resonance (NMR), together with select coordinates from a 4E-BP1:eIF4E crystal structure. Structural sampling within dynamic complexes is often under-appreciated, with NMR and crystal structure data for 4E-BP:eIF4E suggesting different degrees of structural heterogeneity. Our ensemble models validated by solution spectroscopy data enable comparison of free 4E-BP2 and its complex with eIF4E. This shows a delocalization of contacts around canonical regions, which supports previous findings of unidirectional conditional occupancy of the binding sites. Two new contact regions emerged: one between the disordered N -termini of eIF4E and 4E-BP2, which may play an allosteric role in tuning the binding affinity, and the other between the C-terminus of 4E-BP2 and an extended region of eIF4E, which is consistent with the extended, dynamic binding interface that we reported previously. These results support a model of translation regulation in which the dynamic 4E-BP2:eIF4E complex facilitates accessibility of regulatory sites of 4E-BP2 when bound.

Women with pre-pregnancy overweight or obesity are at increased risk of adverse pregnancy outcomes (APOs) and postpartum weight retention (PPWR). We examined which lipid classes were associated with APO and PPWR during pregnancy and postpartum using a subsample from a clinical trial. Data were collected via questionnaires, electronic health records, and participant-collected dried blood spots at three time-points. Lipidomic profiles were measured at all three time points in 49 participants. Using weighted-lipid correlation network analysis, differential lipid analysis, and partial-least squares discriminant analysis, we identified triglyceride (TG)-rich lipid signatures associated with APOs and PPWR. In early pregnancy, three TG networks and seven individual TGs were consistently associated with APOs. Postpartum, several TG networks and individual TGs were associated with APOs and PPWR. These findings highlight TG lipids' crucial role in pregnancy outcomes and the potential of TG-based lipidomic biomarkers for early risk identification to improve maternal and fetal health.

High-strength bolts are critical structural components that are highly susceptible to corrosion in complex environments, posing significant threats to structural safety and reliability. Although acoustic emission (AE) technology has been widely applied in structural health monitoring, existing studies mainly focus on damage mode identification or source localization, while the identification of corrosion evolution stages based on AE signals remains insufficient. This study develops an intelligent corrosion diagnosis framework for high-strength bolts by integrating multimodal feature fusion and optimized machine learning. AE signals are first collected from the near-end and far-end of bolts using a wireless sensor network and then transformed into time-frequency representations via continuous wavelet transform (CWT). The resulting time-frequency images are fed into a modified ResNet-18 network to extract deep features, while statistical features are simultaneously extracted from the raw signals to preserve global information. These heterogeneous features are subsequently fused to form a comprehensive representation of corrosion characteristics. Furthermore, an artificial protozoa optimizer (APO) is introduced to adaptively optimize the hyperparameters of the XGBoost model. The results demonstrate that AE signals generated by hammering bolts with different corrosion levels can be successfully distinguished. The proposed method achieves high accuracy in corrosion stage classification and outperforms conventional approaches. Even when evaluated on an additional M30 bolt dataset, the proposed method maintains robust performance, demonstrating excellent generalization capability across different bolt sizes. These results demonstrate the practical potential of the proposed method for intelligent bolt corrosion diagnosis.

