Large scale discovery of coronavirus-host factor protein interaction motifs reveals SARS-CoV-2 specific mechanisms and vulnerabilities

Viral proteins make extensive use of short peptide interaction motifs to hijack cellular host factors. However, most current large-scale methods do not identify this important class of protein-protein interactions. Uncovering peptide mediated interactions provides both a molecular understanding of viral interactions with their host and the foundation for developing novel antiviral reagents. Here we describe a scalable viral peptide discovery approach covering 229 RNA viruses that provides high resolution information on direct virus-host interactions. We identify 269 peptide-based interactions for 18 coronaviruses including a specific interaction between the human G3BP1/2 proteins and an [FILV]xFG peptide motif in the SARS-CoV-2 nucleocapsid (N) protein. This interaction supports viral replication and through its [FILV]xFG motif N rewires the G3BP1/2 interactome to disrupt stress granules. A peptide-based inhibitor disrupting the G3BP1/2-N interaction blocks SARS-CoV-2 infection showing that our results can be directly translated into novel specific antiviral reagents.

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Genome-wide analysis of protein-protein interactions and involvement of viral proteins in SARS-CoV-2 replication

10.21203/rs.3.rs-337427/v1 ◽

2021 ◽

Author(s):

Yiling Jiang ◽

Kuijie Tong ◽

Roubin Yao ◽

Yuanze Zhou ◽

Hanwen Lin ◽

...

Keyword(s):

Protein Interactions ◽

Viral Protein ◽

Antiviral Drug ◽

Viral Proteins ◽

Protein Protein Interactions ◽

Reporter System ◽

N Protein ◽

Genome Wide ◽

N Terminus ◽

Protein Functions

Abstract Analysis of viral protein-protein interactions is an essential step to uncover the viral protein functions and the molecular mechanism for the assembly of a viral protein complex. We employed a mammalian two-hybrid system to screen all the viral proteins of SARS-CoV-2 for the protein-protein interactions. Our study detected 48 interactions, 14 of which were firstly reported here. Unlike Nsp1 of SARS-CoV, Nsp1 of SARS-CoV-2 has the most interacting partners among all the viral proteins and likely functions as a hub for the viral proteins. Five self-interactions were confirmed, and five interactions, Nsp1/Nsp3.1, Nsp3.1/N, Nsp3.2/Nsp12, Nsp10/Nsp14, and Nsp10/Nsp16, were determined to be positive bidirectionally. Using the replicon reporter system of SARS-CoV-2, we screened all viral proteins for their impacts on the viral replication and revealed Nsp3.1, the N-terminus of Nsp3, significantly inhibited the replicon reporter gene expression. We found Nsp3 interacted with N through its acidic region at N-terminus, while N interacted with Nsp3 through its NTD, which is rich in the basic amino acids. Furthermore, using purified truncated N and Nsp3 proteins, we determined the direct interactions between Nsp3 and N protein. In summary, our findings provided a basis for understanding the functions of coronavirus proteins and supported the potential of interactions as the target for antiviral drug development.

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Genome-wide analysis of protein–protein interactions and involvement of viral proteins in SARS-CoV-2 replication

Cell & Bioscience ◽

10.1186/s13578-021-00644-y ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Yiling Jiang ◽

Kuijie Tong ◽

Roubin Yao ◽

Yuanze Zhou ◽

Hanwen Lin ◽

...

Keyword(s):

Protein Interactions ◽

Viral Protein ◽

Antiviral Drug ◽

Viral Proteins ◽

Protein Protein Interactions ◽

Reporter System ◽

N Protein ◽

Genome Wide ◽

N Terminus ◽

Protein Functions

Abstract Background Analysis of viral protein–protein interactions is an essential step to uncover the viral protein functions and the molecular mechanism for the assembly of a viral protein complex. We employed a mammalian two-hybrid system to screen all the viral proteins of SARS-CoV-2 for the protein–protein interactions. Results Our study detected 48 interactions, 14 of which were firstly reported here. Unlike Nsp1 of SARS-CoV, Nsp1 of SARS-CoV-2 has the most interacting partners among all the viral proteins and likely functions as a hub for the viral proteins. Five self-interactions were confirmed, and five interactions, Nsp1/Nsp3.1, Nsp3.1/N, Nsp3.2/Nsp12, Nsp10/Nsp14, and Nsp10/Nsp16, were determined to be positive bidirectionally. Using the replicon reporter system of SARS-CoV-2, we screened all viral Nsps for their impacts on the viral replication and revealed Nsp3.1, the N-terminus of Nsp3, significantly inhibited the replicon reporter gene expression. We found Nsp3 interacted with N through its acidic region at N-terminus, while N interacted with Nsp3 through its NTD, which is rich in the basic amino acids. Furthermore, using purified truncated N and Nsp3 proteins, we determined the direct interactions between Nsp3 and N protein. Conclusions Our findings provided a basis for understanding the functions of coronavirus proteins and supported the potential of interactions as the target for antiviral drug development.

