Oncogenic protein interfaces: small molecules, big challenges

Targeting protein-protein interactions in drug discovery: Modulators approved or in clinical trials for cancer treatment

Protein-protein interactions (PPIs) form complex cellular networks fundamental to many key biological processes, including signal transduction, cell proliferation and DNA repair. In consequence, their perturbation is often associated with many human diseases. Targeting PPIs offers a promising approach in drug discovery and ongoing advancements in this field hold the potential to provide highly specific therapies for a wide range of complex diseases. Despite the development of PPI modulators is challenging, advances in the genetic, proteomic and computational level have facilitated their discovery and optimization. Focusing on anticancer drugs, in the last years several PPI modulators have entered clinical trials and venetoclax, which targets Bcl-2 family proteins, has been approved for treating different types of leukemia. This review discusses the clinical development status of drugs modulating several PPIs, such as MDM2-4/p53, Hsp90/Hsp90, Hsp90/CDC37, c-Myc/Max, KRAS/SOS1, CCR5/CCL5, CCR2/CCL2 or Smac/XIAP, in cancer drug discovery.

Interface-aware molecular generative framework for protein-protein interaction modulators

Protein-protein interactions (PPIs) play a crucial role in numerous biochemical and biological processes. Although several structure-based molecular generative models have been developed, PPI interfaces and compounds targeting PPIs exhibit distinct physicochemical properties compared to traditional binding pockets and small-molecule drugs. As a result, generating compounds that effectively target PPIs, particularly by considering PPI complexes or interface hotspot residues, remains a significant challenge. In this work, we constructed a comprehensive dataset of PPI interfaces with active and inactive compound pairs. Based on this, we propose a novel molecular generative framework tailored to PPI interfaces, named GENiPPI. Our evaluation demonstrates that GENiPPI captures the implicit relationships between the PPI interfaces and the active molecules, and can generate novel compounds that target these interfaces. Moreover, GENiPPI can generate structurally diverse novel compounds with limited PPI interface modulators. To the best of our knowledge, this is the first exploration of a structure-based molecular generative model focused on PPI interfaces, which could facilitate the design of PPI modulators. The PPI interface-based molecular generative model enriches the existing landscape of structure-based (pocket/interface) molecular generative model. SCIENTIFIC CONTRIBUTION: This study introduces GENiPPI, a protein-protein interaction (PPI) interface-aware molecular generative framework. The framework first employs Graph Attention Networks to capture atomic-level interaction features at the protein complex interface. Subsequently, Convolutional Neural Networks extract compound representations in voxel and electron density spaces. These features are integrated into a Conditional Wasserstein Generative Adversarial Network, which trains the model to generate compound representations targeting PPI interfaces. GENiPPI effectively captures the relationship between PPI interfaces and active/inactive compounds. Furthermore, in fewshot molecular generation, GENiPPI successfully generates compounds comparable to known disruptors. GENiPPI provides an efficient tool for structure-based design of PPI modulators.

New insights into protein-protein interaction modulators in drug discovery and therapeutic advance

Protein-protein interactions (PPIs) are fundamental to cellular signaling and transduction which marks them as attractive therapeutic drug development targets. What were once considered to be undruggable targets have become increasingly feasible due to the progress that has been made over the last two decades and the rapid technological advances. This work explores the influence of technological innovations on PPI research and development. Additionally, the diverse strategies for discovering, modulating, and characterizing PPIs and their corresponding modulators are examined with the aim of presenting a streamlined pipeline for advancing PPI-targeted therapeutics. By showcasing carefully selected case studies in PPI modulator discovery and development, we aim to illustrate the efficacy of various strategies for identifying, optimizing, and overcoming challenges associated with PPI modulator design. The valuable lessons and insights gained from the identification, optimization, and approval of PPI modulators are discussed with the aim of demonstrating that PPI modulators have transitioned beyond early-stage drug discovery and now represent a prime opportunity with significant potential. The selected examples of PPI modulators encompass those developed for cancer, inflammation and immunomodulation, as well as antiviral applications. This perspective aims to establish a foundation for the effective targeting and modulation of PPIs using PPI modulators and pave the way for future drug development.

Cell-permeated peptide P-T3H2 inhibits malignancy on hepatocellular carcinoma through stabilizing HNF4α protein

OBJECTIVES

Hepatocyte nuclear factor 4α (HNF4α) is a key regulator of hepatocyte function and has a strong therapeutic effect on hepatocellular carcinoma (HCC) by inducing the differentiation of hepatoma cell into hepatocytes. Our previous study showed that Tribbles homolog 3 (TRIB3) directly interacts with and promotes the degradation of HNF4α in non-alcoholic fatty liver disease (NAFLD). Disrupting the TRIB3-HNF4α interaction by a cell-permeating peptide, called P-T3H2, stabilized HNF4α protein. This study aimed to assess the anti-tumor impact of P-T3H2 in HCC.

METHODS

The expression of TRIB3 and HNF4α was evaluated using western blot and immunohistochemistry (IHC). Hepatic functions and cellular senescence of HCC cells were evaluated through periodic acid-Schiff (PAS) staining, acetylated low-density lipoprotein (ac-LDL) uptake and senescence-associated β-galactosidase (SA-β-gal) activity staining, respectively. RNA-Seq analysis was performed to identify differentially expressed genes in Huh7 cells treated with P-T3H2. The impact of P-T3H2 on HCC malignancy was assessed in vitro and in vivo.

RESULTS

TRIB3 exhibited a negative correlation with HNF4α in both human and mouse HCC tissues. The administration of P-T3H2 significantly inhibited the malignancy of HCC cells. Additionally, P-T3H2 stabilized HNF4α protein and facilitated the restoration of hepatic functions and the cellular senescence in HCC cells. RNA-Seq analysis demonstrated that P-T3H2 enhanced the transcriptional activity of HNF4α in HCC. Furthermore, P-T3H2 effectively suppressed the carcinogenesis and progression of HCC in mice.

CONCLUSION

P-T3H2 suppressed HCC progression through the stabilization of HNF4α protein and may be a promising therapeutic candidate for clinical application in the treatment of HCC.

Applying Spatiotemporal Modeling of Cell Dynamics to Accelerate Drug Development

Cells act as physical computational programs that utilize input signals to orchestrate molecule-level protein-protein interactions (PPIs), generating and responding to forces, ultimately shaping all of the physiological and pathophysiological behaviors. Genome editing and molecule drugs targeting PPIs hold great promise for the treatments of diseases. Linking genes and molecular drugs with protein-performed cellular behaviors is a key yet challenging issue due to the wide range of spatial and temporal scales involved. Building predictive spatiotemporal modeling systems that can describe the dynamic behaviors of cells intervened by genome editing and molecular drugs at the intersection of biology, chemistry, physics, and computer science will greatly accelerate pharmaceutical advances. Here, we review the mechanical roles of cytoskeletal proteins in orchestrating cellular behaviors alongside significant advancements in biophysical modeling while also addressing the limitations in these models. Then, by integrating generative artificial intelligence (AI) with spatiotemporal multiscale biophysical modeling, we propose a computational pipeline for developing virtual cells, which can simulate and evaluate the therapeutic effects of drugs and genome editing technologies on various cell dynamic behaviors and could have broad biomedical applications. Such virtual cell modeling systems might revolutionize modern biomedical engineering by moving most of the painstaking wet-laboratory effort to computer simulations, substantially saving time and alleviating the financial burden for pharmaceutical industries.

