A General Method to Screen Nanobodies for Cytochrome P450 Enzymes from a Yeast Surface Display Library.

The availability of yeast surface display nanobody (Nb) libraries offers a convenient way to acquire antigen-specific nanobodies that may be useful for protein structure-function studies and/or therapeutic applications, complementary to the conventional method of acquiring nanobodies through immunization in camelids. In this study, we developed a general approach to select nanobodies for cytochrome P450 enzymes from a highly diverse yeast display library. We tested our method on three P450 enzymes including CYP102A1, neuronal nitric oxide synthase (nNOS), and the complex of CYP2B4:POR, using a novel streamlined approach where biotinylated P450s were bound to fluorescent-labeled streptavidin for Nb screening. The Nb-antigen binders were selectively enriched using magnetic-activated cell sorting (MACS) and fluorescence-activated cell sorting (FACS). After two rounds of MACS, the population of positive binders was enriched by >5-fold compared to the naïve library. The subsequent FACS selection, with a gating of 0.1%, identified 634, 270, and 215 positive binders for CYP102A1, nNOS, and CYP2B4:POR, respectively. The positive binders for CYP102A1 were further triaged based on EC50 determined at various antigen concentrations. DNA sequencing of the top 30 binders of CYP102A1 resulted in 26 unique clones, 8 of which were selected for over-expression and characterization. They were found to inhibit CYP102A1-catalyzed oxidation of omeprazole with IC50 values in the range of 0.16-2.8 µM. These results validate our approach and may be applied to other protein targets for the effective selection of specific nanobodies.

LSPConv: local spatial projection convolution for point cloud analysis.

This study introduces a novel approach, Local Spatial Projection Convolution (LSPConv), for point cloud classification and semantic segmentation. Unlike conventional methods utilizing relative coordinates for local geometric information, our motivation stems from the inadequacy of existing techniques for representing the intricate spatial organization of unconsolidated and irregular 3D point clouds. To address this limitation, we propose a Local Spatial Projection Module utilizing a vector projection strategy, designed to capture comprehensive local spatial information more effectively. Moreover, recent studies emphasize the importance of anisotropic kernels for point cloud feature extraction, considering the distinct contributions of individual neighboring points. To cater to this requirement, we introduce the Feature Weight Assignment (FWA) Module to assign weights to neighboring points, enhancing the anisotropy crucial for accurate feature extraction. Additionally, we introduce an Anisotropic Relative Feature Encoding Module that adaptively encodes points based on their relative features, further amplifying the anisotropic characteristics. Our approaches achieve remarkable results for point cloud classification and segmentation in several benchmark datasets based on extensive qualitative and quantitative evaluation.

Mapping interactions of calmodulin and neuronal NO synthase by crosslinking and mass spectrometry.

Neuronal nitric oxide synthase (nNOS) is a homodimeric cytochrome P450-like enzyme that catalyzes the conversion of L-arginine to nitric oxide in the presence of NADPH and molecular oxygen. The binding of calmodulin (CaM) to a linker region between the FAD/FMN-containing reductase domain, and the heme-containing oxygenase domain is needed for electron transfer reactions, reduction of the heme, and NO synthesis. Due to the dynamic nature of the reductase domain and low resolution of available full-length structures, the exact conformation of the CaM-bound active complex during heme reduction is still unresolved. Interestingly, hydrogen-deuterium exchange and mass spectrometry studies revealed interactions of the FMN domain and CaM with the oxygenase domain for iNOS, but not nNOS. This finding prompted us to utilize covalent crosslinking and mass spectrometry to clarify interactions of CaM with nNOS. Specifically, MS-cleavable bifunctional crosslinker disuccinimidyl dibutyric urea was used to identify thirteen unique crosslinks between CaM and nNOS as well as 61 crosslinks within the nNOS. The crosslinks provided evidence for CaM interaction with the oxygenase and reductase domain residues as well as interactions of the FMN domain with the oxygenase dimer. Cryo-EM studies, which gave a high-resolution model of the oxygenase domain, along with crosslink-guided docking provided a model of nNOS that brings the FMN within 15 Å of the heme in support for a more compact conformation than previously observed. These studies also point to the utility of covalent crosslinking and mass spectrometry in capturing transient dynamic conformations that may not be captured by hydrogen-deuterium exchange and mass spectrometry experiments.

