Exploring Molecular Contacts of MUC1 at CIN85 Binding Interface to Address Future Drug Design Efforts

The modulation of protein-protein interactions (PPIs) by small molecules represents a valuable strategy for pharmacological intervention in several human diseases. In this context, computer-aided drug discovery techniques offer useful resources to predict the network of interactions governing the recognition process between protein partners, thus furnishing relevant information for the design of novel PPI modulators. In this work, we focused our attention on the MUC1-CIN85 complex as a crucial PPI controlling cancer progression and metastasis. MUC1 is a transmembrane glycoprotein whose extracellular domain contains a variable number of tandem repeats (VNTRs) regions that are highly glycosylated in normal cells and under-glycosylated in cancer. The hypo-glycosylation fosters the exposure of the backbone to new interactions with other proteins, such as CIN85, that alter the intracellular signalling in tumour cells. Herein, different computational approaches were combined to investigate the molecular recognition pattern of MUC1-CIN85 PPI thus unveiling new structural information useful for the design of MUC1-CIN85 PPI inhibitors as potential anti-metastatic agents.     

Breaking the Fourth Wall: Modulating Quaternary Associations for Protein Regulation and Drug Discovery

Protein–protein interactions (PPIs) are an effective means to orchestrate intricate biological processes required to sustain life. Approximately 650 000 PPIs underlie the human interactome; thus underscoring its complexity and the manifold signaling outputs altered in response to changes in specific PPIs. This minireview illustrates the growing arsenal of PPI assemblies and offers insights into how these varied PPI regulatory modalities are relevant to customized drug discovery, with a focus on cancer. First, known and emerging PPIs and PPI-targeted drugs of both natural and synthetic origin are categorized. Building on these discussions, the merits of PPI-guided therapeutics over traditional drug design are discussed. Finally, a compare-and-contrast section for different PPI blockers, with gain-of-function PPI interventions, such as PROTACS, is provided.     

Protein–Protein Interaction Prediction for Targeted Protein Degradation

Protein–protein interactions (PPIs) play a fundamental role in various biological functions; thus, detecting PPI sites is essential for understanding diseases and developing new drugs. PPI prediction is of particular relevance for the development of drugs employing targeted protein degradation, as their efficacy relies on the formation of a stable ternary complex involving two proteins. However, experimental methods to detect PPI sites are both costly and time-intensive. In recent years, machine learning-based methods have been developed as screening tools. While they are computationally more efficient than traditional docking methods and thus allow rapid execution, these tools have so far primarily been based on sequence information, and they are therefore limited in their ability to address spatial requirements. In addition, they have to date not been applied to targeted protein degradation. Here, we present a new deep learning architecture based on the concept of graph representation learning that can predict interaction sites and interactions of proteins based on their surface representations. We demonstrate that our model reaches state-of-the-art performance using AUROC scores on the established MaSIF dataset. We furthermore introduce a new dataset with more diverse protein interactions and show that our model generalizes well to this new data. These generalization capabilities allow our model to predict the PPIs relevant for targeted protein degradation, which we show by demonstrating the high accuracy of our model for PPI prediction on the available ternary complex data. Our results suggest that PPI prediction models can be a valuable tool for screening protein pairs while developing new drugs for targeted protein degradation.     

Chemical Space Overlap with Critical Protein–Protein Interface Residues in Commercial and Specialized Small-Molecule Libraries

There is growing interest in the use of structure-based virtual screening to identify small molecules that inhibit challenging protein–protein interactions (PPIs). In this study, we investigated how effectively chemical library members docked at the PPI interface mimic the position of critical side-chain residues known as “hot spots”. Three compound collections were considered, a commercially available screening collection (ChemDiv), a collection of diversity-oriented synthesis (DOS) compounds that contains natural-product-like small molecules, and a library constructed using established reactions (the “screenable chemical universe based on intuitive data organization”, SCUBIDOO). Three different tight PPIs for which hot-spot residues have been identified were selected for analysis: uPAR⋅uPA, TEAD4⋅Yap1, and Ca_Vα⋅Ca_Vβ. Analysis of library physicochemical properties was followed by docking to the PPI receptors. A pharmacophore method was used to measure overlap between small-molecule substituents and hot-spot side chains. Fragment-like conformationally restricted small molecules showed better hot-spot overlap for interfaces with well-defined pockets such as uPAR⋅uPA, whereas better overlap was observed for more complex DOS compounds in interfaces lacking a well-defined binding site such as TEAD4⋅Yap1. Virtual screening of conformationally restricted compounds targeting uPAR⋅uPA and TEAD4⋅Yap1 followed by experimental validation reinforce these findings, as the best hits were fragment-like and had few rotatable bonds for the former, while no hits were identified for the latter. Overall, such studies provide a framework for understanding PPIs in the context of additional chemical matter and new PPI definitions.     

