scholarly journals Targeting protein–protein interactions by rational design: mimicry of protein surfaces

2006 ◽  
Vol 3 (7) ◽  
pp. 215-233 ◽  
Author(s):  
Steven Fletcher ◽  
Andrew D Hamilton

Protein–protein interactions play key roles in a range of biological processes, and are therefore important targets for the design of novel therapeutics. Unlike in the design of enzyme active site inhibitors, the disruption of protein–protein interactions is far more challenging, due to such factors as the large interfacial areas involved and the relatively flat and featureless topologies of these surfaces. Nevertheless, in spite of such challenges, there has been considerable progress in recent years. In this review, we discuss this progress in the context of mimicry of protein surfaces: targeting protein–protein interactions by rational design.

2021 ◽  
Vol 9 ◽  
Author(s):  
Xuefei Wang ◽  
Duan Ni ◽  
Yaqin Liu ◽  
Shaoyong Lu

Protein-protein interactions (PPIs) are well-established as a class of promising drug targets for their implications in a wide range of biological processes. However, drug development toward PPIs is inevitably hampered by their flat and wide interfaces, which generally lack suitable pockets for ligand binding, rendering most PPI systems “undruggable.” Here, we summarized drug design strategies for developing peptide-based PPI inhibitors. Importantly, several quintessential examples toward well-established PPI targets such as Bcl-2 family members, p53-MDM2, as well as APC-Asef are presented to illustrate the detailed schemes for peptide-based PPI inhibitor development and optimizations. This review supplies a comprehensive overview of recent progresses in drug discovery targeting PPIs through peptides or peptidomimetics, and will shed light on future therapeutic agent development toward the historically “intractable” PPI systems.


2020 ◽  
Vol 17 (4) ◽  
pp. 271-286
Author(s):  
Chang Xu ◽  
Limin Jiang ◽  
Zehua Zhang ◽  
Xuyao Yu ◽  
Renhai Chen ◽  
...  

Background: Protein-Protein Interactions (PPIs) play a key role in various biological processes. Many methods have been developed to predict protein-protein interactions and protein interaction networks. However, many existing applications are limited, because of relying on a large number of homology proteins and interaction marks. Methods: In this paper, we propose a novel integrated learning approach (RF-Ada-DF) with the sequence-based feature representation, for identifying protein-protein interactions. Our method firstly constructs a sequence-based feature vector to represent each pair of proteins, viaMultivariate Mutual Information (MMI) and Normalized Moreau-Broto Autocorrelation (NMBAC). Then, we feed the 638- dimentional features into an integrated learning model for judging interaction pairs and non-interaction pairs. Furthermore, this integrated model embeds Random Forest in AdaBoost framework and turns weak classifiers into a single strong classifier. Meanwhile, we also employ double fault detection in order to suppress over-adaptation during the training process. Results: To evaluate the performance of our method, we conduct several comprehensive tests for PPIs prediction. On the H. pyloridataset, our method achieves 88.16% accuracy and 87.68% sensitivity, the accuracy of our method is increased by 0.57%. On the S. cerevisiaedataset, our method achieves 95.77% accuracy and 93.36% sensitivity, the accuracy of our method is increased by 0.76%. On the Humandataset, our method achieves 98.16% accuracy and 96.80% sensitivity, the accuracy of our method is increased by 0.6%. Experiments show that our method achieves better results than other outstanding methods for sequence-based PPIs prediction. The datasets and codes are available at https://github.com/guofei-tju/RF-Ada-DF.git.


2020 ◽  
Vol 85 (16) ◽  
pp. 10552-10560
Author(s):  
Peng Sang ◽  
Yan Shi ◽  
Pirada Higbee ◽  
Minghui Wang ◽  
Sami Abdulkadir ◽  
...  

2020 ◽  
Author(s):  
Ramesh K. Jha ◽  
Allison Yankey ◽  
Kalifa Shabazz ◽  
Leslie Naranjo ◽  
Nileena Velappan ◽  
...  

ABSTRACTWhile natural protein-protein interactions have evolved to be induced by complex stimuli, rational design of interactions that can be switched-on-demand still remain challenging in the protein design world. Here, we demonstrate a computationally redesigned natural interface for improved binding affinity could further be mutated to adopt a pH switchable interaction. The redesigned interface of Protein G-IgG Fc domain, when incorporated with histidine and glutamic acid on Protein G (PrG-EHHE), showed a switch in binding affinity by 50-fold when pH was altered from mild acidic to mild basic. The wild type (WT) interface only showed negligible switch. The overall binding affinity at mild acidic pH for PrG-EHHE outperformed the WT PrG interaction. The new reagent PrG-EHHE will be revolutionary in IgG purification since the traditional method of using an extreme acidic pH for elution can be circumvented.Abstract Figure


Author(s):  
Natalia Sanchez de Groot ◽  
Marc Torrent Burgas

ABSTRACTBacteria use protein-protein interactions to infect their hosts and hijack fundamental pathways, which ensures their survival and proliferation. Hence, the infectious capacity of the pathogen is closely related to its ability to interact with host proteins. Here, we show that hubs in the host-pathogen interactome are isolated in the pathogen network by adapting the geometry of the interacting interfaces. An imperfect mimicry of the eukaryotic interfaces allows pathogen proteins to actively bind to the host’s target while preventing deleterious effects on the pathogen interactome. Understanding how bacteria recognize eukaryotic proteins may pave the way for the rational design of new antibiotic molecules.


