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  1. Article ; Online: Engineering microbial chemical factories using metabolic models

    Debolina Sarkar / Costas D. Maranas

    BMC Chemical Engineering, Vol 1, Iss 1, Pp 1-

    2019  Volume 11

    Abstract: Abstract Living organisms in analogy with chemical factories use simple molecules such as sugars to produce a variety of compounds which are necessary for sustaining life and some of which are also commercially valuable. The metabolisms of simple (such ... ...

    Abstract Abstract Living organisms in analogy with chemical factories use simple molecules such as sugars to produce a variety of compounds which are necessary for sustaining life and some of which are also commercially valuable. The metabolisms of simple (such as bacteria) and higher organisms (such as plants) alike can be exploited to convert low value inputs into high value outputs. Unlike conventional chemical factories, microbial production chassis are not necessarily tuned for a single product overproduction. Despite the same end goal, metabolic and industrial engineers rely on different techniques for achieving productivity goals. Metabolic engineers cannot affect reaction rates by manipulating pressure and temperature, instead they have at their disposal a range of enzymes and transcriptional and translational processes to optimize accordingly. In this review, we first highlight how various analytical approaches used in metabolic engineering and synthetic biology are related to concepts developed in systems and control engineering. Specifically, how algorithmic concepts derived in operations research can help explain the structure and organization of metabolic networks. Finally, we consider the future directions and challenges faced by the field of metabolic network modeling and the possible contributions of concepts drawn from the classical fields of chemical and control engineering. The aim of the review is to offer a current perspective of metabolic engineering and all that it entails without requiring specialized knowledge of bioinformatics or systems biology.
    Keywords Systems biology ; Metabolic engineering ; Flux balance analysis ; Control theory ; Chemical engineering ; TP155-156
    Language English
    Publishing date 2019-11-01T00:00:00Z
    Publisher BMC
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: dGPredictor

    Lin Wang / Vikas Upadhyay / Costas D Maranas

    PLoS Computational Biology, Vol 17, Iss 9, p e

    Automated fragmentation method for metabolic reaction free energy prediction and de novo pathway design.

    2021  Volume 1009448

    Abstract: Group contribution (GC) methods are conventionally used in thermodynamics analysis of metabolic pathways to estimate the standard Gibbs energy change (ΔrG'o) of enzymatic reactions from limited experimental measurements. However, these methods are ... ...

    Abstract Group contribution (GC) methods are conventionally used in thermodynamics analysis of metabolic pathways to estimate the standard Gibbs energy change (ΔrG'o) of enzymatic reactions from limited experimental measurements. However, these methods are limited by their dependence on manually curated groups and inability to capture stereochemical information, leading to low reaction coverage. Herein, we introduce an automated molecular fingerprint-based thermodynamic analysis tool called dGPredictor that enables the consideration of stereochemistry within metabolite structures and thus increases reaction coverage. dGPredictor has comparable prediction accuracy compared to existing GC methods and can capture Gibbs energy changes for isomerase and transferase reactions, which exhibit no overall group changes. We also demonstrate dGPredictor's ability to predict the Gibbs energy change for novel reactions and seamless integration within de novo metabolic pathway design tools such as novoStoic for safeguarding against the inclusion of reaction steps with infeasible directionalities. To facilitate easy access to dGPredictor, we developed a graphical user interface to predict the standard Gibbs energy change for reactions at various pH and ionic strengths. The tool allows customized user input of known metabolites as KEGG IDs and novel metabolites as InChI strings (https://github.com/maranasgroup/dGPredictor).
    Keywords Biology (General) ; QH301-705.5
    Subject code 541
    Language English
    Publishing date 2021-09-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: De novo design of high-affinity antibody variable regions (scFv) against the SARS-CoV-2 spike protein

    Veda Sheersh Boorla / Ratul Chowdhury / Costas D. Maranas

    Abstract: AbstractThe emergence of SARS-CoV-2 is responsible for the pandemic of respiratory disease known as COVID-19, which emerged in the city of Wuhan, Hubei province, China in late 2019. Both vaccines and targeted therapeutics for treatment of this disease ... ...