Golgi-localized Ring Finger Protein 121 is necessary for MYCN-driven neuroblastoma tumorigenesis

MYCN amplification predicts poor prognosis in childhood neuroblastoma. To identify MYCN oncogenic signal dependencies we performed N-ethyl-N-nitrosourea (ENU) mutagenesis on the germline of neuroblastoma-prone TH-MYCN transgenic mice to generate founders which had lost tumorigenesis. Sequencing of the mutant mouse genomes identified the Ring Finger Protein 121 (RNF121WT) gene mutated to RNFM158R associated with heritable loss of tumorigenicity. While the RNF121WT protein localised predominantly to the cis-Golgi Complex, the RNF121M158R mutation in Helix 4 of its transmembrane domain caused reduced RNF121 protein stability and absent Golgi localisation. RNF121WT expression markedly increased during TH-MYCN tumorigenesis, whereas hemizygous RNF121WT gene deletion reduced TH-MYCN tumorigenicity. The RNF121WT-enhanced growth of MYCN-amplified neuroblastoma cells depended on RNF121WT transmembrane Helix 5. RNF121WT directly bound MYCN protein and enhanced its stability. High RNF121 mRNA expression associated with poor prognosis in human neuroblastoma tissues and another MYC-driven malignancy, laryngeal cancer. RNF121 is thus an essential oncogenic cofactor for MYCN and a target for drug development.

Binding Modalities and Phase-Specific Regulation of Cyclin/Cyclin-Dependent Kinase Complexes in the Cell Cycle

Cyclin-dependent kinases (CDKs) are activated upon cyclin-binding to enable progression through the cell cycle. Dominant CDKs and cyclins in mammalian cells include CDK1, CDK2, CDK4, and CDK6 and corresponding cyclins A, B, D, and E. While only certain, "typical" cyclin/CDK complexes are primarily responsible for cell cycle progression, "atypical" cyclin/CDK complexes can form and sometimes perform the same roles as typical complexes. We asked what structural features of cyclins and CDKs favor the formation of typical complexes, a vital yet not fully explored question. We use computational docking and biophysical analyses to exhaustively evaluate the structure and stability of all CDK and cyclin complexes listed above. We find that binding of the complexes is generally stronger for typical than for atypical complexes, especially when the CDK is in an active conformation. Typical complexes have denser clusters, indicating that they have more defined cyclin-binding sites than atypical complexes. Our results help explain three notable features of cyclin/CDK function in the cell cycle: (i) why CDK4 and cyclin-D have exceptionally high specificity for each other; (ii) why both cyclin-A and cyclin-B strongly activate CDK1, whereas CDK2 is only strongly activated by cyclin-A; and (iii) why cyclin-E normally activates CDK2 but not CDK1. Overall, this work reveals the binding modalities of cyclin/CDK complexes, how the modalities lead to the preference for typical complexes versus atypical complexes, and how binding modalities differ between typical complexes. Our observations suggest targeting CDK catalytic actions through destabilizing their native differential cyclin interfaces.

DNA-modulated dimerization and oligomerization of cell membrane receptors

DNA-based nanostructures and nanodevices have recently been employed for a broad range of applications in modulating the assemblies and interaction patterns of different cell membrane receptors. These versatile nanodevices can be rationally designed with modular structures, easily programmed and tweaked such that they may act as smart chemical biology and cell biology tools to reveal insights into complicated cellular signaling processes. Their outstanding in vitro and cellular features have also begun to be further validated for some in vivo applications and demonstrated their great biomedical potential. In this review, we will highlight some key current advances in the molecular engineering and biological applications of DNA-based functional nanodevices, with a focus on how these tools have been used to respond and modulate membrane receptor dimerizations and/or oligomerizations, as a way to control cellular signaling processes. Some current challenges and future directions to further develop and apply these DNA nanodevices will also be discussed.

PPI-hotspotID for detecting protein-protein interaction hot spots from the free protein structure

Experimental detection of residues critical for protein-protein interactions (PPI) is a time-consuming, costly, and labor-intensive process. Hence, high-throughput PPI-hot spot prediction methods have been developed, but they have been validated using relatively small datasets, which may compromise their predictive reliability. Here, we introduce PPI-hotspotID, a novel method for identifying PPI-hot spots using the free protein structure, and validated it on the largest collection of experimentally confirmed PPI-hot spots to date. We explored the possibility of detecting PPI-hot spots using (i) FTMap in the PPI mode, which identifies hot spots on protein-protein interfaces from the free protein structure, and (ii) the interface residues predicted by AlphaFold-Multimer. PPI-hotspotID yielded better performance than FTMap and SPOTONE, a webserver for predicting PPI-hot spots given the protein sequence. When combined with the AlphaFold-Multimer-predicted interface residues, PPI-hotspotID yielded better performance than either method alone. Furthermore, we experimentally verified several PPI-hotspotID-predicted PPI-hot spots of eukaryotic elongation factor 2. Notably, PPI-hotspotID can reveal PPI-hot spots not obvious from complex structures, including those in indirect contact with binding partners. PPI-hotspotID serves as a valuable tool for understanding PPI mechanisms and aiding drug design. It is available as a web server (https://ppihotspotid.limlab.dnsalias.org/) and open-source code (https://github.com/wrigjz/ppihotspotid/).

Quantitative Characterization of the Impact of Protein-Protein Interactions on Ligand-Protein Binding: A Multi-Chain Dynamics Perturbation Analysis Method

Many protein-protein interactions (PPIs) affect the ways in which small molecules bind to their constituent proteins, which can impact drug efficacy and regulatory mechanisms. While recent advances have improved our ability to independently predict both PPIs and ligand-protein interactions (LPIs), a comprehensive understanding of how PPIs affect LPIs is still lacking. Here, we examined 63 pairs of ligand-protein complexes in a benchmark dataset for protein-protein docking studies and quantified six typical effects of PPIs on LPIs. A multi-chain dynamics perturbation analysis method, called mcDPA, was developed to model these effects and used to predict small-molecule binding regions in protein-protein complexes. Our results illustrated that the mcDPA can capture the impact of PPI on LPI to varying degrees, with six similar changes in its predicted ligand-binding region. The calculations showed that 52% of the examined complexes had prediction accuracy at or above 50%, and 55% of the predictions had a recall of not less than 50%. When applied to 33 FDA-approved protein-protein-complex-targeting drugs, these numbers improved to 60% and 57% for the same accuracy and recall rates, respectively. The method developed in this study may help to design drug-target interactions in complex environments, such as in the case of protein-protein interactions.

Age-related changes in the architecture and biochemical markers levels in motor-related cortical areas of SHR rats-an ADHD animal model

Introduction

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder whose exact pathophysiology has not been fully understood yet. Numerous studies have suggested disruptions in the cellular architecture and neuronal activity within brain structures of individuals with ADHD, accompanied by imbalances in the immune system, oxidative stress, and metabolism.

Methods

This study aims to assess two functionally and histologically distinct brain areas involved in motor control and coordination: the motor cortex (MC) and prefrontal cortex (PFC). Namely, the morphometric analysis of the MC throughout the developmental stages of Spontaneously Hypertensive Rats (SHRs) and Wistar Kyoto Rats (WKYs). Additionally, the study aimed to investigate the levels and activities of specific immune, oxidative stress, and metabolic markers in the PFC of juvenile and maturing SHRs in comparison to WKYs.

