Browsing by Author "Buchholz, Patrick C. F."

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Open Access
Data integration and data mining for the exploration of enzymatic sequence-structure-function relationships
(2018) Buchholz, Patrick C. F.; Pleiss, Jürgen (Prof. Dr.)
Open Access
Expansin Engineering Database : a navigation and classification tool for expansins and homologues
(2020) Lohoff, Caroline; Buchholz, Patrick C. F.; Le Roes‐Hill, Marilize; Pleiss, Jürgen
Expansins have the remarkable ability to loosen plant cell walls and cellulose material without showing catalytic activity and therefore have potential applications in biomass degradation. To support the study of sequence‐structure‐function relationships and the search for novel expansins, the Expansin Engineering Database (ExED, https://exed.biocatnet.de) collected sequence and structure data on expansins from Bacteria, Fungi, and Viridiplantae, and expansin‐like homologues such as carbohydrate binding modules, glycoside hydrolases, loosenins, swollenins, cerato‐platanins, and EXPNs. Based on global sequence alignment and protein sequence network analysis, the sequences are highly diverse. However, many similarities were found between the expansin domains. Newly created profile hidden Markov models of the two expansin domains enable standard numbering schemes, comprehensive conservation analyses, and genome annotation. Conserved key amino acids in the expansin domains were identified, a refined classification of expansins and carbohydrate binding modules was proposed, and new sequence motifs facilitate the search of novel candidate genes and the engineering of expansins.
Open Access
Modeling of biocatalytic reactions: a workflow for model calibration, selection, and validation using Bayesian statistics
(2019) Eisenkolb, Ina; Jensch, Antje; Eisenkolb, Kerstin; Kramer, Andrei; Buchholz, Patrick C. F.; Pleiss, Jürgen; Spiess, Antje; Radde, Nicole
We present a workflow for kinetic modeling of biocatalytic reactions which combines methods from Bayesian learning and uncertainty quantification for model calibration, model selection, evaluation, and model reduction in a consistent statistical frame-work. Our workflow is particularly tailored to sparse data settings in which a considerable variability of the parameters remains after the models have been adapted to available data, a ubiquitous problem in many real-world applications. Our workflow is exemplified on an enzyme-catalyzed two-substrate reaction mechanism describing the symmetric carboligation of 3,5-dimethoxy-benzaldehyde to (R)-3,3',5,5'-tetramethoxybenzoin catalyzed by benzaldehyde lyase from Pseudomonas fluorescens. Results indicate a substrate-dependent inactivation of enzyme, which is in accordance with other recent studies.
Open Access
Multicopper oxidases : modular structure, sequence space, and evolutionary relationships
(2020) Gräff, Maike; Buchholz, Patrick C. F.; Le Roes‐Hill, Marilize; Pleiss, Jürgen
Multicopper oxidases (MCOs) use copper ions as cofactors to oxidize a variety of substrates while reducing oxygen to water. MCOs have been identified in various taxa, with notable occurrences in fungi. The role of these fungal MCOs in lignin degradation sparked an interest due to their potential for application in biofuel production and various other industries. MCOs consist of different protein domains, which led to their classification into two‐, three‐, and six‐domain MCOs. The previously established Laccase and Multicopper Oxidase Engineering Database (https://lcced.biocatnet.de) was updated and now includes 51 058 sequences and 229 structures of MCOs. Sequences and structures of all MCOs were systematically compared. All MCOs consist of cupredoxin‐like domains. Two‐domain MCOs are formed by the N‐ and C‐terminal domain (domain N and C), while three‐domain MCOs have an additional domain (M) in between, homologous to domain C. The six‐domain MCOs consist of alternating domains N and C, each three times. Two standard numbering schemes were developed for the copper‐binding domains N and C, which facilitated the identification of conserved positions and a comparison to previously reported results from mutagenesis studies. Two sequence motifs for the copper binding sites were identified per domain. Their modularity, depending on the placement of the T1‐copper binding site, was demonstrated. Protein sequence networks showed relationships between two‐ and three‐domain MCOs, allowing for family‐specific annotation and inference of evolutionary relationships.
Open Access
Plastics degradation by hydrolytic enzymes : the Plastics-Active Enzymes Database - PAZy
(2022) Buchholz, Patrick C. F.; Feuerriegel, Golo; Zhang, Hongli; Perez‐Garcia, Pablo; Nover, Lena‐Luisa; Chow, Jennifer; Streit, Wolfgang R.; Pleiss, Jürgen
Petroleum‐based plastics are durable and accumulate in all ecological niches. Knowledge on enzymatic degradation is sparse. Today, less than 50 verified plastics‐active enzymes are known. First examples of enzymes acting on the polymers polyethylene terephthalate (PET) and polyurethane (PUR) have been reported together with a detailed biochemical and structural description. Furthermore, very few polyamide (PA) oligomer active enzymes are known. In this article, the current known enzymes acting on the synthetic polymers PET and PUR are briefly summarized, their published activity data were collected and integrated into a comprehensive open access database. The Plastics‐Active Enzymes Database (PAZy) represents an inventory of known and experimentally verified enzymes that act on synthetic fossil fuel‐based polymers. Almost 3000 homologs of PET‐active enzymes were identified by profile hidden Markov models. Over 2000 homologs of PUR‐active enzymes were identified by BLAST. Based on multiple sequence alignments, conservation analysis identified the most conserved amino acids, and sequence motifs for PET‐ and PUR‐active enzymes were derived.