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A hot spot on interferon ?/? receptor subunit 1 (IFNAR1) underpins its interaction with interferon-? and dictates signaling.

ABSTRACT: The interaction of IFN-? with its receptor IFNAR1 (interferon ?/? receptor subunit 1) is vital for host-protective anti-viral and anti-proliferative responses, but signaling via this interaction can be detrimental if dysregulated. Whereas it is established that IFNAR1 is an essential component of the IFNAR signaling complex, the key residues underpinning the IFN-?-IFNAR1 interaction are unknown. Guided by the crystal structure of the IFN-?-IFNAR1 complex, we used truncation variants and site-directed mutagenesis to investigate domains and residues enabling complexation of IFN-? to IFNAR1. We have identified an interface on IFNAR1-subdomain-3 that is differentially utilized by IFN-? and IFN-? for signal transduction. We used surface plasmon resonance and cell-based assays to investigate this important IFN-? binding interface that is centered on IFNAR1 residues Tyr240 and Tyr274 binding the C and N termini of the B and C helices of IFN-?, respectively. Using IFNAR1 and IFN-? variants, we show that this interface contributes significantly to the affinity of IFN-? for IFNAR1, its ability to activate STAT1, the expression of interferon stimulated genes, and ultimately to the anti-viral and anti-proliferative properties of IFN-?. These results identify a key interface created by IFNAR1 residues Tyr240 and Tyr274 interacting with IFN-? residues Phe63, Leu64, Glu77, Thr78, Val81, and Arg82 that underlie IFN-?-IFNAR1-mediated signaling and biological processes.

SUBMITTER: de Weerd NA

PROVIDER: S-EPMC5418053 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature

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A hot spot on interferon α/β receptor subunit 1 (IFNAR1) underpins its interaction with interferon-β and dictates signaling.

de Weerd Nicole A NA Matthews Antony Y AY Pattie Phillip R PR Bourke Nollaig M NM Lim San S SS Vivian Julian P JP Rossjohn Jamie J Hertzog Paul J PJ

The Journal of biological chemistry 20170313 18

The interaction of IFN-β with its receptor IFNAR1 (interferon α/β receptor subunit 1) is vital for host-protective anti-viral and anti-proliferative responses, but signaling via this interaction can be detrimental if dysregulated. Whereas it is established that IFNAR1 is an essential component of the IFNAR signaling complex, the key residues underpinning the IFN-β-IFNAR1 interaction are unknown. Guided by the crystal structure of the IFN-β-IFNAR1 complex, we used truncation variants and site-dir ...[more]

PMID: 28289093

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Project description:BackgroundProtein language models, inspired by the success of large language models in deciphering human language, have emerged as powerful tools for unraveling the intricate code of life inscribed within protein sequences. They have gained significant attention for their promising applications across various areas, including the sequence-based prediction of secondary and tertiary protein structure, the discovery of new functional protein sequences/folds, and the assessment of mutational impact on protein fitness. However, their utility in learning to predict protein residue properties based on scant datasets, such as protein-protein interaction (PPI)-hotspots whose mutations significantly impair PPIs, remained unclear. Here, we explore the feasibility of using protein language-learned representations as features for machine learning to predict PPI-hotspots using a dataset containing 414 experimentally confirmed PPI-hotspots and 504 PPI-nonhot spots.ResultsOur findings showcase the capacity of unsupervised learning with protein language models in capturing critical functional attributes of protein residues derived from the evolutionary information encoded within amino acid sequences. We show that methods relying on protein language models can compete with methods employing sequence and structure-based features to predict PPI-hotspots from the free protein structure. We observed an optimal number of features for model precision, suggesting a balance between information and overfitting.ConclusionsThis study underscores the potential of transformer-based protein language models to extract critical knowledge from sparse datasets, exemplified here by the challenging realm of predicting PPI-hotspots. These models offer a cost-effective and time-efficient alternative to traditional experimental methods for predicting certain residue properties. However, the challenge of explaining why specific features are important for determining certain residue properties remains.

Dataset Information

A hot spot on interferon ?/? receptor subunit 1 (IFNAR1) underpins its interaction with interferon-? and dictates signaling.

Publications

A hot spot on interferon α/β receptor subunit 1 (IFNAR1) underpins its interaction with interferon-β and dictates signaling.

Similar Datasets

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