Protein-protein interaction modulators: advances, successes and remaining challenges

Enhancing Cytoplasmic Expression of Exogenous mRNA Through Dynamic Mechanical Stimulation

Ionizable lipid nanoparticles (LNPs) are pivotal in combating COVID-19, and numerous preclinical and clinical studies have highlighted their potential in nucleic acid-based therapies and vaccines. However, the effectiveness of endosomal escape for the nucleic acid cargos encapsulated in LNPs is still low, leading to suboptimal treatment outcomes and side effects. Hence, improving endosomal escape is crucial for enhancing the efficacy of nucleic acid delivery using LNPs. Here, a mechanical oscillation (frequency: 65 Hz) is utilized to prompt the LNP-mediated endosomal escape. The results reveal this mechanical oscillation can induce the combination and fusion between LNPs with opposite surface charges, enhance endosomal escape of mRNA, and increase the transfection efficiency of mRNA. Additionally, cell viability remains high at 99.3% after treatment with oscillation, which is comparable to that of untreated cells. Furthermore, there is no obvious damage to mitochondrial membrane potential and Golgi apparatus integrity. Thus, this work presents a user-friendly and safe approach to enhancing endosomal escape of mRNA and boosting gene expression. As a result, this work can be potentially utilized in both research and clinical fields to facilitate LNP-based delivery by enabling more effective release of LNP-encapsulated cargos from endosomes.

Modular Nanotransporters Deliver Anti-Keap1 Monobody into Mouse Hepatocytes, Thereby Inhibiting Production of Reactive Oxygen Species

Background/Objectives: The study of oxidative stress in cells and ways to prevent it attract increasing attention. Antioxidant defense of cells can be activated by releasing the transcription factor Nrf2 from a complex with Keap1, its inhibitor protein. The aim of the work was to study the effect of the modular nanotransporter (MNT) carrying an R1 anti-Keap1 monobody (MNTR1) on cell homeostasis. Methods: The murine hepatocyte AML12 cells were used for the study. The interaction of fluorescently labeled MNTR1 with Keap1 fused to hrGFP was studied using the Fluorescence-Lifetime Imaging Microscopy-Förster Resonance Energy Transfer (FLIM-FRET) technique on living AML12 cells transfected with the Keap1-hrGFP gene. The release of Nrf2 from the complex with Keap1 and its levels in the cytoplasm and nuclei of the AML12 cells were examined using a cellular thermal shift assay (CETSA) and confocal laser scanning microscopy, respectively. The effect of MNT on the formation of reactive oxygen species was studied by flow cytometry using 6-carboxy-2',7'-dichlorodihydrofluorescein diacetate. Results: MNTR1 is able to interact with Keap1 in the cytoplasm, leading to the release of Nrf2 from the complex with Keap1 and a rapid rise in Nrf2 levels both in the cytoplasm and nuclei, ultimately causing protection of cells from the action of hydrogen peroxide. The possibility of cleavage of the monobody in endosomes leads to an increase in the observed effects. Conclusions: These findings open up a new approach to specifically modulating the interaction of intracellular proteins, as demonstrated by the example of the Keap1-Nrf2 system.

Unlocking therapeutic frontiers: harnessing artificial intelligence in drug discovery for neurodegenerative diseases

Neurodegenerative diseases (NDs) pose serious healthcare challenges with limited therapeutic treatments and high social burdens. The integration of artificial intelligence (AI) into drug discovery has emerged as a promising approach to address these challenges. This review explores the application of AI techniques to unravel therapeutic frontiers for NDs. We examine the current landscape of AI-driven drug discovery and discuss the potentials of AI in accelerating the identification of novel therapeutic targets on ND research and drug development, optimization of drug candidates, and expediating personalized medicine approaches. Finally, we outline future directions and challenges in harnessing AI for the advancement of therapeutics in this critical area by emphasizing the importance of interdisciplinary collaboration and ethical considerations.

Enhancing Cytoplasmic Expression of Exogenous mRNA through Dynamic Mechanical Stimulation

Ionizable lipid nanoparticles (LNPs) have been pivotal in combating COVID-19, and numerous preclinical and clinical studies have highlighted their potential in nucleic acid-based therapies and vaccines. However, the effectiveness of endosomal escape for the nucleic acid cargos encapsulated in LNPs is still low, leading to suboptimal treatment outcomes and side effects. Hence, improving endosomal escape is crucial for enhancing the efficacy of nucleic acid delivery using LNPs. Here, a mechanical oscillation (frequency: 65 Hz) is utilized to prompt the LNP-mediated endosomal escape. The results reveal this mechanical oscillation can induce the combination and fusion between LNPs with opposite surface charges, enhance endosomal escape of mRNA, and increase the transfection efficiency of mRNA. Additionally, cell viability remains high at 99.3% after treatment with oscillation, which is comparable to that of untreated cells. Furthermore, there is no obvious damage to mitochondrial membrane potential and Golgi apparatus integrity. Thus, this work presents a user-friendly and safe approach to enhancing endosomal escape of mRNA and boosting gene expression. As a result, our work can be potentially utilized in both research and clinical fields to facilitate LNP-based delivery by enabling more effective release of LNP-encapsulated cargos from endosomes.

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.

INTREPPPID-an orthologue-informed quintuplet network for cross-species prediction of protein-protein interaction

An overwhelming majority of protein-protein interaction (PPI) studies are conducted in a select few model organisms largely due to constraints in time and cost of the associated 'wet lab' experiments. In silico PPI inference methods are ideal tools to overcome these limitations, but often struggle with cross-species predictions. We present INTREPPPID, a method that incorporates orthology data using a new 'quintuplet' neural network, which is constructed with five parallel encoders with shared parameters. INTREPPPID incorporates both a PPI classification task and an orthologous locality task. The latter learns embeddings of orthologues that have small Euclidean distances between them and large distances between embeddings of all other proteins. INTREPPPID outperforms all other leading PPI inference methods tested on both the intraspecies and cross-species tasks using strict evaluation datasets. We show that INTREPPPID's orthologous locality loss increases performance because of the biological relevance of the orthologue data and not due to some other specious aspect of the architecture. Finally, we introduce PPI.bio and PPI Origami, a web server interface for INTREPPPID and a software tool for creating strict evaluation datasets, respectively. Together, these two initiatives aim to make both the use and development of PPI inference tools more accessible to the community.

Supramolecular Nanofibers Ameliorate Bleomycin-Induced Pulmonary Fibrosis by Restoring Autophagy

Idiopathic pulmonary fibrosis (IPF) is a progressive and ultimately fatal interstitial lung disease, with limited therapeutic options available. Impaired autophagy resulting from aberrant TRB3/p62 protein-protein interactions (PPIs) contributes to the progression of IPF. Restoration of autophagy by modulating the TRB3/p62 PPIs has rarely been reported for the treatment of IPF. Herein, peptide nanofibers are developed that specifically bind to TRB3 protein and explored their potential as a therapeutic approach for IPF. By conjugating with the self-assembling fragment (Ac-GFFY), a TRB3-binding peptide motif A2 allows for the formation of nanofibers with a stable α-helix secondary structure. The resulting peptide (Ac-GFFY-A2) nanofibers exhibit specific high-affinity binding to TRB3 protein in saline buffer and better capacity of cellular uptake to A2 peptide. Furthermore, the TRB3-targeting peptide nanofibers efficiently interfere with the aberrant TRB3/p62 PPIs in activated fibroblasts and fibrotic lung tissue of mice, thereby restoring autophagy dysfunction. The TRB3-targeting peptide nanofibers inhibit myofibroblast differentiation, collagen production, and fibroblast migration in vitro is demonstrated, as well as bleomycin-induced pulmonary fibrosis in vivo. This study provides a supramolecular method to modulate PPIs and highlights a promising strategy for treating IPF diseases by restoring autophagy.

