Additional file 1 of LigninGraphs: lignin structure determination with multiscale graph modeling
Creators: name: Wang, Yifan
name: Kalscheur, Jake
name: Ebikade, Elvis
name: Li, Qiang
name: Vlachos, Dionisios G.
Publisher: name: figshare
Year: 2022
Subjects: subject: Biophysics
subject: Medicine
subject: Genetics
subject: FOS: Biological sciences
subject: Biological Sciences not elsewhere classified
subject: Information Systems not elsewhere classified
subject: Cancer
subject: Plant Biology

description: Additional file 1. (1) Software design of LigninGraphs, including the key modules and functions, (2) Details of the Metropolis Monte Carlo-based optimization algorithm, (3) Optimal structure metrics and simulated libraries for pine, poplar, and miscanthus lignin, (4) Computational performance. Figure S1. Units in big graphs where each monomer is a node and each linkage is an edge. In this example, both nodes 0 and 1 are H monomers, and the edge (0,1) represents a beta-O-4 linkage between the two. Figure S2. BigSMILES notation1 for a hypothetical linear lignin structure with only G units and β-O-4 linkages. The corresponding atomic indices, as shown in Figure 2a, are marked for each C/O atom. In this case, the bonding descriptor ([$]) indicates that the 4th or the 8th C can be connected to any other atom with the same bonding descriptor. Such linkages can be written as G-(4,8)-G or G-(8,4)-G in the M1-(C1,C2)-M2 format. Following smiles notation, the lower case ‘c’ indicates aromatic ring carbons, and capital case ‘C’ means non-aromatic carbons. Figure S3. LigninGraphs software design. (a) High-level workflow and (b) main modules, classes, functions (marked in bold), and their usages. Figure S4. Hierarchical optimization scheme in LigninGraphs. Table S1. Proposed structure additions, stopping criteria, and return values in each optimization loop. Figure S5. Illustration of new monomer addition via random sampling. For instance, the experimental monomer distributions are 0, 0.37, and 0.63 for H, G, S; the linkage distributions are 0, 0, 0.78, 0, 0.07, and 0.15 for 4-O-5, 5-5, α-O-4, β-O-4, β -5, β-β; the branching propensity is 0.25. After random sampling for each property, with random numbers r ∈ [0,1] shown in dials, a new G monomer is added via a β-O-4 to a terminal monomer to avoid branching. Table S2. List of hyperparameters in the multiscale optimization framework. Figure S6. Poplar lignin structure metrics for the simulated optima and target values. Monomer percentages: a, H. b, G. c, S. Linkage distributions: d, 4-O-5. e, α-O-4. f, β-O-4. g, 5-5. h, β-5. i, β-β. The population's means (µ) and standard deviations (σ) are shown at the top of each subfigure. Figure S7. Miscanthus lignin structure metrics for the simulated optima and target values. Monomer percentages: a, H. b, G. c, S; Linkage distributions: d, 4-O-5. e, α-O-4. f, β-O-4. g, 5-5. h, β-5. i, β-β. The population's means (µ) and standard deviations (σ) are shown at the top of each subfigure. Figure S8. Correlations between (a) branching propensity, (b) 5-5 linkage percentage, (c) β-O-4 linkage percentage, and (d) β- β simulated branching coefficient for pine and poplar simulations. The values of Pearson correlation coefficients ( $${r}_{pearson}$$ r pearson ) are indicated. Values close to 1, -1, and 0 suggest a strong positive, negative, and weak correlation, respectively. Table S3. Example structure library for pine lignin. The counts of monomers, linkages and functional groups, branching coefficient, and the smiles strings of the structures are shown. Table S4. Example structure library for poplar lignin. The counts of monomers, linkages and functional groups, branching coefficient, and the smiles strings of the structures are shown. Table S5. Example structure library for miscanthus lignin. The counts of monomers, linkages and functional groups, branching coefficient, and the smiles strings of the structures are shown. Figure S9. Computational time complexity of polymerization. For LigninGraphs, blue dots indicate the measured mean CPU execution time for a simulation at a specific polymer size; the blue error bars indicate the 95% confidence interval of the CPU time, averaged over five runs. The blue and orange dashed lines are the power-law fits of the polymerization time complexity for LigninGraphs and Lignin-KMC, respectively. Figure S10. Example distance trajectories in the Metropolis Monte Carlo simulations for (a) a structure population and (b) a single polymer. Table S6. Acceptance ratios, total Monte Carlo (MC) attempts, and runtime for various lignin feedstocks.