Protein-protein Interactions In Cancer Research Articles

Overcoming phenotypic switching: targeting protein-protein interactions in cancer.

Alternative protein-protein interactions (PPIs) arising from mutations or post-translational modifications (PTMs), termed phenotypic switching (PS), are critical for the transmission of alternative pathogenic signals and are particularly significant in cancer. In recent years, PPIs have emerged as promising targets for rational drug design, primarily because their high specificity facilitates targeting of disease-related signaling pathways. However, obstacles exist at the molecular level that arise from the properties of the interaction interfaces and the propensity of small molecule drugs to interact with more than one cleft surface. The difficulty in identifying small molecules that act as activators or inhibitors to counteract the biological effects of mutations raises issues that have not been encountered before. For example, small molecules can bind tightly but may not act as drugs or bind to multiple sites (interaction promiscuity). Another reason is the absence of significant clefts on protein surfaces; if a pocket is present, it may be too small, or its geometry may prevent binding. PS, which arises from oncogenic (alternative) signaling, causes drug resistance and forms the basis for the systemic robustness of tumors. In this review, the properties of PPI interfaces relevant to the design and development of targeting drugs are examined. In addition, the interactions between three tyrosine kinase inhibitors (TKIs) employed as drugs are discussed. Finally, potential novel targets of one of these drugs were identified in silico.

Abstract 877: Protein-protein interactions in cancer: An in-silico analysis of Cdc6 interactions

Abstract Aberrant activity of cell cycle proteins is a hallmark of cancer and therefore, cell cycle regulators are considered attractive targets in cancer therapy. One such regulator, Cell division cycle 6 (Cdc6), is a key conserved cell cycle protein responsible for the initiation of eukaryotic DNA replication. Cdc6 overexpression has been observed as an early event in malignancies. Additionally, Cdc6 has also been implicated in contributing to the aggressiveness of cervical and bladder cancers. In budding yeast, Cdc6 collaborates with proteins such as Cyclin B2 (Clb2) and Cell division control protein 4 (Cdc4) to ensure the fidelity of cell division by preventing re-replication. The perfectly orchestrated sequence of events requires Cdc6 to interact with other cell cycle proteins during the correct stage of the cell cycle. During S-phase, Cyclin-dependent kinase 1 (Cdk1) regulates Cdc6 levels and Cdc6 is targeted for degradation by polyubiquination via Cdc4 (SCF-complex). In mitosis, phosphorylation by Cdk1, induces the binding of Cdc6 to Clb2-Cdk1. Clb2 binds to Cdc6’s 44PEKLQF49 motif at the Cdc6 N-terminus thereby preventing Cdc6 from licensing origins, preventing DNA re-replication, and ensuring genomic integrity. Although, it has been well studied how each Cdc6 phosphorylation site regulates protein binding, in the absence of full-length structures for many of these proteins, the interaction modes and molecular details for Cdc6’s interaction with its protein partners remain unknown. In this study, we have characterized and analyzed Cdc6’s interactions with some of its key interactors. We present a robust computational approach to understand the molecular mechanisms of Cdc6 interactions with Cdc4 and Clb2, which includes homology/ab initio modeling of the full-length structures including their intrinsically disordered regions (IDRs), and predicted interaction scenarios. Our results show that the Cdc6-Cdc4 interaction displays a dynamic shift between S-phase and mitosis. In S-phase, Cdc4 binds to the N’ phosphodegrons on Cdc6, but in mitosis, Cdc4 binding shifts to the C’ phosphodegrons. In addition, we show Clb2’s K270, within its hydrophobic patch, binds E45 in Cdc6’s 44PEKLQF49 motif. We also provide a comparison of Clb2’s interaction with Cdc6 with that of Bud3 to assess the conservation of the binding mechanism. We show that the molecular mechanism is conserved with similar residues and motifs being involved in the interaction. By exploring the molecular details underlying these protein-protein interactions, we can gain insightful information and identify interface residues that are responsible for specific protein-protein interactions. Such molecular details are critical to pave the way to the discovery of druggable targets and ultimately lead to the development of new cancer therapeutics. Citation Format: Andriele Silva Eichner, Jasmin Philip, Amy Ikui, Shaneen Singh. Protein-protein interactions in cancer: An in-silico analysis of Cdc6 interactions [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 877.