In silico design of peptide inhibitors for Dengue virus to treat Dengue virus-associated infections

Dengue virus is a single positive-strand RNA virus that is composed of three structural proteins including capsid, envelope, and precursor membrane while seven non-structural proteins (NS1, NS2A, NS2B, NS3A, NS3B, NS4, and NS5). Dengue is a viral infection caused by the dengue virus (DENV). DENV infections are asymptomatic or produce only mild illness. However, DENV can occasionally cause more severe cases and even death. There is no specific treatment for dengue virus infections. Therapeutic peptides have several important advantages over proteins or antibodies: they are small in size, easy to synthesize, and have the ability to penetrate the cell membranes. They also have high activity, specificity, affinity, and less toxicity. Based on the known peptide inhibitor, the current study designs peptide inhibitors for dengue virus envelope protein using an alanine and residue scanning technique. By replacing I21 with Q21, L14 with H14, and V28 with K28, the binding affinity of the peptide inhibitors was increased. The newly designed peptide inhibitors with single residue mutation improved the binding affinity of the peptide inhibitors. The inhibitory capability of the new promising peptide inhibitors was further confirmed by the utilization of MD simulation and free binding energy calculations. The molecular dynamics simulation demonstrated that the newly engineered peptide inhibitors exhibited greater stability compared to the wild-type peptide inhibitors. According to the binding free energies MM(GB)SA of these developed peptides, the first peptide inhibitor was the most effective against the dengue virus envelope protein. All peptide derivatives had higher binding affinities for the envelope protein and have the potential to treat dengue virus-associated infections. In this study, new peptide inhibitors were developed for the dengue virus envelope protein based on the already reported peptide inhibitor.

Assessing the functional impact of protein binding site definition

Many biomedical applications, such as classification of binding specificities or bioengineering, depend on the accurate definition of protein binding interfaces. Depending on the choice of method used, substantially different sets of residues can be classified as belonging to the interface of a protein. A typical approach used to verify these definitions is to mutate residues and measure the impact of these changes on binding. Besides the lack of exhaustive data, this approach also suffers from the fundamental problem that a mutation introduces an unknown amount of alteration into an interface, which potentially alters the binding characteristics of the interface. In this study we explore the impact of alternative binding site definitions on the ability of a protein to recognize its cognate ligand using a pharmacophore approach, which does not affect the interface. The study also shows that methods for protein binding interface predictions should perform above approximately F-score = 0.7 accuracy level to capture the biological function of a protein.

Impact of GSK-3β and CK-1δ on Wnt signaling pathway in alzheimer disease: A dual target approach

Alzheimer's disease (AD) is an enigmatic neurological illness that offers few treatment options. Recent exploration has highlighted the crucial connection of the Wnt signaling pathway in AD pathogenesis, shedding light on potential therapeutic targets. The present study focuses on the dual targeting of glycogen synthase kinase-3β (GSK-3β) and casein kinase-1δ (CK-1δ) within the framework of the Wnt signaling pathway as a possible technique for AD intervention. GSK-3β and CK-1δ are multifunctional kinases known for their roles in tau hyperphosphorylation, amyloid processing, and synaptic dysfunction, all of which are major hallmarks of Alzheimer's disease. They are intricately linked to Wnt signaling, which plays a pivotal part in sustaining neuronal function and synaptic plasticity. Dysregulation of the Wnt pathway in AD contributes to cognitive decline and neurodegeneration. This review delves into the molecular mechanisms by which GSK-3β and CK-1δ impact the Wnt signaling pathway, elucidating their roles in AD pathogenesis. We discuss the potential of small-molecule inhibitors along with their SAR studies along with the multi-targetd approach targeting GSK-3β and CK-1δ to modulate Wnt signaling and mitigate AD-related pathology. In summary, the dual targeting of GSK-3β and CK-1δ within the framework of the Wnt signaling pathway presents an innovative and promising avenue for future AD therapies, offering new hope for patients and caregivers in the quest to combat this challenging condition.

Cell-penetrating protein-recognizing polymeric nanoparticles through dynamic covalent chemistry and double imprinting

Molecular recognition of proteins is key to their biological functions and processes such as protein-protein interactions (PPIs). The large binding interface involved and an often relatively flat binding surface make the development of selective protein-binding materials extremely challenging. A general method is reported in this work to construct protein-binding polymeric nanoparticles from cross-linked surfactant micelles. Preparation involves first dynamic covalent chemistry that encodes signature surface lysines on a protein template. A double molecular imprinting procedure fixes the binding groups on the nanoparticle for these lysine groups, meanwhile creating a binding interface complementary to the protein in size, shape, and distribution of acidic groups on the surface. These water-soluble nanoparticles possess excellent specificities for target proteins and sufficient affinities to inhibit natural PPIs such as those between cytochrome c (Cytc) and cytochrome c oxidase (CcO). With the ability to enter cells through a combination of energy-dependent and -independent pathways, they intervene apoptosis by inhibiting the PPI between Cytc and the apoptotic protease activating factor-1 (APAF1). Generality of the preparation and the excellent molecular recognition of the materials have the potential to make them powerful tools to probe protein functions in vitro and in cellulo.

Pocket Crafter: a 3D generative modeling based workflow for the rapid generation of hit molecules in drug discovery

We present a user-friendly molecular generative pipeline called Pocket Crafter, specifically designed to facilitate hit finding activity in the drug discovery process. This workflow utilized a three-dimensional (3D) generative modeling method Pocket2Mol, for the de novo design of molecules in spatial perspective for the targeted protein structures, followed by filters for chemical-physical properties and drug-likeness, structure-activity relationship analysis, and clustering to generate top virtual hit scaffolds. In our WDR5 case study, we acquired a focused set of 2029 compounds after a targeted searching within Novartis archived library based on the virtual scaffolds. Subsequently, we experimentally profiled these compounds, resulting in a novel chemical scaffold series that demonstrated activity in biochemical and biophysical assays. Pocket Crafter successfully prototyped an effective end-to-end 3D generative chemistry-based workflow for the exploration of new chemical scaffolds, which represents a promising approach in early drug discovery for hit identification.

