Engineering selective competitors for the discrimination of highly conserved protein-protein interaction modules

Abstract Designing highly specific modulators of protein-protein interactions (PPIs) is especially challenging in the context of multiple paralogs and conserved interaction surfaces. In this case, direct generation of selective and competitive inhibitors is hindered by high similarity within the evolutionary-related protein interfaces. We report here a strategy that uses a semi-rational approach to separate the modulator design into two functional parts. We first achieve specificity toward a region outside of the interface by using phage display selection coupled with molecular and cellular validation. Highly selective competition is then generated by appending the more degenerate interaction peptide to contact the target interface. We apply this approach to specifically bind a single PDZ domain within the postsynaptic protein PSD-95 over highly similar PDZ domains in PSD-93, SAP-97 and SAP-102. Our work provides a paralog-selective and domain specific inhibitor of PSD-95, and describes a method to efficiently target other conserved PPI modules.

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Targeting Kinase Interaction Networks: A New Paradigm in PPI Based Design of Kinase Inhibitors

Current Topics in Medicinal Chemistry ◽

10.2174/1568026619666190304155711 ◽

2019 ◽

Vol 19 (6) ◽

pp. 467-485 ◽

Cited By ~ 2

Author(s):

Pranitha Jenardhanan ◽

Manivel Panneerselvam ◽

Premendu P. Mathur

Keyword(s):

Protein Interactions ◽

Kinase Inhibitors ◽

Disease Onset ◽

Peptide Inhibitors ◽

Protein Protein Interactions ◽

New Paradigm ◽

Diverse Range ◽

Protein Protein Interaction ◽

Domain Specific ◽

And Function

Background: Kinases are key modulators in regulating diverse range of cellular activities and are an essential part of the protein-protein interactome. Understanding the interaction of kinases with different substrates and other proteins is vital to decode the cell signaling machinery as well as causative mechanism for disease onset and progression. Objective: The objective of this review is to present all studies on the structure and function of few important kinases and highlight the protein-protein interaction (PPI) mechanism of kinases and the kinase specific interactome databases and how such studies could be utilized to develop anticancer drugs. Methods: The article is a review of the detailed description of the various domains in kinases that are involved in protein-protein interactions and specific inhibitors developed targeting these PPI domains. Results: The review has surfaced in depth the interacting domains in key kinases and their features and the roles of PPI in the human kinome and the various signaling cascades that are involved in certain types of cancer. Conclusion: The insight availed into the mechanism of existing peptide inhibitors and peptidomimetics against kinases will pave way for the design and generation of domain specific peptide inhibitors with better productivity and efficiency and the various software and servers available can be of great use for the identification and analysis of protein-protein interactions.

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A structural network associated with the kallikrein-kinin and renin-angiotensin systems

Biological Chemistry ◽

10.1515/bc.2010.046 ◽

2010 ◽

Vol 391 (4) ◽

Cited By ~ 7

Author(s):

Veronika Stoka ◽

Vito Turk

Keyword(s):

Protein Interactions ◽

Three Dimensional ◽

Type I ◽

Protein Protein Interactions ◽

Renin Angiotensin ◽

Protein Protein Interaction ◽

Protein Interfaces ◽

Structural Network ◽

Domain Interactions ◽

Fibronectin Type

Abstract The kallikrein-kinin and renin-angiotensin (KKS-RAS) systems represent two highly regulated proteolytic systems that are involved in several physiological and pathological processes. Although their protein-protein interactions can be studied using experimental approaches, it is difficult to differentiate between direct physical interactions and functional associations, which do not involve direct atomic contacts between macromolecules. This information can be obtained from an atomic-resolution characterization of the protein interfaces. As a result of this, various three-dimensional-based protein-protein interaction databases have become available. To gain insight into the multilayered interaction of the KKS-RAS systems, we present a protein network that is built up on three-dimensional domain-domain interactions. The essential domains that link these systems are as follows: Cystatin, Peptidase_C1, Thyroglobulin_1, Insulin, CIMR (Cation-independent mannose-6-phosphate receptor repeat), fn2 (Fibronectin type II domain), fn1 (Fibronectin type I domain), EGF, Trypsin, and Serpin. We found that the CIMR domain is located at the core of the network, thus connecting both systems. From the latter, all domain interactors up to level 4 were retrieved, thus displaying a more comprehensive representation of the KKS-RAS structural network.

