Computational Evidence for Kinetically Controlled Radical Coupling during Lignification

© 2019 American Chemical Society. Lignin is an alkyl-aromatic biopolymer that, despite its abundance, is underutilized as a renewable feedstock because of its highly complex structure. An approach to overcome this challenge that has gained prominence in recent years leverages the plasticity and malleability of lignin biosynthesis to tune lignin structure in planta through genetic approaches. An improved understanding of lignin biosynthesis can thus provide fundamental insights critical for the development of effective tailoring and valorization strategies. Although it is widely accepted that lignin monomers and growing chains are oxidized enzymatically into radicals that then undergo kinetically controlled coupling in planta, direct experimental evidence has been scarce because of the difficulty of exactly replicating in planta lignification conditions. Here, we computationally investigate a set of radical reactions representative of lignin biosynthesis. We show that, contrary to the notion that radical coupling reactions are usually barrierless and dynamically controlled, the computed activation energies can be qualitatively consistent with key structural observations made empirically for native lignin in a variety of biomass types. We also rationalize the origins of regioselectivity in coupling reactions through structural and activation strain analyses. Our findings lay the groundwork for first-principles lignin structural models and more detailed multiscale simulations of the lignification process

Lignin-KMC: A Toolkit for Simulating Lignin Biosynthesis

© 2019 American Chemical Society. Lignin is an abundant biopolymer of phenylpropanoid monomers that is critical for plant structure and function. Based on the abundance of lignin in the biosphere and interest in lignin valorization, a more comprehensive understanding of lignin biosynthesis is imperative. Here, we present an open-source software toolkit, Lignin-KMC, that combines kinetic Monte Carlo and first-principles calculations of radical coupling events to model lignin biosynthesis in silico. Lignification is simulated using the Gillespie algorithm with rates derived from density functional theory calculations of individual fragment couplings. Using this approach, we confirm experimental findings regarding the impact of lignification conditions on the polymer structure such as (1) the positive correlation between sinapyl alcohol fraction and depolymerization yield and (2) the primarily benzodioxane linked structure of C-lignin. Additionally, we identify the in planta monolignol supply rate as a possible control mechanism for lignin biosynthesis based on evolutionary stresses. These examples not only highlight the robustness of our modeling framework but also motivate future studies of new lignin types, unexplored monolignol chemistries, and lignin structure predictions, all with an overarching aim of developing a more comprehensive molecular understanding of native lignin, which, in turn, can advance the biological and chemistry communities interested in this important biopolymer

Thermodynamic Modeling of Several Aqueous Alkanol Solutions Containing Amino Acids with the Perturbed-Chain Statistical Associated Fluid Theory Equation of State

The perturbed-chain statistical associated fluid theory EoS was applied to model the solubilities of glycine, DL-alanine, L-serine, L-threonine, and L-isoleucine in pure water, pure alcohols (ethanol, 1-propanol, and 2-propanol) and in mixed solvent systems. Three pure component nonassociating parameters for the amino acids were fitted to the densities, activity and osmotic coefficients, vapor pressures, and water activity of their aqueous solutions. The solubilities of amino acids in pure and mixed solvent systems were calculated on the basis of the phase equilibrium conditions for a pure solid and a fluid phase. The hypothetical melting properties of each amino acid were fitted, to accurately correlate the solubilities in pure water. Only one temperature independent binary parameter is required for each amino acid/solvent pair. The model can accurately describe the solubility of the amino acids in water, but the correlation for the solubility in pure alcohols was not so satisfactory. The solubility in mixed solvents (ternary systems) was predicted on the basis of the modeling of the solubility in pure solvents, without any additional fitting of the parameters, and the results achieved were reasonable. Fitting the binary parameter for the pair amino acid/alcohol not to the solubility in pure alcohol, but to the solubility in the mixed solvent system, the description of the solubility in the mixed solvent systems was clearly improved and the results were in fair agreement with the experimental data for all mixture compositions. The results showed a global root-mean-square deviation in mole fraction of 0.0032 for correlation and 0.0070 for prediction