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A novel method for achieving an optimal classification of the proteinogenic amino acids.

ABSTRACT: The classification of proteinogenic amino acids is crucial for understanding their commonalities as well as their differences to provide a hint for why life settled on the usage of precisely those amino acids. It is also crucial for predicting electrostatic, hydrophobic, stacking and other interactions, for assessing conservation in multiple alignments and many other applications. While several methods have been proposed to find "the" optimal classification, they have several shortcomings, such as the lack of efficiency and interpretability or an unnecessarily high number of discriminating features. In this study, we propose a novel method involving a repeated binary separation via a minimum amount of five features (such as hydrophobicity or volume) expressed by numerical values for amino acid characteristics. The features are extracted from the AAindex database. By simple separation at the medians, we successfully derive the five properties volume, electron-ion-interaction potential, hydrophobicity, ?-helix propensity, and ?-helix propensity. We extend our analysis to separations other than by the median. We further score our combinations based on how natural the separations are.

SUBMITTER: Then A

PROVIDER: S-EPMC7501307 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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A novel method for achieving an optimal classification of the proteinogenic amino acids.

Then Andre A Mácha Karel K Ibrahim Bashar B Schuster Stefan S

Scientific reports 20200918 1

The classification of proteinogenic amino acids is crucial for understanding their commonalities as well as their differences to provide a hint for why life settled on the usage of precisely those amino acids. It is also crucial for predicting electrostatic, hydrophobic, stacking and other interactions, for assessing conservation in multiple alignments and many other applications. While several methods have been proposed to find "the" optimal classification, they have several shortcomings, such ...[more]

PMID: 32948819

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Project description:Genome-scale metabolism can best be described as a highly interconnected network of biochemical reactions and metabolites. The flow of metabolites, i.e., flux, throughout these networks can be predicted and analyzed using approaches such as flux balance analysis (FBA). By knowing the network topology and employing only a few simple assumptions, FBA can efficiently predict metabolic functions at the genome scale as well as microbial phenotypes. The network topology is represented in the form of genome-scale metabolic models (GEMs), which provide a direct mapping between network structure and function via the enzyme-coding genes and corresponding metabolic capacity. Recently, the role of protein limitations in shaping metabolic phenotypes have been extensively studied following the reconstruction of enzyme-constrained GEMs. This framework has been shown to significantly improve the accuracy of predicting microbial phenotypes, and it has demonstrated that a global limitation in protein availability can prompt the ubiquitous metabolic strategy of overflow metabolism. Being one of the most abundant and differentially expressed proteome sectors, metabolic proteins constitute a major cellular demand on proteinogenic amino acids. However, little is known about the impact and sensitivity of amino acid availability with regards to genome-scale metabolism. Here, we explore these aspects by extending on the enzyme-constrained GEM framework by also accounting for the usage of amino acids in expressing the metabolic proteome. Including amino acids in an enzyme-constrained GEM of Saccharomyces cerevisiae, we demonstrate that the expanded model is capable of accurately reproducing experimental amino acid levels. We further show that the metabolic proteome exerts variable demands on amino acid supplies in a condition-dependent manner, suggesting that S. cerevisiae must have evolved to efficiently fine-tune the synthesis of amino acids for expressing its metabolic proteins in response to changes in the external environment. Finally, our results demonstrate how the metabolic network of S. cerevisiae is robust towards perturbations of individual amino acids, while simultaneously being highly sensitive when the relative amino acid availability is set to mimic a priori distributions of both yeast and non-yeast origins.

Project description:Non-proteinogenic amino Q7 Q8 acids (npAAs) are valuable building blocks for the development of advanced pharmaceuticals and agrochemicals. The surge in interest in their synthesis is primarily due to the potential to enhance and diversify existing bioactive molecules. This can be achieved by altering these bioactive molecules to improve their effectiveness, reducing resistance compared to their natural counterparts or generating molecules with novel functions. Traditional production of npAAs in native hosts requires specialized conditions and complex cultivation media. Furthermore, these compounds are often found in organisms that challenge genetic manipulation. Thus, the recombinant production of these npAAs in a model organism like Escherichia coli paves the way for groundbreaking advancements in synthetic biology. For the first time, we report the synthesis of npAAs β-methylphenylalanine (BmePhe), β- hydroxyenduracididine (BhEnd), and enduracididine (End) from Streptomyces hygroscopicus in E. coli. Through proteomic and metabolomic analyses, we achieved the production of these compounds from two synthetic operons, comprising five heterologous proteins. Interestingly, we discovered that the exogenous addition of pathway precursors to the E. coli system enhanced the yield of BmePhe by 2.5 times, whereas it concurrently attenuated the production of BhEnd and End, signifying a selective precursor-dependent yield enhancement. The synthetic biology landscape is broadened in this study by expanding the repertoire of amino acids beyond the conventional set of 22 standard proteinogenic amino acids. The biosynthesized npAAs, End, BhEnd, and BmePhe hold promise for engineering proteins with modified functions, integrating into novel metabolites and/or enhancing biological stability and activity. Additionally, these amino acids’ biological production and subsequent purification present an alternative to traditional chemical synthesis methods, paving a direct pathway for pharmacological evaluation.

Dataset Information

A novel method for achieving an optimal classification of the proteinogenic amino acids.

Publications

A novel method for achieving an optimal classification of the proteinogenic amino acids.

Similar Datasets

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