Red blood cell folate and benign prostatic hyperplasia: results from the NHANES 2001-2008.

BACKGROUND: Benign prostatic hyperplasia (BPH) affects 30% of men worldwide, folate is essential for life. However, few studies have investigated the relationship between folate levels and BPH. The present study aims to explore the relationship between red blood cell (RBC) folate, a better indicator of long-term folate intake, and BPH in United States (US) men. METHODS: We used statistics from four cycles of the "National Health and Nutrition Examination Survey" (NHANES2001-2008), RBC folate data come from laboratory data and BPH date come from questionnaire data. A multivariate conditional logistic regression model and subgroup analysis were using to assess the association between RBC folate and BPH. RESULTS: 647 males from four survey cycles in the NHANES2001-2008, of which, 574 men (88.7%) had BPH. After adjusting for potential confounders, a considerable correlation was observed between RBC folate and BPH; With the first quintiles of RBC folate as the reference, multivariable-adjusted odds ratios (ORs) and confidence intervals (95% CIs) of the second, third, fourth, and the highest quintiles were 1.19 (0.58 ∼ 2.44), 1.39 (0.65 ∼ 2.97), 2.27 (0.96 ∼ 5.39), 2.26 (1.35 ∼ 3.76) and 5.37 (1.85 ∼ 15.59), respectively. CONCLUSIONS: Individuals with high levels of RBC folate were associated with an increased risk of self-reported benign prostatic hyperplasia of US men.

Assessment of prediction methods for protein structures determined by NMR in CASP14: Impact of AlphaFold2.

NMR studies can provide unique information about protein conformations in solution. In CASP14, three reference structures provided by solution NMR methods were available (T1027, T1029, and T1055), as well as a fourth data set of NMR-derived contacts for an integral membrane protein (T1088). For the three targets with NMR-based structures, the best prediction results ranged from very good (GDT_TS = 0.90, for T1055) to poor (GDT_TS = 0.47, for T1029). We explored the basis of these results by comparing all CASP14 prediction models against experimental NMR data. For T1027, NMR data reveal extensive internal dynamics, presenting a unique challenge for protein structure prediction methods. The analysis of T1029 motivated exploration of a novel method of "inverse structure determination," in which an AlphaFold2 model was used to guide NMR data analysis. NMR data provided to CASP predictor groups for target T1088, a 238-residue integral membrane porin, was also used to assess several NMR-assisted prediction methods. Most groups involved in this exercise generated similar beta-barrel models, with good agreement with the experimental data. However, as was also observed in CASP13, some pure prediction groups that did not use any NMR data generated models for T1088 that better fit the NMR data than the models generated using these experimental data. These results demonstrate the remarkable power of modern methods to predict structures of proteins with accuracies rivaling solution NMR structures, and that it is now possible to reliably use prediction models to guide and complement experimental NMR data analysis.

Protein structure prediction assisted with sparse NMR data in CASP13.

CASP13 has investigated the impact of sparse NMR data on the accuracy of protein structure prediction. NOESY and 15 N-1 H residual dipolar coupling data, typical of that obtained for 15 N,13 C-enriched, perdeuterated proteins up to about 40 kDa, were simulated for 11 CASP13 targets ranging in size from 80 to 326 residues. For several targets, two prediction groups generated models that are more accurate than those produced using baseline methods. Real NMR data collected for a de novo designed protein were also provided to predictors, including one data set in which only backbone resonance assignments were available. Some NMR-assisted prediction groups also did very well with these data. CASP13 also assessed whether incorporation of sparse NMR data improves the accuracy of protein structure prediction relative to nonassisted regular methods. In most cases, incorporation of sparse, noisy NMR data results in models with higher accuracy. The best NMR-assisted models were also compared with the best regular predictions of any CASP13 group for the same target. For six of 13 targets, the most accurate model provided by any NMR-assisted prediction group was more accurate than the most accurate model provided by any regular prediction group; however, for the remaining seven targets, one or more regular prediction method provided a more accurate model than even the best NMR-assisted model. These results suggest a novel approach for protein structure determination, in which advanced prediction methods are first used to generate structural models, and sparse NMR data is then used to validate and/or refine these models.