Protein Surface Mimetics: Understanding How Ruthenium Tris(Bipyridines) Interact with Proteins

Protein surface mimetics achieve high‐affinity binding by exploiting a scaffold to project binding groups over a large area of solvent‐exposed protein surface to make multiple cooperative noncovalent interactions. Such recognition is a prerequisite for competitive/orthosteric inhibition of protein–protein interactions (PPIs). This paper describes biophysical and structural studies on ruthenium(II) tris(bipyridine) surface mimetics that recognize cytochrome (cyt) c and inhibit the cyt c/cyt c peroxidase (CCP) PPI. Binding is electrostatically driven, with enhanced affinity achieved through enthalpic contributions thought to arise from the ability of the surface mimetics to make a greater number of noncovalent interactions than CCP with surface‐exposed basic residues on cyt c. High‐field natural abundance ¹H,¹⁵N HSQC NMR experiments are consistent with surface mimetics binding to cyt c in similar manner to CCP. This provides a framework for understanding recognition of proteins by supramolecular receptors and informing the design of ligands superior to the protein partners upon which they are inspired.     

A Box of Chemistry to Inhibit the MEN1 Tumor Suppressor Gene Promoting Leukemia

Targeting protein-protein interactions (PPIs) with small-molecule inhibitors has become a hotbed of modern drug development. In this review, we describe a new class of PPI inhibitors that block menin from binding to MLL proteins. Menin is encoded by the MEN1 tumor suppressor, but acts as an essential cofactor for MLL/KMT2A-rearranged leukemias. The most promising menin-MLL inhibitors belong to the thienopyrimidine class and have recently entered phase I/II clinical trials for treating acute leukemias characterized by MLL/KMT2A translocations or NPM1 mutations. As single agents, thienopyrimidine compounds eradicate leukemia in a xenograft models of primary leukemic cells belonging to the MLL-rearranged or NPM1-mutant subtypes. These compounds are well tolerated with few or no side effects, which is remarkable given the tumor-suppressor function of menin. The menin-MLL inhibitors highlight how leukemia patients could benefit from a targeted epigenetic therapy with novel PPI inhibitors obtained by directed chemical evolution.     

Selective Disruption of Survivin's Protein-Protein Interactions: A Supramolecular Approach Based on Guanidiniocarbonylpyrrole

Targeting specific protein binding sites to interfere with protein-protein interactions (PPIs) is crucial for the rational modulation of biologically relevant processes. Survivin, which is highly overexpressed in most cancer cells and considered to be a key player of carcinogenesis, features two functionally relevant binding sites. Here, we demonstrate selective disruption of the Survivin/Histone H3 or the Survivin/Crm1 interaction using a supramolecular approach. By rational design we identified two structurally related ligands ( L_NES and L_HIS ), capable of selectively inhibiting these PPIs, leading to a reduction in cancer cell proliferation.     

Bacterially Secretable Single-Chain Tandem Macrocyclic Peptides for High Affinity and Inhibitory Activity

The inhibition of protein-protein interactions (PPIs) is an effective approach for therapy. Owing to their large binding surface areas to target proteins, macrocyclic peptides are suitable molecules for PPI inhibition. In this study, we developed single-chain tandem macrocyclic peptides (STaMPtides) that inhibits the vascular endothelial growth factor (VEGF) receptor 2 (VEGFR2). They were artificially designed to comprise two different VEGFR2-binding macrocyclic peptides linked in tandem by peptide linkers and secreted by Corynebacterium glutamicum. Most potent VEGFR2-inhibitory STaMPtides with length-optimized linkers exhibited >1000 times stronger inhibitory activity than their parental monomeric peptides, possibly due to the avidity effect of heterodimerization. Our approach of using STaMPtides for PPI inhibition may be used to inhibit other extracellular factors, such as growth factors and cytokines.     

