Overcoming Chemical, Biological, and Computational Challenges in the Development of Inhibitors Targeting Protein-Protein Interactions

Targeting peptide based therapeutics: Integrated computational and experimental studies of autophagic regulation in host-parasite interaction

Cutaneous leishmaniasis caused by the intracellular parasite Leishmania major, exhibits significant public health challenge worldwide. With limited treatment options available, the identification of novel therapeutic targets is of paramount importance. Present study manifested the crucial role of ATG8 protein as a potential target in combating L. major infection. Using machine learning algorithms, we identified non-conserved motifs within the ATG8 in L. major. Subsequently, a peptide library was generated based on these motifs, and three peptides were selected for further investigation through molecular docking and molecular dynamics simulations. Surface Plasmon Resonance (SPR) experiments confirmed the direct interaction between ATG8 and the identified peptides. Remarkably, these peptides demonstrated the ability to cross the parasite membrane and exert profound effects on L. major. Peptide treatment significantly impacted parasite survival, inducing alterations in the cell cycle and morphology. Furthermore, the peptides were found to modulate autophagosome formation, particularly under starved conditions, indicating their involvement in autophagy regulation within L. major. In vitro studies revealed that the selected peptides effectively decreased the parasite load within the infected host cells. Encouragingly, in vivo experiments corroborated these findings, demonstrating a reduction in parasite burden upon peptide administration. Additionally, the peptides were observed to affect the levels of LC3II, a known autophagy marker within the host cells. Collectively, our findings highlight the efficacy of these novel peptides in targeting L. major ATG8 and disrupting parasite survival, wherein P2 is showing prominent effect on L. major as compared to P1. These results provide valuable insights into the development of innovative therapeutic strategies against leishmaniasis.

Optimization Efforts for Identification of Novel Highly Potent Keap1-Nrf2 Protein-Protein Interaction Inhibitors

In research focused on protein-protein interaction (PPI) inhibitors, the optimization process to achieve both high inhibitory activity and favorable physicochemical properties remains challenging. Our previous study reported the discovery of novel and bioavailable Keap1-Nrf2 PPI inhibitor 8 which exhibited moderate in vivo activity in rats. In this work, we present our subsequent efforts to optimize this compound. Two distinct approaches were employed, targeting high energy water molecules and Ser602 as "hot spots" from the anchor with good aqueous solubility, metabolic stability, and membrane permeability. Through ligand efficiency (LE)-guided exploration, we identified two novel inhibitors 22 and 33 with good pharmacokinetics (PK) profiles and more potent in vivo activities, which appear to be promising chemical probes among the existing inhibitors.

Drug discovery by targeting the protein-protein interactions involved in autophagy

Autophagy is a cellular process in which proteins and organelles are engulfed in autophagosomal vesicles and transported to the lysosome/vacuole for degradation. Protein-protein interactions (PPIs) play a crucial role at many stages of autophagy, which present formidable but attainable targets for autophagy regulation. Moreover, selective regulation of PPIs tends to have a lower risk in causing undesired off-target effects in the context of a complicated biological network. Thus, small-molecule regulators, including peptides and peptidomimetics, targeting the critical PPIs involved in autophagy provide a new opportunity for innovative drug discovery. This article provides general background knowledge of the critical PPIs involved in autophagy and reviews a range of successful attempts on discovering regulators targeting those PPIs. Successful strategies and existing limitations in this field are also discussed.

Expanding the Structural Diversity at the Phenylene Core of Ligands for the von Hippel-Lindau E3 Ubiquitin Ligase: Development of Highly Potent Hypoxia-Inducible Factor-1α Stabilizers

Hypoxia-inducible factor-1α (HIF-1α) constitutes the principal mediator of cellular adaptation to hypoxia in humans. The HIF-1α protein level and activity are tightly regulated by the ubiquitin E3 ligase von Hippel-Lindau (VHL). Here, we performed a structure-guided and bioactivity-driven design of new VHL inhibitors. Our iterative and combinatorial strategy focused on chemical variability at the phenylene unit and encompassed further points of diversity. The exploitation of tailored phenylene fragments and the stereoselective installation of the benzylic methyl group provided potent VHL ligands. Three high-resolution structures of VHL-ligand complexes were determined, and bioactive conformations of these ligands were explored. The most potent inhibitor (30) exhibited dissociation constants lower than 40 nM, independently determined by fluorescence polarization and surface plasmon resonance and an enhanced cellular potency, as evidenced by its superior ability to induce HIF-1α transcriptional activity. Our work is anticipated to inspire future efforts toward HIF-1α stabilizers and new ligands for proteolysis-targeting chimera (PROTAC) degraders.

