Hot spot-based design of small-molecule inhibitors for protein-protein interactions

ProBAN: Neural network algorithm for predicting binding affinity in protein-protein complexes

Determining binding affinities in protein-protein and protein-peptide complexes is a challenging task that directly impacts the development of peptide and protein pharmaceuticals. Although several models have been proposed to predict the value of the dissociation constant and the Gibbs free energy, they are currently not capable of making stable predictions with high accuracy, in particular for complexes consisting of more than two molecules. In this work, we present ProBAN, a new method for predicting binding affinity in protein-protein complexes based on a deep convolutional neural network. Prediction is carried out for the spatial structures of complexes, presented in the format of a 4D tensor, which includes information about the location of atoms and their abilities to participate in various types of interactions realized in protein-protein and protein-peptide complexes. The effectiveness of the model was assessed both on an internal test data set containing complexes consisting of three or more molecules, as well as on an external test for the PPI-Affinity service. As a result, we managed to achieve the best prediction quality on these data sets among all the analyzed models: on the internal test, Pearson correlation R = 0.6, MAE = 1.60, on the external test, R = 0.55, MAE = 1.75. The open-source code, the trained ProBAN model, and the collected dataset are freely available at the following link https://github.com/EABogdanova/ProBAN.

14-3-3 Protein-Protein Interactions: From Mechanistic Understanding to Their Small-Molecule Stabilization

Protein-protein interactions (PPIs) are of utmost importance for maintenance of cellular homeostasis. Herein, a central role can be found for 14-3-3 proteins. These hub-proteins are known to bind hundreds of interaction partners, thereby regulating their activity, localization, and/or stabilization. Due to their ability to bind a large variety of client proteins, studies of 14-3-3 protein complexes flourished over the last decades, aiming to gain greater molecular understanding of these complexes and their role in health and disease. Because of their crucial role within the cell, 14-3-3 protein complexes are recognized as highly interesting therapeutic targets, encouraging the discovery of small molecule modulators of these PPIs. We discuss various examples of 14-3-3-mediated regulation of its binding partners on a mechanistic level, highlighting the versatile and multi-functional role of 14-3-3 within the cell. Furthermore, an overview is given on the development of stabilizers of 14-3-3 protein complexes, from initially used natural products to fragment-based approaches. These studies show the potential of 14-3-3 PPI stabilizers as novel agents in drug discovery and as tool compounds to gain greater molecular understanding of the role of 14-3-3-based protein regulation.

Deciphering the Lexicon of Protein Targets: A Review on Multifaceted Drug Discovery in the Era of Artificial Intelligence

Understanding protein sequence and structure is essential for understanding protein-protein interactions (PPIs), which are essential for many biological processes and diseases. Targeting protein binding hot spots, which regulate signaling and growth, with rational drug design is promising. Rational drug design uses structural data and computational tools to study protein binding sites and protein interfaces to design inhibitors that can change these interactions, thereby potentially leading to therapeutic approaches. Artificial intelligence (AI), such as machine learning (ML) and deep learning (DL), has advanced drug discovery and design by providing computational resources and methods. Quantum chemistry is essential for drug reactivity, toxicology, drug screening, and quantitative structure-activity relationship (QSAR) properties. This review discusses the methodologies and challenges of identifying and characterizing hot spots and binding sites. It also explores the strategies and applications of artificial-intelligence-based rational drug design technologies that target proteins and protein-protein interaction (PPI) binding hot spots. It provides valuable insights for drug design with therapeutic implications. We have also demonstrated the pathological conditions of heat shock protein 27 (HSP27) and matrix metallopoproteinases (MMP2 and MMP9) and designed inhibitors of these proteins using the drug discovery paradigm in a case study on the discovery of drug molecules for cancer treatment. Additionally, the implications of benzothiazole derivatives for anticancer drug design and discovery are deliberated.

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.

A Multimodal Deep Learning Framework for Predicting PPI-Modulator Interactions

Protein-protein interactions (PPIs) are essential for various biological processes and diseases. However, most existing computational methods for identifying PPI modulators require either target structure or reference modulators, which restricts their applicability to novel PPI targets. To address this challenge, we propose MultiPPIMI, a sequence-based deep learning framework that predicts the interaction between any given PPI target and modulator. MultiPPIMI integrates multimodal representations of PPI targets and modulators and uses a bilinear attention network to capture intermolecular interactions. Experimental results on our curated benchmark data set show that MultiPPIMI achieves an average AUROC of 0.837 in three cold-start scenarios and an AUROC of 0.994 in the random-split scenario. Furthermore, the case study shows that MultiPPIMI can assist molecular docking simulations in screening inhibitors of Keap1/Nrf2 PPI interactions. We believe that the proposed method provides a promising way to screen PPI-targeted modulators.

β-catenin inhibitors in cancer therapeutics: intricacies and way forward

β-catenin is an evolutionary conserved, quintessential, multifaceted protein that plays vital roles in cellular homeostasis, embryonic development, organogenesis, stem cell maintenance, cell proliferation, migration, differentiation, apoptosis, and pathogenesis of various human diseases including cancer. β-catenin manifests both signaling and adhesive features. It acts as a pivotal player in intracellular signaling as a component of versatile WNT signaling cascade involved in embryonic development, homeostasis as well as in carcinogenesis. It is also involved in Ca2+ dependent cell adhesion via interaction with E-cadherin at the adherens junctions. Aberrant β-catenin expression and its nuclear accumulation promote the transcription of various oncogenes including c-Myc and cyclinD1, thereby contributing to tumor initiation, development, and progression. β-catenin's expression is closely regulated at various levels including its stability, sub-cellular localization, as well as transcriptional activity. Understanding the molecular mechanisms of regulation of β-catenin and its atypical expression will provide researchers not only the novel insights into the pathogenesis and progression of cancer but also will help in deciphering new therapeutic avenues. In the present review, we have summarized the dual functions of β-catenin, its role in signaling, associated mutations as well as its role in carcinogenesis and tumor progression of various cancers. Additionally, we have discussed the challenges associated with targeting β-catenin molecule with the presently available drugs and suggested the possible way forward in designing new therapeutic alternatives against this oncogene.