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Functional interplay between protein domains in a supramodular structure involving the postsynaptic density protein PSD-95

Journal of Biological Chemistry ◽

10.1074/jbc.ra119.011050 ◽

2019 ◽

Vol 295 (7) ◽

pp. 1992-2000 ◽

Cited By ~ 4

Author(s):

Louise Laursen ◽

Elin Karlsson ◽

Stefano Gianni ◽

Per Jemth

Keyword(s):

Conformational Changes ◽

Protein Interactions ◽

Pdz Domain ◽

Postsynaptic Density ◽

Protein Protein Interactions ◽

Conformational States ◽

Protein Protein Interaction ◽

Natural Ligand ◽

C Terminus ◽

Postsynaptic Density Protein

Cell scaffolding and signaling are governed by protein–protein interactions. Although a particular interaction is often defined by two specific domains binding to each other, this interaction often occurs in the context of other domains in multidomain proteins. How such adjacent domains form supertertiary structures and modulate protein–protein interactions has only recently been addressed and is incompletely understood. The postsynaptic density protein PSD-95 contains a three-domain supramodule, denoted PSG, which consists of PDZ, Src homology 3 (SH3), and guanylate kinase-like domains. The PDZ domain binds to the C terminus of its proposed natural ligand, CXXC repeat–containing interactor of PDZ3 domain (CRIPT), and results from previous experiments using only the isolated PDZ domain are consistent with the simplest scenario for a protein–protein interaction; namely, a two-state mechanism. Here we analyzed the binding kinetics of the PSG supramodule with CRIPT. We show that PSG binds CRIPT via a more complex mechanism involving two conformational states interconverting on the second timescale. Both conformational states bound a CRIPT peptide with similar affinities but with different rates, and the distribution of the two conformational states was slightly shifted upon CRIPT binding. Our results are consistent with recent structural findings of conformational changes in PSD-95 and demonstrate how conformational transitions in supertertiary structures can shape the ligand-binding energy landscape and modulate protein–protein interactions.

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Identification of Toll-like receptor signaling inhibitors based on selective activation of hierarchically acting signaling proteins

Science Signaling ◽

10.1126/scisignal.aaq1077 ◽

2018 ◽

Vol 11 (543) ◽

pp. eaaq1077 ◽

Cited By ~ 9

Author(s):

Sirish K. Ippagunta ◽

Julie A. Pollock ◽

Naina Sharma ◽

Wenwei Lin ◽

Taosheng Chen ◽

...

Keyword(s):

Protein Interactions ◽

Interleukin 1 ◽

Specific Inhibitor ◽

Bioactive Compound ◽

Adaptor Proteins ◽

Protein Protein Interactions ◽

Protein Protein Interaction ◽

Relationship Analysis ◽

Starting Point ◽

Signaling Inhibitors

Toll-like receptors (TLRs) recognize various pathogen- and host tissue–derived molecules and initiate inflammatory immune responses. Exaggerated or prolonged TLR activation, however, can lead to etiologically diverse diseases, such as bacterial sepsis, metabolic and autoimmune diseases, or stroke. Despite the apparent medical need, no small-molecule drugs against TLR pathways are clinically available. This may be because of the complex signaling mechanisms of TLRs, which are governed by a series of protein-protein interactions initiated by Toll/interleukin-1 receptor homology domains (TIR) found in TLRs and the cytoplasmic adaptor proteins TIRAP and MyD88. Oligomerization of TLRs with MyD88 or TIRAP leads to the recruitment of members of the IRAK family of kinases and the E3 ubiquitin ligase TRAF6. We developed a phenotypic drug screening system based on the inducible homodimerization of either TIRAP, MyD88, or TRAF6, that ranked hits according to their hierarchy of action. From a bioactive compound library, we identified methyl-piperidino-pyrazole (MPP) as a TLR-specific inhibitor. Structure-activity relationship analysis, quantitative proteomics, protein-protein interaction assays, and cellular thermal shift assays suggested that MPP targets the TIR domain of MyD88. Chemical evolution of the original MPP scaffold generated compounds with selectivity for distinct TLRs that interfered with specific TIR interactions. Administration of an MPP analog to mice protected them from TLR4-dependent inflammation. These results validate this phenotypic screening approach and suggest that the MPP scaffold could serve as a starting point for the development of anti-inflammatory drugs.

