4.6 Article

Lignin-KMC: A Toolkit for Simulating Lignin Biosynthesis

Journal

ACS SUSTAINABLE CHEMISTRY & ENGINEERING
Volume 7, Issue 22, Pages 18313-18322

Publisher

AMER CHEMICAL SOC
DOI: 10.1021/acssuschemeng.9b03534

Keywords

lignin biosynthesis; lignin structure; polymerization; radical coupling; lignin polymerization; kinetic Monte Carlo

Funding

  1. National Science Foundation, CBET Award [1454299]
  2. Center for Bioenergy Innovation, a U.S. Department of Energy Research Center - Office of Biological and Environmental Research in the DOE Office of Science
  3. U.S. Department of Energy (DOE) [DE-AC36-08GO28308]
  4. National Science Foundation [ACI-1548562, TG-MCB090159, ACI-1053575]
  5. National Science Foundation Graduate Research Fellowship Program
  6. National Science Foundation Graduate Research Fellowship [1122374]
  7. Div Of Chem, Bioeng, Env, & Transp Sys
  8. Directorate For Engineering [1454299] Funding Source: National Science Foundation

Ask authors/readers for more resources

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.

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