Protein structure determination by combining sparse NMR data with evolutionary couplings.

Accurate determination of protein structure by NMR spectroscopy is challenging for larger proteins, for which experimental data are often incomplete and ambiguous. Evolutionary sequence information together with advances in maximum entropy statistical methods provide a rich complementary source of structural constraints. We have developed a hybrid approach (evolutionary coupling-NMR spectroscopy; EC-NMR) combining sparse NMR data with evolutionary residue-residue couplings and demonstrate accurate structure determination for several proteins 6-41 kDa in size.

A Hybrid Approach for Protein Structure Determination Combining Sparse NMR with Evolutionary Coupling Sequence Data.

While 3D structure determination of small (<15 kDa) proteins by solution NMR is largely automated and routine, structural analysis of larger proteins is more challenging. An emerging hybrid strategy for modeling protein structures combines sparse NMR data that can be obtained for larger proteins with sequence co-variation data, called evolutionary couplings (ECs), obtained from multiple sequence alignments of protein families. This hybrid "EC-NMR" method can be used to accurately model larger (15-60 kDa) proteins, and more rapidly determine structures of smaller (5-15 kDa) proteins using only backbone NMR data. The resulting structures have accuracies relative to reference structures comparable to those obtained with full backbone and sidechain NMR resonance assignments. The requirement that evolutionary couplings (ECs) are consistent with NMR data recorded on a specific member of a protein family, under specific conditions, potentially also allows identification of ECs that reflect alternative allosteric or excited states of the protein structure.

A New Lignanamide from the Root of Lycium yunnanense Kuang and Its Antioxidant Activity.

A new lignanamide (1), lyciumamide K, together with four known analogues (2-5), was isolated from the root of Lycium yunnanense Kuang. Based on HR-ESI-MS, NMR spectral data and quantum chemistry ECD calculations, the structure of this new compound was confirmed, including its absolute configuration. Evaluation of the antioxidant activity of compounds 1-5 in the oxygen radical absorption capacity (ORAC) assay showed that they all exhibited significant antioxidant activities. Particularly, compound 1 showed the best activity with ORAC values (U/mol) of 7.90 ± 0.52. Thus, the new lignanamide may be a good source of bioavtive and protective compounds.

Guiding automated NMR structure determination using a global optimization metric, the NMR DP score.

ASDP is an automated NMR NOE assignment program. It uses a distinct bottom-up topology-constrained network anchoring approach for NOE interpretation, with 2D, 3D and/or 4D NOESY peak lists and resonance assignments as input, and generates unambiguous NOE constraints for iterative structure calculations. ASDP is designed to function interactively with various structure determination programs that use distance restraints to generate molecular models. In the CASD-NMR project, ASDP was tested and further developed using blinded NMR data, including resonance assignments, either raw or manually-curated (refined) NOESY peak list data, and in some cases (15)N-(1)H residual dipolar coupling data. In these blinded tests, in which the reference structure was not available until after structures were generated, the fully-automated ASDP program performed very well on all targets using both the raw and refined NOESY peak list data. Improvements of ASDP relative to its predecessor program for automated NOESY peak assignments, AutoStructure, were driven by challenges provided by these CASD-NMR data. These algorithmic improvements include (1) using a global metric of structural accuracy, the discriminating power score, for guiding model selection during the iterative NOE interpretation process, and (2) identifying incorrect NOESY cross peak assignments caused by errors in the NMR resonance assignment list. These improvements provide a more robust automated NOESY analysis program, ASDP, with the unique capability of being utilized with alternative structure generation and refinement programs including CYANA, CNS, and/or Rosetta.

Solution NMR structures of homeodomains from human proteins ALX4, ZHX1, and CASP8AP2 contribute to the structural coverage of the Human Cancer Protein Interaction Network.

High-quality solution NMR structures of three homeodomains from human proteins ALX4, ZHX1 and CASP8AP2 were solved. These domains were chosen as targets of a biomedical theme project pursued by the Northeast Structural Genomics Consortium. This project focuses on increasing the structural coverage of human proteins associated with cancer.

