11,506 research outputs found
Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models.
Knowing the catalytic turnover numbers of enzymes is essential for understanding the growth rate, proteome composition, and physiology of organisms, but experimental data on enzyme turnover numbers is sparse and noisy. Here, we demonstrate that machine learning can successfully predict catalytic turnover numbers in Escherichia coli based on integrated data on enzyme biochemistry, protein structure, and network context. We identify a diverse set of features that are consistently predictive for both in vivo and in vitro enzyme turnover rates, revealing novel protein structural correlates of catalytic turnover. We use our predictions to parameterize two mechanistic genome-scale modelling frameworks for proteome-limited metabolism, leading to significantly higher accuracy in the prediction of quantitative proteome data than previous approaches. The presented machine learning models thus provide a valuable tool for understanding metabolism and the proteome at the genome scale, and elucidate structural, biochemical, and network properties that underlie enzyme kinetics
The protein cost of metabolic fluxes: prediction from enzymatic rate laws and cost minimization
Bacterial growth depends crucially on metabolic fluxes, which are limited by
the cell's capacity to maintain metabolic enzymes. The necessary enzyme amount
per unit flux is a major determinant of metabolic strategies both in evolution
and bioengineering. It depends on enzyme parameters (such as kcat and KM
constants), but also on metabolite concentrations. Moreover, similar amounts of
different enzymes might incur different costs for the cell, depending on
enzyme-specific properties such as protein size and half-life. Here, we
developed enzyme cost minimization (ECM), a scalable method for computing
enzyme amounts that support a given metabolic flux at a minimal protein cost.
The complex interplay of enzyme and metabolite concentrations, e.g. through
thermodynamic driving forces and enzyme saturation, would make it hard to solve
this optimization problem directly. By treating enzyme cost as a function of
metabolite levels, we formulated ECM as a numerically tractable, convex
optimization problem. Its tiered approach allows for building models at
different levels of detail, depending on the amount of available data.
Validating our method with measured metabolite and protein levels in E. coli
central metabolism, we found typical prediction fold errors of 3.8 and 2.7,
respectively, for the two kinds of data. ECM can be used to predict enzyme
levels and protein cost in natural and engineered pathways, establishes a
direct connection between protein cost and thermodynamics, and provides a
physically plausible and computationally tractable way to include enzyme
kinetics into constraint-based metabolic models, where kinetics have usually
been ignored or oversimplified
Interplay of water and a supramolecular capsule for catalysis of reductive elimination reaction from gold.
Supramolecular assemblies have gained tremendous attention due to their ability to catalyze reactions with the efficiencies of natural enzymes. Using ab initio molecular dynamics, we identify the origin of the catalysis by the supramolecular capsule Ga4L612- on the reductive elimination reaction from gold complexes and assess their similarity to natural enzymes. By comparing the free energies of the reactants and transition states for the catalyzed and uncatalyzed reactions, we determine that an encapsulated water molecule generates electric fields that contributes the most to the reduction in the activation free energy. Although this is unlike the biomimetic scenario of catalysis through direct host-guest interactions, the electric fields from the nanocage also supports the transition state to complete the reductive elimination reaction with greater catalytic efficiency. However it is also shown that the nanocage poorly organizes the interfacial water, which in turn creates electric fields that misalign with the breaking bonds of the substrate, thus identifying new opportunities for catalytic design improvements in nanocage assemblies
Dynamic optimization of metabolic networks coupled with gene expression
The regulation of metabolic activity by tuning enzyme expression levels is
crucial to sustain cellular growth in changing environments. Metabolic networks
are often studied at steady state using constraint-based models and
optimization techniques. However, metabolic adaptations driven by changes in
gene expression cannot be analyzed by steady state models, as these do not
account for temporal changes in biomass composition. Here we present a dynamic
optimization framework that integrates the metabolic network with the dynamics
of biomass production and composition, explicitly taking into account enzyme
production costs and enzymatic capacity. In contrast to the established dynamic
flux balance analysis, our approach allows predicting dynamic changes in both
the metabolic fluxes and the biomass composition during metabolic adaptations.
