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Synthetic Microbial Consortium for Biological Breakdown and Conversion of Lignin

Sale, Kenneth L.; Rodriguez Ruiz, Jose A.; Light, Yooli K.; Tran-Gyamfi, Mary B.; Hirakawa, Matthew H.; George, Anthe G.; Geiselman, Gina M.; Martinez, Salvador M.

The plant polymer lignin is the most abundant renewable source of aromatics on the planet and conversion of it to valuable fuels and chemicals is critical to the economic viability of a lignocellulosic biofuels industry and to meeting the DOE’s 2022 goal of $\$2.50$/gallon mean biofuel selling price. Presently, there is no efficient way of converting lignin into valuable commodities. Current biological approaches require mixtures of expensive ligninolytic enzymes and engineered microbes. This project was aimed at circumventing these problems by discovering commensal relationships among fungi and bacteria involved in biological lignin utilization and using this knowledge to engineer microbial communities capable of converting lignin into renewable fuels and chemicals. Essentially, we aimed to learn from, mimic and improve on nature. We discovered fungi that synergistically work together to degrade lignin, engineered fungal systems to increase expression of the required enzymes and engineered organisms to produce products such as biodegradable plastics precursors.

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Depolymerization of lignin for biological conversion through sulfonation and a chelator-mediated Fenton reaction

Green Chemistry

Martinez, Daniella V.; Sale, Kenneth L.; Simmons, Blake A.; Sale, Kenneth L.; Simmons, Blake A.; Singer, Steven W.; Martinez, Daniella V.; Rodriguez Ruiz, Jose A.; Juarros, Miranda A.; Martinez, Estevan J.; Alam, Todd M.; Sale, Kenneth L.; Kent, Michael S.

Generating value from lignin through depolymerization and biological conversion to valuable fuels, chemicals, or intermediates has great promise but is limited by several factors including lack of cost-effective depolymerization methods, toxicity within the breakdown products, and low bioconversion of the breakdown products. High yield depolymerization of natural lignins requires cleaving carbon-carbon bonds in addition to ether bonds. To address that need, we report that a chelator-mediated Fenton reaction can efficiently cleave C-C bonds in sulfonated polymers at or near room temperature, and that unwanted repolymerization can be minimized through optimizing reaction conditions. This method was used to depolymerize lignosulfonate from Mw = 28 000 g mol−1 to Mw = 800 g mol−1. The breakdown products were characterized by SEC, FTIR and NMR and evaluated for bioavailability. The breakdown products are rich in acid, aldehyde, and alcohol functionalities but are largely devoid of aromatics and aliphatic dienes. A panel of nine organisms were tested for the ability to grow on the breakdown products. Growth at a low level was observed for several monocultures on the depolymerized lignosulfonate in the absence of glucose. Much stronger growth was observed in the presence of 0.2% glucose and for one organism we demonstrate doubling of melanin production in the presence of depolymerized lignosulfonate. The results suggest that this chelator-mediated Fenton method is a promising new approach for biological conversion of lignin into higher value chemicals or intermediates.

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A multiplexed nanostructure-initiator mass spectrometry (NIMS) assay for simultaneously detecting glycosyl hydrolase and lignin modifying enzyme activities

Scientific Reports

Ing, Nicole; Deng, Kai; Chen, Yan; Aulitto, Martina; Gin, Jennifer W.; Pham, Thanh L.; Petzold, Christopher J.; Singer, Steve W.; Bowen, Benjamin; Sale, Kenneth L.; Simmons, Blake A.; Singh, Anup K.; Adams, Paul D.; Northen, Trent R.

Lignocellulosic biomass is composed of three major biopolymers: cellulose, hemicellulose and lignin. Analytical tools capable of quickly detecting both glycan and lignin deconstruction are needed to support the development and characterization of efficient enzymes/enzyme cocktails. Previously we have described nanostructure-initiator mass spectrometry-based assays for the analysis of glycosyl hydrolase and most recently an assay for lignin modifying enzymes. Here we integrate these two assays into a single multiplexed assay against both classes of enzymes and use it to characterize crude commercial enzyme mixtures. Application of our multiplexed platform based on nanostructure-initiator mass spectrometry enabled us to characterize crude mixtures of laccase enzymes from fungi Agaricus bisporus (Ab) and Myceliopthora thermophila (Mt) revealing activity on both carbohydrate and aromatic substrates. Using time-series analysis we determined that crude laccase from Ab has the higher GH activity and that laccase from Mt has the higher activity against our lignin model compound. Inhibitor studies showed a significant reduction in Mt GH activity under low oxygen conditions and increased activities in the presence of vanillin (common GH inhibitor). Ultimately, this assay can help to discover mixtures of enzymes that could be incorporated into biomass pretreatments to deconstruct diverse components of lignocellulosic biomass.