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Target-Templated de novo Design of Macrocyclic D-/L-Peptides: Inhibitors of the PD-1/PD-L1 Interaction

10.26434/chemrxiv.11663337.v3 ◽

2020 ◽

Author(s):

Salvador Guardiola ◽

Monica Varese ◽

Xavier Roig ◽

Jesús Garcia ◽

Ernest Giralt

Keyword(s):

Protein Interactions ◽

Cyclic Peptides ◽

General Framework ◽

Large Scale ◽

De Novo ◽

Inhibitory Effect ◽

Original Text ◽

Protein Protein Interactions ◽

Retraction Notice ◽

Pharmaceutical Properties

NOTE: This preprint has been retracted by consensus from all authors. See the retraction notice in place above; the original text can be found under "Version 1", accessible from the version selector above. ------------------------------------------------------------------------ Peptides, together with antibodies, are among the most potent biochemical tools to modulate challenging protein-protein interactions. However, current structure-based methods are largely limited to natural peptides and are not suitable for designing target-specific binders with improved pharmaceutical properties, such as macrocyclic peptides. Here we report a general framework that leverages the computational power of Rosetta for large-scale backbone sampling and energy scoring, followed by side-chain composition, to design heterochiral cyclic peptides that bind to a protein surface of interest. To showcase the applicability of our approach, we identified two peptides (PD-i3 and PD-i6) that target PD-1, a key immune checkpoint, and work as protein ligand decoys. A comprehensive biophysical evaluation confirmed their binding mechanism to PD-1 and their inhibitory effect on the PD-1/PD-L1 interaction. Finally, elucidation of their solution structures by NMR served as validation of our de novo design approach. We anticipate that our results will provide a general framework for designing target-specific drug-like peptides.

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Target-Templated de novo Design of Macrocyclic D-/L-Peptides: Inhibitors of the PD-1/PD-L1 Interaction

10.26434/chemrxiv.11663337 ◽

2020 ◽

Author(s):

Salvador Guardiola ◽

Monica Varese ◽

Xavier Roig ◽

Jesús Garcia ◽

Ernest Giralt

Keyword(s):

Protein Interactions ◽

Cyclic Peptides ◽

General Framework ◽

Large Scale ◽

De Novo ◽

Inhibitory Effect ◽

Original Text ◽

Protein Protein Interactions ◽

Retraction Notice ◽

Pharmaceutical Properties

NOTE: This preprint has been retracted by consensus from all authors. See the retraction notice in place above; the original text can be found under "Version 1", accessible from the version selector above. ------------------------------------------------------------------------ Peptides, together with antibodies, are among the most potent biochemical tools to modulate challenging protein-protein interactions. However, current structure-based methods are largely limited to natural peptides and are not suitable for designing target-specific binders with improved pharmaceutical properties, such as macrocyclic peptides. Here we report a general framework that leverages the computational power of Rosetta for large-scale backbone sampling and energy scoring, followed by side-chain composition, to design heterochiral cyclic peptides that bind to a protein surface of interest. To showcase the applicability of our approach, we identified two peptides (PD-i3 and PD-i6) that target PD-1, a key immune checkpoint, and work as protein ligand decoys. A comprehensive biophysical evaluation confirmed their binding mechanism to PD-1 and their inhibitory effect on the PD-1/PD-L1 interaction. Finally, elucidation of their solution structures by NMR served as validation of our de novo design approach. We anticipate that our results will provide a general framework for designing target-specific drug-like peptides.