A General Global and Local Pre-Training Framework for 3D Medical Image Segmentation.

Accurate target segmentation from computed tomography (CT) scans is crucial for surgical robots to perform clinical surgeries successfully. However, the lack of medical image data and annotations has been the biggest obstacle to learning robust medical image segmentation models. Self-supervised learning can effectively address this problem by providing a strategy to pre-train a model with unlabeled data, and then fine-tune downstream tasks with limited labeled data. Existing self-supervised methods fail to simultaneously utilize the abundant global anatomical structure information and local feature differences in medical imaging. In this work, we propose a new strategy for the pre-training framework, which uses the three-dimensional anatomical structure of medical images and specific task and background cues to segment volumetric medical images with limited annotations. Specifically, we propose (1) learning intrinsic patterns of volumetric medical image structures through multiple sub-tasks, and (2) designing a multi-level background cube contrastive learning strategy to enhance the target feature representation by exploiting the differences between the specific target and background. We conduct extensive evaluations on two publicly available datasets. Under limited annotation settings, the proposed method yields significant improvements compared to other self-supervised learning techniques. The proposed method achieves within 6% of the baseline performance using only five labeled CT volumes for training. Once the paper is online, the code and dataset will be available at https://github.com/PinkGhost0812/SGL.

Point-NAS: A Novel Neural Architecture Search Framework for Point Cloud Analysis.

Recently, point-based networks have exhibited extraordinary potential for 3D point cloud processing. However, owing to the meticulous design of both parameters and hyperparameters inside the network, constructing a promising network for each point cloud task can be an expensive endeavor. In this work, we develop a novel one-shot search framework called Point-NAS to automatically determine optimum architectures for various point cloud tasks. Specifically, we design an elastic feature extraction (EFE) module that serves as a basic unit for architecture search, which expands seamlessly alongside both the width and depth of the network for efficient feature extraction. Based on the EFE module, we devise a searching space, which is encoded into a supernet to provide a wide number of latent network structures for a particular point cloud task. To fully optimize the weights of the supernet, we propose a weight coupling sandwich rule that samples the largest, smallest, and multiple medium models at each iteration and fuses their gradients to update the supernet. Furthermore, we present a united gradient adjustment algorithm that mitigates gradient conflict induced by distinct gradient directions of sampled models and supernet, thus expediting the convergence of the supernet and assuring that it can be comprehensively trained. Pursuant to the provided techniques, the trained supernet enables a multitude of subnets to be incredibly well-optimized. Finally, we conduct an evolutionary search for the supernet under resource constraints to find promising architectures for different tasks. Experimentally, the searched Point-NAS with weights inherited from the supernet realizes outstanding results across a variety of benchmarks. i.e., 94.2% and 88.9% overall accuracy under ModelNet40 and ScanObjectNN, 68.6% mIoU under S3DIS, 63.6% and 69.3% mAP@0.25 under SUN RGB-D and ScanNet V2 datasets.

Oxytocin enhances the triangular association among behavior, resting-state, and task-state functional connectivity.

