Molecular basis for protein-protein interactions

Polymers for Disrupting Protein-Protein Interactions: Where Are We and Where Should We Be?

Protein-protein interactions (PPIs) are central to the cellular signaling and regulatory networks that underlie many physiological and pathophysiological processes. It is challenging to target PPIs using traditional small molecule or peptide-based approaches due to the frequent lack of well-defined binding pockets at the large and flat PPI interfaces. Synthetic polymers offer an opportunity to circumvent these challenges by providing unparalleled flexibility in tuning their physiochemical properties to achieve the desired binding properties. In this review, we summarize the current state of the field pertaining to polymer-protein interactions in solution, highlighting various polyelectrolyte systems, their tunable parameters, and their characterization. We provide an outlook on how these architectures can be improved by incorporating sequence control, foldability, and machine learning to mimic proteins at every structural level. Advances in these directions will enable the design of more specific protein-binding polymers and provide an effective strategy for targeting dynamic proteins, such as intrinsically disordered proteins.

Studying protein-protein interactions: Latest and most popular approaches

PPIs, or protein-protein interactions, are essential for many biological processes. According to the findings, abnormal PPIs have been linked to several diseases, such as cancer and infectious and neurological disorders. Consequently, focusing on PPIs is a path toward disease treatment and a crucial tool for producing novel medications. Many methods exist to investigate PPIs, including low- and high-throughput studies. Since many PPIs have been discovered using in vitro and in vivo experimental approaches, the use of computational methods to predict PPIs has grown due to the expanding scale of PPI data and the intrinsic complexity of interacting mechanisms. Recognizing PPI networks offers a systematic means of predicting protein functions, and pathways that are included. These investigations can help uncover the underlying molecular mechanisms of complex phenotypes and clarify the biological processes related to health and diseases. Therefore, our goal in this study is to provide an overview of the latest and most popular approaches for investigating PPIs. We also overview some important clinical approaches based on the PPIs and how these interactions can be targeted.

Recent Advances in the Field of Amino Acid-Conjugated Aminoferrocenes-A Personal Perspective

The development of turn-based inhibitors of protein-protein interactions has attracted considerable attention in medicinal chemistry. Our group has synthesized a series of peptides derived from an amino-functionalized ferrocene to investigate their potential to mimic protein turn structures. Detailed DFT and spectroscopic studies (IR, NMR, CD) have shown that, for peptides, the backbone chirality and bulkiness of the amino acid side chains determine the hydrogen-bond pattern, allowing tuning of the size of the preferred hydrogen-bonded ring in turn-folded structures. However, their biological potential is more dependent on their lipophilicity. In addition, our pioneering work on the chiroptical properties of aminoferrocene-containing peptides enables the correlation of their geometry with the sign of the CD signal in the absorption region of the ferrocene chromophore. These studies have opened up the possibility of using aminoferrocene and its derivatives as chirooptical probes for the determination of various chirality elements, such as the central chirality of amino acids and the helicity of peptide sequences.

Dual-Capped Helical Interface Mimics

Disruption of protein-protein interactions is medicinally important. Interface helices may be mimicked in helical probes featuring enhanced rigidities, binding to protein targets, stabilities in serum, and cell uptake. This form of mimicry is dominated by stapling between side chains of helical residues: there has been less progress on helical N-caps, and there were no generalizable C-caps. Conversely, in natural proteins, helicities are stabilized and terminated by C- and N-caps but not staples. Bicyclic caps previously introduced by us enable interface helical mimicry featuring rigid synthetic caps at both termini in this work. An unambiguously helical dual-capped system proved to be conformationally stable, binding cyclins A and E, and showed impressive cellular uptake. In addition, the dual-capped mimic was completely resistant to proteolysis in serum over an extended period when compared with "gold standard" hydrocarbon-stapled controls. Dual-capped peptidomimetics are a new, generalizable paradigm for helical interface probe design.

Using Passive Microrheology to Measure the Evolution of the Rheological Properties of NIST mAb Formulations during Adsorption to the Air-Water Interface

The development of novel protein-based therapeutics, such as monoclonal antibodies (mAbs), is often limited due to challenges associated with maintaining the stability of these formulations during manufacturing, storage, and clinical administration. An undesirable consequence of the instability of protein therapeutics is the formation of protein particles. MAbs can adsorb to interfaces and have the potential to undergo partial unfolding as well as to form viscoelastic gels. Further, the viscoelastic properties may be correlated with their aggregation potential. In this work, a passive microrheology technique was used to correlate the evolution of surface adsorption with the evolution of surface rheology of the National Institute of Standards and Technology (NIST) mAb reference material (NIST mAb) and interface-induced subvisible protein particle formation. The evolution of the surface adsorption and interfacial shear rheological properties of the NIST mAb was recorded in four formulation conditions: two different buffers (histidine vs phosphate-buffered saline) and two different pHs (6.0 and 7.6). Our results together demonstrate the existence of multiple stages for both surface adsorption and surface rheology, characterized by an induction period that appears to be purely viscous, followed by a sharp increase in protein molecules at the interface when the film rheology is viscoelastic and ultimately a slowdown in the surface adsorption that corresponds to the formation of solid-like or glassy films at the interface. When the transitions between the different stages occurred, they were dependent on the buffer/pH of the formulations. The onset of these transitions can also be correlated to the number of protein particles formed at the interface. Finally, the addition of polysorbate 80, an FDA-approved surfactant used to mitigate protein particle formation, led to the interface being surfactant-dominated, and the resulting interface remained purely viscous.