We applied our algorithm in two case studies: a minimal nutrient uptake
network, and an abstraction of core metabolic processes in bacteria. In the
minimal model, we show that the optimized uptake rates reproduce the empirical
Monod growth for bacterial cultures. For the network of core metabolic
processes, the dynamic optimization algorithm predicted commonly observed
metabolic adaptations, such as a diauxic switch with a preference ranking for
different nutrients, re-utilization of waste products after depletion of the
original substrate, and metabolic adaptation to an impending nutrient
depletion. These examples illustrate how dynamic adaptations of enzyme
expression can be predicted solely from an optimization principle
Operability-Based Design of Energy Systems: Application to Natural Gas Utilization Processes
Process operability emerged in the last decades as a powerful tool for the design and control of complex chemical processes. The design of such processes is a challenging task as they are represented by nonlinear models with large numbers of differential and algebraic equations that demand high computational effort for their solution. In particular, process operability was proposed as a method for verifying the ability of a process design, defined by the available input set, to reach an achievable output set that considers production targets. However, existing operability methods for nonlinear systems are limited by the problem size that they can address.;In this thesis, a novel operability framework for process design and intensification of high-dimensional nonlinear chemical and energy processes is developed. This proposed framework bridges the gap in the literature by addressing the challenges of process nonlinearity and model size. This framework also broadens the scope of the traditional path of operability approaches for design and control, mainly oriented to obtain the achievable output set from the available input set, and compare the computed achievable output set to a desired output set. In particular, an optimization algorithm based on nonlinear programming tools is formulated for the high-dimensional calculations of the desired input set that is feasible considering process constraints, performance levels, and intensification targets. The high computational effort required for the high-dimensional calculations is addressed by the incorporation of bilevel and parallel programming approaches into the classical process operability concepts.;To illustrate the effectiveness of the developed methods, two natural gas utilization processes of different dimensionalities are addressed: i) a catalytic membrane reactor for the direct methane aromatization conversion to benzene and hydrogen, for which an intensified reactor design footprint reduction up to 90% when compared to the base case is obtained; and ii) a natural gas combined cycle system for power generation, for which a dramatic reduction in size, from 400 to 0.11 [MW], is produced by specifying conditions of the gas and steam turbine cycles, while still keeping the high net plant efficiency between 55 and 56.5 [%]. These results indicate that this novel operability framework can be a powerful tool for enabling process intensification and modularity. Moreover, results on the implementation of the bilevel and parallel computing methods show a reduction in computational time up to 2 orders of magnitude, when compared to the original results. The results in this thesis have culminated in four peer reviewed publications and four delivered presentations by the time of the defense
Roadmap on semiconductor-cell biointerfaces.
This roadmap outlines the role semiconductor-based materials play in understanding the complex biophysical dynamics at multiple length scales, as well as the design and implementation of next-generation electronic, optoelectronic, and mechanical devices for biointerfaces. The roadmap emphasizes the advantages of semiconductor building blocks in interfacing, monitoring, and manipulating the activity of biological components, and discusses the possibility of using active semiconductor-cell interfaces for discovering new signaling processes in the biological world
Synergistic Niobium Doped Two-Dimensional Zirconium Diselenide: An Efficient Electrocatalyst for O Reduction Reaction
The development of high-activity and low-price cathodic catalysts to
facilitate the electrochemical sluggish O reduction reaction (ORR) is very
important to achieve the commercial application of fuel cells. Here, we have
investigated the electrocatalytic activity of two-dimensional single-layer
Nb-doped zirconium diselenide (2D Nb-ZrSe) towards ORR by employing the
dispersion corrected Density Functional Theory (DFT-D) method. Through our
study, we computed structural properties, electronic properties, and energetics
of the 2D Nb-ZrSe and ORR intermediates to analyze the electrocatalytic
performance of the 2D Nb-ZrSe. The electronic properties calculations
depict that the 2D monolayer ZrSe has a large band gap of 1.48 eV, which is
not favorable for the ORR mechanism. After the doping of Nb, the electronic
band gap vanishes and 2D Nb-ZrSe acts as a conductor. We studied both the
dissociative and associative pathways through which the ORR can proceed to
reduce the oxygen molecule (O). Our results show that the more favorable
path for O reduction on the surface of the 2D Nb-ZrSe is the 4e
associative path. The detailed ORR mechanisms (both associated and
dissociative) have been explored by computing the changes of Gibbs free energy
({\Delta}G). All the ORR reaction intermediate steps are thermodynamically
stable and energetically favorable. The free energy profile for the associative
path shows the downhill behavior of the free energy vs. the reaction steps,
suggesting that all ORR intermediate structures are catalytically active for
the 4e associative path and a high 4e reduction pathway selectivity.
Therefore, 2D Nb-ZrSe is a promising catalyst for the ORR which can be used
as an alternative ORR catalyst compared with expensive platinum (Pt)
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