Deep learning-based method for predicting and classifying the binding affinity of protein-protein complexes

Protein-protein interactions (PPIs) play a critical role in various biological processes. Accurately estimating the binding affinity of PPIs is essential for understanding the underlying molecular recognition mechanisms. In this study, we employed a deep learning approach to predict the binding affinity (ΔG) of protein-protein complexes. To this end, we compiled a dataset of 903 protein-protein complexes, each with its corresponding experimental binding affinity, which belong to six functional classes. We extracted 8 to 20 non-redundant features from the sequence information as well as the predicted three-dimensional structures using feature selection methods for each protein functional class. Our method showed an overall mean absolute error of 1.05 kcal/mol and a correlation of 0.79 between experimental and predicted ΔG values. Additionally, we evaluated our model for discriminating high and low affinity protein-protein complexes and it achieved an accuracy of 87% with an F1 score of 0.86 using 10-fold cross-validation on the selected features. Our approach presents an efficient tool for studying PPIs and provides crucial insights into the underlying mechanisms of the molecular recognition process. The web server can be freely accessed at https://web.iitm.ac.in/bioinfo2/DeepPPAPred/index.html.

Insilico design of an allosteric modulator targeting the protein-protein interaction site of 3 Phosphoinositide dependent Kinase-1: design, synthesis and biological activity

The signalling pathways in human cells mostly rely on protein-protein interactions (PPI) for their function. Such a PPI site in 3 Phosphoinositide dependent Kinase-1 (PDK1) is targeted to design the small molecule modulators. Based on the hotspot residues in its PPI site, a pharmacophore with seven different features was developed and screened against 2.9 million lead like compounds in Zinc database. A phthalazine derivative was identified as a potent allosteric inhibitor through virtual screening, molecular docking and 100 ns dynamics simulations. The modified hit possessed hydrogen bonds with Lys115, Arg131, Thr148, Glu150 as well as pi-pi stacking interactions with Phe157 which are the key residues in the PIF pocket of PDK1. Comparison between the free energy profiles by metadynamics simulation with the presence and absence of the modified ligand (MH) in the binding pocket indicates that the binding of MH enhances the hinge motion making PDK1 to adopt open conformation also and stabilizes the fluctuation of the end-to-end distance in αB helix of PDK1. The modified hit compound was synthesized, characterized and found to be cytotoxic to cancerous cells that are rich in PDK1 expression. These results propose that MH can serve as a new scaffold template for the design of novel drugs targeting PDK1 as well as promising allosteric regulator of PDK1 targeting its protein-protein interface.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-023-00160-6.

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.

Insilico molecular modelling to identify PDK-1 targeting agents based on its protein-protein docking interaction

PDK1, an attractive cancer target that downstreams 23 other kinases towards cell growth, survival and metabolism has gaining attention due to allosteric effect of ligands bound to it. Generally, the drug design strategy using pharmacophores is either a single protein structure or ensemble or ligand-based. Apart from these methods, yet another new approach of protein-protein docking with state of art computational tool like Schrodinger Suite to generate pharmacophores based on the interacting partners of the protein is proposed in this work. The structure-based pharmacophoric features were picked up from docking the ten interacting partners of PDK1 and screened against the Enamine libraries containing protein-protein interacting compound collection, advanced, protein mimetic and allosteric compounds. High throughput virtual screening against the PIF pocket of PDK1 yields an indole scaffold. The identified indole derivative is proposed to be a strong activator that binds in the protein-protein interaction site of PDK1 which was further confirmed by molecular metadynamics simulations, free energy surface analysis and MM-GBSA calculations. Thus, the pharmacophores generated by the interacting proteins for PPI can facilitate the virtual screening in structure-based drug discovery of similar therapeutic targets.Communicated by Ramaswamy H. Sarma.

UBL3 Interacts with Alpha-Synuclein in Cells and the Interaction Is Downregulated by the EGFR Pathway Inhibitor Osimertinib

Ubiquitin-like 3 (UBL3) acts as a post-translational modification (PTM) factor and regulates protein sorting into small extracellular vesicles (sEVs). sEVs have been reported as vectors for the pathology propagation of neurodegenerative diseases, such as α-synucleinopathies. Alpha-synuclein (α-syn) has been widely studied for its involvement in α-synucleinopathies. However, it is still unknown whether UBL3 interacts with α-syn, and is influenced by drugs or compounds. In this study, we investigated the interaction between UBL3 and α-syn, and any ensuing possible functional and pathological implications. We found that UBL3 can interact with α-syn by the Gaussia princeps based split luciferase complementation assay in cells and immunoprecipitation, while cysteine residues at its C-terminal, which are considered important as PTM factors for UBL3, were not essential for the interaction. The interaction was upregulated by 1-methyl-4-phenylpyridinium exposure. In drug screen results, the interaction was significantly downregulated by the treatment of osimertinib. These results suggest that UBL3 interacts with α-syn in cells and is significantly downregulated by epidermal growth factor receptor (EGFR) pathway inhibitor osimertinib. Therefore, the UBL3 pathway may be a new therapeutic target for α-synucleinopathies in the future.

Applying deep learning to iterative screening of medium-sized molecules for protein-protein interaction-targeted drug discovery

We combined a library of medium-sized molecules with iterative screening using multiple machine learning algorithms that were ligand-based, which resulted in a large increase of the hit rate against a protein-protein interaction target. This was demonstrated by inhibition assays using a PPI target, Kelch-like ECH-associated protein 1/nuclear factor erythroid 2-related factor 2 (Keap1/Nrf2), and a deep neural network model based on the first-round assay data showed a highest hit rate of 27.3%. Using the models, we identified novel active and non-flat compounds far from public datasets, expanding the chemical space.

Medicinal Chemistry Strategies for the Development of Inhibitors Disrupting β-Catenin's Interactions with Its Nuclear Partners

Dysregulation of the Wnt/β-catenin signaling pathway is strongly associated with various aspects of cancer, including tumor initiation, proliferation, and metastasis as well as antitumor immunity, and presents a promising opportunity for cancer therapy. Wnt/β-catenin signaling activation increases nuclear dephosphorylated β-catenin levels, resulting in β-catenin binding to TCF and additional cotranscription factors, such as BCL9, CBP, and p300. Therefore, directly disrupting β-catenin's interactions with these nuclear partners holds promise for the effective and selective suppression of the aberrant activation of Wnt/β-catenin signaling. Herein, we summarize recent advances in biochemical techniques and medicinal chemistry strategies used to identify potent peptide-based and small-molecule inhibitors that directly disrupt β-catenin's interactions with its nuclear binding partners. We discuss the challenges involved in developing drug-like inhibitors that target the interactions of β-catenin and its nuclear binding partner into therapeutic agents.