Utilization of macrocyclic peptides to target protein-protein interactions in cancer.

Protein-protein interactions (PPIs) play vital roles in normal cellular processes. Dysregulated PPIs are involved in the process of various diseases, including cancer. Thus, these PPIs may serve as potential therapeutic targets in cancer treatment. However, despite rapid advances in small-molecule drugs and biologics, it is still hard to target PPIs, especially for those intracellular PPIs. Macrocyclic peptides have gained growing attention for their therapeutic properties in targeting dysregulated PPIs. Macrocyclic peptides have some unique features, such as moderate sizes, high selectivity, and high binding affinities, which make them good drug candidates. In addition, some oncology macrocyclic peptide drugs have been approved by the US Food and Drug Administration (FDA) for clinical use. Here, we reviewed the recent development of macrocyclic peptides in cancer treatment. The opportunities and challenges were also discussed to inspire new perspectives.

Analysis of Protein-Protein Interactions by Protein Microarrays.

The analysis of the proteome and the interactome would be useful for a better understanding of the pathophysiology of several disorders, allowing the identification of potential specific markers for early diagnosis and prognosis, as well as potential targets of intervention. Among different proteomic approaches, high-density protein microarrays have become an interesting tool for the screening of protein-protein interactions and the interactome definition of disease-associated dysregulated proteins. This information might contribute to the identification of altered signaling pathways and protein functions involved in the pathogenesis of a disease. Remarkably, protein microarrays have been already satisfactorily employed for the study of protein-protein interactions in cancer, allergy, or neurodegenerative diseases. Here, we describe the utilization of recombinant protein microarrays for the identification of protein-protein interactions to help in the definition of disease-specific dysregulated interactomes.

Engineering "Antimicrobial Peptides" and Other Peptides to Modulate Protein-Protein Interactions in Cancer.

Antimicrobial peptides (AMPs) are a class of peptides found across a wide array of organisms that play key roles in host defense. AMPs induce selective death in target cells and orchestrate specific or nonspecific immune responses. Many AMPs exhibit native anticancer activity in addition to antibacterial activity, and others have been engineered as antineoplastic agents. We discuss the use of AMPs in the detection and treatment of cancer as well as mechanisms of AMP-induced cell death. We present key examples of cathelicidins and transferrins, which are major AMP families. Further, we discuss the critical roles of protein-protein interactions (PPIs) in cancer and how AMPs are well-suited to target PPIs based on their unique drug-like properties not exhibited by small molecules or antibodies. While peptides, including AMPs, can have limited stability and bioavailability, these issues can be overcome by peptide backbone modification or cyclization (e.g., stapling) and by the use of delivery systems such as cellpenetrating peptides (CPPs), respectively. We discuss approaches for optimizing drug properties of peptide and peptidomimetic leads (modified peptides), providing examples of promising techniques that may be applied to AMPs. These molecules represent an exciting resource as anticancer agents with unique therapeutic advantages that can target challenging mechanisms involving PPIs. Indeed, AMPs are suitable drug leads for further development of cancer therapeutics, and many studies to this end are underway.

Macropinocytosis as a Key Determinant of Peptidomimetic Uptake in Cancer Cells.

Peptides and peptidomimetics represent the middle space between small molecules and large proteins-they retain the relatively small size and synthetic accessibility of small molecules while providing high binding specificity for biomolecular partners typically observed with proteins. During the course of our efforts to target intracellular protein-protein interactions in cancer, we observed that the cellular uptake of peptides is critically determined by the cell line-specifically, we noted that peptides show better uptake in cancer cells with enhanced macropinocytic indices. Here, we describe the results of our analysis of cellular penetration by different classes of conformationally stabilized peptides. We tested the uptake of linear peptides, peptide macrocycles, stabilized helices, β-hairpin peptides, and cross-linked helix dimers in 11 different cell lines. Efficient uptake of these conformationally defined constructs directly correlated with the macropinocytic activity of each cell line: high uptake of compounds was observed in cells with mutations in certain signaling pathways. Significantly, the study shows that constrained peptides follow the same uptake mechanism as proteins in macropinocytic cells, but unlike proteins, peptide mimics can be readily designed to resist denaturation and proteolytic degradation. Our findings expand the current understanding of cellular uptake in cancer cells by designed peptidomimetics and suggest that cancer cells with certain mutations are suitable mediums for the study of biological pathways with peptide leads.