Considerable advances in the role of oxytocin (OT) effect on behavior and the brain network have been made, but the effect of OT on the association between inter-individual differences in functional connectivity (FC) and behavior is elusive. Here, by using a face-perception task and multiple connectome-based predictive models, we aimed to (1) determine whether OT could enhance the association among behavioral performance, resting-state FC (rsFC), and task-state FC (tsFC) and (2) if so, explore the role of OT in enhancing this triangular association. We found that in the OT group, the prediction performance of using rsFC or tsFC to predict task behavior was higher than that of the PL group. Additionally, the correlation coefficient between rsFC and tsFC was substantially higher in the OT group than in the PL group. The strength of these associations could be partly explained by OT altering the brain's FCs related to social cognition and face perception in both the resting and task states, mainly in brain regions such as the limbic system, prefrontal cortex, temporal poles, and temporoparietal junction. Taken together, these results provide novel evidence and a corresponding mechanism for how neuropeptides cause increased associations among inter-individual differences across different levels (e.g., behavior and large-scale brain networks in both resting and task-state), and may inspire future research on the role of neuropeptides in the cross levels association of both clinical and nonclinical use.

Discovery of N-Arylsulfonyl-Indole-2-Carboxamide Derivatives as Galectin-3 and Galectin-8 C-Terminal Domain Inhibitors.

Both galectin-3 and galectin-8 are involved in cell adhesion, migration, apoptosis, angiogenesis, and inflammatory processes by recognizing galactose-containing glycoproteins. Inhibiting galectin-3/8 activities is a potential treatment for cancer and tissue fibrosis. Herein, a series of novel N-arylsulfonyl-5-aryloxy-indole-2-carboxamide derivatives was disclosed as dual inhibitors toward galectin-3 and galectin-8 C-terminal domain with Kd values of low micromolar level (Cpd53, gal-3: Kd= 4.12 µM, gal-8C: Kd= 6.04 µM; Cpd57, gal-3: Kd= 12.8 µM, gal-8C: Kd= 2.06 µM), which are the most potent and selective noncarbohydrate-based inhibitors toward gal-3/8 isoforms to date. The molecular docking investigations suggested that the unique amino acids Arg144 in galectin-3 and Ser213 in galectin-8C could contribute to their potency and selectivity. The scratch wound assay demonstrated that Cpd53 and Cpd57 were able to inhibit the MRC-5 lung fibroblast cells migration as well. This class of inhibitors could serve as a new starting point for further discovering structurally distinct gal-3 and gal-8C inhibitors to be used in cancer and tissue fibrosis treatment.

Computational redesign of cytochrome P450 CYP102A1 for highly stereoselective omeprazole hydroxylation by UniDesign.

Cytochrome P450 CYP102A1 is a prototypic biocatalyst that has great potential in chemical synthesis, drug discovery, and biotechnology. CYP102A1 variants engineered by directed evolution and/or rational design are capable of catalyzing the oxidation of a wide range of organic compounds. However, it is difficult to foresee the outcome of engineering CYP102A1 for a compound of interest. Here, we introduce UniDesign as a computational framework for enzyme design and engineering. We tested UniDesign by redesigning CYP102A1 for stereoselective metabolism of omeprazole (OMP), a proton pump inhibitor, starting from an active but nonstereoselective triple mutant (TM: A82F/F87V/L188Q). To shift stereoselectivity toward (R)-OMP, we computationally scanned three active site positions (75, 264, and 328) for mutations that would stabilize the binding of the transition state of (R)-OMP while destabilizing that of (S)-OMP and picked three variants, namely UD1 (TM/L75I), UD2 (TM/A264G), and UD3 (TM/A328V), for experimentation, based on computed energy scores and models. UD1, UD2, and UD3 exhibit high turnover rates of 55 ± 4.7, 84 ± 4.8, and 79 ± 5.7 min-1, respectively, for (R)-OMP hydroxylation, whereas the corresponding rates for (S)-OMP are only 2.2 ± 0.19, 6.0 ± 0.68, and 14 ± 2.8 min-1, yielding an enantiomeric excess value of 92, 87, and 70%, respectively. These results suggest the critical roles of L75I, A264G, and A328V in steering OMP in the optimal orientation for stereoselective oxidation and demonstrate the utility of UniDesign for engineering CYP102A1 to produce drug metabolites of interest. The results are discussed in the context of protein structures.

The Versatile Biocatalyst of Cytochrome P450 CYP102A1: Structure, Function, and Engineering.

