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Discovery of protein interactions using parallel analysis of translated ORFs (PLATO).

ABSTRACT: Parallel analysis of translated open reading frames (ORFs) (PLATO) can be used for the unbiased discovery of interactions between full-length proteins encoded by a library of 'prey' ORFs and surface-immobilized 'bait' antibodies, polypeptides or small-molecular-weight compounds. PLATO uses ribosome display (RD) to link ORF-derived mRNA molecules to the proteins they encode, and recovered mRNA from affinity enrichment is subjected to analysis using massively parallel DNA sequencing. Compared with alternative in vitro methods, PLATO provides several advantages including library size and cost. A unique advantage of PLATO is that an alternative reverse transcription-quantitative PCR (RT-qPCR) protocol can be used to test binding of specific, individual proteins. To illustrate a typical experimental workflow, we demonstrate PLATO for the identification of the immune target of serum antibodies from patients with inclusion body myositis (IBM). Beginning with an ORFeome library in an RD vector, the protocol can produce samples for deep sequencing or RT-qPCR within 4 d.

SUBMITTER: Larman HB

PROVIDER: S-EPMC4129458 | biostudies-literature | 2014 Jan

REPOSITORIES: biostudies-literature

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Discovery of protein interactions using parallel analysis of translated ORFs (PLATO).

Larman H Benjamin HB Liang Anthony C AC Elledge Stephen J SJ Zhu Jian J

Nature protocols 20131212 1

Parallel analysis of translated open reading frames (ORFs) (PLATO) can be used for the unbiased discovery of interactions between full-length proteins encoded by a library of 'prey' ORFs and surface-immobilized 'bait' antibodies, polypeptides or small-molecular-weight compounds. PLATO uses ribosome display (RD) to link ORF-derived mRNA molecules to the proteins they encode, and recovered mRNA from affinity enrichment is subjected to analysis using massively parallel DNA sequencing. Compared with ...[more]

PMID: 24336473

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Project description:Small proteins of <51 amino acids are abundant across all domains of life but are often overlooked because their small size makes them difficult to predict computationally, and they are refractory to standard proteomic approaches. Ribosome profiling has been used to infer the existence of small proteins by detecting the translation of the corresponding open reading frames (ORFs). Detection of translated short ORFs by ribosome profiling can be improved by treating cells with drugs that stall ribosomes at specific codons. Here, we combine the analysis of ribosome profiling data for Escherichia coli cells treated with antibiotics that stall ribosomes at either start or stop codons. Thus, we identify ribosome-occupied start and stop codons with high sensitivity for ∼400 novel putative ORFs. The newly discovered ORFs are mostly short, with 365 encoding proteins of <51 amino acids. We validate translation of several selected short ORFs, and show that many likely encode unstable proteins. Moreover, we present evidence that most of the newly identified short ORFs are not under purifying selection, suggesting they do not impact cell fitness, although a small subset have the hallmarks of functional ORFs. IMPORTANCE Small proteins of <51 amino acids are abundant across all domains of life but are often overlooked because their small size makes them difficult to predict computationally, and they are refractory to standard proteomic approaches. Recent studies have discovered small proteins by mapping the location of translating ribosomes on RNA using a technique known as ribosome profiling. Discovery of translated sORFs using ribosome profiling can be improved by treating cells with drugs that trap initiating ribosomes. Here, we show that combining these data with equivalent data for cells treated with a drug that stalls terminating ribosomes facilitates the discovery of small proteins. We use this approach to discover 365 putative genes that encode small proteins in Escherichia coli.

Project description:Protein-ligand interactions (PLIs) are essential for biochemical functionality and their identification is crucial for estimating biophysical properties for rational therapeutic design. Currently, experimental characterization of these properties is the most accurate method, however, this is very time-consuming and labor-intensive. A number of computational methods have been developed in this context but most of the existing PLI prediction heavily depends on 2D protein sequence data. Here, we present a novel parallel graph neural network (GNN) to integrate knowledge representation and reasoning for PLI prediction to perform deep learning guided by expert knowledge and informed by 3D structural data. We develop two distinct GNN architectures: [Formula: see text] is the base implementation that employs distinct featurization to enhance domain-awareness, while [Formula: see text] is a novel implementation that can predict with no prior knowledge of the intermolecular interactions. The comprehensive evaluation demonstrated that GNN can successfully capture the binary interactions between ligand and protein's 3D structure with 0.979 test accuracy for [Formula: see text] and 0.958 for [Formula: see text] for predicting activity of a protein-ligand complex. These models are further adapted for regression tasks to predict experimental binding affinities and [Formula: see text] crucial for compound's potency and efficacy. We achieve a Pearson correlation coefficient of 0.66 and 0.65 on experimental affinity and 0.50 and 0.51 on [Formula: see text] with [Formula: see text] and [Formula: see text], respectively, outperforming similar 2D sequence based models. Our method can serve as an interpretable and explainable artificial intelligence (AI) tool for predicted activity, potency, and biophysical properties of lead candidates. To this end, we show the utility of [Formula: see text] on SARS-Cov-2 protein targets by screening a large compound library and comparing the prediction with the experimentally measured data.

Dataset Information

Discovery of protein interactions using parallel analysis of translated ORFs (PLATO).

Publications

Discovery of protein interactions using parallel analysis of translated ORFs (PLATO).

Similar Datasets

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