Machine learning methods for protein-protein binding affinity prediction in protein design

Protein-protein interactions govern a wide range of biological activity. A proper estimation of the protein-protein binding affinity is vital to design proteins with high specificity and binding affinity toward a target protein, which has a variety of applications including antibody design in immunotherapy, enzyme engineering for reaction optimization, and construction of biosensors. However, experimental and theoretical modelling methods are time-consuming, hinder the exploration of the entire protein space, and deter the identification of optimal proteins that meet the requirements of practical applications. In recent years, the rapid development in machine learning methods for protein-protein binding affinity prediction has revealed the potential of a paradigm shift in protein design. Here, we review the prediction methods and associated datasets and discuss the requirements and construction methods of binding affinity prediction models for protein design.

Fragment screening using biolayer interferometry reveals ligands targeting the SHP-motif binding site of the AAA+ ATPase p97

Biosensor techniques have become increasingly important for fragment-based drug discovery during the last years. The AAA+ ATPase p97 is an essential protein with key roles in protein homeostasis and a possible target for cancer chemotherapy. Currently available p97 inhibitors address its ATPase activity and globally impair p97-mediated processes. In contrast, inhibition of cofactor binding to the N-domain by a protein-protein-interaction inhibitor would enable the selective targeting of specific p97 functions. Here, we describe a biolayer interferometry-based fragment screen targeting the N-domain of p97 and demonstrate that a region known as SHP-motif binding site can be targeted with small molecules. Guided by molecular dynamics simulations, the binding sites of selected screening hits were postulated and experimentally validated using protein- and ligand-based NMR techniques, as well as X-ray crystallography, ultimately resulting in the first structure of a small molecule in complex with the N-domain of p97. The identified fragments provide insights into how this region could be targeted and present first chemical starting points for the development of a protein-protein interaction inhibitor preventing the binding of selected cofactors to p97.

Increased slow dynamics defines ligandability of BTB domains

Efficient determination of protein ligandability, or the propensity to bind small-molecules, would greatly facilitate drug development for novel targets. Ligandability is currently assessed using computational methods that typically consider the static structural properties of putative binding sites or by experimental fragment screening. Here, we evaluate ligandability of conserved BTB domains from the cancer-relevant proteins LRF, KAISO, and MIZ1. Using fragment screening, we discover that MIZ1 binds multiple ligands. However, no ligands are uncovered for the structurally related KAISO or LRF. To understand the principles governing ligand-binding by BTB domains, we perform comprehensive NMR-based dynamics studies and find that only the MIZ1 BTB domain exhibits backbone µs-ms time scale motions. Interestingly, residues with elevated dynamics correspond to the binding site of fragment hits and recently defined HUWE1 interaction site. Our data argue that examining protein dynamics using NMR can contribute to identification of cryptic binding sites, and may support prediction of the ligandability of novel challenging targets.

Implications of the Essential Role of Small Molecule Ligand Binding Pockets in Protein-Protein Interactions

Protein-protein interactions (PPIs) and protein-metabolite interactions play a key role in many biochemical processes, yet they are often viewed as being independent. However, the fact that small molecule drugs have been successful in inhibiting PPIs suggests a deeper relationship between protein pockets that bind small molecules and PPIs. We demonstrate that 2/3 of PPI interfaces, including antibody-epitope interfaces, contain at least one significant small molecule ligand binding pocket. In a representative library of 50 distinct protein-protein interactions involving hundreds of mutations, >75% of hot spot residues overlap with small molecule ligand binding pockets. Hence, ligand binding pockets play an essential role in PPIs. In representative cases, evolutionary unrelated monomers that are involved in different multimeric interactions yet share the same pocket are predicted to bind the same metabolites/drugs; these results are confirmed by examples in the PDB. Thus, the binding of a metabolite can shift the equilibrium between monomers and multimers. This implicit coupling of PPI equilibria, termed "metabolic entanglement", was successfully employed to suggest novel functional relationships among protein multimers that do not directly interact. Thus, the current work provides an approach to unify metabolomics and protein interactomics.

Targeting p53-MDM2 interaction by small-molecule inhibitors: learning from MDM2 inhibitors in clinical trials

p53, encoded by the tumor suppressor gene TP53, is one of the most important tumor suppressor factors in vivo and can be negatively regulated by MDM2 through p53–MDM2 negative feedback loop. Abnormal p53 can be observed in almost all tumors, mainly including p53 mutation and functional inactivation. Blocking MDM2 to restore p53 function is a hotspot in the development of anticancer candidates. Till now, nine MDM2 inhibitors with different structural types have entered clinical trials. However, no MDM2 inhibitor has been approved for clinical application. This review focused on the discovery, structural modification, preclinical and clinical research of the above compounds from the perspective of medicinal chemistry. Based on this, the possible defects in MDM2 inhibitors in clinical development were analyzed to suggest that the multitarget strategy or targeted degradation strategy based on MDM2 has the potential to reduce the dose-dependent hematological toxicity of MDM2 inhibitors and improve their anti-tumor activity, providing certain guidance for the development of agents targeting the p53–MDM2 interaction.

Myostatin and its Regulation: A Comprehensive Review of Myostatin Inhibiting Strategies

Myostatin (MSTN) is a well-reported negative regulator of muscle growth and a member of the transforming growth factor (TGF) family. MSTN has important functions in skeletal muscle (SM), and its crucial involvement in several disorders has made it an important therapeutic target. Several strategies based on the use of natural compounds to inhibitory peptides are being used to inhibit the activity of MSTN. This review delivers an overview of the current state of knowledge about SM and myogenesis with particular emphasis on the structural characteristics and regulatory functions of MSTN during myogenesis and its involvements in various muscle related disorders. In addition, we review the diverse approaches used to inhibit the activity of MSTN, especially in silico approaches to the screening of natural compounds and the design of novel short peptides derived from proteins that typically interact with MSTN.