Results

The most significant MC volume reductions occurred in juvenile SHRs, accompanied by alterations in neuronal density in these brain areas compared to WKYs. Furthermore, juvenile SHRs exhibit heightened levels and activity of various markers, including interleukin-1α (IL-1α), IL-6, serine/threonine-protein mammalian target of rapamycin, RAC-alpha serine/threonine-protein kinase, glucocorticoid receptor β, malondialdehyde, sulfhydryl groups, superoxide dismutase, peroxidase, glutathione reductase, glutathione S-transferase, glucose, fructosamine, iron, lactic acid, alanine, aspartate transaminase, and lactate dehydrogenase.

Discussion

Significant changes in the MC morphometry and elevated levels of inflammatory, oxidative, and metabolic markers in PFC might be associated with disrupted brain development and maturation in ADHD.

Microscale measurements of protein complexes from single cells

Proteins execute numerous cell functions in concert with one another in protein-protein interactions (PPI). While essential in each cell, such interactions are not identical from cell to cell. Instead, PPI heterogeneity contributes to cellular phenotypic heterogeneity in health and diseases such as cancer. Understanding cellular phenotypic heterogeneity thus requires measurements of properties of PPIs such as abundance, stoichiometry, and kinetics at the single-cell level. Here, we review recent, exciting progress in single-cell PPI measurements. Novel technology in this area is enabled by microscale and microfluidic approaches that control analyte concentration in timescales needed to outpace PPI disassembly kinetics. We describe microscale innovations, needed technical capabilities, and methods poised to be adapted for single-cell analysis in the near future.

Exploring Ubiquitin-specific proteases as therapeutic targets in Glioblastoma

Glioblastoma (GB) remains a formidable challenge and requires new treatment strategies. The vital part of the Ubiquitin-proteasome system (UPS) in cellular regulation has positioned it as a potentially crucial target in GB treatment, given its dysregulation oncolines. The Ubiquitin-specific proteases (USPs) in the UPS system were considered due to the garden role in the cellular processes associated with oncolines and their vital function in the apoptotic process, cell cycle regulation, and autophagy. The article provides a comprehensive summary of the evidence base for targeting USPs as potential factors for neoplasm treatment. The review considers the participation of the UPS system in the development, resulting in the importance of p53, Rb, and NF-κB, and evaluates specific goals for therapeutic administration using midnight proteasomal inhibitors and small molecule antagonists of E1 and E2 enzymes. Despite the slowed rate of drug creation, recent therapeutic discoveries based on USP system dynamics hold promise for specialized therapies. The review concludes with an analysis of future wanderers and the feasible effects of targeting USPs on personalized GB therapies, which can improve patient hydration in this current and unattractive therapeutic landscape. The manuscript emphasizes the possibility of USP oncogene therapy as a promising alternative treatment line for GB. It stresses the direct creation of research on the medical effectiveness of the approach.

Unraveling the role of physicochemical differences in predicting protein-protein interactions

The ability to accurately predict protein-protein interactions is critically important for understanding major cellular processes. However, current experimental and computational approaches for identifying them are technically very challenging and still have limited success. We propose a new computational method for predicting protein-protein interactions using only primary sequence information. It utilizes the concept of physicochemical similarity to determine which interactions will most likely occur. In our approach, the physicochemical features of proteins are extracted using bioinformatics tools for different organisms. Then they are utilized in a machine-learning method to identify successful protein-protein interactions via correlation analysis. It was found that the most important property that correlates most with the protein-protein interactions for all studied organisms is dipeptide amino acid composition (the frequency of specific amino acid pairs in a protein sequence). While current approaches often overlook the specificity of protein-protein interactions with different organisms, our method yields context-specific features that determine protein-protein interactions. The analysis is specifically applied to the bacterial two-component system that includes histidine kinase and transcriptional response regulators, as well as to the barnase-barstar complex, demonstrating the method's versatility across different biological systems. Our approach can be applied to predict protein-protein interactions in any biological system, providing an important tool for investigating complex biological processes' mechanisms.

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.

Molecular Modeling of Single- and Double-Hydrocarbon-Stapled Coiled-Coil Inhibitors against Bcr-Abl: Toward a Treatment Strategy for CML

The chimeric oncoprotein Bcr-Abl is the causative agent of virtually all chronic myeloid leukemias and a subset of acute lymphoblastic leukemias. As a result of the so-called Philadelphia chromosome translocation t(9;22), Bcr-Abl manifests as a constitutively active tyrosine kinase, which promotes leukemogenesis by activation of cell cycle signaling pathways. Constitutive and oncogenic activation is mediated by an N-terminal coiled-coil oligomerization domain in Bcr (Bcr-CC), presenting a therapeutic target for inhibition of Bcr-Abl activity toward the treatment of Bcr-Abl+ leukemias. Previously, we demonstrated that a rationally designed Bcr-CC mutant, CCmut3, exerts a dominant negative effect upon Bcr-Abl activity by preferential oligomerization with Bcr-CC. Moreover, we have shown that conjugation to a leukemia-specific cell-penetrating peptide (CPP-CCmut3) improves intracellular delivery and activity. However, our full-length CPP-CCmut3 construct (81 aa) is encumbered by an intrinsically high degree of conformational variability and susceptibility to proteolytic degradation relative to traditional small-molecule therapeutics. Here, we iterate a new generation of CCmut3 inhibitors against Bcr-CC-mediated Bcr-Abl assembly designed to address these constraints through incorporation of all-hydrocarbon staples spanning i and i + 7 positions in α-helix 2 (CPP-CCmut3-st). We utilize computational modeling and biomolecular simulation to evaluate single- and double-stapled CCmut3 candidates in silico for dynamics and binding energetics. We further model a truncated system characterized by the deletion of α-helix 1 and the flexible loop linker, which are known to impart high conformational variability. To study the impact of the N-terminal cyclic CPP toward model stability and inhibitor activity, we also model the full-length and truncated systems devoid of the CPP, with a cyclized CPP, and with an open-configuration CPP, for a total of six systems that comprise our library. From this library, we present lead-stapled peptide candidates to be synthesized and evaluated experimentally as our next iteration of inhibitors against Bcr-Abl.

Megalin-related mechanism of hemolysis-induced acute kidney injury and the therapeutic strategy

Hemolysis-induced acute kidney injury (AKI) is attributed to heme-mediated proximal tubule epithelial cell (PTEC) injury and tubular cast formation due to intratubular protein condensation. Megalin is a multiligand endocytic receptor for proteins, peptides, and drugs in PTECs and mediates the uptake of free hemoglobin and the heme-scavenging protein α1-microglobulin. However, understanding of how megalin is involved in the development of hemolysis-induced AKI remains elusive. Here, we investigated the megalin-related pathogenesis of hemolysis-induced AKI and a therapeutic strategy using cilastatin, a megalin blocker. A phenylhydrazine-induced hemolysis model developed in kidney-specific mosaic megalin knockout (MegKO) mice confirmed megalin-dependent PTEC injury revealed by the co-expression of kidney injury molecule-1 (KIM-1). In the hemolysis model in kidney-specific conditional MegKO mice, the uptake of hemoglobin and α1-microglobulin as well as KIM-1 expression in PTECs was suppressed, but tubular cast formation was augmented, likely due to the nonselective inhibition of protein reabsorption in PTECs. Quartz crystal microbalance analysis revealed that cilastatin suppressed the binding of megalin with hemoglobin and α1-microglobulin. Cilastatin also inhibited the specific uptake of fluorescent hemoglobin by megalin-expressing rat yolk sac tumor-derived L2 cells. In a mouse model of hemolysis-induced AKI, repeated cilastatin administration suppressed PTEC injury by inhibiting the uptake of hemoglobin and α1-microglobulin and also prevented cast formation. Hemopexin, another heme-scavenging protein, was also found to be a novel ligand of megalin, and its binding to megalin and uptake by PTECs in the hemolysis model were suppressed by cilastatin. Mass spectrometry-based semiquantitative analysis of urinary proteins in cilastatin-treated C57BL/6J mice indicated that cilastatin suppressed the reabsorption of a limited number of megalin ligands in PTECs, including α1-microglobulin and hemopexin. Collectively, cilastatin-mediated selective megalin blockade is an effective therapeutic strategy to prevent both heme-mediated PTEC injury and cast formation in hemolysis-induced AKI. © 2024 The Pathological Society of Great Britain and Ireland.

