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.

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.

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.

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.

The Efficacy of Clostridium Butyricum Triple Viable in Enhancing Fitness and Performance in Athletes: A Case-Control Study

Objective: This case-control study aimed to assess the impact of Clostridium butyricum triple viable (CBTV) on the performance and gut microbiota of athletes. Methods: We recruited 47 athletes from various sports disciplines and divided them into two groups: Group A received a standard fitness regimen, while Group B received the same fitness regimen along with CBTV supplementation for a period of 4 weeks. Performance measurements were recorded, and gut microbiota analysis was conducted using 16S rRNA sequencing and bioinformatics. Results: After 4 weeks, there were no significant differences in the performance measurements between the two groups (Group A: 159.1±42.4 vs. Group B: 150.8±34.8, p = 0.42). However, the quality of life improvement in Group B was significantly higher than in Group A (Group A: 86.2±26.2 vs. Group B: 89.7±40.7, p < 0.01). Additionally, the gut microbiota analysis revealed that certain bacterial species, including Megamanos, Pseudonocardia, Corynebacterium, and Veillonell, were less abundant in Group B compared to Group A after 4 weeks. Conclusion: This case-control study suggests that CBTV supplementation can enhance the quality of life and influence the abundance of specific bacteria, including a reduction in Megamanos, in athletes. These findings lay the groundwork for further research into the mechanisms by which probiotic bacteria impact gut microbiota in the context of sports performance. (AU)

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.

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) 15 N- 1 H 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. Highlights: AF2 models assessed against NMR data for 9 monomeric proteins not used in training.AF2 models fit NMR data almost as well as the experimentally-determined structures. RPF-DP, PSVS , and PDBStat software provide structure quality and RDC assessment. RPF-DP analysis using AF2 models suggests multiple conformational states.

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.

SpecDB: A relational database for archiving biomolecular NMR spectral data.

NMR is a valuable experimental tool in the structural biologist's toolkit to elucidate the structures, functions, and motions of biomolecules. The progress of machine learning, particularly in structural biology, reveals the critical importance of large, diverse, and reliable datasets in developing new methods and understanding in structural biology and science more broadly. Biomolecular NMR research groups produce large amounts of data, and there is renewed interest in organizing these data to train new, sophisticated machine learning architectures and to improve biomolecular NMR analysis pipelines. The foundational data type in NMR is the free-induction decay (FID). There are opportunities to build sophisticated machine learning methods to tackle long-standing problems in NMR data processing, resonance assignment, dynamics analysis, and structure determination using NMR FIDs. Our goal in this study is to provide a lightweight, broadly available tool for archiving FID data as it is generated at the spectrometer, and grow a new resource of FID data and associated metadata. This study presents a relational schema for storing and organizing the metadata items that describe an NMR sample and FID data, which we call Spectral Database (SpecDB). SpecDB is implemented in SQLite and includes a Python software library providing a command-line application to create, organize, query, backup, share, and maintain the database. This set of software tools and database schema allow users to store, organize, share, and learn from NMR time domain data. SpecDB is freely available under an open source license at https://github.rpi.edu/RPIBioinformatics/SpecDB.

AlphaFold Models of Small Proteins Rival the Accuracy of Solution NMR Structures.