Fragment hopping protocol for the design of small-molecule protein-protein interaction inhibitors

Fragment-based ligand discovery (FBLD) is one of the most successful approaches to designing small-molecule protein-protein interaction (PPI) inhibitors. The incorporation of computational tools to FBLD allows the exploration of chemical space in a time- and cost-efficient manner. Herein, a computational protocol for the development of small-molecule PPI inhibitors using fragment hopping, a fragment-based de novo design approach, is described and a case study is presented to illustrate the efficiency of this protocol. Fragment hopping facilitates the design of PPI inhibitors from scratch solely based on key binding features in the PPI complex structure. This approach is an open system that enables the inclusion of different state-of-the-art programs and softwares to improve its performances.

Abiotic Synthetic Antibodies to Target a Specific Protein Domain and Inhibit Its Function

The Bacillus thuringiensis (Bt) Cry proteins are widely used in insect pest control. Despite their economic benefits, remaining concerns over potential ecological and health risks warrant their ongoing surveillance. Affinity reagents, most often antibodies, protein scaffolds, and aptamers, are the traditional tools used for protein binding and detection. We report a synthetic antibody (SA) alternative to traditional biological affinity reagents for binding Bt Cry proteins. Analysis of hotspots of the Bt Cry protein-insect midgut cadherin-like receptor complexes was used for the design of the SA. The SA was selected from a small focused library of hydrogel copolymers containing functional monomers complementary to key exposed hotspots of Bt Cry proteins. A directed chemical evolution identified a SA, APhe-NP23, with affinity and selectivity for Bt Cry1Ab/Ac proteins. The putative intermolecular polymer-protein interfaces were identified by the SA's uptake of Bt Cry1Ac pepsin hydrolysates, binding epitope mutation studies, and protein-protein inhibition studies of the toxin binding to its native insect receptor binding domains. The SA inhibitor binds to the same protein domains as the insect's cadherin-like receptors, Bt-R₁ and SeCad1b. The SA binds rapidly to Bt Cry1Ab/Ac with high capacity, is pH-responsive, and is synthesized reproducibly. We believe that a hotspot-directed approach is general for creation of abiotic protein affinity reagents that target functional protein domains. Affinity ligands are typically high-information content biologicals. Their structure and function are determined from their amino acid or oligo sequence. In contract, the SA described in this work is a statistical copolymer that lacks sequence specificity. These results are an important contribution to the concept that randomness and biospecificity are not mutually exclusive.

Small Molecule Agents Targeting PD-1 Checkpoint Pathway for Cancer Immunotherapy: Mechanisms of Action and Other Considerations for Their Advanced Development

Pioneering success of antibodies targeting immune checkpoints such as programmed cell death protein 1 (PD-1) and cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) has changed the outlook of cancer therapy. Although these antibodies show impressive durable clinical activity, low response rates and immune-related adverse events are becoming increasingly evident in antibody-based approaches. For further strides in cancer immunotherapy, novel treatment strategies including combination therapies and alternate therapeutic modalities are highly warranted. Towards this discovery and development of small molecule, checkpoint inhibitors are actively being pursued, and the efforts have culminated in the ongoing clinical testing of orally bioavailable checkpoint inhibitors. This review focuses on the small molecule agents targeting PD-1 checkpoint pathway for cancer immunotherapy and highlights various chemotypes/scaffolds and their characterization including binding and functionality along with reported mechanism of action. The learnings from the ongoing small molecule clinical trials and crucial points to be considered for their clinical development are also discussed.

Abrogating the Interaction Between p53 and Mortalin (Grp75/HSPA9/mtHsp70) for Cancer Therapy: The Story so far

p53 is a transcription factor that activates the expression of a set of genes that serve as a critical barrier to oncogenesis. Inactivation of p53 is the most common characteristic in sporadic human cancers. Mortalin is a differentially sub-cellularly localized member of the heat shock protein 70 family of chaperones that has essential mitochondrial and extra-mitochondrial functions. Elevated mortalin levels in multiple cancerous tissues and tumor-derived cell lines emphasized its key role in oncogenesis. One of mortalin’s major oncogenic roles is the inactivation of p53. Mortalin binds to p53 sequestering it in the cytoplasm. Hence, p53 cannot freely shuttle to the nucleus to perform its tumor suppressor functions as a transcription factor. This protein-protein interaction was reported to be cancer-specific, hence, a selective druggable target for a rationalistic cancer therapeutic strategy. In this review article, the chronological identification of mortalin-p53 interactions is summarized, the challenges and general strategies for targeting protein-protein interactions are briefly discussed, and information about compounds that have been reported to abrogate mortalin-p53 interaction is provided. Finally, the reasons why the disruption of this druggable interaction has not yet been applied clinically are discussed.

Discovery of potential small molecular SARS-CoV-2 entry blockers targeting the spike protein

An epidemic of pneumonia caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is spreading worldwide. SARS-CoV-2 relies on its spike protein to invade host cells by interacting with the human receptor protein Angiotensin-Converting Enzymes 2 (ACE2). Therefore, designing an antibody or small-molecular entry blockers is of great significance for virus prevention and treatment. This study identified five potential small molecular anti-virus blockers via targeting SARS-CoV-2 spike protein by combining in silico technologies with in vitro experimental methods. The five molecules were natural products that binding to the RBD domain of SARS-CoV-2 was qualitatively and quantitively validated by both native Mass Spectrometry (MS) and Surface Plasmon Resonance (SPR). Anti-viral activity assays showed that the optimal molecule, H69C2, had a strong binding affinity (dissociation constant K_D) of 0.0947 µM and anti-virus IC₅₀ of 85.75 µM.

Discovery of phenylpyrrolidine derivatives as a novel class of retinol binding protein 4 (RBP4) reducers

Retinol-binding protein 4 (RBP4) is a potential drug target for metabolic and ophthalmologic diseases. A high-throughput screening of our compound library has identified a small-molecule RBP4 reducer 7a, as a hit compound. Aiming to provide a suitable tool for investigating the pharmacological effects of RBP4 reducers, we conducted a structure-activity relationship study of 7a. Exploration of the aryl head, oxazole core, and propanoic acid tail of 7a resulted in the discovery of novel, potent, and orally available phenylpyrrolidine derivatives 43b and 43c. Compound 43b had a potent and long-lasting blood RBP4-level-reducing effect when orally administered to mice at a dose as low as 0.3 mg/kg.

