ResearchPad - reactants https://www.researchpad.co Default RSS Feed en-us © 2020 Newgen KnowledgeWorks <![CDATA[Effective coupling of rapid freeze-quench to high-frequency electron paramagnetic resonance]]> https://www.researchpad.co/article/elastic_article_7690 We report an easy, efficient and reproducible way to prepare Rapid-Freeze-Quench samples in sub-millimeter capillaries and load these into the probe head of a 275 GHz Electron Paramagnetic Resonance spectrometer. Kinetic data obtained for the binding reaction of azide to myoglobin demonstrate the feasibility of the method for high-frequency EPR. Experiments on the same samples at 9.5 GHz show that only a single series of Rapid-Freeze-Quench samples is required for studies at multiple microwave frequencies.

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<![CDATA[Prediction of perturbed proton transfer networks]]> https://www.researchpad.co/article/5c1ab879d5eed0c4840282dc

The transfer of protons through proton translocating channels is a complex process, for which direct samplings of different protonation states and side chain conformations in a transition network calculation provide an efficient, bias-free description. In principle, a new transition network calculation is required for every unsampled change in the system of interest, e.g. an unsampled protonation state change, which is associated with significant computational costs. Transition networks void of or including an unsampled change are termed unperturbed or perturbed, respectively. Here, we present a prediction method, which is based on an extensive coarse-graining of the underlying transition networks to speed up the calculations. It uses the minimum spanning tree and a corresponding sensitivity analysis of an unperturbed transition network as initial guess and refinement parameter for the determination of an unknown, perturbed transition network. Thereby, the minimum spanning tree defines a sub-network connecting all nodes without cycles and minimal edge weight sum, while the sensitivity analysis analyzes the stability of the minimum spanning tree towards individual edge weight reductions. Using the prediction method, we are able to reduce the calculation costs in a model system by up to 80%, while important network properties are maintained in most predictions.

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<![CDATA[A Theoretical Study on the Antioxidant Activity of Piceatannol and Isorhapontigenin Scavenging Nitric Oxide and Nitrogen Dioxide Radicals]]> https://www.researchpad.co/article/5989d9e6ab0ee8fa60b6b43c

The antioxidant activity of naturally occurring stilbene compounds piceatannol (PIC) and isorhapontigenin (ISO) scavenging two free radicals (NO and NO2) were studied using density functional theory (DFT) method. Four reaction mechanisms have been considered: hydrogen atom transfer (HAT), radical adduct formation (RAF), single electron transfer (SET), and sequential proton loss electron transfer (SPLET). The reaction channels in water solution were traced independently, and the respective thermodynamic and kinetic parameters were obtained. We found PIC and ISO scavenge NO mainly through RAF mechanism, and scavenge NO2 through HAT mechanism. The capacity of PIC scavenging NO2 is much higher than ISO, but the reactivity of scavenging NO is lower than ISO.

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<![CDATA[Cut-Offs and Response Criteria for the Hospital Universitario La Princesa Index (HUPI) and Their Comparison to Widely-Used Indices of Disease Activity in Rheumatoid Arthritis]]> https://www.researchpad.co/article/5989d9d7ab0ee8fa60b661f9

Objective

To estimate cut-off points and to establish response criteria for the Hospital Universitario La Princesa Index (HUPI) in patients with chronic polyarthritis.

Methods

Two cohorts, one of early arthritis (Princesa Early Arthritis Register Longitudinal [PEARL] study) and other of long-term rheumatoid arthritis (Estudio de la Morbilidad y Expresión Clínica de la Artritis Reumatoide [EMECAR]) including altogether 1200 patients were used to determine cut-off values for remission, and for low, moderate and high activity through receiver operating curve (ROC) analysis. The areas under ROC (AUC) were compared to those of validated indexes (SDAI, CDAI, DAS28). ROC analysis was also applied to establish minimal and relevant clinical improvement for HUPI.