Solution NMR structures of immunoglobulin-like domains 7 and 12 from obscurin-like protein 1 contribute to the structural coverage of the Human Cancer Protein Interaction Network.

High-quality solution NMR structures of immunoglobulin-like domains 7 and 12 from human obscurin-like protein 1 were solved. The two domains share 30% sequence identity and their structures are, as expected, rather similar. The new structures contribute to structural coverage of human cancer associated proteins. Mutations of Arg 812 in domain 7 cause the rare 3-M syndrome, and this site is located in a surface area predicted to be involved in protein-protein interactions.

The expanded FindCore method for identification of a core atom set for assessment of protein structure prediction.

Maximizing the scientific impact of NMR-based structure determination requires robust and statistically sound methods for assessing the precision of NMR-derived structures. In particular, a method to define a core atom set for calculating superimpositions and validating structure predictions is critical to the use of NMR-derived structures as targets in the CASP competition. FindCore (Snyder and Montelione, Proteins 2005;59:673-686) is a superimposition independent method for identifying a core atom set and partitioning that set into domains. However, as FindCore optimizes superimposition by sensitively excluding not-well-defined atoms, the FindCore core may not comprise all atoms suitable for use in certain applications of NMR structures, including the CASP assessment process. Adapting the FindCore approach to assess predicted models against experimental NMR structures in CASP10 required modification of the FindCore method. This paper describes conventions and a standard protocol to calculate an "Expanded FindCore" atom set suitable for validation and application in biological and biophysical contexts. A key application of the Expanded FindCore method is to identify a core set of atoms in the experimental NMR structure for which it makes sense to validate predicted protein structure models. We demonstrate the application of this Expanded FindCore method in characterizing well-defined regions of 18 NMR-derived CASP10 target structures. The Expanded FindCore protocol defines "expanded core atom sets" that match an expert's intuition of which parts of the structure are sufficiently well defined to use in assessing CASP model predictions. We also illustrate the impact of this analysis on the CASP GDT assessment scores.

Assessment of template-based protein structure predictions in CASP10.

Template-based modeling (TBM) is a major component of the critical assessment of protein structure prediction (CASP). In CASP10, some 41,740 predicted models submitted by 150 predictor groups were assessed as TBM predictions. The accuracy of protein structure prediction was assessed by geometric comparison with experimental X-ray crystal and NMR structures using a composite score that included both global alignment metrics and distance-matrix-based metrics. These included GDT-HA and GDC-all global alignment scores, and the superimposition-independent LDDT distance-matrix-based score. In addition, a superimposition-independent RPF metric, similar to that described previously for comparing protein models against experimental NMR data, was used for comparing predicted protein structure models against experimental protein structures. To score well on all four of these metrics, models must feature accurate predictions of both backbone and side-chain conformations. Performance rankings were determined independently for server and the combined server plus human-curated predictor groups. Final rankings were made using paired head-to-head Student's t-test analysis of raw metric scores among the top 25 performing groups in each category.

Definition and classification of evaluation units for CASP10.

For the 10th experiment on Critical Assessment of the techniques of protein Structure Prediction (CASP), the prediction target proteins were broken into independent evaluation units (EUs), which were then classified into template-based modeling (TBM) or free modeling (FM) categories. We describe here how the EUs were defined and classified, what issues arose in the process, and how we resolved them. EUs are frequently not the whole target proteins but the constituting structural domains. However, the assessors from CASP7 on combined more than one domain into 1 EU for some targets, which implied that the assessment also included evaluation of the prediction of the relative position and orientation of these domains. In CASP10, we followed and expanded this notion by defining multidomain EUs for a number of targets. These included 3 EUs, each made of two domains of familiar fold but arranged in a novel manner and for which the focus of evaluation was the interdomain arrangement. An EU was classified to the TBM category if a template could be found by sequence similarity searches and to FM if a structural template could not be found by structural similarity searches. The EUs that did not fall cleanly in either of these cases were classified case-by-case, often including consideration of the overall quality and characteristics of the predictions.