Exploring the chemical space of protein-protein interaction inhibitors through machine learning

Although protein-protein interactions (PPIs) have emerged as the basis of potential new therapeutic approaches, targeting intracellular PPIs with small molecule inhibitors is conventionally considered highly challenging. Driven by increasing research efforts, success rates have increased significantly in recent years. In this study, we analyze the physicochemical properties of 9351 non-redundant inhibitors present in the iPPI-DB and TIMBAL databases to define a computational model for active compounds acting against PPI targets. Principle component analysis (PCA) and k-means clustering were used to identify plausible PPI targets in regions of interest in the active group in the chemical space between active and inactive iPPI compounds. Notably, the uniquely defined active group exhibited distinct differences in activity compared with other active compounds. These results demonstrate that active compounds with regions of interest in the chemical space may be expected to provide insights into potential PPI inhibitors for particular protein targets.

Proposing novel MDM2 inhibitors: Combined physics-driven high-throughput virtual screening and in vitro studies

The mouse double minute 2 (MDM2) protein acts as a negative regulator of the p53 tumor suppressor. It directly binds to the N terminus of p53 and promotes p53 ubiquitination and degradation. Since the most common p53-suppressing mechanisms involve the MDM2, proposing novel inhibitors has been the focus of many in silico and also experimental studies. Thus, here we screened around 500,000 small organic molecules from Enamine database at the binding pocket of this oncogenic target. The screening was achieved systematically with starting from the high-throughput virtual screening method followed by more sophisticated docking approaches. The initial high number of screened molecules was reduced to 100 hits which then were studied extensively for their therapeutic activity and pharmacokinetic properties using binary QSAR models. The structural and dynamical profiles of the selected molecules at the binding pocket of the target were studied thoroughly by all-atom molecular dynamics simulations. The free energy of the binding of the hit molecules was estimated by the MM/GBSA method. Based on docking simulations, binary QSAR model results, and free energy calculations, 11 compounds (E1-E11) were selected for in vitro studies. HUVEC vascular endothelium, colon cancer, and breast cancer cell lines were used for testing the binding affinities of the identified hits and for further cellular effects on human cancer cell. Based on in vitro studies, six compounds (E1, E2, E5, E6, E9, and E11) in breast cancer cell lines and six compounds (E1, E2, E5, E6, E8, and E10) in colon cancer cell lines were found as active. Our results showed that these compounds inhibit proliferation and lead to apoptosis.

Conformational dynamics is critical for the allosteric inhibition of cGAS upon acetyl-mimic mutations

Detection of cytosolic dsDNA by cyclic GMP-AMP synthase (cGAS) is critical for the immune system to sense and fight against infection, but chronic activation of cGAS by self-DNA leads to autoimmune diseases without effective treatment yet. It was found that acetylation on either Lys384, Lys394, or Lys414 could inhibit the catalytic production of cGAMP by cGAS, and further suppressed self-DNA-induced autoimmunity. However, the implied mechanism remains unclear. Here, extensive molecular dynamics simulations combined with multiple analytical approaches were employed to uncover the allosteric inhibition mechanisms by using the K-to-Q mutations to mimic acetylation. Results suggested that the exterior loops contributed most to the conformational dynamics of cGAS, and two concerted intrinsic motions were observed: the inward/outward or twisting movement for the outer appendage of lobe 1 and the open/closed swing of the active-site loops. Mutations slightly affected the binding of dsDNA and cGAMP. The shift of the conformational sampling of the active-site loops or residues around cGAMP upon mutation might potentially explain the inhibition of cGAS activity. Moreover, the intra- and inter-molecular coupling was weakened upon mutations more or less but via distinct pathways. Hence, conformational dynamics play a vital role in the allosteric inhibition of cGAS upon the studied acetyl-mimic mutations. As the studied acetyl-mimic mutations are located at either the inter-lobe or inter-molecular interfaces, hence except for acetylation, our findings might help the development of new therapeutics against autoimmune diseases due to abnormal cGAS activation by designing inter-lobe or intermolecular allosteric inhibitors.

Exploring the Conformational Landscape of the Neh4 and Neh5 Domains of Nrf2 Using Two Different Force Fields and Circular Dichroism

The nuclear factor erythroid 2-related factor 2 (Nrf2)-ARE transcriptional response pathway plays a critical role in protecting the cell from oxidative stresses via the upregulation of cytoprotective genes. Aberrant activation of Nrf2 in cancer cells can confer this cytoprotectivity, thereby reducing the efficacy of both chemotherapeutics and radiotherapies. Key to this antioxidant pathway is the interaction between Nrf2 and CREB binding protein (CBP), mediated by the Neh4 and Neh5 domains of Nrf2. Disruption of this interaction via small-molecule therapeutics could negate the effects of aberrant Nrf2 upregulation. Due to the disordered nature of these domains, there remains no three-dimensional structure of Neh4 or Neh5, making structure-based drug design a challenge. Here, we performed 48 μs of unbiased molecular dynamics (MD) simulations with the Amber99SB*-ILDNP and CHARMM36m force fields and circular dichroism (CD) spectropolarimetry experiments to elucidate the free-state structures of these domains; no previous data regarding their conformational landscapes exists. There are two main findings: First, we find Neh5 to be markedly more disordered than Neh4, which has nine residues in the middle of the domain showing α-helical propensity, thus pointing to Neh4 and Neh5 having different binding mechanisms. Second, the two force fields show strong differences for the glutamic acid-rich Neh5 peptide but are in reasonable agreement for Neh4, which has no glutamic acid. The CHARMM36m force field agrees more closely with the CD results.