Results

The best cut-off points for HUPI are 2, 5 and 9, classifying RA activity as remission if ≤2, low disease activity if >2 and ≤5), moderate if >5 and <9 and high if ≥9. HUPI’s AUC to discriminate between low-moderate activity was 0.909 and between moderate-high activity 0.887. DAS28’s AUCs were 0.887 and 0.846, respectively; both indices had higher accuracy than SDAI (AUCs: 0.832 and 0.756) and CDAI (AUCs: 0.789 and 0.728). HUPI discriminates remission better than DAS28-ESR in early arthritis, but similarly to SDAI. The HUPI cut-off for minimal clinical improvement was established at 2 and for relevant clinical improvement at 4. Response criteria were established based on these cut-off values.

Conclusions

The cut-offs proposed for HUPI perform adequately in patients with either early or long term arthritis.

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<![CDATA[Investigating Information Dynamics in Living Systems through the Structure and Function of Enzymes]]> https://www.researchpad.co/article/5989da78ab0ee8fa60b9775d

Enzymes are proteins that accelerate intracellular chemical reactions often by factors of 105−1012s−1. We propose the structure and function of enzymes represent the thermodynamic expression of heritable information encoded in DNA with post-translational modifications that reflect intra- and extra-cellular environmental inputs. The 3 dimensional shape of the protein, determined by the genetically-specified amino acid sequence and post translational modifications, permits geometric interactions with substrate molecules traditionally described by the key-lock best fit model. Here we apply Kullback-Leibler (K-L) divergence as metric of this geometric “fit” and the information content of the interactions. When the K-L ‘distance’ between interspersed substrate pn and enzyme rn positions is minimized, the information state, reaction probability, and reaction rate are maximized. The latter obeys the Arrhenius equation, which we show can be derived from the geometrical principle of minimum K-L distance. The derivation is first limited to optimum substrate positions for fixed sets of enzyme positions. However, maximally improving the key/lock fit, called ‘induced fit,’ requires both sets of positions to be varied optimally. We demonstrate this permits and is maximally efficient if the key and lock particles pn, rn are quantum entangled because the level of entanglement obeys the same minimized value of the Kullback-Leibler distance that occurs when all pnrn. This implies interchanges pnbrn randomly taking place during a reaction successively improves key/lock fits, reducing the activation energy Ea and increasing the reaction rate k. Our results demonstrate the summation of heritable and environmental information that determines the enzyme spatial configuration, by decreasing the K-L divergence, is converted to thermodynamic work by reducing Ea and increasing k of intracellular reactions. Macroscopically, enzyme information increases the order in living systems, similar to the Maxwell demon gedanken, by selectively accelerating specific reaction thus generating both spatial and temporal concentration gradients.

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<![CDATA[On the Effect of Planetary Stable Isotope Compositions on Growth and Survival of Terrestrial Organisms]]> https://www.researchpad.co/article/5989da4eab0ee8fa60b8d4df

Isotopic compositions of reactants affect the rates of chemical and biochemical reactions. Usually it is assumed that heavy stable isotope enrichment leads to progressively slower reactions. Yet the effect of stable isotopes may be nonlinear, as exemplified by the “isotopic resonance” phenomenon. Since the isotopic compositions of other planets of Solar system, including Mars and Venus, are markedly different from terrestrial (e.g., deuterium content is ≈5 and ≈100 times higher, respectively), it is far from certain that terrestrial life will thrive in these isotopic conditions. Here we found that Martian deuterium content negatively affected survival of shrimp in semi-closed biosphere on a year-long time scale. Moreover, the bacterium Escherichia coli grows slower at Martian isotopic compositions and even slower at Venus’s compositions. Thus, the biological impact of varying stable isotope compositions needs to be taken into account when planning interplanetary missions.

