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Minimal genome encoding proteins with constrained amino acid repertoire.

ABSTRACT: Minimal bacterial gene set comprises the genetic elements needed for survival of engineered bacterium on a rich medium. This set is estimated to include 300-350 protein-coding genes. One way of simplifying an organism with such a minimal genome even further is to constrain the amino acid content of its proteins. In this study, comparative genomics approaches and the results of gene knockout experiments were used to extrapolate the minimal gene set of mollicutes, and bioinformatics combined with the knowledge-based analysis of the structure-function relationships in these proteins and their orthologs, paralogs and analogs was applied to examine the challenges of completely replacing the rarest residue, cysteine. Among several known functions of cysteine residues, their roles in the active centers of the enzymes responsible for deoxyribonucleoside synthesis and transfer RNA modification appear to be crucial, as no alternative chemistry is known for these reactions. Thus, drastic reduction of the content of the rarest amino acid in a minimal proteome appears to be possible, but its complete elimination is challenging.

SUBMITTER: Tsoy O

PROVIDER: S-EPMC3794579 | biostudies-other | 2013 Oct

REPOSITORIES: biostudies-other

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Minimal genome encoding proteins with constrained amino acid repertoire.

Tsoy Olga O Yurieva Marina M Kucharavy Andrey A O'Reilly Mary M Mushegian Arcady A

Nucleic acids research 20130719 18

Minimal bacterial gene set comprises the genetic elements needed for survival of engineered bacterium on a rich medium. This set is estimated to include 300-350 protein-coding genes. One way of simplifying an organism with such a minimal genome even further is to constrain the amino acid content of its proteins. In this study, comparative genomics approaches and the results of gene knockout experiments were used to extrapolate the minimal gene set of mollicutes, and bioinformatics combined with ...[more]

PMID: 23873957

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Project description:BackgroundThe number of applications of deep learning algorithms in bioinformatics is increasing as they usually achieve superior performance over classical approaches, especially, when bigger training datasets are available. In deep learning applications, discrete data, e.g. words or n-grams in language, or amino acids or nucleotides in bioinformatics, are generally represented as a continuous vector through an embedding matrix. Recently, learning this embedding matrix directly from the data as part of the continuous iteration of the model to optimize the target prediction - a process called 'end-to-end learning' - has led to state-of-the-art results in many fields. Although usage of embeddings is well described in the bioinformatics literature, the potential of end-to-end learning for single amino acids, as compared to more classical manually-curated encoding strategies, has not been systematically addressed. To this end, we compared classical encoding matrices, namely one-hot, VHSE8 and BLOSUM62, to end-to-end learning of amino acid embeddings for two different prediction tasks using three widely used architectures, namely recurrent neural networks (RNN), convolutional neural networks (CNN), and the hybrid CNN-RNN.ResultsBy using different deep learning architectures, we show that end-to-end learning is on par with classical encodings for embeddings of the same dimension even when limited training data is available, and might allow for a reduction in the embedding dimension without performance loss, which is critical when deploying the models to devices with limited computational capacities. We found that the embedding dimension is a major factor in controlling the model performance. Surprisingly, we observed that deep learning models are capable of learning from random vectors of appropriate dimension.ConclusionOur study shows that end-to-end learning is a flexible and powerful method for amino acid encoding. Further, due to the flexibility of deep learning systems, amino acid encoding schemes should be benchmarked against random vectors of the same dimension to disentangle the information content provided by the encoding scheme from the distinguishability effect provided by the scheme.

Dataset Information

Minimal genome encoding proteins with constrained amino acid repertoire.

Publications

Minimal genome encoding proteins with constrained amino acid repertoire.

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