Background/Objectives: Substance use disorder (SUD) in pregnancy is an increasingly complex public health challenge that is known to worsen maternal and neonatal outcomes. Rates of polysubstance use are steadily rising. The objective of this study was to assess the impact of co-occurring opioid and stimulant use disorder on adverse pregnancy outcomes (APOs) among inpatient pregnancy hospitalizations. Methods: We conducted a cross-sectional analysis of inpatient pregnancy hospitalizations for delivery admissions from the Healthcare Cost and Utilization Project (HCUP) National Inpatient Sample (NIS) from 2016 to 2020. ICD-10 codes were used to identify patients with opioid and stimulant use disorder and with APOs. APO was defined as a composite to include hypertensive disorders of pregnancy, antepartum hemorrhage, postpartum hemorrhage, preterm birth, and fetal growth restriction. Multivariable logistic regression analyses were undertaken to predict the likelihood of APOs among pregnancy hospitalizations with opioid use, stimulant use, or co-occurring (opioid and stimulant) use disorders. Sociodemographic covariates, including age, race and/or ethnicity, insurance payor type, and income level, were accounted for. Results: From 2016 to 2020, 32,602 delivery hospitalizations complicated by stimulant or opioid use disorder were identified. Of these admissions, 21,049 (64.6%) had opioid use disorder, 9472 (29.1%) had stimulant use disorder, and 2081 (6.4%) had co-occurring opioid and stimulant use disorder. In the entire cohort, the prevalence of APOs was significantly highest among pregnancy delivery hospitalizations with co-occurring opioid use and stimulant use disorder (1136/2081-54.6%, p < 0.001), as compared with opioid use disorder (8923/21,049-42.4%) or stimulant use disorder alone (4654/9472-49.1%). Rates of APOs increased in subsequent years for all cohort groups. Adjusting for relevant sociodemographic covariates, co-occurring opioid and stimulant use disorder was an independent predictor of APO (aOR 3.65; CI 95%, 3.34-3.99). In comparison, opioid use disorder and stimulant use disorder were independent predictors of APOs with a less strong correlation, aOR 2.22 (CI 95%, 2.16-2.29) and aOR 2.89 (CI 95%, 2.77-3.02), respectively. Conclusions: Patients with co-occurring opioid and stimulant use disorder have the highest exposure risk for APOs, acting as an independent predictor for APOs when adjusting for sociodemographic covariates.

The structures predicted by AlphaFold can provide unprecedented opportunities for docking screens; however, experimentally validated examples of using the apo-form are limited. This study reports novel chemotype inhibitors targeting the leucine-rich repeat kinase 2 (LRRK2) kinase domain through a docking screen using one of the ensemble structures starting from the template deposited by AlphaFold2. The MODELLER software generated the ensemble. The conformer that showed the best early enrichment of true positives with the mixture of known ligands and their property-matched decoys was selected. The docking screen against approximately 1.3 million small molecules and enzyme-based assays with the LRRK2 kinase domain followed. We selected 17 molecules, excluding those similar to all known kinase inhibitors. Combined with analogs-by-catalog, ten new small molecules with Ki values below 15 μM were discovered, including one sub-μM inhibitor. To test selectivity, enzyme assays with a mutant and six additional kinases, including known off-targets of existing LRRK2 inhibitors, were performed using three inhibitors. The data suggest that the novelty in chemical structure may be insufficient for providing selectivity. Our approach is generally applicable to cases where information on known binders is available but experimental structure is not.

This study introduces two recently developed bio-inspired metaheuristic algorithms, Artificial Protozoa Optimizer (APO, 2024) and Dung Beetle Optimizer (DBO, 2023), into long short-term memory (LSTM) networks for monthly pan evaporation prediction under limited climatic data. Representing the first application of these algorithms to hydrological modeling, these models integrate APO and DBO into the LSTM framework to optimize hyperparameters and enhance accuracy and generalization. Their performance is benchmarked against the standard LSTM and two established hybrids, LSTM-GWO and LSTM-HHO. A case study in southeast China using 40 years of data from two stations shows that both LSTM-APO and LSTM-DBO consistently outperform the alternatives across three data-splitting scenarios (M1, M2, M3). For the best test case (M3, Station 1), LSTM-APO reduced RMSE and MAE by 46.5% and 47.2%, respectively, compared to the best LSTM, while in Station 2 (M2) it achieved reductions of 43.9% and 40.7%, with gains of about 9% in R[2] and NSE. LSTM-DBO also yielded notable improvements, reducing errors by 20-30% and demonstrating robust predictive stability. Visual analyses confirm that LSTM-APO provides predictions closely aligned with observations, with LSTM-DBO performing comparably well. These findings highlight the role of metaheuristic optimization in boosting LSTM performance for nonlinear evaporation processes with sparse inputs. Overall, APO- and DBO-based hybrids show strong promise for reliable pan evaporation forecasting. Future research should assess their real-time applicability and transferability across diverse climates.