Chemical Translational Biology: Redefining Druggability of Protein-Protein Interactions

Chemical biologists use chemical tools to answer biological questions. The translational application of these principles has led to an explosion in the discovery and druggability of new protein targets, including protein-protein interactions (PPIs). Proteins tend to interact with other macromolecules using relatively large and featureless binding surfaces, which has hampered traditional drug discovery efforts, particularly for interactions with weaker affinity. In this article, I discuss several emerging strategies for targeting PPIs, including computational and structural methods and novel screening approaches. In particular, I focus on hijacking intrinsic protein allosteric pathways for the discovery and design of small-molecule and peptide ligands.     

From Affinity to Proximity Techniques to Investigate Protein Complexes in Plants

The study of protein–protein interactions (PPIs) is fundamental in understanding the unique role of proteins within cells and their contribution to complex biological systems. While the toolkit to study PPIs has grown immensely in mammalian and unicellular eukaryote systems over recent years, application of these techniques in plants remains under-utilized. Affinity purification coupled to mass spectrometry (AP-MS) and proximity labeling coupled to mass spectrometry (PL-MS) are two powerful techniques that have significantly enhanced our understanding of PPIs. Relying on the specific binding properties of a protein to an immobilized ligand, AP is a fast, sensitive and targeted approach used to detect interactions between bait (protein of interest) and prey (interacting partners) under near-physiological conditions. Similarly, PL, which utilizes the close proximity of proteins to identify potential interacting partners, has the ability to detect transient or hydrophobic interactions under native conditions. Combined, these techniques have the potential to reveal an unprecedented spatial and temporal protein interaction network that better understands biological processes relevant to many fields of interest. In this review, we summarize the advantages and disadvantages of two increasingly common PPI determination techniques: AP-MS and PL-MS and discuss their important application to plant systems.     

A Case Study on the Keap1 Interaction with Peptide Sequence Epitopes Selected by the Peptidomic mRNA Display

Many protein–protein and peptide–protein interactions (PPIs) play key roles in the regulation of biological functions, and therefore, the modulation of PPIs has become an attractive target of new drug development. Although a number of PPIs have already been identified, over 100 000 unknown PPIs are predicted to exist. To uncover such unknown PPIs, it is important to devise a conceptually distinct method from that of currently available methods. Herein, an mRNA display by using a total RNA library derived from various human tissues, which serves as a unique method to physically isolate peptide epitopes that potentially bind to a target protein of interest, is reported. In this study, selection was performed against Kelch-like ECH-associated protein (Keap1) as a model target protein, leading to a peptide epitope originating from astrotactin-1 (ASTN1). It turned out that this ASTN1 peptide was able to interact with Keap1 more strongly than that with a known peptide derived from Nrf2; a well-known, naturally occurring Keap1 binder. This case study demonstrates the applicability of peptidomic mRNA display for the rapid exploration of consensus binding peptide motifs and the potential for the discovery of unknown PPIs with other proteins of interest.     

Quantitative Estimate Index for Early-Stage Screening of Compounds Targeting Protein-Protein Interactions

Drug-likeness quantification is useful for screening drug candidates. Quantitative estimates of drug-likeness (QED) are commonly used to assess quantitative drug efficacy but are not suitable for screening compounds targeting protein-protein interactions (PPIs), which have recently gained attention. Therefore, we developed a quantitative estimate index for compounds targeting PPIs (QEPPI), specifically for early-stage screening of PPI-targeting compounds. QEPPI is an extension of the QED method for PPI-targeting drugs that models physicochemical properties based on the information available for drugs/compounds, specifically those reported to act on PPIs. FDA-approved drugs and compounds in iPPI-DB, which comprise PPI inhibitors and stabilizers, were evaluated using QEPPI. The results showed that QEPPI is more suitable than QED for early screening of PPI-targeting compounds. QEPPI was also considered an extended concept of the “Rule-of-Four” (RO4), a PPI inhibitor index. We evaluated the discriminatory performance of QEPPI and RO4 for datasets of PPI-target compounds and FDA-approved drugs using F-score and other indices. The F-scores of RO4 and QEPPI were 0.451 and 0.501, respectively. QEPPI showed better performance and enabled quantification of drug-likeness for early-stage PPI drug discovery. Hence, it can be used as an initial filter to efficiently screen PPI-targeting compounds.     