Identification of novel EED-EZH2 PPI inhibitors using an in silico fragment mapping method

Enhancer of zeste homolog 2 (EZH2) is a histone lysine methyltransferase that is overexpressed in many cancers. Numerous EZH2 inhibitors have been developed as anticancer agents, but recent studies have also focused on protein-protein interaction (PPI) between embryonic ectoderm development (EED) and EZH2 as a novel drug discovery target. Because EED indirectly enhances EZH2 enzymatic activity, EED-EZH2 PPI inhibitors suppress the methyltransferase activity and inhibit cancer growth. By contrast to the numerous promising EZH2 inhibitors, there are a paucity of EED-EZH2 PPI inhibitors reported in the literature. Here, we aimed to discover novel EED-EZH2 PPI inhibitors by first identifying possible binders of EED using an in-house knowledge-based in silico fragment mapping method. Next, 3D pharmacophore models were constructed from the arrangement pattern of the potential binders mapped onto the EED surface. In all, 16 compounds were selected by 3D pharmacophore-based virtual screening followed by docking-based virtual screening. In vitro evaluation revealed that five of these compounds exhibited inhibitory activities. This study has provided structural insights into the discovery and the molecular design of novel EED-EZH2 PPI inhibitors using an in silico fragment mapping method.

Current and Future Challenges in Modern Drug Discovery

Drug discovery is an expensive, time-consuming, and risky business. To avoid late-stage failure, learnings from past projects and the development of new approaches are crucial. New modalities and emerging new target spaces allow the exploration of unprecedented indications or to address so far undrugable targets. Late-stage attrition is usually attributed to the lack of efficacy or to compound-related safety issues. Efficacy has been shown to be related to a strong genetic link to human disease, a better understanding of the target biology, and the availability of biomarkers to bridge from animals to humans. Compound safety can be improved by ligand optimization, which is becoming increasingly demanding for difficult targets. Therefore, new strategies include the design of allosteric ligands, covalent binders, and other modalities. Design methods currently heavily rely on artificial intelligence and advanced computational methods such as free energy calculations and quantum chemistry. Especially for quantum chemical methods, a more detailed overview is given in this chapter.

Direct targeting of β-catenin in the Wnt signaling pathway: Current progress and perspectives

Aberrant activation of the Wnt/β-catenin signaling circuit is associated with cancer recurrence and relapse, cancer invasion and metastasis, and cancer immune evasion. Direct targeting of β-catenin, the central hub in this signaling pathway, is a promising strategy to suppress the hyperactive β-catenin signaling but has proven to be highly challenging. Substantial efforts have been made to discover compounds that bind with β-catenin, block β-catenin-mediated protein-protein interactions, and suppress β-catenin signaling. Herein, we characterize potential small-molecule binding sites in β-catenin, summarize bioactive small molecules that directly target β-catenin, and review structure-based inhibitor optimization, structure-activity relationship, and biological activities of reported inhibitors. This knowledge will benefit future inhibitor development and β-catenin-related drug discovery.

Diameter Is a Key 3D Characteristic for Assessments of Efficient Inhibitors of Protein-Protein Interactions

Three-dimensional (3D) molecular descriptors, including physicochemical and shape properties, for protein-protein interaction (PPI) interface inhibitors have become a topic of discussion. However, the relationships between such properties and binding free energy have not been adequately investigated. In this study, we focused on identifying key 3D molecular descriptors related to the binding free energy and/or the ligand efficiency (LE) of PPI interface inhibitors. A positive correlation was found between the binding free energy and the diameter (D) of cylindrical 3D molecules, in addition to a correlation between LE and D/heavy atom count (HAC). In addition, we showed a correlation between LE and D/HAC for macrocyclic compounds, suggesting that the present findings could be applied during assessments of the potential of macrocyclic PPI interface inhibitors in drug discovery processes.

Activity Mapping the Acyl Carrier Protein: Elongating Ketosynthase Interaction in Fatty Acid Biosynthesis

Elongating ketosynthases (KSs) catalyze carbon-carbon bond-forming reactions during the committed step for each round of chain extension in both fatty acid synthases (FASs) and polyketide synthases (PKSs). A small α-helical acyl carrier protein (ACP) shuttles fatty acyl intermediates between enzyme active sites. To accomplish this task, the ACP relies on a series of dynamic interactions with multiple partner enzymes of FAS and associated FAS-dependent pathways. Recent structures of the Escherichia coli FAS ACP, AcpP, in covalent complexes with its two cognate elongating KSs, FabF and FabB, provide high-resolution details of these interfaces, but a systematic analysis of specific interfacial interactions responsible for stabilizing these complexes has not yet been undertaken. Here, we use site-directed mutagenesis with both in vitro and in vivo activity analyses to quantitatively evaluate these contacting surfaces between AcpP and FabF. We delineate the FabF interface into three interacting regions and demonstrate the effects of point mutants, double mutants, and region deletion variants. Results from these analyses reveal a robust and modular FabF interface capable of tolerating seemingly critical interface mutations with only the deletion of an entire region significantly compromising activity. Structure and sequence analyses of FabF orthologs from related type II FAS pathways indicate significant conservation of type II FAS KS interface residues and, overall, support its delineation into interaction regions. These findings strengthen our mechanistic understanding of molecular recognition events between ACPs and FAS enzymes and provide a blueprint for engineering ACP-dependent biosynthetic pathways.

Computational methods-guided design of modulators targeting protein-protein interactions (PPIs)

Protein-protein interactions (PPIs) play a pivotal role in extensive biological processes and are thus crucial to human health and the development of disease states. Due to their critical implications, PPIs have been spotlighted as promising drug targets of broad-spectrum therapeutic interests. However, owing to the general properties of PPIs, such as flat surfaces, featureless conformations, difficult topologies, and shallow pockets, previous attempts were faced with serious obstacles when targeting PPIs and almost portrayed them as "intractable" for decades. To date, rapid progress in computational chemistry and structural biology methods has promoted the exploitation of PPIs in drug discovery. These techniques boost their cost-effective and high-throughput traits, and enable the study of dynamic PPI interfaces. Thus, computational methods represent an alternative strategy to target "undruggable" PPI interfaces and have attracted intense pharmaceutical interest in recent years, as exemplified by the accumulating number of successful cases. In this review, we first introduce a diverse set of computational methods used to design PPI modulators. Herein, we focus on the recent progress in computational strategies and provide a comprehensive overview covering various methodologies. Importantly, a list of recently-reported successful examples is highlighted to verify the feasibility of these computational approaches. Finally, we conclude the general role of computational methods in targeting PPIs, and also discuss future perspectives on the development of such aids.