A Hierarchical Graph Neural Network Framework for Predicting Protein-Protein Interaction Modulators With Functional Group Information and Hypergraph Structure

Accurate prediction of small molecule modulators targeting protein-protein interactions (PPIMs) remains a significant challenge in drug discovery. Existing machine learning-based models rely on manual feature engineering, which is tedious and task-specific. Recently, deep learning models based on graph neural networks have made remarkable progress in molecular representation learning. However, many graph-based approaches ignore molecular hierarchical structure modeling guided by domain knowledge. In chemistry, the functional groups of a molecule determine its interaction with specific targets. Therefore, we propose a hierarchical graph neural network framework (called HiGPPIM) for predicting PPIMs by integrating atom-level and functional group-level features of molecules. HiGPPIM constructs atom-level and functional group-level graphs based on chemical knowledge and learns graph representations using graph attention networks. Furthermore, a hypergraph attention network is designed in HiGPPIM to aggregate and transform two-level graph information. We evaluate the performance of HiGPPIM on eight PPI families and two prediction tasks, namely PPIM identification and potency prediction. Experimental results demonstrate that HiGPPIM achieves state-of-the-art performance on both tasks and that using functional group information to guide PPIM prediction is effective.

Identification of WNK1 as a therapeutic target to suppress IgH/MYC expression in multiple myeloma

Multiple myeloma (MM) remains an incurable hematological malignancy demanding innovative therapeutic strategies. Targeting MYC, the notorious yet traditionally undruggable oncogene, presents an appealing avenue. Here, using a genome-scale CRISPR-Cas9 screen, we identify the WNK lysine-deficient protein kinase 1 (WNK1) as a regulator of MYC expression in MM cells. Genetic and pharmacological inhibition of WNK1 reduces MYC expression and, further, disrupts the MYC-dependent transcriptional program. Mechanistically, WNK1 inhibition attenuates the activity of the immunoglobulin heavy chain (IgH) enhancer, thus reducing MYC transcription when this locus is translocated near the MYC locus. WNK1 inhibition profoundly impacts MM cell behaviors, leading to growth inhibition, cell-cycle arrest, senescence, and apoptosis. Importantly, the WNK inhibitor WNK463 inhibits MM growth in primary patient samples as well as xenograft mouse models and exhibits synergistic effects with various anti-MM compounds. Collectively, our study uncovers WNK1 as a potential therapeutic target in MM.

SWISH-X, an Expanded Approach to Detect Cryptic Pockets in Proteins and at Protein-Protein Interfaces

Protein-protein interactions mediate most molecular processes in the cell, offering a significant opportunity to expand the set of known druggable targets. Unfortunately, targeting these interactions can be challenging due to their typically flat and featureless interaction surfaces, which often change as the complex forms. Such surface changes may reveal hidden (cryptic) druggable pockets. Here, we analyze a set of well-characterized protein-protein interactions harboring cryptic pockets and investigate the predictive power of current computational methods. Based on our observations, we developed a new computational strategy, SWISH-X (SWISH Expanded), which combines the established cryptic pocket identification capabilities of SWISH with the rapid temperature range exploration of OPES MultiThermal. SWISH-X is able to reliably identify cryptic pockets at protein-protein interfaces while retaining its predictive power for revealing cryptic pockets in isolated proteins, such as TEM-1 β-lactamase.

Validation of Core Ingredients and Molecular Mechanism of Cinobufotalin Injection Against Liver Cancer

Purpose

Cinobufotalin injection has obvious curative effects on liver cancer patients with less toxicity and fewer side effects than other therapeutic approaches. However, the core ingredients and mechanism underlying these anti-liver cancer effects have not been fully clarified due to its complex composition.

Methods

Multidimensional network analysis was used to screen the core ingredients, key targets and pathways underlying the therapeutic effects of cinobufotalin injection on liver cancer, and in vitro and in vivo experiments were performed to confirm the findings.

Results

By construction of ingredient networks and integrated analysis, eight core ingredients and ten key targets were finally identified in cinobufotalin injection, and all of the core ingredients are tightly linked with the key targets, and these key targets are highly associated with the cell cycle-related pathways, supporting that both cinobufotalin injection and its core ingredients exert anti-liver cancer roles by blocking cell cycle-related pathways. Moreover, in vitro and in vivo experiments confirmed that either cinobufotalin injection or one of its core ingredients, cinobufagin, significantly inhibited cell proliferation, colony formation, cell cycle progression and xenograft tumor growth, and the key target molecules involved in the cell cycle pathway such as CDK1, CDK4, CCNB1, CHEK1 and CCNE1, exhibit consistent changes in expression after treatment with cinobufotalin injection or cinobufagin. Interestingly, some key targets CDK1, CDK4, PLK1, CHEK1, TTK were predicted to bind with multiple of core ingredients of cinobufotalin injection, and the affinity between one of the critical ingredients cinobufagin and key target CDK1 was further confirmed by SPR assay.

Conclusion

Cinobufotalin injection was confirmed to includes eight core ingredients, and they play therapeutic effects in liver cancer by blocking cell cycle-related pathways, which provides important insights for the mechanism of cinobufotalin injection antagonizing liver cancer and the development of novel small molecule anti-cancer drugs.

Small Molecule Antagonists of the DNA Repair ERCC1/XPA Protein-Protein Interaction

The DNA excision repair protein ERCC1 and the DNA damage sensor protein, XPA are highly overexpressed in patient samples of cisplatin-resistant solid tumors including lung, bladder, ovarian, and testicular cancer. The repair of cisplatin-DNA crosslinks is dependent upon nucleotide excision repair (NER) that is modulated by protein-protein binding interactions of ERCC1, the endonuclease, XPF, and XPA. Thus, inhibition of their function is a potential therapeutic strategy for the selective sensitization of tumors to DNA-damaging platinum-based cancer therapy. Here, we report on new small-molecule antagonists of the ERCC1/XPA protein-protein interaction (PPI) discovered using a high-throughput competitive fluorescence polarization binding assay. We discovered a unique structural class of thiopyridine-3-carbonitrile PPI antagonists that block a truncated XPA polypeptide from binding to ERCC1. Preliminary hit-to-lead studies from compound 1 reveal structure-activity relationships (SAR) and identify lead compound 27 o with an EC50 of 4.7 μM. Furthermore, chemical shift perturbation mapping by NMR confirms that 1 binds within the same site as the truncated XPA67-80 peptide. These novel ERCC1 antagonists are useful chemical biology tools for investigating DNA damage repair pathways and provide a good starting point for medicinal chemistry optimization as therapeutics for sensitizing tumors to DNA damaging agents and overcoming resistance to platinum-based chemotherapy.