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Faculty Opinions recommendation of Comparative assessment of large-scale data sets of protein-protein interactions.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1006598.82257 ◽

2002 ◽

Author(s):

Rob Russell

Keyword(s):

Protein Interactions ◽

Large Scale ◽

Comparative Assessment ◽

Data Sets ◽

Protein Protein Interactions ◽

Large Scale Data ◽

Scale Data ◽

Large Scale Data Sets

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Short loop functional commonality identified in leukaemia proteome highlights crucial protein sub-networks

NAR Genomics and Bioinformatics ◽

10.1093/nargab/lqab010 ◽

2021 ◽

Vol 3 (1) ◽

Author(s):

Sun Sook Chung ◽

Joseph C F Ng ◽

Anna Laddach ◽

N Shaun B Thomas ◽

Franca Fraternali

Keyword(s):

Protein Interactions ◽

Large Scale ◽

Interaction Network ◽

Protein Protein Interactions ◽

Protein Protein Interaction ◽

Ppi Networks ◽

Short Loop ◽

New Strategy ◽

Loop Network ◽

Protein Protein Interaction Network

Abstract Direct drug targeting of mutated proteins in cancer is not always possible and efficacy can be nullified by compensating protein–protein interactions (PPIs). Here, we establish an in silico pipeline to identify specific PPI sub-networks containing mutated proteins as potential targets, which we apply to mutation data of four different leukaemias. Our method is based on extracting cyclic interactions of a small number of proteins topologically and functionally linked in the Protein–Protein Interaction Network (PPIN), which we call short loop network motifs (SLM). We uncover a new property of PPINs named ‘short loop commonality’ to measure indirect PPIs occurring via common SLM interactions. This detects ‘modules’ of PPI networks enriched with annotated biological functions of proteins containing mutation hotspots, exemplified by FLT3 and other receptor tyrosine kinase proteins. We further identify functional dependency or mutual exclusivity of short loop commonality pairs in large-scale cellular CRISPR–Cas9 knockout screening data. Our pipeline provides a new strategy for identifying new therapeutic targets for drug discovery.

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Investigating the Role of Large-Scale Domain Dynamics in Protein-Protein Interactions

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2016.00054 ◽

2016 ◽

Vol 3 ◽

Cited By ~ 8

Author(s):

Elise Delaforge ◽

Sigrid Milles ◽

Jie-rong Huang ◽

Denis Bouvier ◽

Malene Ringkjøbing Jensen ◽

...

Keyword(s):

Protein Interactions ◽

Large Scale ◽

Protein Protein Interactions ◽

Domain Dynamics

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A MapReduce-Based Parallel Random Forest Approach for Predicting Large-Scale Protein-Protein Interactions

Intelligent Computing Methodologies - Lecture Notes in Computer Science ◽

10.1007/978-3-030-60796-8_34 ◽

2020 ◽

pp. 400-407

Author(s):

Bo-Ya Ji ◽

Zhu-Hong You ◽

Long Yang ◽

Ji-Ren Zhou ◽

Peng-Wei Hu

Keyword(s):

Random Forest ◽

Protein Interactions ◽

Large Scale ◽

Protein Protein Interactions

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FrustratometeR: an R-package to compute Local frustration in protein structures, point mutants and MD simulations

10.1101/2020.11.26.400432 ◽

2020 ◽

Author(s):

Atilio O. Rausch ◽

Maria I. Freiberger ◽

Cesar O. Leonetti ◽

Diego M. Luna ◽

Leandro G. Radusky ◽

...

Keyword(s):

Protein Interactions ◽

Large Scale ◽

Protein Structures ◽

Md Simulations ◽

R Package ◽

Protein Protein Interactions ◽

Large Scale Analysis ◽

Functional Aspects ◽

Catalytic Sites ◽

Polypeptide Chains

Once folded natural protein molecules have few energetic conflicts within their polypeptide chains. Many protein structures do however contain regions where energetic conflicts remain after folding, i.e. they have highly frustrated regions. These regions, kept in place over evolutionary and physiological timescales, are related to several functional aspects of natural proteins such as protein-protein interactions, small ligand recognition, catalytic sites and allostery. Here we present FrustratometeR, an R package that easily computes local energetic frustration on a personal computer or a cluster. This package facilitates large scale analysis of local frustration, point mutants and MD trajectories, allowing straightforward integration of local frustration analysis in to pipelines for protein structural analysis.Availability and implementation: https://github.com/proteinphysiologylab/frustratometeR

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