Screening approaches for the identification of Nrf2-Keap1 protein-protein interaction inhibitors targeting hot spot residues

Protein-protein interactions (PPIs) play a crucial role in most biological processes and are important targets in the development of therapeutic agents. However, small molecule drug discovery that targets PPIs remains very challenging. Targeting hot spot residues is considered the best option for inhibiting such interactions, but there are few examples of how knowledge of hot spots can be used in high throughput screening to find hit compounds. A substrate adaptor protein for a ubiquitin ligase complex, Kelch-like ECH-associated protein 1 (Keap1), negatively modulates the expression of genes involved in cellular protection against oxidative stress. Here, we focused on three arginine hot spot residues in the Keap1 substrate binding pocket (Arg380, Arg415, and Arg483), and screened the carboxylic acid library owned by Japan Tobacco Inc. for compounds that interact with the arginine residues in differential scanning fluorescence assays. Furthermore, we identified several small molecule compounds that specifically bind to the Keap1 Kelch domain hot spots by comparing binding to alanine mutant proteins (R380A, R415A, and R483A) with binding to the wild-type protein using surface plasmon resonance (SPR) screening. These compounds inhibited the protein-protein interaction between the Keap1 Kelch domain and the nuclear factor erythroid 2-related factor 2 (Nrf2) peptide, and the ubiquitination of Nrf2 catalyzed by the Cul3/RINGBox 1 E3 ligase. In addition, the binding mode of one compound (Compound 4) was determined by X-ray crystallography after validation of binding by isothermal titration calorimetry, native mass spectrometry, and nuclear magnetic resonance. Compound 4 had favorable thermodynamic properties, and noncovalently bound to Keap1 with a stoichiometry of 1:1. Our results suggest that Compound 4 could potentially be developed into effective therapeutic or preventive agents for a variety of diseases and conditions such as oxidative stress response, inflammation, and carcinogenesis. We believe that the use of a set of complementary biophysical techniques including the SPR assay with single alanine mutant of hot spots provides opportunities to identify hit compounds for developing inhibitors of PPIs.

Inhibition of protein-protein interactions using biodegradable depsipeptide nanoassemblies

Although peptides notoriously have poor intrinsic pharmacokinetic properties, it is well-known that nanostructures with excellent pharmacokinetic properties can be designed. Noticing that peptide inhibitors are generally nonpolar, here, we consolidate the peptide inhibitor targeting intracellular protein-protein interactions (PPIs) as an integral part of biodegradable self-assembled depsipeptide nanostructures (SdPNs). Because the peptide inhibitor has the dual role of PPI inhibition and self-assembly in this design, problems associated with the poor pharmacokinetics of peptides and encapsulation/entrapment processes can be overcome. Optimized SdPNs displayed better tumor targeting and PPI inhibition properties than the comparable small molecule inhibitor in vivo. Kinetics of PPI inhibition for SdPNs were gradual and controllable in contrast to the rapid inhibition kinetics of the small molecule. Because SdPN is modular, any appropriate peptide inhibitor can be incorporated into the platform without concern for the poor pharmacokinetic properties of the peptide.

Apoptosis Inhibitor 5: A Multifaceted Regulator of Cell Fate

Apoptosis, or programmed cell death, is a fundamental process that maintains tissue homeostasis, eliminates damaged or infected cells, and plays a crucial role in various biological phenomena. The deregulation of apoptosis is involved in many human diseases, including cancer. One of the emerging players in the intricate regulatory network of apoptosis is apoptosis inhibitor 5 (API5), also called AAC-11 (anti-apoptosis clone 11) or FIF (fibroblast growth factor-2 interacting factor). While it may not have yet the same level of notoriety as some other cancer-associated proteins, API5 has garnered increasing attention in the cancer field in recent years, as elevated API5 levels are often associated with aggressive tumor behavior, resistance to therapy, and poor patient prognosis. This review aims to shed light on the multifaceted functions and regulatory mechanisms of API5 in cell fate decisions as well as its interest as therapeutic target in cancer.

Functional Classification and Interaction Selectivity Landscape of the Human SH3 Domain Superfamily

SRC homology 3 (SH3) domains are critical interaction modules that orchestrate the assembly of protein complexes involved in diverse biological processes. They facilitate transient protein-protein interactions by selectively interacting with proline-rich motifs (PRMs). A database search revealed 298 SH3 domains in 221 human proteins. Multiple sequence alignment of human SH3 domains is useful for phylogenetic analysis and determination of their selectivity towards PRM-containing peptides (PRPs). However, a more precise functional classification of SH3 domains is achieved by constructing a phylogenetic tree only from PRM-binding residues and using existing SH3 domain-PRP structures and biochemical data to determine the specificity within each of the 10 families for particular PRPs. In addition, the C-terminal proline-rich domain of the RAS activator SOS1 covers 13 of the 14 recognized proline-rich consensus sequence motifs, encompassing differential PRP pattern selectivity among all SH3 families. To evaluate the binding capabilities and affinities, we conducted fluorescence dot blot and polarization experiments using 25 representative SH3 domains and various PRPs derived from SOS1. Our analysis has identified 45 interacting pairs, with binding affinities ranging from 0.2 to 125 micromolar, out of 300 tested and potential new SH3 domain-SOS1 interactions. Furthermore, it establishes a framework to bridge the gap between SH3 and PRP interactions and provides predictive insights into the potential interactions of SH3 domains with PRMs based on sequence specifications. This novel framework has the potential to enhance the understanding of protein networks mediated by SH3 domain-PRM interactions and be utilized as a general approach for other domain-peptide interactions.

Discovery of 14-3-3 PPI Stabilizers by Extension of an Amidine-Substituted Thiophene Fragment

Protein-protein interaction (PPI) modulation is a promising approach in drug discovery with the potential to expand the 'druggable' proteome and develop new therapeutic strategies. While there have been significant advancements in methodologies for developing PPI inhibitors, there is a relative scarcity of literature describing the 'bottom-up' development of PPI stabilizers (Molecular Glues). The hub protein 14-3-3 and its interactome provide an excellent platform for exploring conceptual approaches to PPI modulation, including evolution of chemical matter for Molecular Glues. In this study, we employed a fragment extension strategy to discover stabilizers for the complex of 14-3-3 protein and an Estrogen Receptor alpha-derived peptide (ERα). A focused library of analogues derived from an amidine-substituted thiophene fragment enhanced the affinity of the 14-3-3/ERα complex up to 6.2-fold. Structure-activity relationship (SAR) analysis underscored the importance of the newly added, aromatic side chain with a certain degree of rigidity. X-ray structural analysis revealed a unique intermolecular π-π stacking binding mode of the most active analogues, resulting in the simultaneous binding of two molecules to the PPI binding pocket. Notably, analogue 11 displayed selective stabilization of the 14-3-3/ERα complex.