Modulating Secondary Structure Motifs Through Photo-Labile Peptide Staples

Peptide-protein interactions (PPIs) are facilitated by the well-defined three-dimensional structure of bioactive peptides, interesting compounds for the development of new therapeutic agents. Their secondary structure and thus their propensity to engage in PPIs can be influenced by the introduction of peptide staples on the side chains. In particular, light-controlled staples based on azobenzene photoswitches and their structural influence on helical peptides have been studied extensively. In contrast, photolabile staples bearing photocages as a structural key motif, have mainly been used to block supramolecular interactions. Their influence on the secondary structure of the target peptide is under-investigated. Thus, in this study we use a combination of spectroscopic techniques and in silico simulations to systematically study a series of helical peptides with varying length of the photo-labile staple to obtain a detailed insight into the structure-property relationship in such photoresponsive biomolecules.     

Microscale Thermophoresis as a Tool to Study Protein Interactions and Their Implication in Human Diseases

The review highlights how protein–protein interactions (PPIs) have determining roles in most life processes and how interactions between protein partners are involved in various human diseases. The study of PPIs and binding interactions as well as their understanding, quantification and pharmacological regulation are crucial for therapeutic purposes. Diverse computational and analytical methods, combined with high-throughput screening (HTS), have been extensively used to characterize multiple types of PPIs, but these procedures are generally laborious, long and expensive. Rapid, robust and efficient alternative methods are proposed, including the use of Microscale Thermophoresis (MST), which has emerged as the technology of choice in drug discovery programs in recent years. This review summarizes selected case studies pertaining to the use of MST to detect therapeutically pertinent proteins and highlights the biological importance of binding interactions, implicated in various human diseases. The benefits and limitations of MST to study PPIs and to identify regulators are discussed.     

Gastroesophageal reflux disease (GERD): is there more to the story?

Gastroesophageal reflux disease (GERD) affects both men and women worldwide, with the most common symptom of GERD being frequent heartburn. If left untreated, more serious diseases including esophagitis and/or esophageal cancer may result. GERD has been commonly held to be the result of gastric acid refluxing into the esophagus. Recent work, however, has shown that there are acid-producing cells in the upper aerodigestive tract. In addition, acid-producing bacteria located within the upper gastrointestinal tract and oral cavity may also be a contributing factor in the onset of GERD. Proton pump inhibitors (PPIs) are commonly prescribed for treating GERD; these drugs are designed to stop the production of gastric acid by shutting down the H(+)/K(+)-ATPase enzyme located in parietal cells. PPI treatment is systemic and therefore significantly different than traditional antacids. Although a popular treatment choice, PPIs exhibit substantial interpatient variability and commonly fail to provide a complete cure to the disease. Recent studies have shown that H(+)/K(+)-ATPases are expressed in tissues outside the stomach, and the effects of PPIs in these nongastric tissues have not been fully explored. Likewise, acid-producing bacteria containing proton pumps are present in both the oral cavity and esophagus, and PPI use may also adversely affect these bacteria. The use of PPI therapy is further complicated by the two philosophical approaches to treating this disease: to treat only symptoms or to treat continuously. The latter approach frequently results in unwanted side effects which may be due to the PPIs acting on nongastric tissues or the microbes which colonize the upper aerodigestive tract.     

Prediction of Protein–Protein Interaction with Pairwise Kernel Support Vector Machine

Protein–protein interactions (PPIs) play a key role in many cellular processes. Unfortunately, the experimental methods currently used to identify PPIs are both time-consuming and expensive. These obstacles could be overcome by developing computational approaches to predict PPIs. Here, we report two methods of amino acids feature extraction: (i) distance frequency with PCA reducing the dimension (DFPCA) and (ii) amino acid index distribution (AAID) representing the protein sequences. In order to obtain the most robust and reliable results for PPI prediction, pairwise kernel function and support vector machines (SVM) were employed to avoid the concatenation order of two feature vectors generated with two proteins. The highest prediction accuracies of AAID and DFPCA were 94% and 93.96%, respectively, using the 10 CV test, and the results of pairwise radial basis kernel function are considerably improved over those based on radial basis kernel function. Overall, the PPI prediction tool, termed PPI-PKSVM, which is freely available at http://159.226.118.31/PPI/index.html, promises to become useful in such areas as bio-analysis and drug development.     