Wild-type cytochrome P450 CYP102A1 from Bacillus megaterium is a highly efficient monooxygenase for the oxidation of long-chain fatty acids. The unique features of CYP102A1, such as high catalytic activity, expression yield, regio- and stereoselectivity, and self-sufficiency in electron transfer as a fusion protein, afford the requirements for an ideal biocatalyst. In the past three decades, remarkable progress has been made in engineering CYP102A1 for applications in drug discovery, biosynthesis, and biotechnology. The repertoire of engineered CYP102A1 variants has grown tremendously, whereas the substrate repertoire is avalanched to encompass alkanes, alkenes, aromatics, organic solvents, pharmaceuticals, drugs, and many more. In this article, we highlight the major advances in the past five years in our understanding of the structure and function of CYP102A1 and the methodologies used to engineer CYP102A1 for novel applications. The objective is to provide a succinct review of the latest developments with reference to the body of CYP102A1-related literature.

Mortality burden based on the associations of ambient PM2.5 with cause-specific mortality in China: Evidence from a death-spectrum wide association study (DWAS).

Although studies have estimated the associations of PM2.5 with total mortality or cardiopulmonary mortality, few have comprehensively examined cause-specific mortality risk and burden caused by ambient PM2.5. Thus, this study investigated the association of short-term exposure to PM2.5 with cause-specific mortality using a death-spectrum wide association study (DWAS). Individual information of 5,450,764 deaths during 2013-2018 were collected from six provinces in China. Daily PM2.5 concentration in the case and control days were estimated by a random forest model. A time-stratified case-crossover study design was applied to estimate the associations (access risk, ER) of PM2.5 with cause-specific mortality, which was then used to calculate the population-attributable fraction (PAF) of mortality and the corresponding mortality burden caused by PM2.5. Each 10 µg/m3 increase in PM2.5 concentration (lag03) was associated with a 0.80 % [95 % confidence interval (CI): 0.73 %, 0.86 %] rise in total mortality. We found greater mortality effect at PM2.5 concentrations < 50 µg/m3. Stratified analyses showed greater ERs in females (1.01 %, 95 %CI: 0.91 %, 1.11 %), children ≤ 5 years (2.17 %, 95 %CI: 0.85 %, 3.51 %), and old people ≥ 70 years. We identified 33 specific causes (level 2) of death which had significant associations with PM2.5, including 16 circulatory diseases, 9 respiratory diseases, and 8 other causes. The PAF estimated based on the overall association between PM2.5 and total mortality was 3.16 % (95 %CI: 2.89 %, 3.40 %). However, the PAF was reduced to 2.88 % (95 %CI: 1.88 %, 3.81 %) using the associations of PM2.5 with 33 level 2 causes of death, based on which 250.15 (95 %CI: 163.29, 330.93) thousand deaths were attributable to short-term PM2.5 exposure across China in 2019. Overall, this study provided a comprehensive picture on the death-spectrum wide association between PM2.5 and morality in China. We observed robust positive cause-specific associations of PM2.5 with mortality risk, which may provide more precise basis in assessing the mortality burden of air pollution.

Social conformity is associated with inter-trial electroencephalogram variability.

Human society encompasses diverse social influences, and people experience events differently and may behave differently under such influence, including in forming an impression of others. However, little is known about the underlying neural relevance of individual differences in following others' opinions or social norms. In the present study, we designed a series of tasks centered on social influence to investigate the underlying relevance between an individual's degree of social conformity and their neural variability. We found that individual differences under the social influence are associated with the amount of inter-trial electroencephalogram (EEG) variability over multiple stages in a conformity task (making face judgments and receiving social influence). This association was robust in the alpha band over the frontal and occipital electrodes for negative social influence. We also found that inter-trial EEG variability is a very stable, participant-driven internal state measurement and could be interpreted as mindset instability. Overall, these findings support the hypothesis that higher inter-trial EEG variability may be related to higher mindset instability, which makes participants more vulnerable to exposed external social influence. The present study provides a novel approach that considers the stability of one's endogenous neural signal during tasks and links it to human social behaviors.