Selective inhibitors targeting Fis1/Mid51 protein-protein interactions protect against hypoxia-induced damage in cardiomyocytes

Cardiovascular diseases (CVDs) are the most common non-communicable diseases globally. An estimated 17.9 million people died from CVDs in 2019, representing 32% of all global deaths. Mitochondria play critical roles in cellular metabolic homeostasis, cell survival, and cell death, as well as producing most of the cell's energy. Protein-protein interactions (PPIs) have a significant role in physiological and pathological processes, and aberrant PPIs are associated with various diseases, therefore they are potential drug targets for a broad range of therapeutic areas. Due to their ability to mimic natural interaction motifs and cover relatively larger interaction region, peptides are very promising as PPI inhibitors. To expedite drug discovery, computational approaches are widely used for screening potential lead compounds. Here, we developed peptides that inhibit mitochondrial fission 1 (Fis1)/mitochondrial dynamics 51 kDa (Mid51) PPI to reduce the cellular damage that can lead to various human pathologies, such as CVDs. Based on a rational design approach we developed peptide inhibitors of the Fis1/Mid51 PPI. In silico and in vitro studies were done to evaluate the biological activity and molecular interactions of the peptides. Two peptides, CVP-241 and CVP-242 were identified based on low binding energy and molecular dynamics simulations. These peptides inhibit Fis1/Mid51 PPI (-1324.9 kcal mol-1) in docking calculations (CVP-241, -741.3 kcal mol-1, and CVP-242, -747.4 kcal mol-1), as well as in vitro experimental studies Fis1/Mid51 PPI (KD 0.054 µM) Fis1/Mid51 PPI + CVP-241 (KD 3.43 µM), and Fis1/Mid51 PPI + CVP-242 (KD 44.58 µM). Finally, these peptides have no toxicity to H9c2 cells, and they increase cell viability in cardiomyocytes (H9c2 cells). Consequently, the identified inhibitor peptides could serve as potent molecules in basic research and as leads for therapeutic development.

Stabilized cyclic peptides as modulators of protein-protein interactions: promising strategies and biological evaluation

Protein-protein interactions (PPIs) control many essential biological pathways which are often misregulated in disease. As such, selective PPI modulators are desirable to unravel complex functions of PPIs and thus expand the repertoire of therapeutic targets. However, the large size and relative flatness of PPI interfaces make them challenging molecular targets for conventional drug modalities, rendering most PPIs "undruggable". Therefore, there is a growing need to discover innovative molecules that are able to modulate crucial PPIs. Peptides are ideal candidates to deliver such therapeutics attributed to their ability to closely mimic structural features of protein interfaces. However, their inherently poor proteolysis resistance and cell permeability inevitably hamper their biomedical applications. The introduction of a constraint (i.e., peptide cyclization) to stabilize peptides' secondary structure is a promising strategy to address this problem as witnessed by the rapid development of cyclic peptide drugs in the past two decades. Here, we comprehensively review the recent progress on stabilized cyclic peptides in targeting challenging PPIs. Technological advancements and emerging chemical approaches for stabilizing active peptide conformations are categorized in terms of α-helix stapling, β-hairpin mimetics and macrocyclization. To discover potent and selective ligands, cyclic peptide library technologies were updated based on genetic, biochemical or synthetic methodologies. Moreover, several advances to improve the permeability and oral bioavailability of biologically active cyclic peptides enable the de novo development of cyclic peptide ligands with pharmacological properties. In summary, the development of cyclic peptide-based PPI modulators carries tremendous promise for the next generation of therapeutic agents to target historically "intractable" PPI systems.

Structural modulation of insulin by hydrophobic and hydrophilic molecules

In the bloodstream, insulin interacts with various kinds of molecules, which can alter its structure and modulate its function. In this work, we have synthesized two molecules having extremely hydrophilic and hydrophobic side chains. The effects of hydrophilic and hydrophobic molecules on the binding with insulin have been investigated through a multi-spectroscopic approach. We found that hydrophilic molecules have a slightly higher binding affinity towards insulin. Insulin can bind with the hydrophilic molecules as it binds glucose. The high insulin binding affinity of a hydrophobic molecule indicates its dual nature. The hydrophobic molecule binds at the hydrophobic pocket of the insulin surface, where hydrophilic molecules interact at the polar surface of the insulin. Such binding with the hydrophobic molecule perturbs strongly the secondary structure of the insulin much more in comparison to hydrophilic molecules. Therefore, the stability of insulin decreases in the presence of hydrophobic molecules.

Structural Insights into Protein-Aptamer Recognitions Emerged from Experimental and Computational Studies

Aptamers are synthetic nucleic acids that are developed to target with high affinity and specificity chemical entities ranging from single ions to macromolecules and present a wide range of chemical and physical properties. Their ability to selectively bind proteins has made these compounds very attractive and versatile tools, in both basic and applied sciences, to such an extent that they are considered an appealing alternative to antibodies. Here, by exhaustively surveying the content of the Protein Data Bank (PDB), we review the structural aspects of the protein-aptamer recognition process. As a result of three decades of structural studies, we identified 144 PDB entries containing atomic-level information on protein-aptamer complexes. Interestingly, we found a remarkable increase in the number of determined structures in the last two years as a consequence of the effective application of the cryo-electron microscopy technique to these systems. In the present paper, particular attention is devoted to the articulated architectures that protein-aptamer complexes may exhibit. Moreover, the molecular mechanism of the binding process was analyzed by collecting all available information on the structural transitions that aptamers undergo, from their protein-unbound to the protein-bound state. The contribution of computational approaches in this area is also highlighted.

ADP release can explain spatially-dependent kinesin binding times

The self-organization of cells relies on the profound complexity of protein-protein interactions. Challenges in directly observing these events have hindered progress toward understanding their diverse behaviors. One notable example is the interaction between molecular motors and cytoskeletal systems that combine to perform a variety of cellular functions. In this work, we leverage theory and experiments to identify and quantify the rate-limiting mechanism of the initial association between a cargo-bound kinesin motor and a microtubule track. Recent advances in optical tweezers provide binding times for several lengths of kinesin motors trapped at varying distances from a microtubule, empowering the investigation of competing models. We first explore a diffusion-limited model of binding. Through Brownian dynamics simulations and simulation-based inference, we find this simple diffusion model fails to explain the experimental binding times, but an extended model that accounts for the ADP state of the molecular motor agrees closely with the data, even under the scrutiny of penalizing for additional model complexity. We provide quantification of both kinetic rates and biophysical parameters underlying the proposed binding process. Our model suggests that most but not every motor binding event is limited by their ADP state. Lastly, we predict how these association rates can be modulated in distinct ways through variation of environmental concentrations and spatial distances.

YWHAG Deficiency Disrupts the EMT-Associated Network to Induce Oxidative Cell Death and Prevent Metastasis

Metastasis involves epithelial-to-mesenchymal transition (EMT), a process that is regulated by complex gene networks, where their deliberate disruption may yield a promising outcome. However, little is known about mechanisms that coordinate these metastasis-associated networks. To address this gap, hub genes with broad engagement across various human cancers by analyzing the transcriptomes of different cancer cell types undergoing EMT are identified. The oncogenic signaling adaptor protein tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein gamma (YWHAG) is ranked top for its clinical relevance and impact. The cellular kinome and transcriptome data are surveyed to construct the regulome of YWHAG, revealing stress responses and metabolic processes during cancer EMT. It is demonstrated that a YWHAG-dependent cytoprotective mechanism in the regulome is embedded in EMT-associated networks to protect cancer cells from oxidative catastrophe through enhanced autophagy during EMT. YWHAG deficiency results in a rapid accumulation of reactive oxygen species (ROS), delayed EMT, and cell death. Tumor allografts show that metastasis potential and overall survival time are correlated with the YWHAG expression level of cancer cell lines. Metastasized tumors have higher expression of YWHAG and autophagy-related genes than primary tumors. Silencing YWHAG diminishes primary tumor volumes, prevents metastasis, and prolongs the median survival period of the mice.