    Abstract AbstractThe emergence of SARS-CoV-2 is responsible for the pandemic of respiratory disease known as COVID-19, which emerged in the city of Wuhan, Hubei province, China in late 2019. Both vaccines and targeted therapeutics for treatment of this disease are currently lacking. Viral entry requires binding of the viral spike receptor binding domain (RBD) with the human angiotensin converting enzyme (ACE2). In an earlier paper1, we report on the specific residue interactions underpinning this event. Here we report on the de novo computational design of high affinity antibody variable regions through the recombination of VDJ genes targeting the most solvent-exposed ACE2-binding residues of the SARS-CoV-2 spike protein using the software tool OptMAVEn-2.02. Subsequently, we carry out computational affinity maturation of the designed prototype variable regions through point mutations for improved binding with the target epitope. Immunogenicity was restricted by preferring designs that match sequences from a 9-mer library of “human string content” (HSC)3. We generated 60 different variable region designs and report in detail on the top five that trade-off the greatest affinity for the spike epitope (quantified using the Rosetta binding energies) with low immunogenicity scores. By grafting these designed variable regions with frameworks, high-affinity monoclonal antibodies can be constructed. Having a potent antibody that can recognize the viral spike protein with high affinity would be enabling for both the design of sensitive SARS-CoV-2 detection devices and for their deployment as neutralizing antibodies.
    Keywords covid19
    Publisher biorxiv
    Document type Article ; Online
    DOI 10.1101/2020.04.09.034868
    Database COVID19

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  4. Article ; Online: Computational biophysical characterization of the SARS-CoV-2 spike protein binding with the ACE2 receptor and implications for infectivity

    Ratul Chowdhury / Veda Sheersh Boorla / Costas D. Maranas

    Computational and Structural Biotechnology Journal, Vol 18, Iss , Pp 2573-

    2020  Volume 2582

    Abstract: SARS-CoV-2 is a novel highly virulent pathogen which gains entry to human cells by binding with the cell surface receptor – angiotensin converting enzyme (ACE2). We computationally contrasted the binding interactions between human ACE2 and coronavirus ... ...

    Abstract SARS-CoV-2 is a novel highly virulent pathogen which gains entry to human cells by binding with the cell surface receptor – angiotensin converting enzyme (ACE2). We computationally contrasted the binding interactions between human ACE2 and coronavirus spike protein receptor binding domain (RBD) of the 2002 epidemic-causing SARS-CoV-1, SARS-CoV-2, and bat coronavirus RaTG13 using the Rosetta energy function. We find that the RBD of the spike protein of SARS-CoV-2 is highly optimized to achieve very strong binding with human ACE2 (hACE2) which is consistent with its enhanced infectivity. SARS-CoV-2 forms the most stable complex with hACE2 compared to SARS-CoV-1 (23% less stable) or RaTG13 (11% less stable). Notably, we calculate that the SARS-CoV-2 RBD lowers the binding strength of angiotensin 2 receptor type I (ATR1) which is the native binding partner of ACE2 by 44.2%. Strong binding is mediated through strong electrostatic attachments with every fourth residue on the N-terminus alpha-helix (starting from Ser19 to Asn53) as the turn of the helix makes these residues solvent accessible. By contrasting the spike protein SARS-CoV-2 Rosetta binding energy with ACE2 of different livestock and pet species we find strongest binding with bat ACE2 followed by human, feline, equine, canine and finally chicken. This is consistent with the hypothesis that bats are the viral origin and reservoir species. These results offer a computational explanation for the increased infection susceptibility by SARS-CoV-2 and allude to therapeutic modalities by identifying and rank-ordering the ACE2 residues involved in binding with the virus.
    Keywords Biophysics ; SARS CoV-2 ; COVID 19 ; ATR1 ; Human ACE2 ; Biotechnology ; TP248.13-248.65 ; covid19
    Subject code 500
    Language English
    Publishing date 2020-01-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article ; Online: EnZymClass

    Deepro Banerjee / Michael A. Jindra / Alec J. Linot / Brian F. Pfleger / Costas D. Maranas

    Current Research in Biotechnology, Vol 4, Iss , Pp 1-

    Substrate specificity prediction tool of plant acyl-ACP thioesterases based on ensemble learning

    2022  Volume 9

    Abstract: Characterizing the functional properties of plant acyl-ACP thioesterases (TEs), a key enzyme class used in the production of renewable oleochemicals in microbial hosts, experimentally, can be an expensive and time consuming process since it requires ... ...