Combining structural and coevolution information to unveil allosteric sites

Understanding allosteric regulation in biomolecules is of great interest to pharmaceutical research and computational methods emerged during the last decades to characterize allosteric coupling. However, the prediction of allosteric sites in a protein structure remains a challenging task. Here, we integrate local binding site information, coevolutionary information, and information on dynamic allostery into a structure-based three-parameter model to identify potentially hidden allosteric sites in ensembles of protein structures with orthosteric ligands. When tested on five allosteric proteins (LFA-1, p38-α, GR, MAT2A, and BCKDK), the model successfully ranked all known allosteric pockets in the top three positions. Finally, we identified a novel druggable site in MAT2A confirmed by X-ray crystallography and SPR and a hitherto unknown druggable allosteric site in BCKDK validated by biochemical and X-ray crystallography analyses. Our model can be applied in drug discovery to identify allosteric pockets.

SVSBI: sequence-based virtual screening of biomolecular interactions

Virtual screening (VS) is a critical technique in understanding biomolecular interactions, particularly in drug design and discovery. However, the accuracy of current VS models heavily relies on three-dimensional (3D) structures obtained through molecular docking, which is often unreliable due to the low accuracy. To address this issue, we introduce a sequence-based virtual screening (SVS) as another generation of VS models that utilize advanced natural language processing (NLP) algorithms and optimized deep K-embedding strategies to encode biomolecular interactions without relying on 3D structure-based docking. We demonstrate that SVS outperforms state-of-the-art performance for four regression datasets involving protein-ligand binding, protein-protein, protein-nucleic acid binding, and ligand inhibition of protein-protein interactions and five classification datasets for protein-protein interactions in five biological species. SVS has the potential to transform current practices in drug discovery and protein engineering.

Predicting the Likelihood of Molecules to Act as Modulators of Protein-Protein Interactions

Targeting protein-protein interactions (PPIs) by small molecule modulators (iPPIs) is an attractive strategy for drug therapy, and some iPPIs have already been introduced into the clinic. Blocking PPIs is however considered to be a more difficult task than inhibiting enzymes or antagonizing receptor activity. In this paper, we examine whether it is possible to predict the likelihood of molecules to act as iPPIs. Using our in-house iterative stochastic elimination (ISE) algorithm, we constructed two classification models that successfully distinguish between iPPIs from the iPPI-DB database and decoy molecules from either the Enamine HTS collection (ISE 1) or the ZINC database (ISE 2). External test sets of iPPIs taken from the TIMBAL database and decoys from Enamine HTS or ZINC were screened by the models: the area under the curve for the receiver operating characteristic curve was 0.85-0.89, and the Enrichment Factor increased from an initial 1 to as much as 66 for ISE 1 and 57 for ISE 2. Screening of the Enamine HTS and ZINC data sets through both models results in a library of ∼1.3 million molecules that pass either one of the models. This library is enriched with iPPI candidates that are structurally different from known iPPIs, and thus, it is useful for target-specific screenings and should accelerate the discovery of iPPI drug candidates. The entire library is available in Table S6.

Peptide-based covalent inhibitors of protein-protein interactions

Protein-protein interactions (PPI) are involved in all cellular processes and many represent attractive therapeutic targets. However, the frequently rather flat and large interaction areas render the identification of small molecular PPI inhibitors very challenging. As an alternative, peptide interaction motifs derived from a PPI interface can serve as starting points for the development of inhibitors. However, certain proteins remain challenging targets when applying inhibitors with a competitive mode of action. For that reason, peptide-based ligands with an irreversible binding mode have gained attention in recent years. This review summarizes examples of covalent inhibitors that employ peptidic binders and have been tested in a biological context.

Computational Evaluation of Peptide-Protein Binding Affinities: Application of Potential of Mean Force Calculations to SH2 Domains

Many biological functions are mediated by protein-protein interactions (PPIs), often involving specific structural modules, such as SH2 domains. Inhibition of PPIs is a pharmaceutical strategy of growing importance. However, a major challenge in the design of PPI inhibitors is the large interface involved in these interactions, which, in many cases, makes inhibition by small organic molecules ineffective. Peptides, which cover a wide range of dimensions and can be opportunely designed to mimic protein sequences at PPI interfaces, represent a valuable alternative to small molecules. Computational techniques able to predict the binding affinity of peptides for the target domain or protein represent a crucial stage in the workflow for the design of peptide-based drugs. This chapter describes a protocol to obtain the potential of mean force (PMF) for peptide-SH2 domain binding, starting from umbrella sampling (US) molecular dynamics (MD) simulations. The PMF profiles can be effectively used to predict the relative standard binding free energies of different peptide sequences.