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<![CDATA[Kinetic Reaction Mechanism of Sinapic Acid Scavenging NO2 and OH Radicals: A Theoretical Study]]> https://www.researchpad.co/article/5989da81ab0ee8fa60b9aa8c

The mechanism and kinetics underlying reactions between the naturally-occurring antioxidant sinapic acid (SA) and the very damaging ·NO2 and ·OH were investigated through the density functional theory (DFT). Two most possible reaction mechanisms were studied: hydrogen atom transfer (HAT) and radical adduct formation (RAF). Different reaction channels of neutral and anionic sinapic acid (SA-) scavenging radicals in both atmosphere and water medium were traced independently, and the thermodynamic and kinetic parameters were calculated. We find the most active site of SA/SA- scavenging ·NO2 and ·OH is the –OH group in benzene ring by HAT mechanism, while the RAF mechanism for SA/SA- scavenging ·NO2 seems thermodynamically unfavorable. In water phase, at 298 K, the total rate constants of SA eliminating ·NO2 and ·OH are 1.30×108 and 9.20×109 M-1 S-1 respectively, indicating that sinapic acid is an efficient scavenger for both ·NO2 and ·OH.

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<![CDATA[Characterising Complex Enzyme Reaction Data]]> https://www.researchpad.co/article/5989dac7ab0ee8fa60bb2c9a

The relationship between enzyme-catalysed reactions and the Enzyme Commission (EC) number, the widely accepted classification scheme used to characterise enzyme activity, is complex and with the rapid increase in our knowledge of the reactions catalysed by enzymes needs revisiting. We present a manual and computational analysis to investigate this complexity and found that almost one-third of all known EC numbers are linked to more than one reaction in the secondary reaction databases (e.g., KEGG). Although this complexity is often resolved by defining generic, alternative and partial reactions, we have also found individual EC numbers with more than one reaction catalysing different types of bond changes. This analysis adds a new dimension to our understanding of enzyme function and might be useful for the accurate annotation of the function of enzymes and to study the changes in enzyme function during evolution.

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<![CDATA[Defining the Product Chemical Space of Monoterpenoid Synthases]]> https://www.researchpad.co/article/5989dac8ab0ee8fa60bb330a

Terpenoid synthases create diverse carbon skeletons by catalyzing complex carbocation rearrangements, making them particularly challenging for enzyme function prediction. To begin to address this challenge, we have developed a computational approach for the systematic enumeration of terpenoid carbocations. Application of this approach allows us to systematically define a nearly complete chemical space for the potential carbon skeletons of products from monoterpenoid synthases. Specifically, 18758 carbocations were generated, which we cluster into 74 cyclic skeletons. Five of the 74 skeletons are found in known natural products; some of the others are plausible for new functions, either in nature or engineered. This work systematizes the description of function for this class of enzymes, and provides a basis for predicting functions of uncharacterized enzymes. To our knowledge, this is the first computational study to explore the complete product chemical space of this important class of enzymes.

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<![CDATA[Cord Blood Acute Phase Reactants Predict Early Onset Neonatal Sepsis in Preterm Infants]]> https://www.researchpad.co/article/5989dad5ab0ee8fa60bb7d58

Background

Early onset sepsis (EOS) is a major cause of morbidity and mortality in preterm infants, yet diagnosis remains inadequate resulting in missed cases or prolonged empiric antibiotics with adverse consequences. Evaluation of acute phase reactant (APR) biomarkers in umbilical cord blood at birth may improve EOS detection in preterm infants with intrauterine infection.

Methods

In this nested case-control study, infants (29.7 weeks gestation, IQR: 27.7–32.2) were identified from a longitudinal cohort with archived cord blood and placental histopathology. Patients were categorized using culture, laboratory, clinical, and antibiotic treatment data into sepsis groups: confirmed sepsis (cEOS, n = 12); presumed sepsis (PS, n = 30); and no sepsis (controls, n = 30). Nine APRs were measured in duplicate from cord blood using commercially available multiplex immunoassays (Bio-Plex Pro™). In addition, placental histopathologic data were linked to biomarker results.