In-Bridge Stereochemistry: A Determinant of Stapled Peptide Conformation and Activity

Peptide side chain stapling has been proven to be an effective strategy for fine-tuning peptide properties. This innovative approach leads to the creation of stapled peptides characterized by stabilized α-helical conformations, enhanced protein-binding affinity, improved cell permeability, superior enzymatic stability, and numerous other advantages. Extensive research has explored the impact of various stapling bridges on the properties of these peptides, with limited investigation into the influence of bridge chirality, until very recently. In this concise review, we provide a brief overview of the current state of knowledge regarding the stereochemistry within the bridges of stapled peptides, offering insights into the potential applications of chiral bridges in the design and development of stapled peptides.

Physical-Chemical Features Selection Reveals That Differences in Dipeptide Compositions Correlate Most with Protein-Protein Interactions

The ability to accurately predict protein-protein interactions is critically important for our understanding of major cellular processes. However, current experimental and computational approaches for identifying them are technically very challenging and still have limited success. We propose a new computational method for predicting protein-protein interactions using only primary sequence information. It utilizes a concept of physical-chemical similarity to determine which interactions will most probably occur. In our approach, the physical-chemical features of protein are extracted using bioinformatics tools for different organisms, and then they are utilized in a machine-learning method to identify successful protein-protein interactions via correlation analysis. It is found that the most important property that correlates most with the protein-protein interactions for all studied organisms is dipeptide amino acid compositions. The analysis is specifically applied to the bacterial two-component system that includes histidine kinase and transcriptional response regulators. Our theoretical approach provides a simple and robust method for quantifying the important details of complex mechanisms of biological processes.

Selection of collagen IV fragments forming the outer sphere of the native protein: Assessment of biological activity for regenerative medicine

The aim of this research was to select the fragments that make up the outer layer of the collagen IV (COL4A6) protein and to assess their potential usefulness for regenerative medicine. It was expected that because protein-protein interactions take place via contact between external domains, the set of peptides forming the outer sphere of collagen IV will determine its interaction with other proteins. Cellulose-immobilized protein fragment libraries treated with polyclonal anti-collagen IV antibodies were used to select the peptides forming the outer sphere of collagen IV. In the first test, 33 peptides that strongly interacted with the polyclonal anti-collagen IV antibodies were selected from a library of non-overlapping fragments of collagen IV. The selected fragments of collagen IV (cleaved from the cellulose matrix) were tested for their cytotoxicity, their effects on cell viability and proliferation, and their impact on the formation of reactive oxygen species (ROS). The studies used RAW 264.7 mouse macrophage cells and Hs 680.Tr human fibroblasts. PrestoBlue, ToxiLight™, and ToxiLight 100% Lysis Control assays were conducted. The viability of fibroblasts cultured with the addition of increasing concentrations of the peptide mix did not show statistically significant differences from the control. Fragments 161-170, 221-230, 721-730, 1331-1340, 1521-1530, and 1661-1670 of COL4A6 were examined for cytotoxicity against BJ normal human foreskin fibroblasts. None of the collagen fragments were found to be cytotoxic. Further research is underway on the potential uses of collagen IV fragments in regenerative medicine.

Class-specific recognition and monitoring of environmental steroid estrogens in real water systems utilizing aptamer base substitution mutagenesis approach

The same class of environmental steroid estrogens (SEs) with the highest estrogenic activity share the same chemical core structure and are often found together in the environment, posing significant risks to organismal health and environmental safety due to toxicity accumulation. In this study, a novel method for constructing the group-targeting aptasensor was developed to comprehensively analyze SEs. Through artificial intervention base substitution mutagenesis of adjacent bases T13 and C20 of the aptamer-binding domain recognizing 17β-estradiol, combined with docking calculations, the group-targeting SEs-aptamer for class-specific recognition SEs, such as estrone, estradiol, estriol, and ethinylestradiol were obtained. The binding constant of the SEs-aptamer to the SEs was 108 M-1. The established group-targeting SEs aptasensor exhibited high sensitivity within a concentration range from 0.1 to 10 nM and demonstrated strong interference resistance, as well as high stability and wide pH water applicability. It was further applied to analyze real water samples and monitor changes in SEs concentrations during the removal process by Chlorella pyrenoidosa. These successful applications have demonstrated the excellent ability of this aptasensor to monitor SE in the environment. The method offered a new approach and idea for recognizing and detecting the same class of environmental pollutants in complex systems.

Target-based drug discovery: Applications of fluorescence techniques in high throughput and fragment-based screening

Target-based discovery of first-in-class therapeutics demands an in-depth understanding of the molecular mechanisms underlying human diseases. Precise measurements of cellular and biochemical activities are critical to gain mechanistic knowledge of biomolecules and their altered function in disease conditions. Such measurements enable the development of intervention strategies for preventing or treating diseases by modulation of desired molecular processes. Fluorescence-based techniques are routinely employed for accurate and robust measurements of in-vitro activity of molecular targets and for discovering novel chemical molecules that modulate the activity of molecular targets. In the current review, the authors focus on the applications of fluorescence-based high throughput screening (HTS) and fragment-based ligand discovery (FBLD) techniques such as fluorescence polarization (FP), Förster resonance energy transfer (FRET), fluorescence thermal shift assay (FTSA) and microscale thermophoresis (MST) for the discovery of chemical probe to exploring target's role in disease biology and ultimately, serve as a foundation for drug discovery. Some recent advancements in these techniques for compound library screening against important classes of drug targets, such as G-protein-coupled receptors (GPCRs) and GTPases, as well as phosphorylation- and acetylation-mediated protein-protein interactions, are discussed. Overall, this review presents a landscape of how these techniques paved the way for the discovery of small-molecule modulators and biologics against these targets for therapeutic benefits.

Current trends and future prospects of drug repositioning in gastrointestinal oncology

Gastrointestinal (GI) cancers comprise a significant number of cancer cases worldwide and contribute to a high percentage of cancer-related deaths. To improve survival rates of GI cancer patients, it is important to find and implement more effective therapeutic strategies with better prognoses and fewer side effects. The development of new drugs can be a lengthy and expensive process, often involving clinical trials that may fail in the early stages. One strategy to address these challenges is drug repurposing (DR). Drug repurposing is a developmental strategy that involves using existing drugs approved for other diseases and leveraging their safety and pharmacological data to explore their potential use in treating different diseases. In this paper, we outline the existing therapeutic strategies and challenges associated with GI cancers and explore DR as a promising alternative approach. We have presented an extensive review of different DR methodologies, research efforts and examples of repurposed drugs within various GI cancer types, such as colorectal, pancreatic and liver cancers. Our aim is to provide a comprehensive overview of employing the DR approach in GI cancers to inform future research endeavors and clinical trials in this field.

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.