Recent advances in molecular modeling using deep learning have the potential to revolutionize the field of structural biology. In particular, AlphaFold has been observed to provide models of protein structures with accuracies rivaling medium-resolution X-ray crystal structures, and with excellent atomic coordinate matches to experimental protein NMR and cryo-electron microscopy structures. Here we assess the hypothesis that AlphaFold models of small, relatively rigid proteins have accuracies (based on comparison against experimental data) similar to experimental solution NMR structures. We selected six representative small proteins with structures determined by both NMR and X-ray crystallography, and modeled each of them using AlphaFold. Using several structure validation tools integrated under the Protein Structure Validation Software suite (PSVS), we then assessed how well these models fit to experimental NMR data, including NOESY peak lists (RPF-DP scores), comparisons between predicted rigidity and chemical shift data (ANSURR scores), and 15N-1H residual dipolar coupling data (RDC Q factors) analyzed by software tools integrated in the PSVS suite. Remarkably, the fits to NMR data for the protein structure models predicted with AlphaFold are generally similar, or better, than for the corresponding experimental NMR or X-ray crystal structures. Similar conclusions were reached in comparing AlphaFold2 predictions and NMR structures for three targets from the Critical Assessment of Protein Structure Prediction (CASP). These results contradict the widely held misperception that AlphaFold cannot accurately model solution NMR structures. They also document the value of PSVS for model vs. data assessment of protein NMR structures, and the potential for using AlphaFold models for guiding analysis of experimental NMR data and more generally in structural biology.

An NIR-II Responsive Nanoplatform for Cancer Photothermal and Oxidative Stress Therapy.

Chemodynamic therapy as an emerging therapeutic strategy has been implemented for oncotherapy. However, the reactive oxygen species can be counteracted by the exorbitant glutathione (GSH) produced by the tumor cells before exerting the antitumor effect. Herein, borneol (NB) serving as a monoterpenoid sensitizer, and copper sulfide (CuS NPs) as an NIR-II photothermal agent were loaded in a thermo-responsive vehicle (NB/CuS@PCM NPs). Under 1,060-nm laser irradiation, the hyperthermia produced by CuS NPs can be used for photothermal therapy and melt the phase change material for drug delivery. In the acidity microenvironment, the CuS NPs released from NB/CuS@PCM NPs could degrade to Cu2+, then Cu2+ was reduced to Cu+ during the depletion of GSH. As Fenton-like catalyst, the copper ion could convert hydrogen peroxide into hydroxyl radicals for chemodynamic therapy. Moreover, the NB originated from NB/CuS@PCM NPs could increase the intracellular ROS content to improve the treatment outcome of chemodynamic therapy. The animal experimental results indicated that the NB/CuS@PCM NPs could accumulate at the tumor site and exhibit an excellent antitumor effect. This work confirmed that the combination of oxidative stress-induced damage and photothermal therapy is a potential therapeutic strategy for cancer treatment.

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.

Lyciiterpenoids A-D, four new rearranged eudesmane sesquiterpenoids from Lycii Cortex.

Four new rearranged eudesmane sesquiterpenoids, Lyciiterpenoids A-D (1-4), together with four known compounds (5-8) were isolated from the aqueous extract of Lycii Cortex. Their structures were elucidated by spectroscopic analysis, and the absolute configurations of compounds 1-4 were determined by the quantum chemical ECD calculations. The absolute configuration of 1 was further confirmed by X-ray crystallographic data analysis. All the isolated compounds were evaluated for their cytotoxicity against three human cancer cell lines (MCF-7, HepG2, and A549). However, no significant activities were detected even with a concentration up to 100 µM.

Effect of Kangquan Recipe () on BAMBI Expression in Hypothalamic-Pituitary-Prostate in Rats with Benign Prostatic Hyperplasia.