Designing Cyclic-Constrained Peptides to Inhibit Human Phosphoglycerate Dehydrogenase

Although loop epitopes at protein-protein binding interfaces often play key roles in mediating oligomer formation and interaction specificity, their binding sites are underexplored as drug targets owing to their high flexibility, relatively few hot spots, and solvent accessibility. Prior attempts to develop molecules that mimic loop epitopes to disrupt protein oligomers have had limited success. In this study, we used structure-based approaches to design and optimize cyclic-constrained peptides based on loop epitopes at the human phosphoglycerate dehydrogenase (PHGDH) dimer interface, which is an obligate homo-dimer with activity strongly dependent on the oligomeric state. The experimental validations showed that these cyclic peptides inhibit PHGDH activity by directly binding to the dimer interface and disrupting the obligate homo-oligomer formation. Our results demonstrate that loop epitope derived cyclic peptides with rationally designed affinity-enhancing substitutions can modulate obligate protein homo-oligomers, which can be used to design peptide inhibitors for other seemingly intractable oligomeric proteins.

Molecular Glue Discovery: Current and Future Approaches

The intracellular interactions of biomolecules can be maneuvered to redirect signaling, reprogram the cell cycle, or decrease infectivity using only a few dozen atoms. Such "molecular glues," which can drive both novel and known interactions between protein partners, represent an enticing therapeutic strategy. Here, we review the methods and approaches that have led to the identification of small-molecule molecular glues. We first classify current FDA-approved molecular glues to facilitate the selection of discovery methods. We then survey two broad discovery method strategies, where we highlight the importance of factors such as experimental conditions, software packages, and genetic tools for success. We hope that this curation of methodologies for directed discovery will inspire diverse research efforts targeting a multitude of human diseases.

Intracellular Ca2+ signalling: unexpected new roles for the usual suspect

Cytosolic Ca2+ signals are organized in complex spatial and temporal patterns that underlie their unique ability to regulate multiple cellular functions. Changes in intracellular Ca2+ concentration ([Ca2+]i) are finely tuned by the concerted interaction of membrane receptors and ion channels that introduce Ca2+ into the cytosol, Ca2+-dependent sensors and effectors that translate the elevation in [Ca2+]i into a biological output, and Ca2+-clearing mechanisms that return the [Ca2+]i to pre-stimulation levels and prevent cytotoxic Ca2+ overload. The assortment of the Ca2+ handling machinery varies among different cell types to generate intracellular Ca2+ signals that are selectively tailored to subserve specific functions. The advent of novel high-speed, 2D and 3D time-lapse imaging techniques, single-wavelength and genetic Ca2+ indicators, as well as the development of novel genetic engineering tools to manipulate single cells and whole animals, has shed novel light on the regulation of cellular activity by the Ca2+ handling machinery. A symposium organized within the framework of the 72nd Annual Meeting of the Italian Society of Physiology, held in Bari on 14-16th September 2022, has recently addressed many of the unexpected mechanisms whereby intracellular Ca2+ signalling regulates cellular fate in healthy and disease states. Herein, we present a report of this symposium, in which the following emerging topics were discussed: 1) Regulation of water reabsorption in the kidney by lysosomal Ca2+ release through Transient Receptor Potential Mucolipin 1 (TRPML1); 2) Endoplasmic reticulum-to-mitochondria Ca2+ transfer in Alzheimer's disease-related astroglial dysfunction; 3) The non-canonical role of TRP Melastatin 8 (TRPM8) as a Rap1A inhibitor in the definition of some cancer hallmarks; and 4) Non-genetic optical stimulation of Ca2+ signals in the cardiovascular system.

Prediction of anti-microtubular target proteins of tubulins and their interacting proteins using Gene Ontology tools

BACKGROUND

Tubulins are highly conserved globular proteins involved in stabilization of cellular cytoskeletal microtubules during cell cycle. Different isoforms of tubulins are differentially expressed in various cell types, and their protein-protein interactions (PPIs) analysis will help in identifying the anti-microtubular drug targets for cancer and neurological disorders. Numerous web-based PPIs analysis methods are recently being used, and in this paper, I used Gene Ontology (GO) tools, e.g., Stringbase, ProteomeHD, GeneMANIA, and ShinyGO, to identify anti-microtubular target proteins by selecting strongly interacting proteins of tubulins.

RESULTS

I used 6 different human tubulin isoforms (two from each of α-, β-, and γ-tubulin) and found several thousands of node-to-node protein interactions (highest 4956 in GeneMANIA) and selected top 10 strongly interacting node-to-node interactions with highest score, which included 7 tubulin family protein and 6 non-tubulin family proteins (total 13). Functional enrichment analysis indicated a significant role of these 13 proteins in nucleation, polymerization or depolymerization of microtubules, membrane tethering and docking, dorsal root ganglion development, mitotic cycle, and cytoskeletal organization. I found γ-tubulins (TUBG1, TUBGCP4, and TUBBGCP6) were known to contribute majorly for tubulin-associated functions followed by α-tubulin (TUBA1A) and β-tubulins (TUBB AND TUBB3). In PPI results, I found several non-tubular proteins interacting with tubulins, and six of them (HTT, DPYSL2, SKI, UNC5C, NINL, and DDX41) were found closely associated with their functions.

CONCLUSIONS

Increasing number of regulatory proteins and subpopulation of tubulin proteins are being reported with poor understanding in their association with microtubule assembly and disassembly. The functional enrichment analysis of tubulin isoforms using recent GO tools resulted in identification of γ-tubulins playing a key role in microtubule functions and observed non-tubulin family of proteins HTT, DPYSL2, SKI, UNC5C, NINL, and DDX41 strongly interacting functional proteins of tubulins. The present study yields a promising model system using GO tools to narrow down tubulin-associated proteins as a drug target in cancer, Alzheimer's, neurological disorders, etc.

A novel approach for the analysis of single-cell RNA sequencing identifies TMEM14B as a novel poor prognostic marker in hepatocellular carcinoma

A fundamental goal in cancer-associated genome sequencing is to identify the key genes. Protein-protein interactions (PPIs) play a crucially important role in this goal. Here, human reference interactome (HuRI) map was generated and 64,006 PPIs involving 9094 proteins were identified. Here, we developed a physical link and co-expression combinatory network construction (PLACE) method for genes of interest, which provides a rapid way to analyze genome sequencing datasets. Next, Kaplan‒Meier survival analysis, CCK8 assays, scratch wound assays and Transwell assays were applied to confirm the results. In this study, we selected single-cell sequencing data from patients with hepatocellular carcinoma (HCC) in GSE149614. The PLACE method constructs a protein connection network for genes of interest, and a large fraction (80%) of the genes (screened by the PLACE method) were associated with survival. Then, PLACE discovered that transmembrane protein 14B (TMEM14B) was the most significant prognostic key gene, and target genes of TMEM14B were predicted. The TMEM14B-target gene regulatory network was constructed by PLACE. We also detected that TMEM14B-knockdown inhibited proliferation and migration. The results demonstrate that we proposed a new effective method for identifying key genes. The PLACE method can be used widely and make outstanding contributions to the tumor research field.