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An efficient transient assays system using Agrobacterium-mediated transformation of onion (Allium cepa) epidermal cells

Indian Journal of Genetics and Plant Breeding (The) ◽

10.31742/ijgpb.80.3.17 ◽

2020 ◽

Vol 80 (03) ◽

Author(s):

Yu-Miao Zhang ◽

Jun Wang ◽

Tao Wu

Keyword(s):

Subcellular Localization ◽

Protein Interaction ◽

Protein Interactions ◽

Epidermal Cells ◽

Cyclin Dependent Kinase ◽

Protein Subcellular Localization ◽

Protein Protein Interactions ◽

Efficient System ◽

Protein Protein Interaction ◽

Onion Epidermal Cells

In this study, the Agrobacterium infection medium, infection duration, detergent, and cell density were optimized. The sorghum-based infection medium (SbIM), 10-20 min infection time, addition of 0.01% Silwet L-77, and Agrobacterium optical density at 600 nm (OD600), improved the competence of onion epidermal cells to support Agrobacterium infection at >90% efficiency. Cyclin-dependent kinase D-2 (CDKD-2) and cytochrome c-type biogenesis protein (CYCH), protein-protein interactions were localized. The optimized procedure is a quick and efficient system for examining protein subcellular localization and protein-protein interaction.

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Decoding Protein-protein Interactions: An Overview

Current Topics in Medicinal Chemistry ◽

10.2174/1568026620666200226105312 ◽

2020 ◽

Vol 20 (10) ◽

pp. 855-882

Author(s):

Olivia Slater ◽

Bethany Miller ◽

Maria Kontoyianni

Keyword(s):

Drug Discovery ◽

Protein Interactions ◽

Drug Repurposing ◽

Protein Docking ◽

Target Space ◽

Protein Protein Interactions ◽

X Ray Crystallography ◽

Protein Protein Interaction ◽

Interaction Sites ◽

Long Time

Drug discovery has focused on the paradigm “one drug, one target” for a long time. However, small molecules can act at multiple macromolecular targets, which serves as the basis for drug repurposing. In an effort to expand the target space, and given advances in X-ray crystallography, protein-protein interactions have become an emerging focus area of drug discovery enterprises. Proteins interact with other biomolecules and it is this intricate network of interactions that determines the behavior of the system and its biological processes. In this review, we briefly discuss networks in disease, followed by computational methods for protein-protein complex prediction. Computational methodologies and techniques employed towards objectives such as protein-protein docking, protein-protein interactions, and interface predictions are described extensively. Docking aims at producing a complex between proteins, while interface predictions identify a subset of residues on one protein that could interact with a partner, and protein-protein interaction sites address whether two proteins interact. In addition, approaches to predict hot spots and binding sites are presented along with a representative example of our internal project on the chemokine CXC receptor 3 B-isoform and predictive modeling with IP10 and PF4.

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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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Universal Screening Methods and Applications of ThermoFluor®

CrossRef Listing of Deleted DOIs ◽

10.1177/1087057106292746 ◽

2006 ◽

Vol 11 (7) ◽

pp. 854-863 ◽

Cited By ~ 124

Author(s):

Maxwell D. Cummings ◽

Michael A. Farnum ◽

Marina I. Nelen

Keyword(s):