Computational Modeling of Stapled Coiled-Coil Inhibitors Against Bcr-Abl: Toward a Treatment Strategy for CML

The chimeric oncoprotein Bcr-Abl is the causative agent of virtually all chronic myeloid leukemias (CML) and a subset of acute lymphoblastic leukemias (ALL). As a result of the so-called Philadelphia Chromosome translocation t(9;22), Bcr-Abl manifests as a constitutively active tyrosine kinase which promotes leukemogenesis by activation of cell cycle signaling pathways. Constitutive and oncogenic activation is mediated by an N-terminal coiled-coil oligomerization domain in Bcr (Bcr-CC), presenting a therapeutic target for inhibition of Bcr-Abl activity toward the treatment of Bcr-Abl+ leukemias. Previously, we demonstrated that a rationally designed Bcr-CC mutant, CCmut3, exerts a dominant negative effect upon Bcr-Abl activity by preferential oligomerization with Bcr-CC. Moreover, we have shown conjugation to a leukemia-specific cell-penetrating peptide (CPP-CCmut3) improves intracellular delivery and activity. However, our full-length CPP-CCmut3 construct (81 aa) is encumbered by an intrinsically high degree of conformational variability and susceptibility to proteolytic degradation, relative to traditional small molecule therapeutics. Here, we iterate a new generation of our inhibitor against Bcr-CC mediated Bcr-Abl assembly that is designed to address these constraints through incorporation of all-hydrocarbon staples spanning i, i + 7 positions in helix α2 (CPP-CCmut3-st). We utilize computational modeling and biomolecular simulation to design and characterize single and double staple candidates in silico, evaluating binding energetics and building upon our seminal work modeling single hydrocarbon staples when applied to a truncated Bcr-CC sequence. This strategy enables us to efficiently build, characterize, and screen lead single/double stapled CPP-CCmut3-st candidates for experimental studies and validation in vitro and in vivo. In addition to full-length CPP-CCmut, we model a truncated system characterized by deletion of helix α1 and the flexible-loop linker, which are known to impart high conformational variability. To study the impact of the N-terminal cyclic CPP toward model stability and inhibitor activity, we also model the full-length and truncated systems without CPP, with cyclized CPP, and with linear CPP, for a total of six systems which comprise our library. From this library, we present lead stapled peptide candidates to be synthesized and evaluated experimentally as our next-generation inhibitors against Bcr-Abl.

Cyclobutane-bearing restricted anchoring residues enabled geometry-specific hydrocarbon peptide stapling

Stapled peptides are regarded as the promising next-generation therapeutics because of their improved secondary structure, membrane permeability and metabolic stability as compared with the prototype linear peptides. Usually, stapled peptides are obtained by a hydrocarbon stapling technique, anchoring from paired olefin-terminated unnatural amino acids and the consequent ring-closing metathesis (RCM). To investigate the adaptability of the rigid cyclobutane structure in RCM and expand the chemical diversity of hydrocarbon peptide stapling, we herein described the rational design and efficient synthesis of cyclobutane-based conformationally constrained amino acids, termed (E)-1-amino-3-(but-3-en-1-yl)cyclobutane-1-carboxylic acid (E7) and (Z)-1-amino-3-(but-3-en-1-yl)cyclobutane-1-carboxylic acid (Z7). All four combinations including E7-E7, E7-Z7, Z7-Z7 and Z7-E7 were proven to be applicable in RCM-mediated peptide stapling to afford the corresponding geometry-specific stapled peptides. With the aid of the combined quantum and molecular mechanics, the E7-E7 combination was proven to be optimal in both the RCM reaction and helical stabilization. With the spike protein of SARS-CoV-2 as the target, a series of cyclobutane-bearing stapled peptides were obtained. Among them, E7-E7 geometry-specific stapled peptides indeed exhibit higher α-helicity and thus stronger biological activity than canonical hydrocarbon stapled peptides. We believe that this methodology possesses great potential to expand the scope of the existing peptide stapling strategy. These cyclobutane-bearing restricted anchoring residues served as effective supplements for the existing olefin-terminated unnatural amino acids and the resultant geometry-specific hydrocarbon peptide stapling provided more potential for peptide therapeutics.

Phosphorylation-Assisted Luciferase Complementation Assay Designed to Monitor Kinase Activity and Kinase-Domain-Mediated Protein-Protein Binding

Protein kinases are key regulators of cell signaling and have been important therapeutic targets for three decades. ATP-competitive drugs directly inhibit the activity of kinases but these enzymes work as part of complex protein networks in which protein-protein interactions (often referred to as kinase docking) may govern a more complex activation pattern. Kinase docking is indispensable for many signaling disease-relevant Ser/Thr kinases and it is mediated by a dedicated surface groove on the kinase domain which is distinct from the substrate-binding pocket. Thus, interfering with kinase docking provides an alternative strategy to control kinases. We describe activity sensors developed for p90 ribosomal S6 kinase (RSK) and mitogen-activated protein kinases (MAPKs: ERK, p38, and JNK) whose substrate phosphorylation is known to depend on kinase-docking-groove-mediated protein-protein binding. The in vitro assays were based on fragment complementation of the NanoBit luciferase, which is facilitated upon substrate motif phosphorylation. The new phosphorylation-assisted luciferase complementation (PhALC) sensors are highly selective and the PhALC assay is a useful tool for the quantitative analysis of kinase activity or kinase docking, and even for high-throughput screening of academic compound collections.

Controlling the Chameleonic Behavior and Membrane Permeability of Cyclosporine Derivatives via Backbone and Side Chain Modifications

Some macrocycles exhibit enhanced membrane permeability through conformational switching in different environmental polarities, a trait known as chameleonic behavior. In this study, we demonstrate specific backbone and side chain modifications that can control chameleonic behavior and passive membrane permeability using a cyclosporin O (CsO) scaffold. To quantify chameleonic behavior, we used a ratio of the population of the closed conformation obtained in polar solvent and nonpolar solvent for each CsO derivative. We found that β-hydroxylation at position 1 (1 and 3) can encode chameleonicity and improve permeability. However, the conformational stabilization induced by adding an additional transannular H-bond (2 and 5) leads to a much slower rate of membrane permeation. Our CsO scaffold provides a platform for the systematic study of the relationship among conformation, membrane permeability, solubility, and protein binding. This knowledge contributes to the discovery of potent beyond the rule of five (bRo5) macrocycles capable of targeting undruggable targets.

Recent advances in targeting the "undruggable" proteins: from drug discovery to clinical trials

Undruggable proteins are a class of proteins that are often characterized by large, complex structures or functions that are difficult to interfere with using conventional drug design strategies. Targeting such undruggable targets has been considered also a great opportunity for treatment of human diseases and has attracted substantial efforts in the field of medicine. Therefore, in this review, we focus on the recent development of drug discovery targeting "undruggable" proteins and their application in clinic. To make this review well organized, we discuss the design strategies targeting the undruggable proteins, including covalent regulation, allosteric inhibition, protein-protein/DNA interaction inhibition, targeted proteins regulation, nucleic acid-based approach, immunotherapy and others.

Small molecule-mediated targeting of microRNAs for drug discovery: Experiments, computational techniques, and disease implications

Small molecules have been providing medical breakthroughs for human diseases for more than a century. Recently, identifying small molecule inhibitors that target microRNAs (miRNAs) has gained importance, despite the challenges posed by labour-intensive screening experiments and the significant efforts required for medicinal chemistry optimization. Numerous experimentally-verified cases have demonstrated the potential of miRNA-targeted small molecule inhibitors for disease treatment. This new approach is grounded in their posttranscriptional regulation of the expression of disease-associated genes. Reversing dysregulated gene expression using this mechanism may help control dysfunctional pathways. Furthermore, the ongoing improvement of algorithms has allowed for the integration of computational strategies built on top of laboratory-based data, facilitating a more precise and rational design and discovery of lead compounds. To complement the use of extensive pharmacogenomics data in prioritising potential drugs, our previous work introduced a computational approach based on only molecular sequences. Moreover, various computational tools for predicting molecular interactions in biological networks using similarity-based inference techniques have been accumulated in established studies. However, there are a limited number of comprehensive reviews covering both computational and experimental drug discovery processes. In this review, we outline a cohesive overview of both biological and computational applications in miRNA-targeted drug discovery, along with their disease implications and clinical significance. Finally, utilizing drug-target interaction (DTIs) data from DrugBank, we showcase the effectiveness of deep learning for obtaining the physicochemical characterization of DTIs.

