The architecture of the 10-23 DNAzyme and its implications for DNA-mediated catalysis

Funding Information: The authors acknowledge access to the Jülich‐Düsseldorf Biomolecular NMR Center. HG is grateful for computational support and infrastructure provided by the ‘Zentrum für Informations‐ und Medientechnologie’ (ZIM) at the Heinrich Heine University Düsseldorf and the John von Neumann Institute for Computing (NIC) (user ID: HKF7, VSK33). We thank Hannah Rosenbach for providing activity data. This work was supported by the German Research Foundation (DFG) (103/4‐1, ET 103/4‐3, and the Heisenberg grant ET 103/5‐1) to ME, the Volkswagen Foundation to ME and HG (project no. 9B798) and the European Union‘s Horizon 2020 research and innovation program under the Marie Skłodowska‐Curie grant agreement no. 660258 to AV. Open Access funding enabled and organized by Projekt DEAL. Publisher Copyright: © 2022 The Authors. The FEBS Journal published by John Wiley & Sons Ltd on behalf of Federation of European Biochemical Societies.Understanding the molecular features of catalytically active DNA sequences, so-called DNAzymes, is essential not only for our understanding of the fundamental properties of catalytic nucleic acids in general, but may well be the key to unravelling their full potential via tailored modifications. Our recent findings contributed to the endeavour to assemble a mechanistic picture of DNA-mediated catalysis by providing high-resolution structural insights into the 10-23 DNAzyme (Dz) and exposing a complex interplay between the Dz's unique molecular architecture, conformational plasticity, and dynamic modulation by metal ions as central elements of the DNA catalyst. Here, we discuss key features of our findings and compare them to other studies on similar systems.publishersversionpublishe

Strategies for validation and testing of DNA methylation biomarkers

DNA methylation is a stable covalent epigenetic modification of primarily CpG dinucleotides that has recently gained considerable attention for its use as a biomarker in different clinical settings, including disease diagnosis, prognosis and therapeutic response prediction. Although the advent of genome-wide DNA methylation profiling in primary disease tissue has provided a manifold resource for biomarker development, only a tiny fraction of DNA methylation-based assays have reached clinical testing. Here, we provide a critical overview of different analytical methods that are suitable for biomarker validation, including general study design considerations, which might help to streamline epigenetic marker development. Furthermore, we highlight some of the recent marker validation studies and established markers that are currently commercially available for assisting in clinical management of different cancers

Prevention and Intervention Studies with Telmisartan, Ramipril and Their Combination in Different Rat Stroke Models

The effects of AT1 receptor blocker, telmisartan, and the ACE inhibitor, ramipril, were tested head-to head and in combination on stroke prevention in hypertensive rats and on potential neuroprotection in acute cerebral ischemia in normotensive rats. Normotensive Wistar rats were treated s.c. 5 days prior to middle cerebral artery occlusion (MCAO) for 90 min with reperfusion. Groups (n = 10 each): (1) sham, (2) vehicle (V; 0,9% NaCl), (3) T (0,5 mg/kg once daily), (4) R (0,01 mg/kg twice daily), (5) R (0,1 mg/kg twice daily) or (6) T (0,5 mg/kg once daily) plus R (0,01 mg/kg twice daily). Twenty-four and 48 h after MCAO, neurological outcome (NO) was determined. Forty-eight h after MCAO, infarct volume by MRI, neuronal survival, inflammation factors and neurotrophin receptor (TrkB) were analysed.Stroke incidence was reduced, survival was prolonged and neurological outcome was improved in all treated SHR-SP with no differences between treated groups. In the acute intervention study, T and T+R, but not R alone, improved NO, reduced infarct volume, inflammation (TNFα), and induced TrkB receptor and neuronal survival in comparison to V.T, R or T+R had similar beneficial effects on stroke incidence and NO in hypertensive rats, confirming BP reduction as determinant factor in stroke prevention. In contrast, T and T+R provided superior neuroprotection in comparison to R alone in normotensive rats with induced cerebral ischemia

Potentials of Mean Force for Protein Structure Prediction Vindicated, Formalized and Generalized

Understanding protein structure is of crucial importance in science, medicine and biotechnology. For about two decades, knowledge based potentials based on pairwise distances -- so-called "potentials of mean force" (PMFs) -- have been center stage in the prediction and design of protein structure and the simulation of protein folding. However, the validity, scope and limitations of these potentials are still vigorously debated and disputed, and the optimal choice of the reference state -- a necessary component of these potentials -- is an unsolved problem. PMFs are loosely justified by analogy to the reversible work theorem in statistical physics, or by a statistical argument based on a likelihood function. Both justifications are insightful but leave many questions unanswered. Here, we show for the first time that PMFs can be seen as approximations to quantities that do have a rigorous probabilistic justification: they naturally arise when probability distributions over different features of proteins need to be combined. We call these quantities reference ratio distributions deriving from the application of the reference ratio method. This new view is not only of theoretical relevance, but leads to many insights that are of direct practical use: the reference state is uniquely defined and does not require external physical insights; the approach can be generalized beyond pairwise distances to arbitrary features of protein structure; and it becomes clear for which purposes the use of these quantities is justified. We illustrate these insights with two applications, involving the radius of gyration and hydrogen bonding. In the latter case, we also show how the reference ratio method can be iteratively applied to sculpt an energy funnel. Our results considerably increase the understanding and scope of energy functions derived from known biomolecular structures