Protein Interactions ◽

Protein Function ◽

Protein Unfolding ◽

Direct Detection ◽

Functional Characterization ◽

Screening Methods ◽

Protein Protein Interactions ◽

Protein Protein Interaction ◽

Bacterial Enzyme ◽

Research Problems

The genomics revolution has unveiled a wealth of poorly characterized proteins. Scientists are often able to produce milligram quantities of proteins for which function is unknown or hypothetical, based only on very distant sequence homology. Broadly applicable tools for functional characterization are essential to the illumination of these orphan proteins. An additional challenge is the direct detection of inhibitors of protein-protein interactions (and allosteric effectors). Both of these research problems are relevant to, among other things, the challenge of finding and validating new protein targets for drug action. Screening collections of small molecules has long been used in the pharmaceutical industry as 1 method of discovering drug leads. Screening in this context typically involves a function-based assay. Given a sufficient quantity of a protein of interest, significant effort may still be required for functional characterization, assay development, and assay configuration for screening. Increasingly, techniques are being reported that facilitate screening for specific ligands for a protein of unknown function. Such techniques also allow for function-independent screening with better characterized proteins. ThermoFluor®, a screening instrument based on monitoring ligand effects on temperature-dependent protein unfolding, can be applied when protein function is unknown. This technology has proven useful in the decryption of an essential bacterial enzyme and in the discovery of a series of inhibitors of a cancer-related, protein-protein interaction. The authors review some of the tools relevant to these research problems in drug discovery, and describe our experiences with 2 different proteins.

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Trifunctional cross-linker for mapping protein-protein interaction networks and comparing protein conformational states

eLife ◽

10.7554/elife.12509 ◽

2016 ◽

Vol 5 ◽

Cited By ~ 56

Author(s):

Dan Tan ◽

Qiang Li ◽

Mei-Jun Zhang ◽

Chao Liu ◽

Chengying Ma ◽

...

Keyword(s):

Protein Interactions ◽

Structural Information ◽

Affinity Purification ◽

Protein Protein Interactions ◽

C Elegans ◽

Protein Protein Interaction ◽

Lysine Residues ◽

Spacer Arm ◽

Cross Linker ◽

Protein Nucleic Acid

To improve chemical cross-linking of proteins coupled with mass spectrometry (CXMS), we developed a lysine-targeted enrichable cross-linker containing a biotin tag for affinity purification, a chemical cleavage site to separate cross-linked peptides away from biotin after enrichment, and a spacer arm that can be labeled with stable isotopes for quantitation. By locating the flexible proteins on the surface of 70S ribosome, we show that this trifunctional cross-linker is effective at attaining structural information not easily attainable by crystallography and electron microscopy. From a crude Rrp46 immunoprecipitate, it helped identify two direct binding partners of Rrp46 and 15 protein-protein interactions (PPIs) among the co-immunoprecipitated exosome subunits. Applying it to E. coli and C. elegans lysates, we identified 3130 and 893 inter-linked lysine pairs, representing 677 and 121 PPIs. Using a quantitative CXMS workflow we demonstrate that it can reveal changes in the reactivity of lysine residues due to protein-nucleic acid interaction.

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Structure-aware protein–protein interaction site prediction using deep graph convolutional network

Bioinformatics ◽

10.1093/bioinformatics/btab643 ◽

2021 ◽

Author(s):

Qianmu Yuan ◽

Jianwen Chen ◽

Huiying Zhao ◽

Yaoqi Zhou ◽

Yuedong Yang

Keyword(s):

Protein Interactions ◽

Spatial Information ◽

Screening Tools ◽

Supplementary Information ◽

Protein Protein Interactions ◽

Convolutional Network ◽

Source Codes ◽

Site Prediction ◽

Protein Protein Interaction ◽

Mapping Techniques

Abstract Motivation Protein–protein interactions (PPI) play crucial roles in many biological processes, and identifying PPI sites is an important step for mechanistic understanding of diseases and design of novel drugs. Since experimental approaches for PPI site identification are expensive and time-consuming, many computational methods have been developed as screening tools. However, these methods are mostly based on neighbored features in sequence, and thus limited to capture spatial information. Results We propose a deep graph-based framework deep Graph convolutional network for Protein–Protein-Interacting Site prediction (GraphPPIS) for PPI site prediction, where the PPI site prediction problem was converted into a graph node classification task and solved by deep learning using the initial residual and identity mapping techniques. We showed that a deeper architecture (up to eight layers) allows significant performance improvement over other sequence-based and structure-based methods by more than 12.5% and 10.5% on AUPRC and MCC, respectively. Further analyses indicated that the predicted interacting sites by GraphPPIS are more spatially clustered and closer to the native ones even when false-positive predictions are made. The results highlight the importance of capturing spatially neighboring residues for interacting site prediction. Availability and implementation The datasets, the pre-computed features, and the source codes along with the pre-trained models of GraphPPIS are available at https://github.com/biomed-AI/GraphPPIS. The GraphPPIS web server is freely available at https://biomed.nscc-gz.cn/apps/GraphPPIS. Supplementary information Supplementary data are available at Bioinformatics online.

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