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Genetically encoding an electrophilic amino acid for protein stapling and covalent binding to native receptors.

ABSTRACT: Covalent bonds can be generated within and between proteins by an unnatural amino acid (Uaa) reacting with a natural residue through proximity-enabled bioreactivity. Until now, Uaas have been developed to react mainly with cysteine in proteins. Here we genetically encoded an electrophilic Uaa capable of reacting with histidine and lysine, thereby expanding the diversity of target proteins and the scope of the proximity-enabled protein cross-linking technology. In addition to efficient cross-linking of proteins inter- and intramolecularly, this Uaa permits direct stapling of a protein ?-helix in a recombinant manner and covalent binding of native membrane receptors in live cells. The target diversity, recombinant stapling, and covalent targeting of endogenous proteins enabled by this versatile Uaa should prove valuable in developing novel research tools, biological diagnostics, and therapeutics by exploiting covalent protein linkages for specificity, irreversibility, and stability.

SUBMITTER: Chen XH

PROVIDER: S-EPMC4168779 | biostudies-literature | 2014 Sep

REPOSITORIES: biostudies-literature

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Genetically encoding an electrophilic amino acid for protein stapling and covalent binding to native receptors.

Chen Xiao-Hua XH Xiang Zheng Z Hu Ying S YS Lacey Vanessa K VK Cang Hu H Wang Lei L

ACS chemical biology 20140715 9

Covalent bonds can be generated within and between proteins by an unnatural amino acid (Uaa) reacting with a natural residue through proximity-enabled bioreactivity. Until now, Uaas have been developed to react mainly with cysteine in proteins. Here we genetically encoded an electrophilic Uaa capable of reacting with histidine and lysine, thereby expanding the diversity of target proteins and the scope of the proximity-enabled protein cross-linking technology. In addition to efficient cross-link ...[more]

PMID: 25010185

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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

Genetically encoding an electrophilic amino acid for protein stapling and covalent binding to native receptors.

Publications

Genetically encoding an electrophilic amino acid for protein stapling and covalent binding to native receptors.

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