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High Throughput expression and characterization of laccases in Saccharomyces cerevisiae

Wolski, Paul W.; Lopes, Alberto L.; Deng, Kai; Simmons, Blake A.; Mukhopadhyay, Aindrila M.; Singer, Steven W.; Sale, Kenneth L.

Laccases are oxidative enzymes containing 4 conserved copper heteroatoms. Laccases catalyze cleavage of bonds in lignin using radical chemistry, yet their exact specificity for bonds (such as the β-O-4 or C-C) in lignin remains unknown and may vary with the diversity of laccases across fungi, plants and bacteria. Bond specificity may perhaps even vary for the same enzyme across different reaction conditions. Determining these differences has been difficult due to the fact that heterologous expression of soluble, active laccases has proven difficult. Here we describe the successful heterologous expression of functional laccases in two strains of Saccharomyces cerevisiae, including one we genetically modified with CRISPR. We phylogenically map the enzymes that we successfully expressed, compared to those that did not express. We also describe differences protein sequence differences and pH and temperature profiles and their ability to functionally express, leading to a potential future screening platform for directed evolution of laccases and other ligninolytic enzymes such as peroxidases.

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High Throughput expression and characterization of laccases in Saccharomyces cerevisiae [Poster]

Wolski, Paul W.; Lopes, Alberto L.; Deng, Kai; Simmons, Blake A.; Mukhopadhyay, Aindrila M.; Singer, Steven W.; Sale, Kenneth L.

We are working to generate fundamental understanding of enzymatic depolymerization of lignin and using this understanding to engineer mixtures of enzymes that catalyze the reactions necessary to efficiently depolymerize lignin into defined fragments. Over the years the enzymes involved in these processes have been difficult to study, because 1) the enzymes thought to be most important, fungal laccases and peroxidases, are very difficult to express in soluble, active form; 2) the full complement of required enzymes and whether or not they act synergistically is not known; 3) analysis of bond cleavage events is difficult due to the lack of analytical tools for measuring bond cleavage events in either polymeric lignin or model lignin-like compounds.

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Longitudinal Analysis of Microbiota in Microalga Nannochloropsis salina Cultures

Microbial Ecology

Geng, Haifeng G.; Sale, Kenneth L.; Tran-Gyamfi, Mary B.; Lane, Todd L.; Yu, Eizadora T.

Large-scale open microalgae cultivation has tremendous potential to make a significant contribution to replacing petroleum-based fuels with biofuels. Open algal cultures are unavoidably inhabited with a diversity of microbes that live on, influence, and shape the fate of these ecosystems. However, there is little understanding of the resilience and stability of the microbial communities in engineered semicontinuous algal systems. To evaluate the dynamics and resilience of the microbial communities in microalgae biofuel cultures, we conducted a longitudinal study on open systems to compare the temporal profiles of the microbiota from two multigenerational algal cohorts, which include one seeded with the microbiota from an in-house culture and the other exogenously seeded with a natural-occurring consortia of bacterial species harvested from the Pacific Ocean. From these month-long, semicontinuous open microalga Nannochloropsis salina cultures, we sequenced a time-series of 46 samples, yielding 8804 operational taxonomic units derived from 9,160,076 high-quality partial 16S rRNA sequences. We provide quantitative evidence that clearly illustrates the development of microbial community is associated with microbiota ancestry. In addition, N. salina growth phases were linked with distinct changes in microbial phylotypes. Alteromonadeles dominated the community in the N. salina exponential phase whereas Alphaproteobacteria and Flavobacteriia were more prevalent in the stationary phase. We also demonstrate that the N. salina-associated microbial community in open cultures is diverse, resilient, and dynamic in response to environmental perturbations. This knowledge has general implications for developing and testing design principles of cultivated algal systems.