RPF: a quality assessment tool for protein NMR structures.

We describe the RPF web server, a quality assessment tool for protein NMR structures. The RPF server measures the 'goodness-of-fit' of the 3D structure with NMR chemical shift and unassigned NOESY data, and calculates a discrimination power (DP) score, which estimates the differences between the fits of the query structures and random coil structures to these experimental data. The DP-score is an accuracy predictor of the query structure. The RPF server also maps local structure quality measures onto the 3D structure using an online molecular viewer, and onto the NMR spectra, allowing refinement of the structure and/or NOESY peak list data. The RPF server is available at: http://nmr.cabm.rutgers.edu/rpf.

Hidden Structural States of Proteins Revealed by Conformer Selection with AlphaFold-NMR.

Recent advances in molecular modeling using deep learning can revolutionize our understanding of dynamic protein structures. NMR is particularly well-suited for determining dynamic features of biomolecular structures. The conventional process for determining biomolecular structures from experimental NMR data involves its representation as conformation-dependent restraints, followed by generation of structural models guided by these spatial restraints. Here we describe an alternative approach: generating a distribution of realistic protein conformational models using artificial intelligence-(AI-) based methods and then selecting the sets of conformers that best explain the experimental data. We applied this conformational selection approach to redetermine the solution NMR structure of the enzyme Gaussia luciferase. First, we generated a diverse set of conformer models using AlphaFold2 (AF2) with an enhanced sampling protocol. The models that best-fit NOESY and chemical shift data were then selected with a Bayesian scoring metric. The resulting models include features of both the published NMR structure and the standard AF2 model generated without enhanced sampling. This "AlphaFold-NMR" protocol also generated an alternative "open" conformational state that fits nearly as well to the overall NMR data but accounts for some NOESY data that is not consistent with first "closed" conformational state; while other NOESY data consistent with this second state are not consistent with the first conformational state. The structure of this "open" structural state differs from that of the "closed" state primarily by the position of a thumb-shaped loop between α-helices H5 and H6, revealing a cryptic surface pocket. These alternative conformational states of Gluc are supported by "double recall" analysis of NOESY data and AF2 models. Additional structural states are also indicated by backbone chemical shift data indicating partially-disordered conformations for the C-terminal segment. Considered as a multistate ensemble, these multiple states of Gluc together fit the NOESY and chemical shift data better than the "restraint-based" NMR structure and provide novel insights into its structure-dynamic-function relationships. This study demonstrates the potential of AI-based modeling with enhanced sampling to generate conformational ensembles followed by conformer selection with experimental data as an alternative to conventional restraint satisfaction protocols for protein NMR structure determination.

The structure of vimentin linker 1 and rod 1B domains characterized by site-directed spin-labeling electron paramagnetic resonance (SDSL-EPR) and X-ray crystallography.

Despite the passage of ∼30 years since the complete primary sequence of the intermediate filament (IF) protein vimentin was reported, the structure remains unknown for both an individual protomer and the assembled filament. In this report, we present data describing the structure of vimentin linker 1 (L1) and rod 1B. Electron paramagnetic resonance spectra collected from samples bearing site-directed spin labels demonstrate that L1 is not a flexible segment between coiled-coils (CCs) but instead forms a rigid, tightly packed structure. An x-ray crystal structure of a construct containing L1 and rod 1B shows that it forms a tetramer comprising two equivalent parallel CC dimers that interact with one another in the form of a symmetrical anti-parallel dimer. Remarkably, the parallel CC dimers are themselves asymmetrical, which enables them to tetramerize rather than undergoing higher order oligomerization. This functionally vital asymmetry in the CC structure, encoded in the primary sequence of rod 1B, provides a striking example of evolutionary exploitation of the structural plasticity of proteins. EPR and crystallographic data consistently suggest that a very short region within L1 represents a minor local distortion in what is likely to be a continuous CC from the end of rod 1A through the entirety of rod 1B. The concordance of this structural model with previously published cross-linking and spectral data supports the conclusion that the crystallographic oligomer represents a native biological structure.

ATPase active-site electrostatic interactions control the global conformation of the 100 kDa SecA translocase.

