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.     

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.     

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.     

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.     

D-Peptide-Based Drug Discovery Aided by Chemical Protein Synthesis

Despite a sharp increase in the expenditures for drug research and development (R&D) in the past decade, the declining trend in the number of new drugs approved annually by the US Food and Drug Administration continues. This growing disparity between R&D investment and new drug approvals results in part from the deficiency in promising therapeutic targets and leads to a stagnation exacerbated by the lack of advanced drug discovery tools for harvesting the “high-hanging fruits” such as inhibitors of protein–protein interactions (PPIs). Small peptide inhibitors of PPIs can be of high affinity and specificity, promising an important class of therapeutic agents that target PPIs involved in a great variety of biological processes. However, susceptibility to proteolytic degradation in vivo still remains a major hurdle that limits their therapeutic potential. This limitation can be overcome by mirror-image phage display, a technique that allows, through phage-expressed peptide library screening against the D -enantiomer of a target protein, for the identification of proteolysis-resistant D -peptide inhibitors of PPIs. Recent advances in total protein synthesis via native chemical ligation have significantly expanded the scope of molecular targets for mirror-image phage display. This concise review focuses on the latest development in the combined use of mirror-image phage display and native chemical ligation for D -peptide based anticancer drug discovery.     

PITHIA: Protein Interaction Site Prediction Using Multiple Sequence Alignments and Attention

Cellular functions are governed by proteins, and, while some proteins work independently, most work by interacting with other proteins. As a result it is crucially important to know the interaction sites that facilitate the interactions between the proteins. Since the experimental methods are costly and time consuming, it is essential to develop effective computational methods. We present PITHIA, a sequence-based deep learning model for protein interaction site prediction that exploits the combination of multiple sequence alignments and learning attention. We demonstrate that our new model clearly outperforms the state-of-the-art models on a wide range of metrics. In order to provide meaningful comparison, we update existing test datasets with new information regarding interaction site, as well as introduce an additional new testing dataset which resolves the shortcomings of the existing ones.     

Challenges in Discovering Drugs That Target the Protein–Protein Interactions of Disordered Proteins

Protein–protein interactions (PPIs) outnumber proteins and are crucial to many fundamental processes; in consequence, PPIs are associated with several pathological conditions including neurodegeneration and modulating them by drugs constitutes a potentially major class of therapy. Classically, however, the discovery of small molecules for use as drugs entails targeting individual proteins rather than targeting PPIs. This is largely because discovering small molecules to modulate PPIs has been seen as extremely challenging. Here, we review the difficulties and limitations of strategies to discover drugs that target PPIs directly or indirectly, taking as examples the disordered proteins involved in neurodegenerative diseases.     

Highly Accurate Prediction of Protein-Protein Interactions via Incorporating Evolutionary Information and Physicochemical Characteristics

Protein-protein interactions (PPIs) occur at almost all levels of cell functions and play crucial roles in various cellular processes. Thus, identification of PPIs is critical for deciphering the molecular mechanisms and further providing insight into biological processes. Although a variety of high-throughput experimental techniques have been developed to identify PPIs, existing PPI pairs by experimental approaches only cover a small fraction of the whole PPI networks, and further, those approaches hold inherent disadvantages, such as being time-consuming, expensive, and having high false positive rate. Therefore, it is urgent and imperative to develop automatic in silico approaches to predict PPIs efficiently and accurately. In this article, we propose a novel mixture of physicochemical and evolutionary-based feature extraction method for predicting PPIs using our newly developed discriminative vector machine (DVM) classifier. The improvements of the proposed method mainly consist in introducing an effective feature extraction method that can capture discriminative features from the evolutionary-based information and physicochemical characteristics, and then a powerful and robust DVM classifier is employed. To the best of our knowledge, it is the first time that DVM model is applied to the field of bioinformatics. When applying the proposed method to the Yeast and Helicobacter pylori (H. pylori) datasets, we obtain excellent prediction accuracies of 94.35% and 90.61%, respectively. The computational results indicate that our method is effective and robust for predicting PPIs, and can be taken as a useful supplementary tool to the traditional experimental methods for future proteomics research.     