Targeting epigenetic protein-protein interactions with small-molecule inhibitors

Epigenetic protein-protein interactions (PPIs) play essential roles in regulating gene expression, and their dysregulations have been implicated in many diseases. These PPIs are comprised of reader domains recognizing post-translational modifications on histone proteins, and of scaffolding proteins that maintain integrities of epigenetic complexes. Targeting PPIs have become focuses for development of small-molecule inhibitors and anticancer therapeutics. Here we summarize efforts to develop small-molecule inhibitors targeting common epigenetic PPI domains. Potent small molecules have been reported for many domains, yet small domains that recognize methylated lysine side chains on histones are challenging in inhibitor development. We posit that the development of potent inhibitors for difficult-to-prosecute epigenetic PPIs may be achieved by interdisciplinary approaches and extensive explorations of chemical space.

The In Silico Prediction of Hotspot Residues that Contribute to the Structural Stability of Subunit Interfaces of a Picornavirus Capsid

The assembly of picornavirus capsids proceeds through the stepwise oligomerization of capsid protein subunits and depends on interactions between critical residues known as hotspots. Few studies have described the identification of hotspot residues at the protein subunit interfaces of the picornavirus capsid, some of which could represent novel drug targets. Using a combination of accessible web servers for hotspot prediction, we performed a comprehensive bioinformatic analysis of the hotspot residues at the intraprotomer, interprotomer and interpentamer interfaces of the Theiler’s murine encephalomyelitis virus (TMEV) capsid. Significantly, many of the predicted hotspot residues were found to be conserved in representative viruses from different genera, suggesting that the molecular determinants of capsid assembly are conserved across the family. The analysis presented here can be applied to any icosahedral structure and provides a platform for in vitro mutagenesis studies to further investigate the significance of these hotspots in critical stages of the virus life cycle with a view to identify potential targets for antiviral drug design.

Characterization of PD-L1 binding sites by a combined FMO/GRID-DRY approach

The programmed cell death protein 1 (PD-1) and its ligand, PD-L1, constitute an important co-inhibitory immune checkpoint leading to downregulation of immune system. Tumor cells developed a strategy to trigger PD-1/PD-L1 pathway reducing the T cell anticancer activity. Anti-PD-L1 small drugs, generally with improved pharmacokinetic and technological profiles than monoclonal antibodies, became an attractive research topic. Nevertheless, still few works have been published on the chemical features of possible binding sites. In this work, we applied a novel computational protocol based on the combination of the ab initio Fragment Molecular Orbital (FMO) method and a newly developed GRID-DRY approach in order to characterize the PD-L1 binding sites, starting from PD-1/PD-L1 and PD-L1/BMS-ligands (Bristol-Mayers Squibb ligands) complexes. The FMO method allows the calculation of the pair-residues as well as the ligand-residues interactions with ab initio accuracy, whereas the GRID-DRY approach is an effective tool to investigate hydrophobic interactions, not easily detectable by ab initio methods. The present GRID-DRY protocol is able to determine the energy contributions of each ligand atoms to each hydrophobic interaction, both qualitatively and quantitatively. We were also able to identify the three specific hot regions involved in PD-1/PD-L1 protein-protein interaction and in PD-L1/BMS-ligand interactions, in agreement with preceding theoretical/experimental results, and to suggest a specific pharmacophore for PD-L1 inhibitors.

Prediction and targeting of GPCR oligomer interfaces

GPCR oligomerization has emerged as a hot topic in the GPCR field in the last years. Receptors that are part of these oligomers can influence each other's function, although it is not yet entirely understood how these interactions work. The existence of such a highly complex network of interactions between GPCRs generates the possibility of alternative targets for new therapeutic approaches. However, challenges still exist in the characterization of these complexes, especially at the interface level. Different experimental approaches, such as FRET or BRET, are usually combined to study GPCR oligomer interactions. Computational methods have been applied as a useful tool for retrieving information from GPCR sequences and the few X-ray-resolved oligomeric structures that are accessible, as well as for predicting new and trustworthy GPCR oligomeric interfaces. Machine-learning (ML) approaches have recently helped with some hindrances of other methods. By joining and evaluating multiple structure-, sequence- and co-evolution-based features on the same algorithm, it is possible to dilute the issues of particular structures and residues that arise from the experimental methodology into all-encompassing algorithms capable of accurately predict GPCR-GPCR interfaces. All these methods used as a single or a combined approach provide useful information about GPCR oligomerization and its role in GPCR function and dynamics. Altogether, we present experimental, computational and machine-learning methods used to study oligomers interfaces, as well as strategies that have been used to target these dynamic complexes.

Investigation of protein-protein interactions and hot spot region between PD-1 and PD-L1 by fragment molecular orbital method

Inhibitors to interfere protein-protein interactions (PPI) between programmed cell death 1 (PD-1) and programmed death ligand-1 (PD-L1) block evasion of cancers from immune surveillance. Analyzing hot spot residues in PPI is important for small-molecule drug development. In order to find out hot spots on PPI interface in PD-1/PD-L1 complex, we analyzed PPI in PD-1/PD-L1 with a new analysis method, 3-dimensional scattered pair interactions energies (3D-SPIEs), which assorts significant interactions with fragment molecular orbital (FMO) method. By additionally analyzing PPI in PD-1/antibody and PD-L1/antibody complexes, and small-ligand interactions in PD-L1/peptide and PD-L1/small-molecule complexes, we narrowed down the hot spot region with 3D-SPIEs-based interaction map, which integrates PPI and small-ligand interactions. Based on the map, there are two hot spot regions in PPI of PD-1/PD-L1 and the first hot spot region is important for inhibitors. In particular, _LY56, _LE58, and _LN66 in the first hot spot of PD-L1 are important for PD-L1-antibodies and small-inhibitors in common, while _LM115 is important for small-inhibitors. Therefore, the 3D-SPIEs-based map would provide valuable information for designing new small-molecule inhibitors to inhibit PPI of PD-1/PD-L1 and the FMO/3D-SPIEs method provides an effectual tool to understand PPI and integrate PPI and small-ligand interactions at a quantum mechanical level.

SKEMPI 2.0: an updated benchmark of changes in protein-protein binding energy, kinetics and thermodynamics upon mutation

Motivation

Understanding the relationship between the sequence, structure, binding energy, binding kinetics and binding thermodynamics of protein–protein interactions is crucial to understanding cellular signaling, the assembly and regulation of molecular complexes, the mechanisms through which mutations lead to disease, and protein engineering.