SecA is an intensively studied mechanoenzyme that uses ATP hydrolysis to drive processive extrusion of secreted proteins through a protein-conducting channel in the cytoplasmic membrane of eubacteria. The ATPase motor of SecA is strongly homologous to that in DEAD-box RNA helicases. It remains unclear how local chemical events in its ATPase active site control the overall conformation of an ~100 kDa multidomain enzyme and drive protein transport. In this paper, we use biophysical methods to establish that a single electrostatic charge in the ATPase active site controls the global conformation of SecA. The enzyme undergoes an ATP-modulated endothermic conformational transition (ECT) believed to involve similar structural mechanics to the protein transport reaction. We have characterized the effects of an isosteric glutamate-to-glutamine mutation in the catalytic base, a mutation which mimics the immediate electrostatic consequences of ATP hydrolysis in the active site. Calorimetric studies demonstrate that this mutation facilitates the ECT in Escherichia coli SecA and triggers it completely in Bacillus subtilis SecA. Consistent with the substantial increase in entropy observed in the course of the ECT, hydrogen-deuterium exchange mass spectrometry demonstrates that it increases protein backbone dynamics in domain-domain interfaces at remote locations from the ATPase active site. The catalytic glutamate is one of ~250 charged amino acids in SecA, and yet neutralization of its side chain charge is sufficient to trigger a global order-disorder transition in this 100 kDa enzyme. The intricate network of structural interactions mediating this effect couples local electrostatic changes during ATP hydrolysis to global conformational and dynamic changes in SecA. This network forms the foundation of the allosteric mechanochemistry that efficiently harnesses the chemical energy stored in ATP to drive complex mechanical processes.

Polydopamine-Derived Carbon Catalysts with Optimized Structure-Activity Design towards Electrochemical CO2 Reduction to CO.

Electrochemical reduction of CO2 into chemical feedstocks has been regarded as an attractive way to reconstruct the carbon cycle. In this work, nitrogen-doped carbon was prepared by high temperature pyrolysis using polydopamine (PDA) microspheres as precursors. The effects of doped nitrogen units, surface hydrophilicity and pore structures of the N-Carbon catalysts on the CO2 reduction reaction (CO2 RR) activities were systematically investigated. It was demonstrated that the competition between the hydrogen evolution reaction (HER) and the CO2 RR under reduction potentials was modified by the nature of surface hydrophilicity/hydrophobicity and the doped nitrogen units. The CO2 RR activities were further optimized via the pore structures regulation. Results showed that pore structure with size below 1 nm was favorable for CO2 RR and the developed N-Carbon catalysts with optimized nitrogen units, hydrophilicity, and pore structure achieved a high CO2 to CO Faradaic efficiency of 95 % in the H-cell.

Bushen Tongluo formula ameliorated testosterone propionate-induced benign prostatic hyperplasia in rats.

BACKGROUND: Benign prostatic hyperplasia (BPH) is a common disease in older men worldwide. However, there is currently no effective treatment for BPH. Bushen Tongluo Formula (Kidney-supplementing and collaterals-unblocking formula [KCF]) is a traditional Chinese medicine formula commonly used to ameliorate the symptoms of BPH, although the specific molecular mechanisms remain unclear. PURPOSE: We aimed to discover the effects and potential mechanisms of KCF against BPH. METHODS: Sixty male SD rats were randomly assigned to one of six group (n = 10): control, low-dosage KCF, medium-dosage KCF, high-dosage KCF, BPH model, and finasteride. A rat model of BPH was established by surgical castration followed by subcutaneous injection of testosterone propionate (TP) for 4 weeks. After treatment, the prostate index, histopathological staining, serum levels of estradiol (E2) and dihydrotestosterone (DHT), protein/mRNA levels of E-cadherin, TGF-ß1, caspase-3, Ki67, and vimentin, abundances of serum metabolites, and the proliferation, cell cycle, and apoptosis of BPH-1 cells were documented. RESULTS: KCF treatment for 4 weeks reduced the prostate volume and prostate index, alleviated histopathological changes to the prostate of rats with TP-induced BPH, decreased serum levels of E2 and DHT, reduced protein/mRNA levels of TGF-ß1 and vimentin, and increased E-cadherin levels. Moreover, KCF-spiked serum inhibited proliferation of BPH-1 cells, blocked the cell cycle, and promoted apoptosis. KCF was also found to regulate the contents of three metabolites (D-maltose, citric acid, and fumaric acid). CONCLUSION: The present study was the first to report that KCF exhibited therapeutic effects against BPH by regulating energy metabolism and inhibiting epithelial-mesenchymal transition in prostate tissues. Hence, KCF presents a viable treatment option for BPH.