The binding of cyclic adenosine monophosphate (cAMP) to hyperpolarization-activated cyclic nucleotide-gated (HCN) ion channels regulates cardiac pacemaking but key aspects of the mechanism of ligand-dependent regulation remain unresolved. Here, we examine the role of the lipid environment by reconstituting purified human HCN channels into lipid nanodiscs and measuring successive cAMP binding to single HCN channels using nanophotonic waveguides. Regardless of nanodisc size or lipid composition, cAMP molecules bind cooperatively to HCN channels in lipid bilayers, unlike channels solubilized in detergents. The affinity of the first ligand remains unchanged across conditions, indicating that the bilayer selectively alters higher-order ligation states. Cryo-EM structures of apo- and holo-HCN channels reveal additional lipid densities that are weak or absent in detergent-solubilized preparations. Together, these findings show that the lipid bilayer is both necessary and sufficient to induce cooperative ligand binding in HCN channels, thereby enhancing their sensitivity to gating stimuli.

Early-stage drug discovery relies on the availability of stable protein for reliable biophysical characterization of ligand binding. However, many Plasmodium falciparum proteins are challenging to produce in heterologous systems, which limits their experimental utility. To address this, we tested whether ProteinMPNN-guided sequence design could generate stabilized surrogate constructs that retain wild-type-like structure and binding thermodynamics. Designs were generated with constraints to maintain conserved and binding-site residues for three therapeutically relevant targets: PfBDP1-BRD, PfBDP4-BRD, and PfK13-KREP. The resulting constructs showed markedly increased thermal stability. Using PfBDP1-BRD as a benchmark, isothermal titration calorimetry confirmed that the stabilized variants retained wild-type-like binding thermodynamics with a known ligand. Extending this approach to other targets, a PfK13-KREP construct led to an apo structure with a binding pocket closely matching the wild type. For PfBDP4-BRD, virtual screening against a previously reported wild-type crystal structure identified putative binders, while a stabilized surrogate for this otherwise unstable target enabled their experimental validation and the determination of a 1.25 Å co-crystal structure with a newly identified inhibitor. Our findings demonstrate that computationally stabilized surrogates are practical and effective tools for robust biophysics and structure-enabled drug discovery against otherwise challenging malaria proteins.

Acinetobacter baumannii is an opportunistic pathogen known for its extensive antibiotic resistance, posing a significant concern in healthcare settings. Given global concern over broad-spectrum antibiotic use and its impact on the human microbiome, targeting the bacterial cell-division protein Filamenting temperature-sensitive Z (FtsZ) is a promising strategy for developing new antibiotics, as this protein is essential for bacterial cell division. FtsZ polymerizes into filaments to form the Z-ring at the cell division site. This Z-ring orchestrates the recruitment of other crucial proteins in cytokinesis and cell wall synthesis, making it critical for bacterial viability. To target FtsZ from Acinetobacter baumannii (abFtsZ), we have elucidated the first crystal structures in the apo and GDP-bound state. The structural analysis revealed that the abFtsZ-GDP complex exists in a relaxed state conformation with weak inter-subunit interactions. The GTPase activity of abFtsZ showed a Vmax of 3.3 ± 0.4 nmolP/nmolFtsZ/min, and a Km of 2.3 mM. By virtual screening using the crystal structure of abFtsZ, we identified six potential inhibitors: Gossypin, Stafib-1, Tryphostin A51, Sangivamycin, Scutellarin, and Ellagic acid that target the GTP-binding pocket. These inhibitors exhibited high docking scores ranging from -10.2 to -8.4 kcal/mol and remained stable throughout the 500 ns MD simulation. These inhibitors can serve as lead molecules for the development of a new antibacterial agent.