Quercetin Alleviates Pulmonary Fibrosis in Mice Exposed to Silica by Inhibiting Macrophage Senescence.

Quercetin exerts anti-inflammatory, anti-oxidant and other protective effects. Previous studies have shown that senescent cells, such as fibroblasts and type II airway epithelial cells, are strongly implicated in the development of pulmonary fibrosis pathology. However, the role of senescent macrophages during silicosis remains unclear. We investigated the effects of quercetin on macrophage senescence and pulmonary fibrosis, and explored underlying mechanisms. Mice were randomized to six model groups. Vitro model was also established by culturing RAW264.7 macrophages with silica (SiO2). We examined the effects of quercetin on fibrosis, senescence-associated ß-galactosidase (SA-ß-Gal) activity, and senescence-specific genes (p16, p21, and p53). We showed that quercetin reduced pulmonary fibrosis and inhibited extracellular matrix (ECM) formation. Quercetin also attenuated macrophage senescence induced by SiO2 both in vitro and in vivo. In addition, quercetin significantly decreased the expressions of the senescence-associated secretory phenotype (SASP), including proinflammatory factors (interleukin-1α (Il-1α), Il-6, tumor necrosis factor-α (TNF-α), and transforming growth factor-ß1 (TGF-ß1)) and matrix metalloproteinases (MMP2, MMP9, and MMP12). In conclusion, quercetin mediated its anti-fibrotic effects by inhibiting macrophage senescence, possibly via SASP.

Dynamic Parameter Calibration Framework for Opinion Dynamics Models.

In the past decade, various opinion dynamics models have been built to depict the evolutionary mechanism of opinions and use them to predict trends in public opinion. However, model-based predictions alone cannot eliminate the deviation caused by unforeseeable external factors, nor can they reduce the impact of the accumulated random error over time. To solve this problem, we propose a dynamic framework that combines a genetic algorithm and a particle filter algorithm to dynamically calibrate the parameters of the opinion dynamics model. First, we design a fitness function in accordance with public opinion and search for a set of model parameters that best match the initial observation. Second, with successive observations, we tracked the state of the opinion dynamic system by the average distribution of particles. We tested the framework by using several typical opinion dynamics models. The results demonstrate that the proposed method can dynamically calibrate the parameters of the opinion dynamics model to predict public opinion more accurately.

Implications of Gut Microbiota in Neurodegenerative Diseases.

The morbidity associated with neurodegenerative diseases (NDs) is increasing, posing a threat to the mental and physical quality of life of humans. The crucial effect of microbiota on brain physiological processes is mediated through a bidirectional interaction, termed as the gut-brain axis (GBA), which is being investigated in studies. Many clinical and laboratory trials have indicated the importance of microbiota in the development of NDs via various microbial molecules that transmit from the gut to the brain across the GBA or nervous system. In this review, we summarize the implications of gut microbiota in ND, which will be beneficial for understanding the etiology and progression of NDs that may in turn help in developing ND interventions and clinical treatments for these diseases.

Household Transmission of Severe Acute Respiratory Syndrome Coronavirus 2 in the United States: Living Density, Viral Load, and Disproportionate Impact on Communities of Color.