Multivalent Ligands with Tailor-Made Anion Binding Motif as Stabilizers of Protein–Protein Interactions

Modulation of protein–protein interactions (PPIs) is essential for understanding and tuning biologically relevant processes. Although inhibitors for PPIs are widely used, the field still lacks the targeted design of stabilizers. Here, we report unnatural stabilizers based on the combination of multivalency effects and the artificial building block guanidiniocarbonylpyrrol (GCP), an arginine mimetic. Unlike other GCP-based ligands that modulate PPIs in different protein targets, only a tetrameric design shows potent activity as stabilizer of the 14-3-3ζ/C-Raf and 14-3-3ζ/Tau complexes in the low-micromolar range. This evidences the role of multivalency for achieving higher specificity in the modulation of PPIs.     

dSTORM-Based Single-Cell Protein Quantitative Analysis Can Effectively Evaluate the Degradation Ability of PROTACs

As an emerging therapeutic strategy, proteolysis-targeting chimeras (PROTACs) have been proven to be superior to traditional drugs in many aspects. However, due to their unique mechanism of action, existing methods for evaluating the degradation still have many limitations, which seriously restricts the development of PROTACs. In this methodological study, using direct stochastic optical reconstruction microscopy (dSTORM)-based single-cell protein quantitative analysis, we systematically investigated the dynamic degradation characteristics of FLT3 protein during PROTACs treatment. We found that the distribution of FLT3 varies between FLT3-ITD mutation and FLT3-WT cells. PROTACs had an obvious time-course effect on protein degradation and present two distinct phases; this provided a basis for deciding when to evaluate protein degradation. High concentrations of PROTACs were more effective than long-time administration because a higher D_max was achieved. Two-color dSTORM-based colocalization analysis efficiently detected the proportion of ternary complexes, making it very useful in screening PROTACs. Taken together, our findings show that the dSTORM method is an ideal tool for evaluating PROTACs and will accelerate the development of new PROTACs.     

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 Protein–Protein Interactions with Visible-Light-Responsive Peptide Backbone Photoswitches

Life relies on a myriad of carefully orchestrated processes, in which proteins and their direct interplay ultimately determine cellular function and disease. Modulation of this complex crosstalk has recently attracted attention, even as a novel therapeutic strategy. Herein, we describe the synthesis and characterization of two visible-light-responsive peptide backbone photoswitches based on azobenzene derivatives, to exert optical control over protein–protein interactions (PPI). The novel peptidomimetics undergo fast and reversible isomerization with low photochemical fatigue under alternatively blue-/green-light irradiation cycles. Both bind in the nanomolar range to the protein of interest. Importantly, the best peptidomimetic displays a clear difference between isomers in its protein-binding capacity and, in turn, in its potential to inhibit enzymatic activity through PPI disruption. In addition, crystal structure determination, docking and molecular dynamics calculations allow a molecular interpretation and open up new avenues in the design and synthesis of future photoswitchable PPI modulators.     

Biophysical and Computational Approaches to Study Ternary Complexes: A ‘Cooperative Relationship’ to Rationalize Targeted Protein Degradation

Degraders have illustrated that compound-induced proximity to E3 ubiquitin ligases can prompt the ubiquitination and degradation of disease-relevant proteins. Hence, this pharmacology is becoming a promising alternative and complement to available therapeutic interventions (e. g., inhibitors). Degraders rely on protein binding instead of inhibition and, hence, they hold the promise to broaden the druggable proteome. Biophysical and structural biology approaches have been the cornerstone of understanding and rationalizing degrader-induced ternary complex formation. Computational models have now started to harness the experimental data from these approaches with the aim to identify and rationally help design new degraders. This review outlines the current experimental and computational strategies used to study ternary complex formation and degradation and highlights the importance of effective crosstalk between these approaches in the advancement of the targeted protein degradation (TPD) field. As our understanding of the molecular features that govern drug-induced interactions grows, faster optimizations and superior therapeutic innovations for TPD and other proximity-inducing modalities are sure to follow.     

In Silico Identification of Potential Druggable Binding Sites on CIN85 SH3 Domain

Protein-protein interactions (PPIs) play a pivotal role in the regulation of many physiological processes. The dysfunction of some PPIs interactions led to the alteration of different biological pathways causing various diseases including cancer. In this context, the inhibition of PPIs represents an attractive strategy for the design of new antitumoral agents. In recent years, computational approaches were successfully used to study the interactions between proteins, providing useful hints for the design of small molecules able to modulate PPIs. Targeting PPIs presents several challenges mainly due to the large and flat binding surface that lack the typical binding pockets of traditional drug targets. Despite these hurdles, substantial progress has been made in the last decade resulting in the identification of PPI modulators where some of them even found clinical use. This study focuses on MUC1-CIN85 PPI which is involved in the migration and invasion of cancer cells. Particularly, we investigated the presence of druggable binding sites on the CIN85 surface which provided new insights for the structure-based design of novel MUC1-CIN85 PPI inhibitors as anti-metastatic agents.     

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.     

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.