Are fibrinaloid microclots a cause of autoimmunity in Long Covid and other post-infection diseases?

It is now well established that the blood-clotting protein fibrinogen can polymerise into an anomalous form of fibrin that is amyloid in character; the resultant clots and microclots entrap many other molecules, stain with fluorogenic amyloid stains, are rather resistant to fibrinolysis, can block up microcapillaries, are implicated in a variety of diseases including Long COVID, and have been referred to as fibrinaloids. A necessary corollary of this anomalous polymerisation is the generation of novel epitopes in proteins that would normally be seen as 'self', and otherwise immunologically silent. The precise conformation of the resulting fibrinaloid clots (that, as with prions and classical amyloid proteins, can adopt multiple, stable conformations) must depend on the existing small molecules and metal ions that the fibrinogen may (and is some cases is known to) have bound before polymerisation. Any such novel epitopes, however, are likely to lead to the generation of autoantibodies. A convergent phenomenology, including distinct conformations and seeding of the anomalous form for initiation and propagation, is emerging to link knowledge in prions, prionoids, amyloids and now fibrinaloids. We here summarise the evidence for the above reasoning, which has substantial implications for our understanding of the genesis of autoimmunity (and the possible prevention thereof) based on the primary process of fibrinaloid formation.

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.

Disrupting the α-synuclein-ESCRT interaction with a peptide inhibitor mitigates neurodegeneration in preclinical models of Parkinson's disease

Accumulation of α-synuclein into toxic oligomers or fibrils is implicated in dopaminergic neurodegeneration in Parkinson's disease. Here we performed a high-throughput, proteome-wide peptide screen to identify protein-protein interaction inhibitors that reduce α-synuclein oligomer levels and their associated cytotoxicity. We find that the most potent peptide inhibitor disrupts the direct interaction between the C-terminal region of α-synuclein and CHarged Multivesicular body Protein 2B (CHMP2B), a component of the Endosomal Sorting Complex Required for Transport-III (ESCRT-III). We show that α-synuclein impedes endolysosomal activity via this interaction, thereby inhibiting its own degradation. Conversely, the peptide inhibitor restores endolysosomal function and thereby decreases α-synuclein levels in multiple models, including female and male human cells harboring disease-causing α-synuclein mutations. Furthermore, the peptide inhibitor protects dopaminergic neurons from α-synuclein-mediated degeneration in hermaphroditic C. elegans and preclinical Parkinson's disease models using female rats. Thus, the α-synuclein-CHMP2B interaction is a potential therapeutic target for neurodegenerative disorders.

PRPF19 facilitates colorectal cancer liver metastasis through activation of the Src-YAP1 pathway via K63-linked ubiquitination of MYL9

Distant metastasis is one of the leading causes of cancer-related mortality of colorectal cancer (CRC). Dysregulation of E3 ubiquitin ligases has been implicated in acting vital roles in multiple cancers. In this study, we found that the E3 ubiquitin ligase, PRPF19 was positively correlated with liver metastasis, and predicted a worse clinical outcome in CRC. However, the biological effects and the underlying molecular mechanisms of PRPF19 in CRC remain elusive thus far. We illustrated that PRPF19 promoted the migration and invasion capability of CRC cells in both gain- and loss- of function assays. Mechanistically, we uncovered that myosin light chain 9 (MYL9) was the downstream substrate of PRPF19. PRPF19 enhanced the stability of MYL9 via K63-linked ubiquitination, and promoted the migration and invasion capability of CRC cells in an MYL9-mediated manner. Furthermore, the Src-YAP1 cascade was identified as the downstream effector mechanism by which the PRPF19/MYL9 axis promoted metastasis in CRC. Taken together, our findings highlighted that the PRPF19/MYL9 axis served as a novel mechanism in CRC metastasis, which provided an attractive therapeutic strategy for CRC treatment.

Lnc-ing epigenetic mechanisms with autophagy and cancer drug resistance

Long noncoding RNAs (lncRNAs) comprise a diverse class of RNA molecules that regulate various physiological processes and have been reported to be involved in several human pathologies ranging from neurodegenerative disease to cancer. Therapeutic resistance is a major hurdle for cancer treatment. Over the past decade, several studies has emerged on the role of lncRNAs in cancer drug resistance and many trials have been conducted employing them. LncRNAs also regulate different cell death pathways thereby maintaining a fine balance of cell survival and death. Autophagy is a complex cell-killing mechanism that has both cytoprotective and cytotoxic roles. Similarly, autophagy can lead to the induction of both chemosensitization and chemoresistance in cancer cells upon therapeutic intervention. Recently the role of lncRNAs in the regulation of autophagy has also surfaced. Thus, lncRNAs can be used in cancer therapeutics to alleviate the challenges of chemoresistance by targeting the autophagosomal axis. In this chapter, we discuss about the role of lncRNAs in autophagy-mediated cancer drug resistance and its implication in targeted cancer therapy.

Inhibitors of the Oncogenic PA2G4-MYCN Protein-Protein Interface

MYCN is a major oncogenic driver for neuroblastoma tumorigenesis, yet there are no direct MYCN inhibitors. We have previously identified PA2G4 as a direct protein-binding partner of MYCN and drive neuroblastoma tumorigenesis. A small molecule known to bind PA2G4, WS6, significantly decreased tumorigenicity in TH-MYCN neuroblastoma mice, along with the inhibition of PA2G4 and MYCN interactions. Here, we identified a number of novel WS6 analogues, with 80% structural similarity, and used surface plasmon resonance assays to determine their binding affinity. Analogues #5333 and #5338 showed direct binding towards human recombinant PA2G4. Importantly, #5333 and #5338 demonstrated a 70-fold lower toxicity for normal human myofibroblasts compared to WS6. Structure-activity relationship analysis showed that a 2,3 dimethylphenol was the most suitable substituent at the R1 position. Replacing the trifluoromethyl group on the phenyl ring at the R2 position, with a bromine or hydrogen atom, increased the difference between efficacy against neuroblastoma cells and normal myofibroblast toxicity. The WS6 analogues inhibited neuroblastoma cell phenotype in vitro, in part through effects on apoptosis, while their anti-cancer effects required both PA2G4 and MYCN expression. Collectively, chemical inhibition of PA2G4-MYCN binding by WS6 analogues represents a first-in-class drug discovery which may have implications for other MYCN-driven cancers.

Computational approaches for the design of modulators targeting protein-protein interactions

BACKGROUND

Protein-protein interactions (PPIs) are intriguing targets for designing novel small-molecule inhibitors. The role of PPIs in various infectious and neurodegenerative disorders makes them potential therapeutic targets . Despite being portrayed as undruggable targets, due to their flat surfaces, disorderedness, and lack of grooves. Recent progresses in computational biology have led researchers to reconsider PPIs in drug discovery.

AREAS COVERED

In this review, we introduce in-silico methods used to identify PPI interfaces and present an in-depth overview of various computational methodologies that are successfully applied to annotate the PPIs. We also discuss several successful case studies that use computational tools to understand PPIs modulation and their key roles in various physiological processes.

EXPERT OPINION

Computational methods face challenges due to the inherent flexibility of proteins, which makes them expensive, and result in the use of rigid models. This problem becomes more significant in PPIs due to their flexible and flat interfaces. Computational methods like molecular dynamics (MD) simulation and machine learning can integrate the chemical structure data into biochemical and can be used for target identification and modulation. These computational methodologies have been crucial in understanding the structure of PPIs, designing PPI modulators, discovering new drug targets, and predicting treatment outcomes.

