Rational Design of Peptide-Based Inhibitors Disrupting Protein-Protein Interactions

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.

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

Journal of The Royal Society Interface ◽

10.1098/rsif.2006.0115 ◽

2006 ◽

Vol 3 (7) ◽

pp. 215-233 ◽

Cited By ~ 113

Author(s):

Steven Fletcher ◽

Andrew D Hamilton

Keyword(s):

Protein Interactions ◽

Active Site ◽

Rational Design ◽

Considerable Progress ◽

Biological Processes ◽

Protein Protein Interactions ◽

Protein Surfaces ◽

Enzyme Active Site ◽

Novel Therapeutics ◽

Interfacial Areas

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.

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Socket2: A Program for Locating, Visualising, and Analysing Coiled-coil Interfaces in Protein Structures

Bioinformatics ◽

10.1093/bioinformatics/btab631 ◽

2021 ◽

Author(s):

Prasun Kumar ◽

Derek N Woolfson

Keyword(s):

Protein Interactions ◽

De Novo ◽

Protein Structures ◽

Coiled Coil ◽

Biological Research ◽

Biological Processes ◽

Protein Protein Interactions ◽

Wide Range ◽

Core Packing ◽

Natural Amino Acids

Abstract Motivation Protein-protein interactions are central to all biological processes. One frequently observed mode of such interactions is the α-helical coiled coil (CC). Thus, an ability to extract, visualise, and analyse CC interfaces quickly and without expert guidance would facilitate a wide range of biological research. In 2001, we reported Socket, which locates and characterises CCs in protein structures based on the knobs-into-holes (KIH) packing between helices in CCs. Since then, studies of natural and de novo designed CCs have boomed, and the number of CCs in the RCSB PDB has increased rapidly. Therefore, we have updated Socket and made it accessible to expert and non-expert users alike. Results The original Socket only classified CCs with up to 6 helices. Here, we report Socket2, which rectifies this oversight to identify CCs with any number of helices, and KIH interfaces with any of the 20 proteinogenic residues or incorporating non-natural amino acids. In addition, we have developed a new and easy-to-use web server with additional features. These include the use of NGL Viewer for instantly visualising CCs, and tabs for viewing the sequence repeats, helix-packing angles, and core-packing geometries of CCs identified and calculated by Socket2. Availability and implementation Socket2 has been tested on all modern browsers. It can be accessed freely at http://coiledcoils.chm.bris.ac.uk/socket2/home.html. The source code is distributed using an MIT license and available to download under the Downloads tab of the Socket2 home page.

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Regulation of p53 by the 14-3-3 protein interaction network: new opportunities for drug discovery in cancer

Cell Death Discovery ◽

10.1038/s41420-020-00362-3 ◽

2020 ◽

Vol 6 (1) ◽

Author(s):

Marta Falcicchio ◽

Jake A. Ward ◽

Salvador Macip ◽

Richard G. Doveston

Keyword(s):

Protein Interactions ◽

Drug Targets ◽

Interaction Network ◽

Malignant Cell ◽

Protein Protein Interactions ◽

P53 Pathway ◽

Regulatory Pathways ◽

Wide Range ◽

Suppressor Mechanism ◽

New Compounds

AbstractMost cancers evolve to disable the p53 pathway, a key tumour suppressor mechanism that prevents transformation and malignant cell growth. However, only ~50% exhibit inactivating mutations of p53, while in the rest its activity is suppressed by changes in the proteins that modulate the pathway. Therefore, restoring p53 activity in cells in which it is still wild type is a highly attractive therapeutic strategy that could be effective in many different cancer types. To this end, drugs can be used to stabilise p53 levels by modulating its regulatory pathways. However, despite the emergence of promising strategies, drug development has stalled in clinical trials. The need for alternative approaches has shifted the spotlight to the 14-3-3 family of proteins, which strongly influence p53 stability and transcriptional activity through direct and indirect interactions. Here, we present the first detailed review of how 14-3-3 proteins regulate p53, with special emphasis on the mechanisms involved in their binding to different members of the pathway. This information will be important to design new compounds that can reactivate p53 in cancer cells by influencing protein–protein interactions. The intricate relationship between the 14-3-3 isoforms and the p53 pathway suggests that many potential drug targets for p53 reactivation could be identified and exploited to design novel antineoplastic therapies with a wide range of applications.

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Protein-Protein Interactions and Prediction: A Comprehensive Overview

Protein and Peptide Letters ◽

10.2174/09298665113209990056 ◽

2013 ◽

Vol 21 (8) ◽

pp. 779-789 ◽

Cited By ~ 10

Author(s):

Gopichandran Sowmya ◽

Shoba Ranganathan

Keyword(s):

Protein Interactions ◽

Protein Protein Interactions ◽

Comprehensive Overview

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An Integrated Prediction Method for Identifying Protein-Protein Interactions

Current Proteomics ◽

10.2174/1570164616666190306152318 ◽

2020 ◽

Vol 17 (4) ◽

pp. 271-286

Author(s):

Chang Xu ◽

Limin Jiang ◽

Zehua Zhang ◽

Xuyao Yu ◽

Renhai Chen ◽

...