Structure-based design, synthesis and biological evaluation of a novel series of isoquinolone and pyrazolo[4,3-c]pyridine inhibitors of fascin 1 as potential anti-metastatic agents

Fascin is an actin binding and bundling protein that is not expressed in normal epithelial tissues but overexpressed in a variety of invasive epithelial tumors. It has a critical role in cancer cell metastasis by promoting cell migration and invasion. Here we report the crystal structures of fascin in complex with a series of novel and potent inhibitors. Structure-based elaboration of these compounds enabled the development of a series with nanomolar affinities for fascin, good physicochemical properties and the ability to inhibit fascin-mediated bundling of filamentous actin. These compounds provide promising starting points for fascin-targeted anti-metastatic therapies

Molecular determinants of binding to the Plasmodium subtilisin-like protease 1.

PfSUB1, a subtilisin-like protease of the human malaria parasite Plasmodium falciparum, is known to play important roles during the life cycle of the parasite and has emerged as a promising antimalarial drug target. In order to provide a detailed understanding of the origin of binding determinants of PfSUB1 substrates, we performed molecular dynamics simulations in combination with MM-GBSA free energy calculations using a homology model of PfSUB1 in complex with different substrate peptides. Key interactions, as well as residues that potentially make a major contribution to the binding free energy, are identified at the prime and nonprime side of the scissile bond and comprise peptide residues P4 to P2'. This finding stresses the requirement for peptide substrates to interact with both prime and nonprime side residues of the PfSUB1 binding site. Analyzing the energetic contributions of individual amino acids within the peptide-PfSUB1 complexes indicated that van der Waals interactions and the nonpolar part of solvation energy dictate the binding strength of the peptides and that the most favorable interactions are formed by peptide residues P4 and P1. Hot spot residues identified in PfSUB1 are dispersed over the entire binding site, but clustered areas of hot spots also exist and suggest that either the S4-S2 or the S1-S2' binding site should be exploited in efforts to design small molecule inhibitors. The results are discussed with respect to which binding determinants are specific to PfSUB1 and, therefore, might allow binding selectivity to be obtained

Molecular Recognition of H3/H4 Histone Tails by the Tudor Domains of JMJD2A: A Comparative Molecular Dynamics Simulations Study

Background: Histone demethylase, JMJD2A, specifically recognizes and binds to methylated lysine residues at histone H3 and H4 tails (especially trimethylated H3K4 (H3K4me3), trimethylated H3K9 (H3K9me3) and di, trimethylated H4K20 (H4K20me2, H4K20me3)) via its tandem tudor domains. Crystal structures of JMJD2A-tudor binding to H3K4me3 and H4K20me3 peptides are available whereas the others are not. Complete picture of the recognition of the four histone peptides by the tandem tudor domains yet remains to be clarified. Methodology/Principal Findings: We report a detailed molecular dynamics simulation and binding energy analysis of the recognition of JMJD2A-tudor with four different histone tails. 25 ns fully unrestrained molecular dynamics simulations are carried out for each of the bound and free structures. We investigate the important hydrogen bonds and electrostatic interactions between the tudor domains and the peptide molecules and identify the critical residues that stabilize the complexes. Our binding free energy calculations show that H4K20me2 and H3K9me3 peptides have the highest and lowest affinity to JMJD2A-tudor, respectively. We also show that H4K20me2 peptide adopts the same binding mode with H4K20me3 peptide, and H3K9me3 peptide adopts the same binding mode with H3K4me3 peptide. Decomposition of the enthalpic and the entropic contributions to the binding free energies indicate that the recognition of the histone peptides is mainly driven by favourable van der Waals interactions. Residue decomposition of the binding free energies with backbone and side chain contributions as well as their energetic constituents identify the hotspots in the binding interface of the structures. Conclusion: Energetic investigations of the four complexes suggest that many of the residues involved in the interactions are common. However, we found two receptor residues that were related to selective binding of the H3 and H4 ligands. Modifications or mutations on one of these residues can selectively alter the recognition of the H3 tails or the H4 tails

The association between retraction of the torn rotator cuff and increasing expression of hypoxia inducible factor 1α and vascular endothelial growth factor expression: an immunohistological study