BACKGROUND: Households are hot spots for severe acute respiratory syndrome coronavirus 2 transmission. METHODS: This prospective study enrolled 100 coronavirus disease 2019 (COVID-19) cases and 208 of their household members in North Carolina though October 2020, including 44% who identified as Hispanic or non-White. Households were enrolled a median of 6 days from symptom onset in the index case. Incident secondary cases within the household were detected using quantitative polymerase chain reaction of weekly nasal swabs (days 7, 14, 21) or by seroconversion at day 28. RESULTS: Excluding 73 household contacts who were PCR-positive at baseline, the secondary attack rate (SAR) among household contacts was 32% (33 of 103; 95% confidence interval [CI], 22%-44%). The majority of cases occurred by day 7, with later cases confirmed as household-acquired by viral sequencing. Infected persons in the same household had similar nasopharyngeal viral loads (intraclass correlation coefficient = 0.45; 95% CI, .23-.62). Households with secondary transmission had index cases with a median viral load that was 1.4 log10 higher than those without transmission (P = .03), as well as higher living density (more than 3 persons occupying fewer than 6 rooms; odds ratio, 3.3; 95% CI, 1.02-10.9). Minority households were more likely to experience high living density and had a higher risk of incident infection than did White households (SAR, 51% vs 19%; P = .01). CONCLUSIONS: Household crowding in the context of high-inoculum infections may amplify the spread of COVID-19, potentially contributing to disproportionate impact on communities of color.

Urban-rural disparity of the short-term association of PM2.5 with mortality and its attributable burden.

Although studies have investigated the associations between PM2.5 and mortality risk, evidence from rural areas is scarce. We aimed to compare the PM2.5-mortality associations between urban cities and rural areas in China. Daily mortality and air pollution data were collected from 215 locations during 2014-2017 in China. A two-stage approach was employed to estimate the location-specific and combined cumulative associations between short-term exposure to PM2.5 (lag 0-3 days) and mortality risks. The excess risks (ER) of all-cause, respiratory disease (RESP), cardiovascular disease (CVD), and cerebrovascular disease (CED) mortality for each 10 µg/m3 increment in PM2.5 across all locations were 0.54% (95% confidence interval [CI]: 0.38%, 0.70%), 0.51% (0.10%, 0.93%), 0.74% (0.50%, 0.97%), and 0.52% (0.20%, 0.83%), respectively. Slightly stronger associations for CVD (0.80% versus 0.60%) and CED (0.61% versus 0.26%) mortality were observed in urban cities than in rural areas, and slightly greater associations for RESP mortality (0.51% versus 0.43%) were found in rural areas than in urban cities. A mean of 2.11% (attributable fraction [AF], 95% CI: 1.48%, 2.76%) of all-cause mortality was attributable to PM2.5 exposure in China, with a larger AF in urban cities (2.89% [2.12%, 3.67%]) than in rural areas (0.61% [-0.60%, 1.84%]). Disparities in PM2.5-mortality associations between urban cities and rural areas were also found in some subgroups classified by sex and age. This study provided robust evidence on the associations of PM2.5 with mortality risks in China and demonstrated urban-rural disparities of PM2.5-mortality associations for various causes of death.

EEGdenoiseNet: a benchmark dataset for deep learning solutions of EEG denoising.

Objective.Deep learning (DL) networks are increasingly attracting attention across various fields, including electroencephalography (EEG) signal processing. These models provide comparable performance to that of traditional techniques. At present, however, there is a lack of well-structured and standardized datasets with specific benchmark limit the development of DL solutions for EEG denoising.Approach.Here, we present EEGdenoiseNet, a benchmark EEG dataset that is suited for training and testing DL-based denoising models, as well as for performance comparisons across models. EEGdenoiseNet contains 4514 clean EEG segments, 3400 ocular artifact segments and 5598 muscular artifact segments, allowing users to synthesize contaminated EEG segments with the ground-truth clean EEG.Main results.We used EEGdenoiseNet to evaluate denoising performance of four classical networks (a fully-connected network, a simple and a complex convolution network, and a recurrent neural network). Our results suggested that DL methods have great potential for EEG denoising even under high noise contamination.Significance.Through EEGdenoiseNet, we hope to accelerate the development of the emerging field of DL-based EEG denoising. The dataset and code are available athttps://github.com/ncclabsustech/EEGdenoiseNet.

Ambient Temperature and Years of Life Lost: A National Study in China.

