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

Nature Communications, Journal Year: 2021, Volume and Issue: 12(1)

Published: Nov. 19, 2021

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 protein-protein interactions. Uncovering mediated interactions provides both a molecular understanding viral with their and the foundation for developing novel antiviral reagents. Here we describe discovery approach covering 23 coronavirus strains that high resolution information on direct virus-host We 269 peptide-based 18 coronaviruses including specific between human G3BP1/2 an ΦxFG motif in SARS-CoV-2 nucleocapsid (N) protein. This supports replication through its N rewires interactome disrupt stress granules. A inhibitor disrupting G3BP1/2-N dampened infection showing our results can be directly translated into

Language: Английский

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Myostatin and its Regulation: A Comprehensive Review of Myostatin Inhibiting Strategies

Frontiers in Physiology, Journal Year: 2022, Volume and Issue: 13

Published: June 23, 2022

Myostatin (MSTN) is a well-reported negative regulator of muscle growth and member the transforming factor (TGF) family. MSTN has important functions in skeletal (SM), its crucial involvement several disorders made it an therapeutic target. Several strategies based on use natural compounds to inhibitory peptides are being used inhibit activity MSTN. This review delivers overview current state knowledge about SM myogenesis with particular emphasis structural characteristics regulatory during involvements various related disorders. In addition, we diverse approaches MSTN, especially silico screening design novel short derived from proteins that typically interact

Language: Английский

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PPI-Affinity: A Web Tool for the Prediction and Optimization of Protein–Peptide and Protein–Protein Binding Affinity

Journal of Proteome Research, Journal Year: 2022, Volume and Issue: 21(8), P. 1829 - 1841

Published: June 2, 2022

Virtual screening of protein–protein and protein–peptide interactions is a challenging task that directly impacts the processes hit identification hit-to-lead optimization in drug design projects involving peptide-based pharmaceuticals. Although several tools designed to predict binding affinity complexes have been proposed, methods specifically developed are comparatively scarce. Frequently, predictors trained score small molecules used for peptides indistinctively, despite larger complexity heterogeneity rendered by peptide binders. To address this issue, we introduce PPI-Affinity, tool leverages support vector machine (SVM) screen datasets complexes, as well generate rank mutants given structure. The performance SVM models was assessed on four benchmark datasets, which include data. In addition, evaluated our model set EPI-X4, an endogenous inhibitor chemokine receptor CXCR4, serine proteases HTRA1 HTRA3 with peptides. PPI-Affinity freely accessible at https://protdcal.zmb.uni-due.de/PPIAffinity.

Language: Английский

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Machine learning solutions for predicting protein–protein interactions

Wiley Interdisciplinary Reviews Computational Molecular Science, Journal Year: 2022, Volume and Issue: 12(6)

Published: March 29, 2022

Abstract Proteins are “social molecules.” Recent experimental evidence supports the notion that large protein aggregates, known as biomolecular condensates, affect structurally and functionally many biological processes. Condensate formation may be permanent and/or time dependent, suggesting processes can occur locally, depending on cell needs. The question then arises to which extent we monitor protein‐aggregate formation, both experimentally theoretically predict/simulate functional aggregate formation. Available data relative mesoscopic interacting networks at a proteome level, protein‐binding affinity data, complexes, solved with atomic resolution. Powerful algorithms based machine learning (ML) extract information from sets infer properties of never‐seen‐before examples. ML tools address problem protein–protein interactions (PPIs) adopting different sets, input features, architectures. According recent publications, deep is most successful method. However, in ML‐computational biology, convincing success story comes out by performing general benchmarks blind sets. Results indicate state‐of‐the‐art approaches, traditional learning, still ameliorated, irrespectively power method richness features. This being case, it quite evident powerful methods not trained whole possible spectrum PPIs more investigations necessary complete our knowledge PPI‐functional interactions. article categorized under: Software > Molecular Modeling Structure Mechanism Computational Biochemistry Biophysics Data Science Artificial Intelligence/Machine Learning Statistical Mechanics Interactions

Language: Английский

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Lysine-Targeted Reversible Covalent Ligand Discovery for Proteins via Phage Display

Journal of the American Chemical Society, Journal Year: 2022, Volume and Issue: 144(34), P. 15885 - 15893

Published: Aug. 17, 2022

Binding via reversible covalent bond formation presents a novel and powerful mechanism to enhance the potency of synthetic inhibitors for therapeutically important proteins. Work on this front has yielded anticancer drug bortezomib as well antisickling voxelotor. However, rational design remains difficult even when noncovalent are available scaffold. Herein, we report chemically modified phage libraries, both linear cyclic, that incorporate 2-acetylphenylboronic acid (APBA) warhead bind lysines iminoboronate formation. To demonstrate their utility, these APBA-presenting libraries were screened against sortase A Staphylococcus aureus, spike protein SARS-CoV-2. For targets, peptide ligands readily identified with single-digit micromolar excellent specificity, enabling live-cell inhibition highly sensitive detection, respectively. Furthermore, our structure-activity studies unambiguously benefit APBA binding. Overall, contribution shows first time can be developed display interest. The platform should widely applicable proteins including those involved in protein-protein interactions.

Language: Английский

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