It Takes Two to Tango! Protein-Protein Interactions behind cAMP-Mediated CFTR Regulation

Over the last fifteen years, with the approval of the first molecular treatments, a breakthrough era has begun for patients with cystic fibrosis (CF), the rare genetic disease caused by mutations in the gene encoding the cystic fibrosis transmembrane conductance regulator (CFTR). These molecules, known as CFTR modulators, have led to unprecedented improvements in the lung function and quality of life of most CF patients. However, the efficacy of these drugs is still suboptimal, and the clinical response is highly variable even among individuals bearing the same mutation. Furthermore, not all patients carrying rare CFTR mutations are eligible for CFTR modulator therapies, indicating the need for alternative and/or add-on therapeutic approaches. Because the second messenger 3',5'-cyclic adenosine monophosphate (cAMP) represents the primary trigger for CFTR activation and a major regulator of different steps of the life cycle of the channel, there is growing interest in devising ways to fine-tune the cAMP signaling pathway for therapeutic purposes. This review article summarizes current knowledge regarding the role of cAMP signalosomes, i.e., multiprotein complexes bringing together key enzymes of the cAMP pathway, in the regulation of CFTR function, and discusses how modulating this signaling cascade could be leveraged for therapeutic intervention in CF.

Using a stacked ensemble learning framework to predict modulators of protein-protein interactions

Identifying small molecule protein-protein interaction modulators (PPIMs) is a highly promising and meaningful research direction for drug discovery, cancer treatment, and other fields. In this study, we developed a stacking ensemble computational framework, SELPPI, based on a genetic algorithm and tree-based machine learning method for effectively predicting new modulators targeting protein-protein interactions. More specifically, extremely randomized trees (ExtraTrees), adaptive boosting (AdaBoost), random forest (RF), cascade forest, light gradient boosting machine (LightGBM), and extreme gradient boosting (XGBoost) were used as basic learners. Seven types of chemical descriptors were taken as the input characteristic parameters. Primary predictions were obtained with each basic learner-descriptor pair. Then, the 6 methods mentioned above were used as meta learners and trained on the primary prediction in turn. The most efficient method was utilized as the meta learner. Finally, the genetic algorithm was used to select the optimal primary prediction output as the input of the meta learner for secondary prediction to obtain the final result. We systematically evaluated our model on the pdCSM-PPI datasets. To our knowledge, our model outperformed all existing models, which demonstrates its great power.

Bacterial delivery of therapeutic proteins to the nuclei of cancer cells

Targeting nucleic targets with therapeutic proteins would enhance the treatment of hard-to-treat cancers. However, exogenous proteins are excluded from the nucleus by both the cellular and nuclear membranes. We have recently developed Salmonella that deliver active proteins into the cytoplasm of cancer cells. Here, we hypothesized that bacterially delivered proteins accumulate within nuclei, nuclear localization sequences (NLSs) increase delivery, and bacterially delivered proteins kill cancer cells. To test this hypothesis, we developed intranuclear delivering (IND) Salmonella and quantified the delivery of three model proteins. IND Salmonella delivered both ovalbumin and green fluorescent protein to nuclei of MCF7 cancer cells. The amount of protein in nuclei was linearly dependent on the amount delivered to the cytoplasm. The addition of a NLSs increased both the amount of protein in each nucleus and the number of nuclei that received protein. Delivery of Omomyc, a protein inhibitor of the nuclear transcript factor, Myc, altered cell physiology, and significantly induced cell death. These results show that IND Salmonella deliver functional proteins to the nucleus of cancerous cells. Extending this method to other transcription factors will increase the number of accessible targets for cancer therapy.

Atomic-level thermodynamics analysis of the binding free energy of SARS-CoV-2 neutralizing antibodies

Understanding how protein-protein binding affinity is determined from molecular interactions at the interface is essential in developing protein therapeutics such as antibodies, but this has not yet been fully achieved. Among the major difficulties are the facts that it is generally difficult to decompose thermodynamic quantities into contributions from individual molecular interactions and that the solvent effect-dehydration penalty-must also be taken into consideration for every contact formation at the binding interface. Here, we present an atomic-level thermodynamics analysis that overcomes these difficulties and illustrate its utility through application to SARS-CoV-2 neutralizing antibodies. Our analysis is based on the direct interaction energy computed from simulated antibody-protein complex structures and on the decomposition of solvation free energy change upon complex formation. We find that the formation of a single contact such as a hydrogen bond at the interface barely contributes to binding free energy due to the dehydration penalty. On the other hand, the simultaneous formation of multiple contacts between two interface residues favorably contributes to binding affinity. This is because the dehydration penalty is significantly alleviated: the total penalty for multiple contacts is smaller than a sum of what would be expected for individual dehydrations of those contacts. Our results thus provide a new perspective for designing protein therapeutics of improved binding affinity.

Targeting Protein-Protein Interfaces with Peptides: The Contribution of Chemical Combinatorial Peptide Library Approaches

Protein-protein interfaces play fundamental roles in the molecular mechanisms underlying pathophysiological pathways and are important targets for the design of compounds of therapeutic interest. However, the identification of binding sites on protein surfaces and the development of modulators of protein-protein interactions still represent a major challenge due to their highly dynamic and extensive interfacial areas. Over the years, multiple strategies including structural, computational, and combinatorial approaches have been developed to characterize PPI and to date, several successful examples of small molecules, antibodies, peptides, and aptamers able to modulate these interfaces have been determined. Notably, peptides are a particularly useful tool for inhibiting PPIs due to their exquisite potency, specificity, and selectivity. Here, after an overview of PPIs and of the commonly used approaches to identify and characterize them, we describe and evaluate the impact of chemical peptide libraries in medicinal chemistry with a special focus on the results achieved through recent applications of this methodology. Finally, we also discuss the role that this methodology can have in the framework of the opportunities, and challenges that the application of new predictive approaches based on artificial intelligence is generating in structural biology.

Single-molecule fingerprinting of protein-drug interaction using a funneled biological nanopore

In drug discovery, efficient screening of protein-drug interactions (PDIs) is hampered by the limitations of current biophysical approaches. Here, we develop a biological nanopore sensor for single-molecule detection of proteins and PDIs using the pore-forming toxin YaxAB. Using this YaxAB nanopore, we demonstrate label-free, single-molecule detection of interactions between the anticancer Bcl-xL protein and small-molecule drugs as well as the Bak-BH3 peptide. The long funnel-shaped structure and nanofluidic characteristics of the YaxAB nanopore enable the electro-osmotic trapping of diverse folded proteins and high-resolution monitoring of PDIs. Distinctive nanopore event distributions observed in the two-dimensional (ΔI/I_o-versus-I_N) plot illustrate the ability of the YaxAB nanopore to discriminate individual small-molecule drugs bound to Bcl-xL from non-binders. Taken together, our results present the YaxAB nanopore as a robust platform for label-free, ultrasensitive, single-molecule detection of PDIs, opening up a possibility for low-cost, highly efficient drug discovery against diverse drug targets.

