Biology subjects

Nagy, G.

Publications and source records attributed to Nagy, G..

24 records · Page 2Linked to original sources

bioRxiv2020

Small-angle neutron scattering solution structures of NADPH-dependent sulfite reductase

Sulfite reductase (SiR), a dodecameric complex of flavoprotein reductase subunits (SiRFP) and hemoprotein oxidase subunits (SiRHP), reduces sulfur reduction for biomass incorporation. Electron transfer within SiR requires intra- and inter-subunit interactions that are mediated by the relative position of each protein, governed by flexible domain movements. Using small-angle neutron scattering, we report the first solution structures of SiR heterodimers containing a single copy of each subunit. These structures show how the subunits bind and how both subunit binding and oxidation state impact SiRFPs conformation. Neutron contrast matching experiments on selectively deuterated heterodimers allow us to define the contribution of each subunit to the solution scattering. SiRHP binding induces a change in the position of SiRFPs flavodoxin-like domain relative to its ferredoxin-NADP+ reductase domain while compacting SiRHPs N-terminus. Reduction of SiRFP leads to a more open structure relative to its oxidized state, re-positioning SiRFPs N-terminal flavodoxin-like domain towards the SiRHP binding position. These structures show, for the first time, how both SiRHP binding to, and reduction of, SiRFP positions SiRFP for electron transfer between the subunits.

biochemistry↗

bioRxiv2020

Implementation of a Bayesian Secondary Structure Estimation Method for the SESCA Circular Dichroism Analysis Package

Circular dichroism spectroscopy is a structural biology technique frequently applied to determine the secondary structure composition of soluble proteins. Our recently introduced computational analysis package SESCA aids the interpretation of protein circular dichroism spectra and enables the validation of proposed corresponding structural models. To further these aims, we present the implementation and characterization of a new Bayesian secondary structure estimation method in SESCA, termed SESCA_bayes. SESCA_bayes samples possible secondary structures using a Monte Carlo scheme, driven by the likelihood of estimated scaling errors and non-secondary-structure contributions of the measured spectrum. SESCA_bayes provides an estimated secondary structure composition and separate uncertainties on the fraction of residues in each secondary structure class. It also assists efficient model validation by providing a posterior secondary structure probability distribution based on the measured spectrum. Our presented study indicates that SESCA_bayes estimates the secondary structure composition with a significantly smaller uncertainty than its predecessor, SESCA_deconv, which is based on spectrum deconvolution. Further, the mean accuracy of the two methods in our analysis is comparable, but SESCA_bayes provides more accurate estimates for circular dichroism spectra that contain considerable non-SS contributions. PROGRAM SUMMARYProgram Title: SESCA_bayes CPC Library link to program files: (to be added by Technical Editor) Developers repository link: https://www.mpibpc.mpg.de/sesca Code Ocean capsule: (to be added by Technical Editor) Licensing provisions: GPLv3 Programming language: Python Supplementary material: Nature of problem: The circular dichroism spectrum of a protein is strongly correlated with its secondary structure composition. However, determining the secondary structure from a spectrum is hindered by non-secondary structure contributions and by scaling errors due the uncertainty of the protein concentration. If not taken properly into account, these experimental factors can cause considerable errors when conventional secondary-structure estimation methods are used. Because these errors combine with errors of the proposed structural model in a non-additive fashion, it is difficult to assess how much uncertainty the experimental factors introduce to model validation approaches based on circular dichroism spectra. Solution methodFor a given measured circular dichroism spectrum, the SESCA_bayes algorithm applies Bayesian statistics to account for scaling errors and non-secondary structure contributions and to determine the conditional secondary structure probability distribution. This approach relies on fast spectrum predictions based on empirical basis spectrum sets and joint probability distribution maps for scaling factors and non-secondary structure distributions. Because SESCA_bayes estimates the most probable secondary structure composition based on a probability-weighted sample distribution, it avoids the typical fitting errors that occur during conventional spectrum deconvolution methods. It also estimates the uncertainty of circular dichroism based model validation more accurately than previous methods of the SESCA analysis package.

biophysics↗

bioRxiv2020

The Anti-histamine Azelastine, Identified by Computational Drug Repurposing, Inhibits SARS-CoV-2 Infection in Reconstituted Human Nasal Tissue In Vitro