Results

We present SKEMPI 2.0, a major update to our database of binding free energy changes upon mutation for structurally resolved protein–protein interactions. This version now contains manually curated binding data for 7085 mutations, an increase of 133%, including changes in kinetics for 1844 mutations, enthalpy and entropy changes for 443 mutations, and 440 mutations, which abolish detectable binding.

Availability and implementation

The database is available as supplementary data and at https://life.bsc.es/pid/skempi2/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Targeting the Side-Chain Convergence of Hydrophobic α-Helical Hot Spots To Design Small-Molecule Mimetics: Key Binding Features for i, i + 3, and i + 7

The conformational convergence of hydrophobic α-helical hot spots was revealed by analyzing α-helix-mediated protein-protein interaction (PPI) complex structures. The pharmacophore models were derived for hydrophobic α-helical hot spots at positions i, i + 3, and i + 7. These provide the foundation for designing generalizable scaffolds that can directly mimic the binding mode of the side chains of α-helical hot spots, offering a new class of small-molecule α-helix mimetics. For the first time, the protocol was developed to identify the PPI targets that have similar binding pockets, allowing evaluation of inhibitor selectivities between α-helix-mediated PPIs. The mimicry efficiency of the previously designed scaffold 1 was disclosed. The close positioning of this small molecule to the additional α-helical hot spots suggests that the decoration of this series of generalizable scaffolds can conveniently reach the binding pockets of additional α-helical hot spots to produce potent small-molecule inhibitors for α-helix-mediated PPIs.

Small molecules for modulating the localisation of the water channel aquaporin-2-disease relevance and perspectives for targeting local cAMP signalling

The tight spatial and temporal organisation of cyclic adenosine monophosphate (cAMP) signalling plays a key role in arginine-vasopressin (AVP)-mediated water reabsorption in renal collecting duct principal cells and in a plethora of other processes such as in the control of cardiac myocyte contractility. This review critically discusses in vitro- and cell-based screening strategies for the identification of small molecules that interfere with AVP/cAMP signalling in renal principal cells; it features phenotypic screening and approaches for targeting protein-protein interactions of A-kinase anchoring proteins (AKAPs), which organise local cAMP signalling hubs. The discovery of novel chemical entities for the modulation of local cAMP will not only provide tools for elucidating molecular mechanisms underlying cAMP signalling. Novel chemical entities can also serve as starting points for the development of novel drugs for the treatment of human diseases. Examples illustrate how screening for small molecules can pave the way to novel approaches for the treatment of certain forms of diabetes insipidus, a disease caused by defects in AVP-mediated water reabsorption.

Applications of in Silico Methods for Design and Development of Drugs Targeting Protein-Protein Interactions

BACKGROUND

Identification of Protein-Protein Interactions (PPIs) is a major challenge in modern molecular biology and biochemistry research, due to the unquestionable role of proteins in cells, biological process and pathological states. Over the past decade, the PPIs have evolved from being considered a highly challenging field of research to being investigated and examined as targets for pharmacological intervention.

OBJECTIVE

Comprehension of protein interactions is crucial to known how proteins come together to build signalling pathways, to carry out their functions, or to cause diseases, when deregulated. Multiplicity and great amount of PPIs structures offer a huge number of new and potential targets for the treatment of different diseases.

METHODS

Computational techniques are becoming predominant in PPIs studies for their effectiveness, flexibility, accuracy and cost. As a matter of fact, there are effective in silico approaches which are able to identify PPIs and PPI site. Such methods for computational target prediction have been developed through molecular descriptors and data-mining procedures.

RESULTS

In this review, we present different types of interactions between protein-protein and the application of in silico methods for design and development of drugs targeting PPIs. We described computational approaches for the identification of possible targets on protein surface and to detect of stimulator/ inhibitor molecules.

CONCLUSION

A deeper study of the most recent bioinformatics methodologies for PPIs studies is vital for a better understanding of protein complexes and for discover new potential PPI modulators in therapeutic intervention.

Exploring Energy Profiles of Protein-Protein Interactions (PPIs) Using DFT Method

Background:

Large-scale energy landscape characterization of protein-protein interactions (PPIs) is important to understand the interaction mechanism and protein-protein docking methods. The experimental methods for detecting energy landscapes are tedious and the existing computational methods require longer simulation time.

Objective:

The objective of the present work is to ascertain the energy profiles at the interface regions in a rapid manner to analyze the energy landscape of protein-protein interactions.

Methods:

The atomic coordinates obtained from the X-ray and NMR spectroscopy data are considered as inputs to compute cumulative energy profiles for experimentally validated protein-protein complexes. The energies computed by the program were comparable to the standard molecular dynamics simulations.

Results:

The PPI Profiler not only enables rapid generation of energy profiles but also facilitates the detection of hot spot residue atoms involved therein.

Conclusion:

The hotspot residues and their computed energies matched with the experimentally determined hot spot residues and their energies which correlated well by employing the MM/GBSA method. The proposed method can be employed to scan entire proteomes across species at an atomic level to study the key PPI interactions.

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.

Exploiting endogenous opioids: Lessons learned from endomorphin 2 in the female rat

Effective management of chronic pain is demanded by ethical as well as medical considerations. Although opioid analgesics remain among the most effective pharmacotherapies for ameliorating many types of pain, their use is clouded by concerns regarding their addictive properties, underscored by the current epidemic of prescription opioid abuse and attendant deaths. Medicinal harnessing of endogenous opioid antinociception could provide a strategy for continuing to take advantage of the powerful antinociceptive properties of opioids while avoiding their abuse potential. Based on our studies of endogenous mechanism that suppress and facilitate spinal endomorphin 2 antinociception over the rat reproductive cycle, we identified multiple signaling molecules that could serve as targets for activating endogenous opioid analgesia for chronic pain management in women. Our findings emphasize the need for a precision medicine approach that includes stage of menstrual cycle as an important determinant of drug targets for (activating/harnessing) endogenous opioid antinociceptive systems/ capabilities. Utilization of drugs that harness endogenous opioid antinociception in accordance with varying physiological states represents a novel approach for effective pain management.