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MOF-Based Catalysts for Selective Hydrogenolysis of Carbon-Oxygen Ether Bonds

ACS Catalysis

Stavila, Vitalie S.; Ramakrishnan, Parthasarathi R.; Davis, Ryan W.; El Gabaly, Farid; Sale, Kenneth L.; Simmons, Blake S.; Singh, Seema S.; Allendorf, Mark D.

We demonstrate that metal-organic frameworks (MOFs) can catalyze hydrogenolysis of aryl ether bonds under mild conditions. Mg-IRMOF-74(I) and Mg-IRMOF-74(II) are stable under reducing conditions and can cleave phenyl ethers containing β-O-4, α-O-4, and 4-O-5 linkages to the corresponding hydrocarbons and phenols. Reaction occurs at 10 bar H2 and 120 °C without added base. DFT-optimized structures and charge transfer analysis suggest that the MOF orients the substrate near Mg2+ ions on the pore walls. Ti and Ni doping further increase conversions to as high as 82% with 96% selectivity for hydrogenolysis versus ring hydrogenation. Repeated cycling induces no loss of activity, making this a promising route for mild aryl-ether bond scission.

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Improvements of biomass deconstruction enzymes

Sale, Kenneth L.

Sandia National Laboratories and DSM Innovation, Inc. collaborated on the investigation of the structure and function of cellulases from thermophilic fungi. Sandia's role was to use its expertise in protein structure determination and X-ray crystallography to solve the structure of these enzymes in their native state and in their substrate and product bound states. Sandia was also tasked to work with DSM to use the newly solved structure to, using computational approaches, analyze enzyme interactions with both bound substrate and bound product; the goal being to develop approaches for rationally designing improved cellulases for biomass deconstruction. We solved the structures of five cellulases from thermophilic fungi. Several of these were also solved with bound substrate/product, which allowed us to predict mutations that might enhance activity and stability.

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Initiation of the TLR4 signal transduction network : deeper understanding for better therapeutics

Kent, Michael S.; Branda, Steven B.; Hayden, Carl C.; Sasaki, Darryl Y.; Sale, Kenneth L.

The innate immune system represents our first line of defense against microbial pathogens, and in many cases is activated by recognition of pathogen cellular components (dsRNA, flagella, LPS, etc.) by cell surface membrane proteins known as toll-like receptors (TLRs). As the initial trigger for innate immune response activation, TLRs also represent a means by which we can effectively control or modulate inflammatory responses. This proposal focused on TLR4, which is the cell-surface receptor primarily responsible for initiating the innate immune response to lipopolysaccharide (LPS), a major component of the outer membrane envelope of gram-negative bacteria. The goal was to better understand TLR4 activation and associated membrane proximal events, in order to enhance the design of small molecule therapeutics to modulate immune activation. Our approach was to reconstitute the receptor in biomimetic systems in-vitro to allow study of the structure and dynamics with biophysical methods. Structural studies were initiated in the first year but were halted after the crystal structure of the dimerized receptor was published early in the second year of the program. Methods were developed to determine the association constant for oligomerization of the soluble receptor. LPS-induced oligomerization was observed to be a strong function of buffer conditions. In 20 mM Tris pH 8.0 with 200 mM NaCl, the onset of receptor oligomerization occurred at 0.2 uM TLR4/MD2 with E coli LPS Ra mutant in excess. However, in the presence of 0.5 uM CD14 and 0.5 uM LBP, the onset of receptor oligomerization was observed to be less than 10 nM TLR4/MD2. Several methods were pursued to study LPS-induced oligomerization of the membrane-bound receptor, including CryoEM, FRET, colocalization and codiffusion followed by TIRF, and fluorescence correlation spectroscopy. However, there approaches met with only limited success.

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Disparate data fusion for protein phosphorylation prediction

Annals of Operations Research

Gray, Genetha A.; Williams, Pamela J.; Brown, W.M.; Faulon, Jean-Loup M.; Sale, Kenneth L.

New challenges in knowledge extraction include interpreting and classifying data sets while simultaneously considering related information to confirm results or identify false positives. We discuss a data fusion algorithmic framework targeted at this problem. It includes separate base classifiers for each data type and a fusion method for combining the individual classifiers. The fusion method is an extension of current ensemble classification techniques and has the advantage of allowing data to remain in heterogeneous databases. In this paper, we focus on the applicability of such a framework to the protein phosphorylation prediction problem. © Springer Science+Business Media, LLC 2008.