Identification of novel peptide inhibitors for oncogenic KRAS G12D as therapeutic options using mutagenesis-based remodeling and MD simulations

The Kirsten rat sarcoma 2 viral oncogene homolog (KRAS) serves as a molecular switch, cycling between guanosine triphosphate (GTP)-bound and inactive guanosine diphosphate (GDP)-bound states. KRAS modulates numerous signal transduction pathways including the conventional RAF-MEK-ERK pathway. Mutations in the RAS genes have been linked to the formation of malignant tumors. Human malignancies typically show mutations in the Ras gene including HRAS, KRAS, and NRAS. Among all the mutations in exon 12 and exon 13 of the KRAS gene, the G12D mutation is more prevalent in pancreatic and lung cancer and accounts for around 41% of all G12 mutations, making them potential anticancer therapeutic targets. The present study is aimed at repurposing the peptide inhibitor KD2 of the KRAS G12D mutant. We employed an in-silico mutagenesis approach to design novel peptide inhibitors from the experimentally reported peptide inhibitor, and it was found that substitutions (N8W, N8I, and N8Y) might enhance the peptide's binding affinity toward the KRAS. Molecular dynamics simulations and binding energy calculations confirmed that the newly designed peptide inhibitors are stable and that their binding affinities are stronger as compared to the wild-type peptide. The detailed analysis revealed that newly designed peptides have the potential to inhibit KRAS/Raf interaction and the oncogenic signal of the KRAS G12D mutant. Our findings strongly suggest that these peptides should be tested and clinically validated to combat the oncogenic activity of KRAS.Communicated by Ramaswamy H. Sarma.

Structural Adaptation of the Single-Stranded DNA-Binding Protein C-Terminal to DNA Metabolizing Partners Guides Inhibitor Design

Single-stranded DNA-binding protein (SSB) is a bacterial interaction hub and an appealing target for antimicrobial therapy. Understanding the structural adaptation of the disordered SSB C-terminus (SSB-Ct) to DNA metabolizing enzymes (e.g., ExoI and RecO) is essential for designing high-affinity SSB mimetic inhibitors. Molecular dynamics simulations revealed the transient interactions of SSB-Ct with two hot spots on ExoI and RecO. The residual flexibility of the peptide-protein complexes allows adaptive molecular recognition. Scanning with non-canonical amino acids revealed that modifications at both termini of SSB-Ct could increase the affinity, supporting the two-hot-spot binding model. Combining unnatural amino acid substitutions on both segments of the peptide resulted in enthalpy-enhanced affinity, accompanied by enthalpy-entropy compensation, as determined by isothermal calorimetry. NMR data and molecular modeling confirmed the reduced flexibility of the improved affinity complexes. Our results highlight that the SSB-Ct mimetics bind to the DNA metabolizing targets through the hot spots, interacting with both of segments of the ligands.

30th Annual GP2A Medicinal Chemistry Conference

The Group for the Promotion of Pharmaceutical Chemistry in Academia (GP2A) held their 30th annual conference in August 2022 in Trinity College Dublin, Ireland. There were 9 keynote presentations, 10 early career researcher presentations and 41 poster presentations.

Fragment-based drug discovery supports drugging 'undruggable' protein-protein interactions

Protein-protein interactions (PPIs) have important roles in various cellular processes, but are commonly described as 'undruggable' therapeutic targets due to their large, flat, featureless interfaces. Fragment-based drug discovery (FBDD) has achieved great success in modulating PPIs, with more than ten compounds in clinical trials. Here, we highlight the progress of FBDD in modulating PPIs for therapeutic development. Targeting hot spots that have essential roles in both fragment binding and PPIs provides a shortcut for the development of PPI modulators via FBDD. We highlight successful cases of cracking the 'undruggable' problems of PPIs using fragment-based approaches. We also introduce new technologies and future trends. Thus, we hope that this review will provide useful guidance for drug discovery targeting PPIs.

Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence

Only around 20% of the human proteome is considered to be druggable with small-molecule antagonists. This leaves some of the most compelling therapeutic targets outside the reach of ligand discovery. The concept of targeted protein degradation (TPD) promises to overcome some of these limitations. In brief, TPD is dependent on small molecules that induce the proximity between a protein of interest (POI) and an E3 ubiquitin ligase, causing ubiquitination and degradation of the POI. In this perspective, we want to reflect on current challenges in the field, and discuss how advances in multiomics profiling, artificial intelligence, and machine learning (AI/ML) will be vital in overcoming them. The presented roadmap is discussed in the context of small-molecule degraders but is equally applicable for other emerging proximity-inducing modalities.

The Application of High-Throughput Approaches in Identifying Novel Therapeutic Targets and Agents to Treat Diabetes

During the past decades, unprecedented progress in technologies has revolutionized traditional research methodologies. Among these, advances in high-throughput drug screening approaches have permitted the rapid identification of potential therapeutic agents from drug libraries that contain thousands or millions of molecules. Moreover, high-throughput-based therapeutic target discovery strategies can comprehensively interrogate relationships between biomolecules (e.g., gene, RNA, and protein) and diseases and significantly increase the authors' knowledge of disease mechanisms. Diabetes is a chronic disease primarily characterized by the incapacity of the body to maintain normoglycemia. The prevalence of diabetes in modern society has become a severe public health issue that threatens the well-being of millions of patients. Although a number of pharmacological treatments are available, there is no permanent cure for diabetes, and discovering novel therapeutic targets and agents continues to be an urgent need. The present review discusses the technical details of high-throughput screening approaches in drug discovery, followed by introducing the applications of such approaches to diabetes research. This review aims to provide an example of the applicability of high-throughput technologies in facilitating different aspects of disease research.

Machine learning in computational modelling of membrane protein sequences and structures: From methodologies to applications

Membrane proteins mediate a wide spectrum of biological processes, such as signal transduction and cell communication. Due to the arduous and costly nature inherent to the experimental process, membrane proteins have long been devoid of well-resolved atomic-level tertiary structures and, consequently, the understanding of their functional roles underlying a multitude of life activities has been hampered. Currently, computational tools dedicated to furthering the structure-function understanding are primarily focused on utilizing intelligent algorithms to address a variety of site-wise prediction problems (e.g., topology and interaction sites), but are scattered across different computing sources. Moreover, the recent advent of deep learning techniques has immensely expedited the development of computational tools for membrane protein-related prediction problems. Given the growing number of applications optimized particularly by manifold deep neural networks, we herein provide a review on the current status of computational strategies mainly in membrane protein type classification, topology identification, interaction site detection, and pathogenic effect prediction. Meanwhile, we provide an overview of how the entire prediction process proceeds, including database collection, data pre-processing, feature extraction, and method selection. This review is expected to be useful for developing more extendable computational tools specific to membrane proteins.

Assessing the functional impact of protein binding site definition

Many biomedical applications, such as classification of binding specificities or bioengineering, depend on the accurate definition of protein binding interfaces. Depending on the choice of method used, substantially different sets of residues can be classified as belonging to the interface of a protein. A typical approach used to verify these definitions is to mutate residues and measure the impact of these changes on binding. Besides the lack of exhaustive data this approach generates, it also suffers from the fundamental problem that a mutation introduces an unknown amount of alteration into an interface, which potentially alters the binding characteristics of the interface. In this study we explore the impact of alternative binding site definitions on the ability of a protein to recognize its cognate ligand using a pharmacophore approach, which does not affect the interface. The study also provides guidance on the minimum expected accuracy of interface definition that is required to capture the biological function of a protein.