Although numerous studies have investigated premature deaths attributable to temperature, effects of temperature on years of life lost (YLL) remain unclear. We estimated the relationship between temperatures and YLL, and quantified the YLL per death caused by temperature in China. We collected daily meteorological and mortality data, and calculated the daily YLL values for 364 locations (2013-2017 in Yunnan, Guangdong, Hunan, Zhejiang, and Jilin provinces, and 2006-2011 in other locations) in China. A time-series design with a distributed lag nonlinear model was first employed to estimate the location-specific associations between temperature and YLL rates (YLL/100,000 population), and a multivariate meta-analysis model was used to pool location-specific associations. Then, YLL per death caused by temperatures was calculated. The temperature and YLL rates consistently showed U-shaped associations. A mean of 1.02 (95% confidence interval: 0.67, 1.37) YLL per death was attributable to temperature. Cold temperature caused 0.98 YLL per death with most from moderate cold (0.84). The mean YLL per death was higher in those with cardiovascular diseases (1.14), males (1.15), younger age categories (1.31 in people aged 65-74 years), and in central China (1.34) than in those with respiratory diseases (0.47), females (0.87), older people (0.85 in people ≥75 years old), and northern China (0.64) or southern China (1.19). The mortality burden was modified by annual temperature and temperature variability, relative humidity, latitude, longitude, altitude, education attainment, and central heating use. Temperatures caused substantial YLL per death in China, which was modified by demographic and regional characteristics.

Mapping protein-protein interactions in homodimeric CYP102A1 by crosslinking and mass spectrometry.

Covalent crosslinking and mass spectrometry techniques hold great potential in the study of multiprotein complexes, but a major challenge is the inability to differentiate intra- and inter- protein crosslinks in homomeric complexes. In the current study we use CYP102A1, a well-characterized homodimeric P450, to examine a subtractive method that utilizes limited crosslinking with disuccinimidyl dibutyric urea (DSBU) and isolation of the monomer, in addition to the crosslinked dimer, to identify inter-monomer crosslinks. The utility of this approach was examined with the use of MS-cleavable crosslinker DSBU and recently published cryo-EM based structures of the CYP102A1 homodimer. Of the 31 unique crosslinks found, 26 could be fit to the reported structures whereas 5 exceeded the spatial constraints. Not only did these crosslinks validate the cryo-EM structure, they point to new conformations of CYP102A1 that bring the flavins in closer proximity to the heme.

Amphipol-facilitated elucidation of the functional tetrameric complex of full-length cytochrome P450 CYP2B4 and NADPH-cytochrome P450 oxidoreductase.

Interactions of membrane-bound mammalian cytochromes P450 (CYPs) with NADPH-cytochrome P450 oxidoreductase (POR), which are required for metabolism of xenobiotics, are facilitated by membrane lipids. A variety of membrane mimetics, such as phospholipid liposomes and nanodiscs, have been used to simulate the membrane to form catalytically active CYP:POR complexes. However, the exact mechanism(s) of these interactions are unclear because of the absence of structural information of full-length mammalian CYP:POR complexes in membranes. Herein, we report the use of amphipols (APols) to form a fully functional, soluble, homogeneous preparation of full-length CYP:POR complexes amenable to biochemical and structural study. Incorporation of CYP2B4 and POR into APols resulted in a CYP2B4:POR complex with a stoichiometry of 1:1, which was fully functional in demethylating benzphetamine at a turnover rate of 37.7 ± 2.2 min-1, with a coupling efficiency of 40%. Interestingly, the stable complex had a molecular weight (Mw) of 338 ± 22 kDa determined by multiangle light scattering, suggestive of a tetrameric complex of 2CYP2B4:2POR embedded in one APol nanoparticle. Moreover, negative stain electron microscopy (EM) validated the homogeneity of the complex and allowed us to generate a three-dimensional EM map and model consistent with the tetramer observed in solution. This first report of the full-length mammalian CYP:POR complex by transmission EM not only reveals the architecture that facilitates electron transfer but also highlights a potential use of APols in biochemical and structural studies of functional CYP complexes with redox partners.