    Abstract Characterizing the functional properties of plant acyl-ACP thioesterases (TEs), a key enzyme class used in the production of renewable oleochemicals in microbial hosts, experimentally, can be an expensive and time consuming process since it requires manual screening of thousands of candidates in a database. Using amino acid sequence to computationally predict an enzyme’s function might accelerate this process; however obtaining the necessary amount of information on previously characterized enzymes and their respective sequences required by standard Machine Learning (ML) based approaches to accurately infer sequence-function relationships can be prohibitive, especially with a low-throughput testing cycle. Experimental noise, unbalanced dataset where high sequence similarity does not always imply identical functional properties will further prevent robust prediction performance. Herein we present a ML method, Ensemble method for enZyme Classification (EnZymClass), that is specifically designed to address these issues. We used EnZymClass to classify TEs into short, long and mixed free fatty acid substrate specificity categories. While general guidelines for inferring substrate specificity have been proposed before, prediction of chain-length preference from primary sequence has remained elusive for plant acyl-ACP TEs. By applying EnZymClass to a subset of TEs in the ThYme database, we identified two medium chain TEs, ClFatB3 and CwFatB2, with previously uncharacterized activity in E. coli fatty acid production hosts.EnZymClass can be readily applied to other protein classification challenges and is available at: https://github.com/deeprob/ThioesteraseEnzymeSpecificity.
    Keywords Thioesterase ; Enzyme classification ; Machine learning ; Substrate specificity ; Medium-chain oleochemicals ; Synthetic biology ; Biotechnology ; TP248.13-248.65
    Subject code 540
    Language English
    Publishing date 2022-01-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article ; Online: A genome-scale Escherichia coli kinetic metabolic model k-ecoli457 satisfying flux data for multiple mutant strains

    Ali Khodayari / Costas D. Maranas

    Nature Communications, Vol 7, Iss 1, Pp 1-

    2016  Volume 12

    Abstract: Kinetic models of microbial metabolism have great potential to aid metabolic engineering efforts, but the challenge of parameterization has so far limited them to core metabolism. Here, the authors introduce a genome-scale metabolic model of E. ... ...

    Abstract Kinetic models of microbial metabolism have great potential to aid metabolic engineering efforts, but the challenge of parameterization has so far limited them to core metabolism. Here, the authors introduce a genome-scale metabolic model of E. colimetabolism that satisfies fluxomic data for a wild-type and 25 mutant strains in various growth conditions.
    Keywords Science ; Q
    Language English
    Publishing date 2016-12-01T00:00:00Z
    Publisher Nature Portfolio
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Article ; Online: Achieving Metabolic Flux Analysis for S. cerevisiae at a Genome-Scale

    Saratram Gopalakrishnan / Costas D. Maranas

    Metabolites, Vol 5, Iss 3, Pp 521-

    Challenges, Requirements, and Considerations

    2015  Volume 535

    Abstract: Recent advances in 13C-Metabolic flux analysis (13C-MFA) have increased its capability to accurately resolve fluxes using a genome-scale model with narrow confidence intervals without pre-judging the activity or inactivity of alternate metabolic pathways. ...

    Abstract Recent advances in 13C-Metabolic flux analysis (13C-MFA) have increased its capability to accurately resolve fluxes using a genome-scale model with narrow confidence intervals without pre-judging the activity or inactivity of alternate metabolic pathways. However, the necessary precautions, computational challenges, and minimum data requirements for successful analysis remain poorly established. This review aims to establish the necessary guidelines for performing 13C-MFA at the genome-scale for a compartmentalized eukaryotic system such as yeast in terms of model and data requirements, while addressing key issues such as statistical analysis and network complexity. We describe the various approaches used to simplify the genome-scale model in the absence of sufficient experimental flux measurements, the availability and generation of reaction atom mapping information, and the experimental flux and metabolite labeling distribution measurements to ensure statistical validity of the obtained flux distribution. Organism-specific challenges such as the impact of compartmentalization of metabolism, variability of biomass composition, and the cell-cycle dependence of metabolism are discussed. Identification of errors arising from incorrect gene annotation and suggested alternate routes using MFA are also highlighted.
    Keywords genome-scale MFA ; S. cerevisiae ; challenges ; requirements ; considerations ; Biochemistry ; QD415-436 ; Organic chemistry ; QD241-441 ; Chemistry ; QD1-999 ; Science ; Q
    Language English
    Publishing date 2015-09-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article ; Online: Pareto Optimality Explanation of the Glycolytic Alternatives in Nature