Keyword(s):

Protein Interactions ◽

Feature Vector ◽

Prediction Method ◽

Feature Representation ◽

Protein Interaction Networks ◽

Learning Approach ◽

Biological Processes ◽

Integrated Learning ◽

Protein Protein Interactions ◽

Training Process

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.

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Rational Design and Synthesis of Right-Handed d-Sulfono-γ-AApeptide Helical Foldamers as Potent Inhibitors of Protein–Protein Interactions

The Journal of Organic Chemistry ◽

10.1021/acs.joc.0c00996 ◽

2020 ◽

Vol 85 (16) ◽

pp. 10552-10560

Author(s):

Peng Sang ◽

Yan Shi ◽

Pirada Higbee ◽

Minghui Wang ◽

Sami Abdulkadir ◽

...

Keyword(s):

Protein Interactions ◽

Rational Design ◽

Protein Protein Interactions ◽

Design And Synthesis

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Ways into Understanding HIF Inhibition

Cancers ◽

10.3390/cancers13010159 ◽

2021 ◽

Vol 13 (1) ◽

pp. 159

Author(s):

Tina Schönberger ◽

Joachim Fandrey ◽

Katrin Prost-Fingerle

Keyword(s):

Protein Interactions ◽

Target Genes ◽

Protein Protein Interactions ◽

Low Oxygen ◽

Drug Candidates ◽

Open Questions ◽

Wide Range ◽

Hif Pathway

Hypoxia is a key characteristic of tumor tissue. Cancer cells adapt to low oxygen by activating hypoxia-inducible factors (HIFs), ensuring their survival and continued growth despite this hostile environment. Therefore, the inhibition of HIFs and their target genes is a promising and emerging field of cancer research. Several drug candidates target protein–protein interactions or transcription mechanisms of the HIF pathway in order to interfere with activation of this pathway, which is deregulated in a wide range of solid and liquid cancers. Although some inhibitors are already in clinical trials, open questions remain with respect to their modes of action. New imaging technologies using luminescent and fluorescent methods or nanobodies to complement widely used approaches such as chromatin immunoprecipitation may help to answer some of these questions. In this review, we aim to summarize current inhibitor classes targeting the HIF pathway and to provide an overview of in vitro and in vivo techniques that could improve the understanding of inhibitor mechanisms. Unravelling the distinct principles regarding how inhibitors work is an indispensable step for efficient clinical applications and safety of anticancer compounds.

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Elucidating Protein-protein Interactions Through Computational Approaches and Designing Small Molecule Inhibitors Against them for Various Diseases

Current Topics in Medicinal Chemistry ◽

10.2174/1568026618666181025114903 ◽

2018 ◽

Vol 18 (20) ◽

pp. 1719-1736 ◽

Cited By ~ 3

Author(s):

Sharanya Sarkar ◽

Khushboo Gulati ◽

Manikyaprabhu Kairamkonda ◽

Amit Mishra ◽

Krishna Mohan Poluri

Keyword(s):

Small Molecule ◽

Protein Interactions ◽

Small Molecule Inhibitors ◽

Computational Techniques ◽

Protein Protein Interactions ◽

Computational Tools ◽

Computational Approaches ◽

Protein Protein Interaction ◽

Wide Range ◽

Therapeutic Measures

Background: To carry out wide range of cellular functionalities, proteins often associate with one or more proteins in a phenomenon known as Protein-Protein Interaction (PPI). Experimental and computational approaches were applied on PPIs in order to determine the interacting partners, and also to understand how an abnormality in such interactions can become the principle cause of a disease. Objective: This review aims to elucidate the case studies where PPIs involved in various human diseases have been proven or validated with computational techniques, and also to elucidate how small molecule inhibitors of PPIs have been designed computationally to act as effective therapeutic measures against certain diseases. Results: Computational techniques to predict PPIs are emerging rapidly in the modern day. They not only help in predicting new PPIs, but also generate outputs that substantiate the experimentally determined results. Moreover, computation has aided in the designing of novel inhibitor molecules disrupting the PPIs. Some of them are already being tested in the clinical trials. Conclusion: This review delineated the classification of computational tools that are essential to investigate PPIs. Furthermore, the review shed light on how indispensable computational tools have become in the field of medicine to analyze the interaction networks and to design novel inhibitors efficiently against dreadful diseases in a shorter time span.

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