Results

cEOS organisms were Escherichia coli, Streptococcus agalactiae, Proteus mirabilis, Haemophilus influenzae and Listeria monocytogenes. C-reactive protein (CRP), serum amyloid A (SAA), haptoglobin (Hp), serum amyloid P and ferritin were significantly elevated in cEOS compared to controls (p<0.01). SAA, CRP, and Hp were elevated in cEOS but not in PS (p<0.01) and had AUCs of 99%, 96%, and 95% respectively in predicting cEOS. Regression analysis revealed robust associations of SAA, CRP, and Hp with EOS after adjustment for covariates. Procalcitonin, fibrinogen, α-2-macroglobulin and tissue plasminogen activator were not significantly different across groups. Placental acute inflammation was associated with APR elevation and was present in all cEOS, 9 PS, and 17 control infants.

Conclusion

This study shows that certain APRs are elevated in cord blood of premature infants with EOS of intrauterine origin. SAA, CRP, and Hp at birth have potential diagnostic utility for risk stratification and identification of infants with EOS.

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<![CDATA[The Effect of Attractive Interactions and Macromolecular Crowding on Crystallins Association]]> https://www.researchpad.co/article/5989dabeab0ee8fa60bafd93

In living systems proteins are typically found in crowded environments where their effective interactions strongly depend on the surrounding medium. Yet, their association and dissociation needs to be robustly controlled in order to enable biological function. Uncontrolled protein aggregation often causes disease. For instance, cataract is caused by the clustering of lens proteins, i.e., crystallins, resulting in enhanced light scattering and impaired vision or blindness. To investigate the molecular origins of cataract formation and to design efficient treatments, a better understanding of crystallin association in macromolecular crowded environment is needed. Here we present a theoretical study of simple coarse grained colloidal models to characterize the general features of how the association equilibrium of proteins depends on the magnitude of intermolecular attraction. By comparing the analytic results to the available experimental data on the osmotic pressure in crystallin solutions, we identify the effective parameters regimes applicable to crystallins. Moreover, the combination of two models allows us to predict that the number of binding sites on crystallin is small, i.e. one to three per protein, which is different from previous estimates. We further observe that the crowding factor is sensitive to the size asymmetry between the reactants and crowding agents, the shape of the protein clusters, and to small variations of intermolecular attraction. Our work may provide general guidelines on how to steer the protein interactions in order to control their association.

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<![CDATA[Meneco, a Topology-Based Gap-Filling Tool Applicable to Degraded Genome-Wide Metabolic Networks]]> https://www.researchpad.co/article/5989db54ab0ee8fa60bdcfd4

Increasing amounts of sequence data are becoming available for a wide range of non-model organisms. Investigating and modelling the metabolic behaviour of those organisms is highly relevant to understand their biology and ecology. As sequences are often incomplete and poorly annotated, draft networks of their metabolism largely suffer from incompleteness. Appropriate gap-filling methods to identify and add missing reactions are therefore required to address this issue. However, current tools rely on phenotypic or taxonomic information, or are very sensitive to the stoichiometric balance of metabolic reactions, especially concerning the co-factors. This type of information is often not available or at least prone to errors for newly-explored organisms. Here we introduce Meneco, a tool dedicated to the topological gap-filling of genome-scale draft metabolic networks. Meneco reformulates gap-filling as a qualitative combinatorial optimization problem, omitting constraints raised by the stoichiometry of a metabolic network considered in other methods, and solves this problem using Answer Set Programming. Run on several artificial test sets gathering 10,800 degraded Escherichia coli networks Meneco was able to efficiently identify essential reactions missing in networks at high degradation rates, outperforming the stoichiometry-based tools in scalability. To demonstrate the utility of Meneco we applied it to two case studies. Its application to recent metabolic networks reconstructed for the brown algal model Ectocarpus siliculosus and an associated bacterium Candidatus Phaeomarinobacter ectocarpi revealed several candidate metabolic pathways for algal-bacterial interactions. Then Meneco was used to reconstruct, from transcriptomic and metabolomic data, the first metabolic network for the microalga Euglena mutabilis. These two case studies show that Meneco is a versatile tool to complete draft genome-scale metabolic networks produced from heterogeneous data, and to suggest relevant reactions that explain the metabolic capacity of a biological system.