2015 ◽  
Vol 137 (38) ◽  
pp. 12249-12260 ◽  
Author(s):  
Logan R. Hoggard ◽  
Yongqiang Zhang ◽  
Min Zhang ◽  
Vanja Panic ◽  
John A. Wisniewski ◽  
...  

Author(s):  
Sailu Sarvagalla ◽  
Mohane Selvaraj Coumar

Most of the developed kinase inhibitor drugs are ATP competitive and suffer from drawbacks such as off-target kinase activity, development of resistance due to mutation in the ATP binding pocket and unfavorable intellectual property situations. Besides the ATP binding pocket, protein kinases have binding sites that are involved in Protein-Protein Interactions (PPIs); these PPIs directly or indirectly regulate the protein kinase activity. Of recent, small molecule inhibitors of PPIs are emerging as an alternative to ATP competitive agents. Rational design of inhibitors for kinase PPIs could be carried out using molecular modeling techniques. In silico tools available for the prediction of hot spot residues and cavities at the PPI sites and the means to utilize this information for the identification of inhibitors are discussed. Moreover, in silico studies to target the Aurora B-INCENP PPI sites are discussed in context. Overall, this chapter provides detailed in silico strategies that are available to the researchers for carrying out structure-based drug design of PPI inhibitors.


2020 ◽  
Vol 16 ◽  
pp. 2505-2522
Author(s):  
Peter Bayer ◽  
Anja Matena ◽  
Christine Beuck

As one of the few analytical methods that offer atomic resolution, NMR spectroscopy is a valuable tool to study the interaction of proteins with their interaction partners, both biomolecules and synthetic ligands. In recent years, the focus in chemistry has kept expanding from targeting small binding pockets in proteins to recognizing patches on protein surfaces, mostly via supramolecular chemistry, with the goal to modulate protein–protein interactions. Here we present NMR methods that have been applied to characterize these molecular interactions and discuss the challenges of this endeavor.


2019 ◽  
Vol 47 (W1) ◽  
pp. W338-W344 ◽  
Author(s):  
Carlos H M Rodrigues ◽  
Yoochan Myung ◽  
Douglas E V Pires ◽  
David B Ascher

AbstractProtein–protein Interactions are involved in most fundamental biological processes, with disease causing mutations enriched at their interfaces. Here we present mCSM-PPI2, a novel machine learning computational tool designed to more accurately predict the effects of missense mutations on protein–protein interaction binding affinity. mCSM-PPI2 uses graph-based structural signatures to model effects of variations on the inter-residue interaction network, evolutionary information, complex network metrics and energetic terms to generate an optimised predictor. We demonstrate that our method outperforms previous methods, ranking first among 26 others on CAPRI blind tests. mCSM-PPI2 is freely available as a user friendly webserver at http://biosig.unimelb.edu.au/mcsm_ppi2/.


2015 ◽  
Vol 112 (14) ◽  
pp. 4501-4506 ◽  
Author(s):  
Marie Filteau ◽  
Guillaume Diss ◽  
Francisco Torres-Quiroz ◽  
Alexandre K. Dubé ◽  
Andrea Schraffl ◽  
...  

Cellular processes and homeostasis control in eukaryotic cells is achieved by the action of regulatory proteins such as protein kinase A (PKA). Although the outbound signals from PKA directed to processes such as metabolism, growth, and aging have been well charted, what regulates this conserved regulator remains to be systematically identified to understand how it coordinates biological processes. Using a yeast PKA reporter assay, we identified genes that influence PKA activity by measuring protein–protein interactions between the regulatory and the two catalytic subunits of the PKA complex in 3,726 yeast genetic-deletion backgrounds grown on two carbon sources. Overall, nearly 500 genes were found to be connected directly or indirectly to PKA regulation, including 80 core regulators, denoting a wide diversity of signals regulating PKA, within and beyond the described upstream linear pathways. PKA regulators span multiple processes, including the antagonistic autophagy and methionine biosynthesis pathways. Our results converge toward mechanisms of PKA posttranslational regulation by lysine acetylation, which is conserved between yeast and humans and that, we show, regulates protein complex formation in mammals and carbohydrate storage and aging in yeast. Taken together, these results show that the extent of PKA input matches with its output, because this kinase receives information from upstream and downstream processes, and highlight how biological processes are interconnected and coordinated by PKA.


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