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Genome scale enzyme - Metabolite and drug - Target interaction predictions using the signature molecular descriptor

Bioinformatics

Faulon, Jean-Loup M.; Misra, Milind; Martin, Shawn; Sale, Kenneth L.; Sapra, Rajat

Motivation: Identifying protein enzymatic or pharmacological activities are important areas of research in biology and chemistry. Biological and chemical databases are increasingly being populated with linkages between protein sequences and chemical structures. There is now sufficient information to apply machine - learning techniques to predict interactions between chemicals and proteins at a genome scale. Current machine-learning techniques use as input either protein sequences and structures or chemical information. We propose here a method to infer protein - chemical interactions using heterogeneous input consisting of both protein sequence and chemical information. Results: Our method relies on expressing proteins and chemicals with a common cheminformatics representation. We demonstrate our approach by predicting whether proteins can catalyze reactions not present in training sets. We also predict whether a given drug can bind a target, in the absence of prior binding information for that drug and target. Such predictions cannot be made with current machine - learning techniques requiring binding information for individual reactions or individual targets. © 2007 The Author(s).

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Developing algorithms for predicting protein-protein interactions of homology modeled proteins

Roe, Diana C.; Sale, Kenneth L.; Faulon, Jean-Loup M.

The goal of this project was to examine the protein-protein docking problem, especially as it relates to homology-based structures, identify the key bottlenecks in current software tools, and evaluate and prototype new algorithms that may be developed to improve these bottlenecks. This report describes the current challenges in the protein-protein docking problem: correctly predicting the binding site for the protein-protein interaction and correctly placing the sidechains. Two different and complementary approaches are taken that can help with the protein-protein docking problem. The first approach is to predict interaction sites prior to docking, and uses bioinformatics studies of protein-protein interactions to predict theses interaction site. The second approach is to improve validation of predicted complexes after docking, and uses an improved scoring function for evaluating proposed docked poses, incorporating a solvation term. This scoring function demonstrates significant improvement over current state-of-the art functions. Initial studies on both these approaches are promising, and argue for full development of these algorithms.

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Chemical crosslinking and mass spectrometry studies of the structure and dynamics of membrane proteins and receptors

Schoeniger, Joseph S.; Ayson, Marites J.; Jacobsen, Rick B.; Lane, Pamela L.; Sale, Kenneth L.; Young, Malin M.

Membrane proteins make up a diverse and important subset of proteins for which structural information is limited. In this study, chemical cross-linking and mass spectrometry were used to explore the structure of the G-protein-coupled photoreceptor bovine rhodopsin in the dark-state conformation. All experiments were performed in rod outer segment membranes using amino acid 'handles' in the native protein sequence and thus minimizing perturbations to the native protein structure. Cysteine and lysine residues were covalently cross-linked using commercially available reagents with a range of linker arm lengths. Following chemical digestion of cross-linked protein, cross-linked peptides were identified by accurate mass measurement using liquid chromatography-fourier transform mass spectrometry and an automated data analysis pipeline. Assignments were confirmed and, if necessary, resolved, by tandem MS. The relative reactivity of lysine residues participating in cross-links was evaluated by labeling with NHS-esters. A distinct pattern of cross-link formation within the C-terminal domain, and between loop I and the C-terminal domain, emerged. Theoretical distances based on cross-linking were compared to inter-atomic distances determined from the energy-minimized X-ray crystal structure and Monte Carlo conformational search procedures. In general, the observed cross-links can be explained by re-positioning participating side-chains without significantly altering backbone structure. One exception, between C3 16 and K325, requires backbone motion to bring the reactive atoms into sufficient proximity for cross-linking. Evidence from other studies suggests that residues around K325 for a region of high backbone mobility. These findings show that cross-linking studies can provide insight into the structural dynamics of membrane proteins in their native environment.

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A deterministic algorithm for constrained enumeration of transmembrane protein folds

Faulon, Jean-Loup M.; Sale, Kenneth L.; Schoeniger, Joseph S.; Young, Malin M.

A deterministic algorithm for enumeration of transmembrane protein folds is presented. Using a set of sparse pairwise atomic distance constraints (such as those obtained from chemical cross-linking, FRET, or dipolar EPR experiments), the algorithm performs an exhaustive search of secondary structure element packing conformations distributed throughout the entire conformational space. The end result is a set of distinct protein conformations, which can be scored and refined as part of a process designed for computational elucidation of transmembrane protein structures.

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49 Results
49 Results