Development of a V5-tag-directed nanobody and its implementation as an intracellular biosensor of GPCR signaling

Protein-protein interactions (PPIs) form the foundation of any cell signaling network. Considering that PPIs are highly dynamic processes, cellular assays are often essential for their study because they closely mimic the biological complexities of cellular environments. However, incongruity may be observed across different PPI assays when investigating a protein partner of interest; these discrepancies can be partially attributed to the fusion of different large functional moieties, such as fluorescent proteins or enzymes, which can yield disparate perturbations to the protein's stability, subcellular localization, and interaction partners depending on the given cellular assay. Owing to their smaller size, epitope tags may exhibit a diminished susceptibility to instigate such perturbations. However, while they have been widely used for detecting or manipulating proteins in vitro, epitope tags lack the in vivo traceability and functionality needed for intracellular biosensors. Herein, we develop NbV5, an intracellular nanobody binding the V5-tag, which is suitable for use in cellular assays commonly used to study PPIs such as BRET, NanoBiT, and Tango. The NbV5:V5 tag system has been applied to interrogate G protein-coupled receptor signaling, specifically by replacing larger functional moieties attached to the protein interactors, such as fluorescent or luminescent proteins (∼30 kDa), by the significantly smaller V5-tag peptide (1.4 kDa), and for microscopy imaging which is successfully detected by NbV5-based biosensors. Therefore, the NbV5:V5 tag system presents itself as a versatile tool for live-cell imaging and a befitting adaptation to existing cellular assays dedicated to probing PPIs.

Protein-protein interactions in plant antioxidant defense

The regulation of reactive oxygen species (ROS) levels in plants is ensured by mechanisms preventing their over accumulation, and by diverse antioxidants, including enzymes and nonenzymatic compounds. These are affected by redox conditions, posttranslational modifications, transcriptional and posttranscriptional modifications, Ca²⁺, nitric oxide (NO) and mitogen-activated protein kinase signaling pathways. Recent knowledge about protein-protein interactions (PPIs) of antioxidant enzymes advanced during last decade. The best-known examples are interactions mediated by redox buffering proteins such as thioredoxins and glutaredoxins. This review summarizes interactions of major antioxidant enzymes with regulatory and signaling proteins and their diverse functions. Such interactions are important for stability, degradation and activation of interacting partners. Moreover, PPIs of antioxidant enzymes may connect diverse metabolic processes with ROS scavenging. Proteins like receptor for activated C kinase 1 may ensure coordination of antioxidant enzymes to ensure efficient ROS regulation. Nevertheless, PPIs in antioxidant defense are understudied, and intensive research is required to define their role in complex regulation of ROS scavenging.

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.

Investigating the Effect of Tyrosine Kinase Inhibitors on the Interaction between Human Serum Albumin by Atomic Force Microscopy

It is important for elucidating the regulation mechanism of life activities, as well as for the prevention, diagnosis, and drug design of diseases, to study protein–protein interactions (PPIs). Here, we investigated the interactions of human serum albumin (HSA) in the presence of tyrosine kinase inhibitors (TKIs: imatinib, nilotinib, dasatinib, bosutinib, and ponatinib) using atomic force microscopy (AFM). The distribution of rupture events including the specific interaction force F_i and the non-specific interaction force F₀ between HSA pairs was analyzed. Based on the force measurements, F_i and F₀ between HSA pairs in the control experiment were calculated to be 47 ± 1.5 and 116.1 ± 1.3 pN. However, F_i was significantly decreased in TKIs, while F₀ was slightly decreased. By measuring the rupture forces at various loading rates and according to the Bell equation, the kinetic parameters of the complexes were investigated in greater detail. Molecular docking was used as a complementary means by which to explore the force of this effect. The whole measurements indicated that TKIs influenced PPIs in a variety of ways, among which hydrogen bonding and hydrophobic interactions were the most important. In conclusion, these outcomes give us a better insight into the mechanisms of PPIs when there are exogenous compounds present as well as in different liquid environments.

Prediction and Modeling of Protein–Protein Interactions Using “Spotted” Peptides with a Template-Based Approach

Protein–peptide interactions (PpIs) are a subset of the overall protein–protein interaction (PPI) network in the living cell and are pivotal for the majority of cell processes and functions. High-throughput methods to detect PpIs and PPIs usually require time and costs that are not always affordable. Therefore, reliable in silico predictions represent a valid and effective alternative. In this work, a new algorithm is described, implemented in a freely available tool, i.e., “PepThreader”, to carry out PPIs and PpIs prediction and analysis. PepThreader threads multiple fragments derived from a full-length protein sequence (or from a peptide library) onto a second template peptide, in complex with a protein target, “spotting” the potential binding peptides and ranking them according to a sequence-based and structure-based threading score. The threading algorithm first makes use of a scoring function that is based on peptides sequence similarity. Then, a rerank of the initial hits is performed, according to structure-based scoring functions. PepThreader has been benchmarked on a dataset of 292 protein–peptide complexes that were collected from existing databases of experimentally determined protein–peptide interactions. An accuracy of 80%, when considering the top predicted 25 hits, was achieved, which performs in a comparable way with the other state-of-art tools in PPIs and PpIs modeling. Nonetheless, PepThreader is unique in that it is able at the same time to spot a binding peptide within a full-length sequence involved in PPI and model its structure within the receptor. Therefore, PepThreader adds to the already-available tools supporting the experimental PPIs and PpIs identification and characterization.