Fructooligosaccharides attenuate non-alcoholic fatty liver disease by remodeling gut microbiota and association with lipid metabolism.

BACKGROUND: Nonalcoholic fatty liver disease (NAFLD) is a common liver disease highly associated with metabolic diseases and gut dysbiosis. Several clinical trials have confirmed that fructooligosaccharides (FOSs) are a viable alternative treatment for NAFLD. However, the mechanisms underlying the activities of FOSs remain unclear. METHODS: In this study, the effects of FOSs were investigated with the use of two C57BL/6 J mouse models of NAFLD induced by a high-fat, high-cholesterol (HFHC) diet and a methionine- and choline-deficient (MCD) diet, respectively. The measured metabolic parameters included body, fat, and liver weights; and blood glucose, glucose tolerance, and serum levels of glutamate transaminase, aspartate transaminase, and triglycerides. Liver tissues were collected for histological analysis. In addition, 16 S rRNA sequencing was conducted to investigate the effects of FOSs on the composition of the gut microbiota of mice in the HFHC and MCD groups and treated with FOSs. RESULTS: FOS treatment attenuated severe metabolic changes and hepatic steatosis caused by the HFHC and MCD diets. In addition, FOSs remodeled the structure of gut microbiota in mice fed the HFHC and MCD diets, as demonstrated by increased abundances of Bacteroidetes (phylum level), Klebsiella variicola, Lactobacillus gasseri, and Clostridium perfringens (species level); and decreased abundances of Verrucomicrobia (phylum level) and the Fissicatena group (genus level). Moreover, the expression levels of genes associated with lipid metabolism and inflammation (i.e., ACC1, PPARγ, CD36, MTTP, APOC3, IL-6, and IL-1ß) were down-regulated after FOS treatment. CONCLUSION: FOSs alleviated the pathological phenotype of NAFLD via remodeling of the gut microbiota composition and decreasing hepatic lipid metabolism, suggesting that FOSs as functional dietary supplements can potentially reduce the risk of NAFLD.

Blind assessment of monomeric AlphaFold2 protein structure models with experimental NMR data.

Recent advances in molecular modeling of protein structures are changing the field of structural biology. AlphaFold-2 (AF2), an AI system developed by DeepMind, Inc., utilizes attention-based deep learning to predict models of protein structures with high accuracy relative to structures determined by X-ray crystallography and cryo-electron microscopy (cryoEM). Comparing AF2 models to structures determined using solution NMR data, both high similarities and distinct differences have been observed. Since AF2 was trained on X-ray crystal and cryoEM structures, we assessed how accurately AF2 can model small, monomeric, solution protein NMR structures which (i) were not used in the AF2 training data set, and (ii) did not have homologous structures in the Protein Data Bank at the time of AF2 training. We identified nine open-source protein NMR data sets for such "blind" targets, including chemical shift, raw NMR FID data, NOESY peak lists, and (for 1 case) 15N-1H residual dipolar coupling data. For these nine small (70-108 residues) monomeric proteins, we generated AF2 prediction models and assessed how well these models fit to these experimental NMR data, using several well-established NMR structure validation tools. In most of these cases, the AF2 models fit the NMR data nearly as well, or sometimes better than, the corresponding NMR structure models previously deposited in the Protein Data Bank. These results provide benchmark NMR data for assessing new NMR data analysis and protein structure prediction methods. They also document the potential for using AF2 as a guiding tool in protein NMR data analysis, and more generally for hypothesis generation in structural biology research.