Quantum Biochemistry


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Quantum Biochemistry: Electronic Structure And Biological Activity. 2 Volume Set

Each redox reaction category is shown in a different color. G1—reduction of carboxyl to aldehyde; G2—reduction of carbonyl ketone or aldehyde to hydroxyl; G3—reduction of carbonyl to amine; G4—reduction of hydroxyl to hydrocarbon. As discussed in the S1 Text , the prediction accuracy of the calibrated model chemistries was evaluated using the experimental data for the G3 reaction category only to avoid overfitting. The labels refer to the quantum model chemistry used to perform a single point energy SPE calculation on geometry-optimized conformers.

Quantum chemistry

Distributions are over the entire set of molecular conformers used in our study. Abstract Thermodynamics dictates the structure and function of metabolism. Author summary Redox reactions define the energetic constraints within which life can exist. Introduction In order to understand life we need to understand the forces that support and constrain it. Results Quantum chemical predictions of biochemical redox potentials To facilitate our analysis we divided redox reactions into several generalized oxidoreductase groups which together cover the vast majority of redox transformations within cellular metabolism Fig 1A : G1 reduction of an unmodified carboxylic acid -COO or an activated carboxylic acid—i.

Download: PPT. Fig 1. Our study is based on predicting biochemical standard redox potentials using a calibrated quantum chemistry strategy. Fig 2. Quantum chemistry model predicts experimentally measured reduction potential with high accuracy. Table 1. Prediction accuracy of the quantum chemistry and group contribution method modeling approaches.

Systematic detection of potentially erroneous experimental values Inconsistencies between our predictions and experimental measurements can be used to identify potentially erroneous experimental values. Comprehensive prediction and analysis of reduction potentials We used the calibrated quantum chemistry model to predict redox potentials for a database of natural and non-natural redox reactions. Fig 3. Fig 4. Comparison between the redox potentials of sub-groups for reactions in the G2 category carbonyl to hydroxycarbon reductions.

Quantum Biochemistry

Fig 5. A schematic showing the location of different types of oxidoreductase reactions oxidoreductase groups 1 to 4 within the extended central metabolic network. Discussion In this work, we present a novel approach for predicting the thermodynamics of biochemical redox reactions. Quantum chemical electronic single point energies SPE and calibration against experimental values Single point energy SPE calculations yield the value of the electronic energy E Electronic for each conformer at their optimized geometry.

Generation of comprehensive database of natural and non-natural redox reactions To generate a database of all possible redox reactions involving natural compounds, we use a decomposition of all metabolites into functional groups as per the group contribution method [ 36 ]. Supporting information. S1 Table. The range of range of potentials for the most important redox cofactors in biochemistry. S2 Table. Linear regression coefficients obtained from calibrating the raw redox potential estimates obtained from the quantum single point energy SPE model chemistry.

S3 Table. S4 Table.

Prediction accuracy of the quantum chemistry, molecular fingerprints, and group contribution method modeling approaches. S1 Fig. Prediction accuracy, as measured using Pearson r coefficient, and average runtimes per molecular conformer for different quantum single point energy SPE model chemistries. S2 Fig. Predicting biochemical redox potentials of carbonyl to hydroxycarbon reactions category G2 with different approaches. S3 Fig. Scatter plots of experimental redox potentials and predicted potentials with the selected calibrated quantum chemistry approach upper four panels and group contribution method GCM lower four panels for all four redox categories.

S4 Fig. Detection of experimental outliers using a calibrated quantum chemistry approach and MACCS fingerprint predictions for all four reaction categories. S5 Fig. S6 Fig. S7 Fig. Cumulative distribution functions of runtimes for geometry optimization and single point energy SPE estimates using our quantum chemistry method. S1 Text.

Kundrecensioner

S1 Dataset. Contains predicted standard redox potentials group contribution method and calibrated quantum chemistry an experimental potentials for all redox pairs considered in this work.


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S2 Dataset. Contains the full set of redox reactions in the extended central metabolic network. S3 Dataset. S4 Dataset. Contains the structural categorization of compounds in the G2 category used to obtain the structure-energy relationships in Fig 4. S5 Dataset. Contains the details of all the model chemistries tested during the optimization procedure.

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S6 Dataset. Contains the raw quantum chemical electronic energies—using a variety of model chemistries—Calculated for up to 10 geometrical conformers of each compound considered in this work. References 1. Thermodynamic constraints shape the structure of carbon fixation pathways. Biochim Biophys Acta. Bar-Even A. Does acetogenesis really require especially low reduction potential? Glycolytic strategy as a tradeoff between energy yield and protein cost.

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Pathway thermodynamics highlights kinetic obstacles in central metabolism. PLoS Comput Biol. Ataman M, Hatzimanikatis V. Heading in the right direction: thermodynamics-based network analysis and pathway engineering. Curr Opin Biotechnol. On the universal core of bioenergetics. KEGG for linking genomes to life and the environment.

Nucleic Acids Res. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. The emergence of life from iron monosulphide bubbles at a submarine hydrothermal redox and pH front. J Geol Soc London. Stangherlin A, Reddy AB.


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Regulation of circadian clocks by redox homeostasis. J Biol Chem. A survey of carbon fixation pathways through a quantitative lens. J Exp Bot. The redox stress hypothesis of aging.

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