BackgroundThe COVID-19 pandemic is an enormous threat for healthcare systems and economies worldwide that urgently demands effective preventive and therapeutic strategies. Unlike the development of vaccines and new drugs specifically targeting SARS-CoV-2, repurposing of approved or clinically tested drugs can provide an immediate solution. MethodsWe applied a novel computational approach to search among approved and clinically tested drugs from the DrugBank database. Candidates were selected based on Shannon entropy homology and predefined activity profiles of three small molecules with proven anti-SARS-CoV activity and a published data set. Antiviral activity of a predicted drug, azelastine, was tested in vitro in SARS-CoV-2 infection assays with Vero E6 monkey kidney epithelial cells and reconstituted human nasal tissue. The effect on viral replication was assessed by quantification of viral genomes by droplet digital PCR. FindingsThe computational approach with four independent queries identified major drug families, most often and in overlapping fashion anti-infective, anti-inflammatory, anti-hypertensive, anti-histamine and neuroactive drugs. Azelastine, an histamine 1 receptor-blocker, was predicted in multiple screens, and based on its attractive safety profile and availability in nasal formulation, was selected for experimental testing. Azelastine significantly reduced cytopathic effect and SARS-CoV-2 infection of Vero E6 cells with an EC50 of [~]6 M both in a preventive and treatment setting. Furthermore, azelastine in a commercially available nasal spray tested at 5-fold dilution was highly potent in inhibiting viral propagation in SARS-CoV-2 infected reconstituted human nasal tissue. InterpretationsAzelastine, an anti-histamine, available in nasal sprays developed against allergic rhinitis may be considered as a topical prevention or treatment of nasal colonization with SARS-CoV-2. As such, it could be useful in reducing viral spread and prophylaxis of COVID-19. Ultimately, its potential benefit should be proven in clinical studies. Fundingprovided by the Hungarian government to the National Laboratory of Virology and by CEBINA GmbH.

microbiology↗

bioRxiv2020

How Accurate Are Circular Dichroism Based Secondary Structure Estimates?

Circular dichroism (CD) spectroscopy is highly sensitive to the secondary structure (SS) composition of proteins. Several methods exist to either estimate the SS composition of a protein or to validate existing structural models using its CD spectrum. The accuracy and precision of these methods depend on the quality of both the measured CD spectrum and the used reference structure. Using a large reference protein set with high quality CD spectra and synthetic data derived from this set, we quantified deviations from both ideal spectra and reference structures due to experimental limitations. We also determined the impact of these deviations on SS estimation, CD prediction, and SS validation methods of the SESCA analysis package. With regard to the CD spectra, our results suggest intensity scaling errors and non-SS contributions as the main causes of inaccuracies. These factors also can lead to overestimated model errors during validation. The errors of the used reference structures combine non-additively with errors caused by the CD spectrum, which increases the uncertainty of model validation. We have further shown that the effects of scaling errors in the CD spectrum can be nearly eliminated by appropriate re-scaling, and that the accuracy of model validation methods can be improved by accounting for typical non-SS contributions. These improvements have now been implemented within the SESCA package.

biophysics↗

bioRxiv2018

Alternative super-enhancers result in similar gene expression in different tissues

Super-enhancers (SEs) are clusters of highly active enhancers, regulating cell type-specific and disease-related genes, including oncogenes1-3. The individual regulatory regions within SEs might be simultaneously bound by different transcription factors (TFs) and co-regulators such as P300, BRD4 and Mediator, which together establish a chromatin environment conducting to effective gene induction4-6. While cells with distinct TF profiles can have different functions, an unanswered question is how different cells control overlapping genetic programmes. Here, we show that the construction of oestrogen receptor alpha (ER)-driven SEs is tissue specific, and both the collaborating TFs and the active SE components are largely differing between human breast cancer-derived MCF-7 and endometrial cancer-derived Ishikawa cells; nonetheless, SEs common to both cell types have similar transcriptional outputs. In the MCF-7 cell line, ER-dominated SEs are also driven by the well-known FoxA1 and AP2{gamma} TFs, as described previously7, whereas in Ishikawa cells, FoxM1, TCF12 and TEAD4 are as important as ER for SE formation. Our results show that SEs can be constructed in several ways, but the overall activity of common SEs is the same between cells with a common master regulator. These findings may reshape our current understanding of how these regulatory units can fine-tune cell functions. From a broader perspective, we show that systems assembled from different components can perform similar tasks if a common functional trigger drives their assembly.

molecular biology↗

bioRxiv2018

SESCA: Predicting the Circular Dichroism Spectra of Proteins from Molecular Structure

Circular dichroism spectroscopy is a highly sensitive, but low-resolution technique to study the structure of proteins. Combined with molecular modelling or other complementary techniques, CD spectroscopy can provide essential information at higher resolution. To this end, we introduce a new computational method to calculate the electronic circular dichroism spectra of proteins from a structural model or ensemble using the average secondary structure composition and a pre-calculated set of basis spectra. We compared the predictive power of our method to existing algorithms - namely DichroCalc and PDB2CD - and found that it predicts CD spectra more accurately, with a 50% smaller average deviation from the measured CD spectra. Our results indicate that the derived basis sets are robust to experimental errors in the reference spectra and to the choice of the secondary structure classification algorithm. For over 80% of the globular reference proteins, our basis sets accurately predict the experimental spectrum solely from their secondary structure composition. For the remaining 20%, correcting for intensity normalization considerably improves the prediction power. Additionally, we show that the predictions for short peptides and intrinsically disordered proteins strongly benefit from accounting for side-chain contributions and structural flexibility. Table Of Content Graphics O_TBL View this table: org.highwire.dtl.DTLVardef@1a1d73forg.highwire.dtl.DTLVardef@3aaa8forg.highwire.dtl.DTLVardef@193c090org.highwire.dtl.DTLVardef@1474f92org.highwire.dtl.DTLVardef@b920da_HPS_FORMAT_FIGEXP M_TBL C_TBL

biophysics↗