Large-Scale Crystallographic Fragment Screening Expedites Compound Optimization and Identifies Putative Protein-Protein Interaction Sites

The identification of starting points for compound development is one of the key steps in early-stage drug discovery. Information-rich techniques such as crystallographic fragment screening can potentially increase the efficiency of this step by providing the structural information of the binding mode of the ligands in addition to the mere binding information. Here, we present the crystallographic screening of our 1000-plus-compound F2X-Universal Library against the complex of the yeast spliceosomal Prp8 RNaseH-like domain and the snRNP assembly factor Aar2. The observed 269 hits are distributed over 10 distinct binding sites on the surface of the protein-protein complex. Our work shows that hit clusters from large-scale crystallographic fragment screening campaigns identify known interaction sites with other proteins and suggest putative additional interaction sites. Furthermore, the inherent binding pose validation within the hit clusters may accelerate downstream compound optimization.

Analysis of Protein-Protein Interactions Identifies NECTIN2 as a Target of N,N-Bis (5-Ethyl-2-hydroxybenzyl) Methylamine for Inhibition of Lung Cancer Metastasis

BACKGROUND/AIM

Metastasis negatively affects the survival of lung cancer patients, however, relatively few compounds have potential in metastasis suppression. This study investigated the molecular targets of N,N-bis (5-ethyl-2-hydroxybenzyl) methylamine (EMD) for metastatic inhibition.

MATERIALS AND METHODS

Proteins were analyzed by proteomic and bioinformatic analyses. Protein-protein interaction (PPI) networks were created with the Search Tool for the Retrieval of Interacting Genes. The Kyoto Encyclopedia of Genes and Genomes database and hub genes were used to determine dominant pathways. Immunofluorescence and western blot analyses validated the proteomic results and investigated signaling pathways in NCI-H23 lung cancer cells.

RESULTS

A total of 1,751 proteins were common to the control, EMD and N,N-bis(5-methoxy-2-hydroxybenzyl) methylamine (MeMD) groups; 1,980 different proteins were categorized using metastatic capacity category and analyzed for unique proteins affected by EMD. Fifteen proteins were associated with cell adhesion and six with cell migration. Nectin cell adhesion molecule 2 (NECTIN2) was expressed in the control and MeMD-treated groups but not the EMD-treated group, suggesting NECTIN2 as an EMD target. PPI network showed association of NECTIN2 with proteins regulating cancer metastasis. Kyoto Encyclopedia of Genes and Genomes pathways revealed that NECTIN2 is an upstream target of cytoskeletal regulation via SRC signaling. Western blot and immunofluorescence analyses confirmed that EMD suppressed NECTIN2, and its downstream targets, including p-SRC (Y146 and Y527) and the epithelial-to-mesenchymal transition markers tight junction protein 1, vimentin, β-catenin, snail family transcriptional repressor 1 (SNAI1), and SNAI2, while increasing E-cadherin.

CONCLUSION

EMD suppressed NECTIN2-induced activation of EMT signaling. These data support the development of EMD to prevent metastasis of lung cancer.

Intrinsically disordered proteins in viral pathogenesis and infections

Disordered proteins serve a crucial part in many biological processes that go beyond the capabilities of ordered proteins. A large number of virus-encoded proteins have extremely condensed proteomes and genomes, which results in highly disordered proteins. The presence of these IDPs allows them to rapidly adapt to changes in their biological environment and play a significant role in viral replication and down-regulation of host defense mechanisms. Since viruses undergo rapid evolution and have a high rate of mutation and accumulation in their proteome, IDPs' insights into viruses are critical for understanding how viruses hijack cells and cause disease. There are many conformational changes that IDPs can adopt in order to interact with different protein partners and thus stabilize the particular fold and withstand high mutation rates. This chapter explains the molecular mechanism behind viral IDPs, as well as the significance of recent research in the field of IDPs, with the goal of gaining a deeper comprehension of the essential roles and functions played by viral proteins.

Recent applications of covalent chemistries in protein-protein interaction inhibitors

Protein-protein interactions (PPIs) are large, often featureless domains whose modulations by small-molecules are challenging. Whilst there are some notable successes, such as the BCL-2 inhibitor venetoclax, the requirement for larger ligands to achieve the desired level of potency and selectivity may result in poor "drug-like" properties. Covalent chemistry is presently enjoying a renaissance. In particular, targeted covalent inhibition (TCI), in which a weakly electrophilic "warhead" is installed onto a protein ligand scaffold, is a powerful strategy to develop potent inhibitors of PPIs that are smaller/more drug-like yet have enhanced affinities by virtue of the reinforcing effect on the existing non-covalent interactions by the resulting protein-ligand covalent bond. Furthermore, the covalent bond delivers sustained inhibition, which may translate into significantly reduced therapeutic dosing. Herein, we discuss recent applications of a spectrum of TCIs, as well as covalent screening strategies, in the discovery of more effective inhibitors of PPIs using the HDM2 and BCL-2 protein families as case studies.

De novo molecular design with deep molecular generative models for PPI inhibitors

We construct a protein-protein interaction (PPI) targeted drug-likeness dataset and propose a deep molecular generative framework to generate novel drug-likeness molecules from the features of the seed compounds. This framework gains inspiration from published molecular generative models, uses the key features associated with PPI inhibitors as input and develops deep molecular generative models for de novo molecular design of PPI inhibitors. For the first time, quantitative estimation index for compounds targeting PPI was applied to the evaluation of the molecular generation model for de novo design of PPI-targeted compounds. Our results estimated that the generated molecules had better PPI-targeted drug-likeness and drug-likeness. Additionally, our model also exhibits comparable performance to other several state-of-the-art molecule generation models. The generated molecules share chemical space with iPPI-DB inhibitors as demonstrated by chemical space analysis. The peptide characterization-oriented design of PPI inhibitors and the ligand-based design of PPI inhibitors are explored. Finally, we recommend that this framework will be an important step forward for the de novo design of PPI-targeted therapeutics.

Allosteric regulation of the inactive to active state conformational transition in CDPK1 protein of Plasmodium falciparum

The aim of the current study is to investigate the role of the CAD domain in the activation mechanism of calcium dependent protein kinase-1 of Plasmodium falciparum (PfCDPK1) and explore the possibility of allosteric inhibition of this kinase. PfCDPK1 belongs to CDPK family of apicomplexan kinases which have a C-terminal CAD domain. Microsecond scale MD simulations were performed on modeled structures of complete PfCDPK1 and its kinase domain alone. The simulations revealed that in absence of CAD the salt bridge between Glu116 in αC-helix and Lys85 in β3-sheet of kinase breaks after 200 ns resulting in inactive conformation of the kinase, but the salt bridge stays intact in the complete protein stabilizing it in active conformation. These results highlight the novel CAD mediated allosteric stabilization of the crucial salt bridge which is a hallmark of active conformation of kinase domains. The mechanistic details of the allosteric activation revealed by our study, opens up the possibility for design of allosteric inhibitors of PfCDPK1 kinase by disrupting the kinase:CAD interactions. Using a combination of machine learning and structure-based in silico screening, we have identified novel PPI modulators for allosteric inactivation of PfCDPK1 kinase.