    Chiam Yu Ng / Lin Wang / Anupam Chowdhury / Costas D. Maranas

    Scientific Reports, Vol 9, Iss 1, Pp 1-

    2019  Volume 15

    Abstract: Abstract The Entner-Doudoroff (ED) and Embden-Meyerhof-Parnas (EMP) glycolytic pathways are largely conserved across glycolytic species in nature. Is this a coincidence, convergent evolution or there exists a driving force towards either of the two ... ...

    Abstract Abstract The Entner-Doudoroff (ED) and Embden-Meyerhof-Parnas (EMP) glycolytic pathways are largely conserved across glycolytic species in nature. Is this a coincidence, convergent evolution or there exists a driving force towards either of the two pathway designs? We addressed this question by first employing a variant of the optStoic algorithm to exhaustively identify over 11,916 possible routes between glucose and pyruvate at different pre-determined stoichiometric yields of ATP. Subsequently, we analyzed the thermodynamic feasibility of all the pathways at physiological metabolite concentrations and quantified the protein cost of the feasible solutions. Pareto optimality analysis between energy efficiency and protein cost reveals that the naturally evolved ED and EMP pathways are indeed among the most protein cost-efficient pathways in their respective ATP yield categories and remain thermodynamically feasible across a wide range of ATP/ADP ratios and pathway intermediate metabolite concentration ranges. In contrast, pathways with higher ATP yield (>2) while feasible, are bound within stringent and often extreme operability ranges of cofactor and intermediate metabolite concentrations. The preponderance of EMP and ED is thus consistent with not only optimally balancing energy yield vs. enzyme cost but also with ensuring operability for wide metabolite concentration ranges and ATP/ADP ratios.
    Keywords Medicine ; R ; Science ; Q
    Subject code 570
    Language English
    Publishing date 2019-02-01T00:00:00Z
    Publisher Nature Publishing Group
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Article ; Online: Pathway design using de novo steps through uncharted biochemical spaces

    Akhil Kumar / Lin Wang / Chiam Yu Ng / Costas D. Maranas

    Nature Communications, Vol 9, Iss 1, Pp 1-

    2018  Volume 15

    Abstract: Existing pathway design tools make use of existing reactions from databases or successively apply retrosynthetic rules. novoStoic provides an integrated optimization-based framework combining known reactions with novel steps in pathway design allowing ... ...

    Abstract Existing pathway design tools make use of existing reactions from databases or successively apply retrosynthetic rules. novoStoic provides an integrated optimization-based framework combining known reactions with novel steps in pathway design allowing for constraints on thermodynamic feasibility, product yield, pathway length and number of novel steps.
    Keywords Science ; Q
    Language English
    Publishing date 2018-01-01T00:00:00Z
    Publisher Nature Portfolio
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Article ; Online: Pathway design using de novo steps through uncharted biochemical spaces

    Akhil Kumar / Lin Wang / Chiam Yu Ng / Costas D. Maranas

    Nature Communications, Vol 9, Iss 1, Pp 1-

    2018  Volume 15

    Abstract: Existing pathway design tools make use of existing reactions from databases or successively apply retrosynthetic rules. novoStoic provides an integrated optimization-based framework combining known reactions with novel steps in pathway design allowing ... ...

    Abstract Existing pathway design tools make use of existing reactions from databases or successively apply retrosynthetic rules. novoStoic provides an integrated optimization-based framework combining known reactions with novel steps in pathway design allowing for constraints on thermodynamic feasibility, product yield, pathway length and number of novel steps.
    Keywords Science ; Q
    Language English
    Publishing date 2018-01-01T00:00:00Z
    Publisher Nature Publishing Group
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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