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<![CDATA[Automated visualization of rule-based models]]> https://www.researchpad.co/article/5ab1ac8f463d7e63213ecdac

Frameworks such as BioNetGen, Kappa and Simmune use “reaction rules” to specify biochemical interactions compactly, where each rule specifies a mechanism such as binding or phosphorylation and its structural requirements. Current rule-based models of signaling pathways have tens to hundreds of rules, and these numbers are expected to increase as more molecule types and pathways are added. Visual representations are critical for conveying rule-based models, but current approaches to show rules and interactions between rules scale poorly with model size. Also, inferring design motifs that emerge from biochemical interactions is an open problem, so current approaches to visualize model architecture rely on manual interpretation of the model. Here, we present three new visualization tools that constitute an automated visualization framework for rule-based models: (i) a compact rule visualization that efficiently displays each rule, (ii) the atom-rule graph that conveys regulatory interactions in the model as a bipartite network, and (iii) a tunable compression pipeline that incorporates expert knowledge and produces compact diagrams of model architecture when applied to the atom-rule graph. The compressed graphs convey network motifs and architectural features useful for understanding both small and large rule-based models, as we show by application to specific examples. Our tools also produce more readable diagrams than current approaches, as we show by comparing visualizations of 27 published models using standard graph metrics. We provide an implementation in the open source and freely available BioNetGen framework, but the underlying methods are general and can be applied to rule-based models from the Kappa and Simmune frameworks also. We expect that these tools will promote communication and analysis of rule-based models and their eventual integration into comprehensive whole-cell models.

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<![CDATA[Fast exploration of an optimal path on the multidimensional free energy surface]]> https://www.researchpad.co/article/5989db5cab0ee8fa60bdfef4

In a reaction, determination of an optimal path with a high reaction rate (or a low free energy barrier) is important for the study of the reaction mechanism. This is a complicated problem that involves lots of degrees of freedom. For simple models, one can build an initial path in the collective variable space by the interpolation method first and then update the whole path constantly in the optimization. However, such interpolation method could be risky in the high dimensional space for large molecules. On the path, steric clashes between neighboring atoms could cause extremely high energy barriers and thus fail the optimization. Moreover, performing simulations for all the snapshots on the path is also time-consuming. In this paper, we build and optimize the path by a growing method on the free energy surface. The method grows a path from the reactant and extends its length in the collective variable space step by step. The growing direction is determined by both the free energy gradient at the end of the path and the direction vector pointing at the product. With fewer snapshots on the path, this strategy can let the path avoid the high energy states in the growing process and save the precious simulation time at each iteration step. Applications show that the presented method is efficient enough to produce optimal paths on either the two-dimensional or the twelve-dimensional free energy surfaces of different small molecules.

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<![CDATA[Cross-linked β-cyclodextrin and carboxymethyl cellulose hydrogels for controlled drug delivery of acyclovir]]> https://www.researchpad.co/article/5989db53ab0ee8fa60bdcbab

To explore the potential role of polymers in the development of drug-delivery systems, this study investigated the use of β-cyclodextrin (β-CD), carboxymethyl cellulose (CMC), acrylic acid (AA) and N’ N’-methylenebis-acrylamide (MBA) in the synthesis of hydrogels for controlled drug delivery of acyclovir (ACV). Different proportions of β-CD, CMC, AA and MBA were blended with each other to fabricate hydrogels via free radical polymerization technique. Fourier transform infrared spectroscopy (FTIR) revealed successful grafting of components into the polymeric network. Thermal and morphological characterization confirmed the formation of thermodynamically stable hydrogels having porous structure. The pH-responsive behaviour of hydrogels has been documented by swelling dynamics and drug release behaviour in simulated gastrointestinal fluids. Drug release kinetics revealed controlled release behaviour of the antiviral drug acyclovir in developed polymeric network. Cross-linked β-cyclodextrin and carboxymethyl cellulose hydrogels can be used as promising candidates for the design and development of controlled drug-delivery systems.

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