Cell-free Biosynthesis of Peptidomimetics

A wide variety of peptidomimetics (peptide analogs) possessing innovative biological functions have been brought forth as therapeutic candidates through cell-free protein synthesis (CFPS) systems. A key feature of these peptidomimetic drugs is the use of non-canonical amino acid building blocks with diverse biochemical properties that expand functional diversity. Here, we summarize recent technologies leveraging CFPS platforms to expand the reach of peptidomimetics drugs. We also offer perspectives on engineering the translational machinery that may open new opportunities for expanding genetically encoded chemistry to transform drug discovery practice beyond traditional boundaries.

Inhibition of Amyloid Protein Aggregation Using Selected Peptidomimetics

Aberrant protein aggregation leads to the formation of amyloid fibrils. This phenomenon is linked to the development of more than 40 irremediable diseases such as Alzheimer's disease, Parkinson's disease, type 2 diabetes, and cancer. Plenty of research efforts have been given to understanding the underlying mechanism of protein aggregation, associated toxicity, and the development of amyloid inhibitors. Recently, the peptidomimetic approach has emerged as a potential tool to modulate several protein-protein interactions (PPIs). In this review, we discussed selected peptidomimetic-based approaches for the modulation of important amyloid proteins (Islet Amyloid Polypeptide, Amyloid Beta, α-synuclein, mutant p53, and insulin) aggregation. This approach holds a powerful platform for creating an essential stepping stone for the vital development of anti-amyloid therapeutic agents.

A knowledge-based protein-protein interaction inhibition (KPI) pipeline: an insight from drug repositioning for COVID-19 inhibition

The inhibition of protein-protein interactions (PPIs) by small molecules is an exciting drug discovery strategy. Here, we aimed to develop a pipeline to identify candidate small molecules to inhibit PPIs. Therefore, KPI, a Knowledge-based Protein-Protein Interaction Inhibition pipeline, was introduced to improve the discovery of PPI inhibitors. Then, phytochemicals from a collection of known Middle Eastern antiviral herbs were screened to identify potential inhibitors of key PPIs involved in COVID-19. Here, the following investigations were sequenced: 1) Finding the binding partner and the interface of the proteins in PPIs, 2) Performing the blind ligand-protein inhibition (LPI) simulations, 3) Performing the local LPI simulations, 4) Simulating the interactions of the proteins and their binding partner in the presence and absence of the ligands, and 5) Performing the molecular dynamics simulations. The pharmacophore groups involved in the LPI were also characterized. Aloin, Genistein, Neoglucobrassicin, and Rutin are our new pipeline candidates for inhibiting PPIs involved in COVID-19. We also propose KPI for drug repositioning studies.Communicated by Ramaswamy H. Sarma.

An overview of PROTACs: a promising drug discovery paradigm

Proteolysis targeting chimeras (PROTACs) technology has emerged as a novel therapeutic paradigm in recent years. PROTACs are heterobifunctional molecules that degrade target proteins by hijacking the ubiquitin-proteasome system. Currently, about 20-25% of all protein targets are being studied, and most works focus on their enzymatic functions. Unlike small molecules, PROTACs inhibit the whole biological function of the target protein by binding to the target protein and inducing subsequent proteasomal degradation. PROTACs compensate for limitations that transcription factors, nuclear proteins, and other scaffolding proteins are difficult to handle with traditional small-molecule inhibitors. Currently, PROTACs have successfully degraded diverse proteins, such as BTK, BRD4, AR, ER, STAT3, IRAK4, tau, etc. And ARV-110 and ARV-471 exhibited excellent efficacy in clinical II trials. However, what targets are appropriate for PROTAC technology to achieve better benefits than small-molecule inhibitors are not fully understood. And how to rationally design an efficient PROTACs and optimize it to be orally effective poses big challenges for researchers. In this review, we summarize the features of PROTAC technology, analyze the detail of general principles for designing efficient PROTACs, and discuss the typical application of PROTACs targeting different protein categories. In addition, we also introduce the progress of relevant clinical trial results of representative PROTACs and assess the challenges and limitations that PROTACs may face. Collectively, our studies provide references for further application of PROTACs.

Designing a Nitro-Induced Sutured Biomacromolecule to Engineer Electroconductive Adhesive Hydrogels

Nitro-functionality, with a large deficit of negative charge, embraces biological importance and has proven its therapeutic essence even in chemotherapy. Functionally, with its strong electron-withdrawing capability, nitro can manipulate the electron density of organic moieties and regulates cellular-biochemical reactions. However, the chemistry of nitro-functionality to introduce physiologically relevant macroscopic properties from the molecular skeleton is unknown. Therefore, herein, a neurotransmitter moiety, dopamine, was chemically modified with a nitro-group to explore its influence on synthesizing a multifunctional biomaterial for therapeutic applications. Chemically, while the nitro-group perturbed the aromatic electron density of nitrocatecholic domain, it facilitated the suturing of nitrocatechol moieties to regain its aromaticity through a radical transfer mechanism, forming a novel macromolecular structure. Incorporation of the sutured-nitrocatecholic strand (S-nCAT) in a gelatin-based hydrogel introduced an electroconductive microenvironment through the delocalization of π-electrons in S-nCAT, while maintaining its catechol-mediated adhesive property for tissue repairing/sealing. Meanwhile, the engineered hydrogel enriched with noncovalent interactions, demonstrated excellent mechano-physical properties to support tissue functions. Cytocompatibility of the bioadhesive was assessed with in vitro and in vivo studies, confirming its potential usage for biomedical applications. In conclusion, this novel chemical approach enabled designing a multifunctional biomaterial by manipulating the electronic properties of small bioactive molecules for various biomedical applications.

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.

Targeted Protein Degradation to Overcome Resistance in Cancer Therapies: PROTAC and N-Degron Pathway

Extensive progress in understanding the molecular mechanisms of cancer growth and proliferation has led to the remarkable development of drugs that target cancer-driving molecules. Most target molecules are proteins such as kinases and kinase-associated receptors, which have enzymatic activities needed for the signaling cascades of cells. The small molecule inhibitors for these target molecules greatly improved therapeutic efficacy and lowered the systemic toxicity in cancer therapies. However, long-term and high-dosage treatment of small inhibitors for cancer has produced other obstacles, such as resistance to inhibitors. Among recent approaches to overcoming drug resistance to cancers, targeted protein degradation (TPD) such as proteolysis-targeting chimera (PROTAC) technology adopts a distinct mechanism of action by which a target protein is destroyed through the cellular proteolytic system, such as the ubiquitin-proteasome system or autophagy. Here, we review the currently developed PROTACs as the representative TPD molecules for cancer therapy and the N-degrons of the N-degron pathways as the potential TPD ligands.

Application of nanopore sensors for biomolecular interactions and drug discovery

Biomolecular interactions, including protein-protein, protein-nucleic acid, and protein/nucleic acid-ligand interactions, play crucial roles in various cellular signaling and biological processes, and offer attractive therapeutic targets in numerous human diseases. Currently, drug discovery is limited by the low efficiency and high cost of conventional ensemble-averaging-based techniques for biomolecular interaction analysis and high-throughput drug screening. Nanopores are an emerging technology for single-molecule sensing of biomolecules. Owing to the robust advantages of single-molecule sensing, nanopore sensors have contributed tremendously to nucleic acid sequencing and disease diagnostics. In this minireview, we summarize the recent developments and outlooks in single-molecule sensing of various biomolecular interactions for drug discovery applications using biological and solid-state nanopore sensors.