OBJECTIVE: To investigate the effect of Kangquan Recipe (, KQR) on bone morphogenetic protein and activin membrane-bound inhibitor (BAMBI) expression and its mechanism in rats with benign prostatic hyperplasia (BPH). METHODS: Forty-eight male Sprague-Dawley rats were divided into 6 groups using a random number table, with 8 in each group: the normal group (normal saline 10 mL/kg), the model group (normal saline 10 mL/kg), the finasteride group (0.5 mg/kg), the low-dose KQR group (3.5 g/kg), the middle-dose KQR group (7 g/kg), and the high-dose KQR group (14 g/kg). The 40 rats were subcutaneously injected with testosterone propionate after castration for 30 days to establish the BPH rat model except for those in the normal group. At the same time, the corresponding drugs were administered by gavage for 30 consecutive days. The effects of different doses of KQR on the protate wet weight, prostate volume and prostate index (PI) were observed. The changes in histopathology were monitored with hematoxylin-eosin staining. BAMBI protein and mRNA expression contents were determined by Western blot and quantitative real-time polymerase chain reaction, respectively. RESULTS: All doses of KQR could decrease prostatic epithelial tissue proliferation. Compared to the model group, the high and middle-dose KQR significantly reduced prostate wet weight, prostate volume and PI; increased BAMBI protein expression in the hypothalamus, pituitary and prostate tissue; all doses of KQR up-regulated BAMBI mRNA expression in serum, prostatic fluid and prostate tissue (P<0.05 or P<0.01). CONCLUSIONS: KQR could inhibit the proliferation of rat prostatic tissue, promote BAMBI protein expression in the hypothalamic-pituitary-prostate of rats with BPH; and increase BAMBI mRNA expression in the blood, prostatic fluid and prostate tissue of rats with BPH, showing a dose-effect relationship. KQR can be used as a potential drug for the treatment of BPH.

Bazhu Decoction, a Traditional Chinese Medical Formula, Ameliorates Cognitive Deficits in the 5xFAD Mouse Model of Alzheimer's Disease.

Alzheimer's disease (AD) is the most common neurodegenerative disorder associated with aging. There are currently no effective treatments for AD. Bazhu decoction (BZD), a traditional Chinese medicine (TCM) formula, has been employed clinically to alleviate AD. However, the underlying molecular mechanisms are still unclear. Here we found that middle- and high-doses of BZD ameliorated the behavioral aspects of 5xFAD transgenic mice in elevated plus maze, Y maze and Morris water maze tests. Moreover, BZD reduced the protein levels of BACE1 and PS1, resulting in a reduction of Aß plaques. We also identified a beneficial effect of BZD on oxidative stress by attenuating MDA levels and SOD activity in the brains of 5xFAD mice. Together, these results indicate that BZD produces a dose-dependent positive effect on 5xFAD transgenic mouse model by decreasing APP processing and Aß plaques, and by ameliorating oxidative damage. BZD may play a protective role in the cognitive and anxiety impairments and may be a complementary therapeutic option for AD.

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.

Gypenosides improve the intestinal microbiota of non-alcoholic fatty liver in mice and alleviate its progression.

Gypenosides (GP) are a type of traditional Chinese medicine (TCM) extracted from plants and commonly applied for treatment of metabolic diseases. This study aims to explore the effects of GP extracts on alleviating non-alcoholic fatty liver disease (NAFLD). In this experiment, C57BL/6 J mice were randomly assigned into normal diet control (ND), HFHC (high-fat and high-cholesterol) and HFHC + GP (GP) groups. Mice in HFHC group were fed HFHC diet combined with fructose drinking water for 12 weeks to induce the animal model of NAFLD, followed by ordinary drinking water until the end of the experiment. In the HFHC + GP group, mice were fed HFHC diet combined with fructose drinking water for 12 weeks, followed by GP-containing drinking water till the end. Mouse body weight was measured weekly. After animal procedures, mouse liver and serum samples were collected. It is shown that GP administration reduced body weight, enhanced the sensitivity to insulin resistance (IR) and decreased serum levels of ALT, AST and TG in NAFLD mice. In addition, GP treatment alleviated steatohepatitis, and downregulated ACC1, PPARγ, CD36, APOC3 and MTTP levels in mice fed with HFHC diet. Furthermore, GP treatment markedly improved intestinal microbiota, and reduced relative abundance ratio of Firmicutes / Bacteroidetes in the feces of NAFLD mice. Our results suggested that GP alleviated NAFLD in mice through improving intestinal microbiota.

Kangquan Recipe Regulates the Expression of BAMBI Protein via the TGF-ß/Smad Signaling Pathway to Inhibit Benign Prostatic Hyperplasia in Rats.

