BRENDA in 2019: a European ELIXIR core data resource

Jeske, Lisa; Placzek, Sandra; Schomburg, Ida; Chang, Antje; Schomburg, Dietmar

doi:10.1093/nar/gky1048

Cited by 372 publications

(334 citation statements)

References 35 publications

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“…We therefore applied the protMLP method to Adenosine Kinases (ADK), an extensively studied (Wolf-Watz et al, 2004;Nguyen et al, 2017;Schrank et al, 2009) family that catalyzes the interconversion of adenosine nucleotides. As has been observed in other proteins (Engqvist, 2018), we found that the ADK homologs' temperatures of optimal activity (Nguyen et al, 2017;Jeske et al, 2019) were correlated with measured originating species' growth temperatures (r = 0.79). While there were too few ADK sequences to train an over-determined MLP, a linear regression was already highly accurate at predicting the originating species' growth temperature (RMSE = 3.9 °C, r = 0.86) (Fig.…”

Section: Adenosine Kinases' Biochemical Characteristics Correlate Witsupporting

confidence: 82%

Using machine learning to predict quantitative phenotypes from protein and nucleic acid sequences

Sauer

Wang

2019

Preprint

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The link between sequence and phenotype is essential to understanding the molecular mechanisms of evolution, and the design of proteins and genes with specific properties. However, it is difficult to describe the relationship between sequence and protein or organismal phenotypes, due to the complex relationship between sequence, protein folding and activity, and organismal physiology. Here, we use machine learning models trained on individual families of proteins or nucleic acids to predict the originating species' optimal growth temperatures or other quantitative phenotypes. Trained multilayer perceptrons (MLPs) outperformed linear regressions in predicting the originating species growth temperature from protein sequences, achieving a root mean squared error of 3.6 °C. Similar machine learning models were able to predict the binding affinity of mutant WW domain sequences, brightness of fluorescent proteins, and enzymatic activity of ribozymes. Notably, the trained models are protein or nucleic acid family specific and therefore useful in the design of biopolymers with particular properties. This method provides a new tool for the in silico prediction of quantitative biophysical and organismal phenotypes directly from sequence.

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Section: Adenosine Kinases' Biochemical Characteristics Correlate Witsupporting

confidence: 82%

Using machine learning to predict quantitative phenotypes from protein and nucleic acid sequences

Sauer

Wang

2019

Preprint

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“…Both reviewed and unreviewed sequences are considered and stored as clustered UniPac sequences (src/uniprot.sh). To increase the sequence pool for a given reaction, alternative EC numbers from BRENDA [37] and from the Enzyme Nomenclature Committee https://www.qmul.ac.uk/sbcs/ iubmb/enzyme/ are integrated (src/altec.R, dat/brenda_ec.csv).…”

Section: Pathway and Sequence Databasesmentioning

confidence: 99%

gapseq: Informed prediction of bacterial metabolic pathways and reconstruction of accurate metabolic models

Zimmermann

Kaleta

Waschina

2020

Preprint

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Microbial metabolic processes greatly impact ecosystem functioning and the physiology of multi-cellular host organisms. The inference of metabolic capabilities and phenotypes from genome sequences with the help of prior biomolecular knowledge stored in online databases remains a major challenge in systems biology. Here, we present gapseq: a novel tool for automated pathway prediction and metabolic network reconstruction from microbial genome sequences. gapseq combines databases of reference protein sequences (UniProt, TCDB), in tandem with pathway and reaction databases (MetaCyc, KEGG, ModelSEED). This enables the statistical prediction of an organism's metabolic capabilities from sequence homology and pathway topology criteria. By incorporating a novel LP-based gap-filling algorithm, gapseq facilitates the construction of genomescale metabolic models that are suitable for metabolic phenotype predictions by using constraint-based flux analysis. We validated gapseq by comparing predictions to experimental data for more than 1, 000 bacterial organisms comprising over 10, 000 phenotypic traits that include enzyme activity, energy sources, fermentation products, and gene essentiality. This large-scale phenotypic trait prediction test showed, that gapseq yields an overall accuracy of 80% and thereby outperforming other commonly used reconstruction tools. Furthermore, we illustrate the application of gapseq-reconstructed models to simulate biochemical interactions between microorganisms in multi-species communities. Altogether, gapseq (https://github.com/jotech/gapseq) is a new method that improves the predictive potential of automated metabolic network reconstructions and further increases their applicability in biotechnological, ecological, and medical research.

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“…Biotechnology, bioengineering, and the universe of enzymes contain large gold mines for developing and improving existing enzyme functions for applications as well as for discovering enzymes with novel functions. The enzymatic reaction diversity, the molecular elucidation of metabolic pathways, their information retrieval, and advanced bioinformatics tools are growing continuously and will have, undoubtedly, a major influence on the further growth of bioeconomies.…”

Section: New Value Chains and Widening Connections Between Industriesmentioning

confidence: 99%

Bioeconomy for Sustainable Development

Aguilar,

Twardowski,

Wohlgemuth

2019

Biotechnology Journal

139

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Bioeconomy is an emerging paradigm under which the creation, development, and revitalization of economic systems based on a sustainable use of renewable biological resources in a balanced way is rapidly spreading globally. Bioeconomy is building bridges between biotechnology and economy as well as between science, industry, and society. Biotechnology, from its ancient origins up to the present is at the core of the scientific and innovative foundation of bioeconomy policies developed in numerous countries. The challenges and perspectives of bioeconomies are immense, from resource‐efficient large‐scale manufacturing of products such as chemicals, materials, food, pharmaceuticals, polymers, flavors, and fragrances to the production of new biomaterials and bioenergy in a sustainable and economic way for a growing world population. Key success factors for different countries working on the bioeconomy vary widely from high‐tech bioeconomies, emerging diversified or diversified bioeconomies to advanced and basic primary sector bioeconomies. Despite the large variety of bioeconomies, several common elements are identified, which are simultaneously needed altogether.

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BRENDA in 2019: a European ELIXIR core data resource

Cited by 372 publications

References 35 publications

Using machine learning to predict quantitative phenotypes from protein and nucleic acid sequences

Using machine learning to predict quantitative phenotypes from protein and nucleic acid sequences

gapseq: Informed prediction of bacterial metabolic pathways and reconstruction of accurate metabolic models

Bioeconomy for Sustainable Development

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