A Review of Stapled Peptides and Small Molecules to Inhibit Protein-Protein Interactions in Cancer.

Disruption of binding of two or more molecules to a protein surface is a common basis of inhibition of many biological activities. Smallmolecule inhibitors, antibodies, proteins, and peptidomimetics have been examined as ways to antagonize receptor activity. The peptide α-helix plays a crucial role in the function of many proteins. Hence, much effort has been invested in mimicking α-helices at the binding interface of two proteins to competitively inhibit their interactions. Peptide stapling involves choosing two amino acids on the same face of a native peptide sequence for substitution with non-native amino acids whose side chains can be "stapled" together. The focus of this review is to survey the prevalence in literature of stapled peptides and small-molecule antagonists of interactions of selected mammalian cancer targets, such as β-catenin, BH3-only members of the Bcl-2 family of proteins, eIF4E/G, estrogen receptor complexes, EZH2, Mdm2, Notch, p110α, and survivin. The increasing interest in protein targets currently considered to be "undruggable" with greater selectivity for existing targets, with the goal of overcoming the omnipresent problem of resistance, could be served well by utilizing information about protein-protein interactions to develop both small-molecule and stapled peptide inhibitors.

Abstract PR15: Predicting the essentialities of protein-protein interactions in cancer

Abstract Many protein-protein interactions (PPIs) are essential to cellular proliferation and survival, and thus represent potential targets for novel compounds with high specificity and therapeutic potential in the treatment of cancer. Despite their therapeutic significance, there are currently no high-throughput experimental platforms to interrogate the essentiality of individual protein-protein interactions. The ability to computationally predict or infer PPI essentiality would help prioritize the development of therapeutic targets and advance understanding of cancer biology, and remains a significant barrier. We have developed a computational method to predict PPI essentiality by combining shRNA studies with network models of protein interaction pathways in an analytic framework. High-throughput single-gene shRNA silencing is a well-established experimental approach to study protein essentiality in genome-wide screens. The silencing of a single gene in an shRNA screen effectively disrupts multiple PPIs, masking their individual contributions to the observed protein essentiality. Our method uses a network model to infer cliques of PPIs that are disrupted as each gene is silenced, then uses these sets to formulate an additive model of how the unknown PPI essentialities combine to form the observed protein essentialities. This process is then deconvoluted to recover the unknown essentialities of each PPI using a regularized solver. We performed this analysis in 108 cell lines characterized for protein essentiality in Project Achilles [1, 2]. A superpathway of 2186 proteins and 11488 protein interactions was constructed from prior knowledge databases [3] and protein interaction screenings. We demonstrate the validity of our approach via prediction of essential PPIs such as TP53-MDM4 in cell lines sensitive to nutlin and CDK4-RB1 interactions in cell lines sensitive to CDK4 inhibitors. [1] Cowley, Weir & Vazquez, et al. Nature Scientific Data 1, Article number: 140035. September 30, 2014 [2] Cheung HW, Cowley GS, Weir BA, et al. Proc Natl Acad Sci. 2011 Jul 26; 108(30):12372-7. [3] Schaefer CF, Anthony K, Krupa S, et al. Nucleic Acids Research. Jan 2009. 37:674-9 Citation Format: Lee AD Cooper, Josue D. Moran, Zenggang Li, Yuhong Du, Sahar Harati, Andrey A. Ivanov, Phillip Webber, Jonathan J. Havel, Margaret A. Johns, Haian Fu, Carlos S. Moreno. Predicting the essentialities of protein-protein interactions in cancer. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr PR15.