BACKGROUND: Kangquan Recipe (KQR) is a traditional Chinese medicine compound made by our research group for the treatment of benign prostatic hyperplasia (BPH). Whether KQR can treat BPH as a single drug or play a role in the treatment of BPH in combination therapy needs further study. AIM OF THE STUDY: To investigate the effect of KQR on the expression of TGF-ß/Smad signaling pathway-related factors in rats with BPH. In-depth analysis revealed the relevant signal transduction mechanism by which KQR acts to treat BPH. MATERIALS AND METHODS: Forty-eight male Sprague-Dawley rats were randomly divided into six groups of 8 rats each. In addition to the control group, 40 rats were castrated and then injected with testosterone propionate to form a prostatic hyperplasia model. After 30 days, three groups received different concentrations of KQR (14 g/kg, 7 g/kg, and 3.5 g/kg), and the finasteride group received 0.5 mg/kg finasteride. The BPH group and the control group received the same volume of saline. All groups were treated for a total of 30 days. Rat body weight, prostate volume, wet weight, index, histology, and the mRNA and protein levels of TGF-ß, TGF-ßR1, TGF-ßR2, p-Smad2, p-Smad3, BAMBI, E-cadherin, and N-cadherin in the prostate tissue were measured after the end of treatment. RESULTS: Compared with the control group, the BPH group had increased prostate wet weight, volume, and index, and the histology showed significant BPH. Compared with the BPH group, the three KQR groups and the finasteride group all had varying levels of reduction in the prostate wet weight, volume, and index of the prostate and varying degrees of improvement in the histological manifestations of BPH. KQR downregulates the mRNA and/or protein expression of TGF-ß, TGF-ßR1, TGF-ßR2, p-Smad2, p-Smad3, and N-cadherin protein in prostate tissue and increases the mRNA and protein expression of BAMBI and E-cadherin protein. CONCLUSIONS: In the model of BPH induced by testosterone propionate after castration, KQR can inhibit the conduction of the TGF-ß/Smad signaling pathway by upregulating the expression of BAMBI protein and reversing EMT in rat prostate tissue.

An ELISA-Based Screening Platform for Ligand-Receptor Discovery.

Cell surface molecules are important for development and function of multicellular organisms. Although several methods are available to identify ligand-receptor pairs, ELISA-based methods are particularly amenable to high-throughput screens. ELISA-based methods have high sensitivity and low false-positive rates for detecting protein-protein interaction (PPI) complexes. Here, we provide a detailed protocol for a 384-well ELISA-based PPI screening protocol for the identification of novel cell surface ligand-receptor interactions, together with considerations for validation of PPIs by biophysical methods. This PPI screen has been developed and tested for discovery of novel ligand-receptor pairs between human synaptic adhesion proteins, believed to play crucial roles in many steps of neurodevelopment, from neuronal maturation, to axon guidance, synapse connectivity, and pruning.

Combining Evolutionary Covariance and NMR Data for Protein Structure Determination.

Accurate protein structure determination by solution-state NMR is challenging for proteins greater than about 20kDa, for which extensive perdeuteration is generally required, providing experimental data that are incomplete (sparse) and ambiguous. However, the massive increase in evolutionary sequence information coupled with advances in methods for sequence covariance analysis can provide reliable residue-residue contact information for a protein from sequence data alone. These "evolutionary couplings (ECs)" can be combined with sparse NMR data to determine accurate 3D protein structures. This hybrid "EC-NMR" method has been developed using NMR data for several soluble proteins and validated by comparison with corresponding reference structures determined by X-ray crystallography and/or conventional NMR methods. For small proteins, only backbone resonance assignments are utilized, while for larger proteins both backbone and some sidechain methyl resonance assignments are generally required. ECs can be combined with sparse NMR data obtained on deuterated, selectively protonated protein samples to provide structures that are more accurate and complete than those obtained using such sparse NMR data alone. EC-NMR also has significant potential for analysis of protein structures from solid-state NMR data and for studies of integral membrane proteins. The requirement that ECs are consistent with NMR data recorded on a specific member of a protein family, under specific conditions, also allows identification of ECs that reflect alternative allosteric or excited states of the protein structure.