Truncated O-Glycan-Bearing MUC16 Enhances Pancreatic Cancer Cells Aggressiveness via α4β1 Integrin Complexes and FAK Signaling

Elevated levels of Mucin-16 (MUC16) in conjunction with a high expression of truncated O-glycans is implicated in playing crucial roles in the malignancy of pancreatic ductal adenocarcinoma (PDAC). However, the mechanisms by which such aberrant glycoforms present on MUC16 itself promote an increased disease burden in PDAC are yet to be elucidated. This study demonstrates that the CRISPR/Cas9-mediated genetic deletion of MUC16 in PDAC cells decreases tumor cell migration. We found that MUC16 enhances tumor malignancy by activating the integrin-linked kinase and focal adhesion kinase (ILK/FAK)-signaling axis. These findings are especially noteworthy in truncated O-glycan (Tn and STn antigen)-expressing PDAC cells. Activation of these oncogenic-signaling pathways resulted in part from interactions between MUC16 and integrin complexes (α4β1), which showed a stronger association with aberrant glycoforms of MUC16. Using a monoclonal antibody to functionally hinder MUC16 significantly reduced the migratory cascades in our model. Together, these findings suggest that truncated O-glycan containing MUC16 exacerbates malignancy in PDAC by activating FAK signaling through specific interactions with α4 and β1 integrin complexes on cancer cell membranes. Targeting these aberrant glycoforms of MUC16 can aid in the development of a novel platform to study and treat metastatic pancreatic cancer.     

Antibody Induction Directed against the Tumor‐Associated MUC4 Glycoprotein

Mucin glycoproteins are important diagnostic and therapeutic targets for cancer treatment. Although several strategies have been developed to explore anti‐tumor vaccines based on MUC1 glycopeptides, only few studies have focused on vaccines directed against the tumor‐associated MUC4 glycoprotein. MUC4 is an important tumor marker overexpressed in lung cancer and uniquely expressed in pancreatic ductual adenocarcinoma. The aberrant glycosylation of MUC4 in tumor cells results in an exposure of its peptide backbone and the formation of tumor‐associated glycopeptide antigens. Due to the low immunogenicity of these endogenous structures, their conjugation with immune stimulating peptide or protein carriers are required. In this study, MUC4 tandem‐repeat glycopeptides were conjugated to the tetanus toxoid and used for vaccination of mice. Immunological evaluations showed that our MUC4‐based vaccines induced very strong antigen‐specific immune responses. In addition, antibody binding epitope analysis on glycopeptide microarrays, were demonstrating a clear glycosylation site dependence of the induced antibodies.     

Chemoenzymatic Synthesis of Glycopeptides to Explore the Role of Mucin 1 Glycosylation in Cell Adhesion

Post-translational modifications affect protein biology under physiological and pathological conditions. Efficient methods for the preparation of peptides and proteins carrying defined, homogeneous modifications are fundamental tools for investigating these functions. In the case of mucin 1 (MUC1), an altered glycosylation pattern is observed in carcinogenesis. To better understand the role of MUC1 glycosylation in the interactions and adhesion of cancer cells, we prepared a panel of homogeneously O-glycosylated MUC1 peptides by using a quantitative chemoenzymatic approach. Cell-adhesion experiments with MCF-7 cancer cells on surfaces carrying up to six differently glycosylated MUC1 peptides demonstrated that different glycans have a significant impact on adhesion. This finding suggests a distinct role for MUC1 glycosylation patterns in cancer cell migration and/or invasion. To decipher the molecular mechanism for the observed adhesion, we investigated the conformation of the glycosylated MUC1 peptides by NMR spectroscopy. These experiments revealed only minor differences in peptide structure, therefore clearly relating the adhesion behaviour to the type and number of glycans linked to MUC1.