<p>Abstract</p> <p>Background</p> <p>Differing levels of tendon retraction are found in full-thickness rotator cuff tears. The pathophysiology of tendon degeneration and retraction is unclear. Neoangiogenesis in tendon parenchyma indicates degeneration. Hypoxia inducible factor 1α (HIF) and vascular endothelial growth factor (VEGF) are important inducers of neoangiogenesis. Rotator cuff tendons rupture leads to fatty muscle infiltration (FI) and muscle atrophy (MA). The aim of this study is to clarify the relationship between HIF and VEGF expression, neoangiogenesis, FI, and MA in tendon retraction found in full-thickness rotator cuff tears.</p> <p>Methods</p> <p>Rotator cuff tendon samples of 33 patients with full-thickness medium-sized rotator cuff tears were harvested during reconstructive surgery. The samples were dehydrated and paraffin embedded. For immunohistological determination of VEGF and HIF expression, sample slices were strained with VEGF and HIF antibody dilution. Vessel density and vessel size were determined after Masson-Goldner staining of sample slices. The extent of tendon retraction was determined intraoperatively according to Patte's classification. Patients were assigned to 4 categories based upon Patte tendon retraction grade, including one control group. FI and MA were measured on standardized preoperative shoulder MRI.</p> <p>Results</p> <p>HIF and VEGF expression, FI, and MA were significantly higher in torn cuff samples compared with healthy tissue (p < 0.05). HIF and VEGF expression, and vessel density significantly increased with extent of tendon retraction (p < 0.05). A correlation between HIF/VEGF expression and FI and MA could be found (p < 0.05). There was no significant correlation between HIF/VEGF expression and neovascularity (p > 0.05)</p> <p>Conclusion</p> <p>Tendon retraction in full-thickness medium-sized rotator cuff tears is characterized by neovascularity, increased VEGF/HIF expression, FI, and MA. VEGF expression and neovascularity may be effective monitoring tools to assess tendon degeneration.</p

Classifying and scoring of molecules with the NGN: new datasets, significance tests, and generalization

<p>Abstract</p> <p/> <p>This paper demonstrates how a Neural Grammar Network learns to classify and score molecules for a variety of tasks in chemistry and toxicology. In addition to a more detailed analysis on datasets previously studied, we introduce three new datasets (BBB, FXa, and toxicology) to show the generality of the approach. A new experimental methodology is developed and applied to both the new datasets as well as previously studied datasets. This methodology is rigorous and statistically grounded, and ultimately culminates in a Wilcoxon significance test that proves the effectiveness of the system. We further include a complete generalization of the specific technique to arbitrary grammars and datasets using a mathematical abstraction that allows researchers in different domains to apply the method to their own work.</p> <p>Background</p> <p>Our work can be viewed as an alternative to existing methods to solve the quantitative structure-activity relationship (QSAR) problem. To this end, we review a number approaches both from a methodological and also a performance perspective. In addition to these approaches, we also examined a number of chemical properties that can be used by generic classifier systems, such as feed-forward artificial neural networks. In studying these approaches, we identified a set of interesting benchmark problem sets to which many of the above approaches had been applied. These included: ACE, AChE, AR, BBB, BZR, Cox2, DHFR, ER, FXa, GPB, Therm, and Thr. Finally, we developed our own benchmark set by collecting data on toxicology.</p> <p>Results</p> <p>Our results show that our system performs better than, or comparatively to, the existing methods over a broad range of problem types. Our method does not require the expert knowledge that is necessary to apply the other methods to novel problems.</p> <p>Conclusions</p> <p>We conclude that our success is due to the ability of our system to: 1) encode molecules losslessly before presentation to the learning system, and 2) leverage the design of molecular description languages to facilitate the identification of relevant structural attributes of the molecules over different problem domains.</p

Constraint-based probabilistic learning of metabolic pathways from tomato volatiles

Clustering and correlation analysis techniques have become popular tools for the analysis of data produced by metabolomics experiments. The results obtained from these approaches provide an overview of the interactions between objects of interest. Often in these experiments, one is more interested in information about the nature of these relationships, e.g., cause-effect relationships, than in the actual strength of the interactions. Finding such relationships is of crucial importance as most biological processes can only be understood in this way. Bayesian networks allow representation of these cause-effect relationships among variables of interest in terms of whether and how they influence each other given that a third, possibly empty, group of variables is known. This technique also allows the incorporation of prior knowledge as established from the literature or from biologists. The representation as a directed graph of these relationship is highly intuitive and helps to understand these processes. This paper describes how constraint-based Bayesian networks can be applied to metabolomics data and can be used to uncover the important pathways which play a significant role in the ripening of fresh tomatoes. We also show here how this methods of reconstructing pathways is intuitive and performs better than classical techniques. Methods for learning Bayesian network models are powerful tools for the analysis of data of the magnitude as generated by metabolomics experiments. It allows one to model cause-effect relationships and helps in understanding the underlying processes