DrugnomeAI is an ensemble machine-learning framework for predicting druggability of candidate drug targets

The druggability of targets is a crucial consideration in drug target selection. Here, we adopt a stochastic semi-supervised ML framework to develop DrugnomeAI, which estimates the druggability likelihood for every protein-coding gene in the human exome. DrugnomeAI integrates gene-level properties from 15 sources resulting in 324 features. The tool generates exome-wide predictions based on labelled sets of known drug targets (median AUC: 0.97), highlighting features from protein-protein interaction networks as top predictors. DrugnomeAI provides generic as well as specialised models stratified by disease type or drug therapeutic modality. The top-ranking DrugnomeAI genes were significantly enriched for genes previously selected for clinical development programs (p value < 1 × 10-308) and for genes achieving genome-wide significance in phenome-wide association studies of 450 K UK Biobank exomes for binary (p value = 1.7 × 10-5) and quantitative traits (p value = 1.6 × 10-7). We accompany our method with a web application ( http://drugnomeai.public.cgr.astrazeneca.com ) to visualise the druggability predictions and the key features that define gene druggability, per disease type and modality.

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.

Using the Cyclotide Scaffold for Targeting Biomolecular Interactions in Drug Development

This review provides an overview of the properties of cyclotides and their potential for developing novel peptide-based therapeutics. The selective disruption of protein–protein interactions remains challenging, as the interacting surfaces are relatively large and flat. However, highly constrained polypeptide-based molecular frameworks with cell-permeability properties, such as the cyclotide scaffold, have shown great promise for targeting those biomolecular interactions. The use of molecular techniques, such as epitope grafting and molecular evolution employing the cyclotide scaffold, has shown to be highly effective for selecting bioactive cyclotides.

Synthesis of Scyllatoxin-Based BH3 Domain Mimetics with Diverse Patterns of Native Disulfide Bonds

This article outlines the design and development of scyllatoxin (ScTx)-based BH3 domain mimetics with diverse patterns of native disulfide bonds. More specifically, this method summarizes the total chemical synthesis of ScTx-based peptides that contain zero, one, two, or three disulfide linkages, including techniques to generate variants with any combination of native disulfides. Each peptide reported herein is generated on solid-phase support using microwave-assisted coupling procedures, and all reaction parameters related to the peptide synthesis are described in detail. The various disulfide patterns of the ScTx-based constructs are established during peptide synthesis and are ultimately verified by mass analysis of trypsin-digested fragments. The BH3 domain mimetics developed herein were generated by transposing residues from the helical BH3 domain of the pro-apoptotic BCL2 protein Bax to the α-helix of wild-type ScTx. Interestingly, we found that the relative binding affinities of ScTx-Bax peptides for the anti-apoptotic BCL2 protein Bcl-2 (proper) were heavily influenced by the number and position of disulfide linkages within the ScTx-Bax sequence. As a consequence, we were able to utilize ScTx-Bax BH3 domain mimetics with varied patterns of disulfide bonds to survey how structural rigidity within the helical Bax BH3 domain affects binding to promiscuous anti-apoptotic BCL2 proteins. More broadly, the ability to generate ScTx-based molecules that contain any combination of native disulfide bonds expands the utility of such constructs as tools to study the molecular nature of protein-protein interactions. © 2022 Wiley Periodicals LLC. Basic Protocol 1: Synthesis and characterization of ScTx-based Bax BH3 domain mimetics Basic Protocol 2: Oxidation of ScTx-Bax BH3 domain mimetics containing one, two, or three disulfide linkages Support Protocol: Mapping of disulfide linkages in oxidized ScTx-Bax BH3 domain mimetics.

Integrated analysis of the clinical consequence and associated gene expression of ALK in ALK-positive human cancers

Anaplastic lymphoma kinase (ALK) is a tyrosine kinase receptor that is genetically altered in several cancers, including NSCLC, melanoma, lymphoma, and other tumors. Although ALK is associated with various cancers, the relationship between ALK expression and patient prognosis in different cancers is poorly understood. Here, using multidimensional approaches, we revealed the correlation between ALK expression and the clinical outcomes of patients with LUAD, melanoma, OV, DLBC, AML, and BC. We analyzed ALK transcriptional expression, patient survival rate, genetic alteration, protein network, and gene and microRNA (miRNA) co-expression. Compared to that in normal tissues, higher ALK expression was found in LUAD, melanoma, and OV, which are associated with poor patient survival rates. In contrast, lower transcriptional expression was found to decrease the survival rate of patients with DLBC, AML, and BC. A total of 202 missense mutations, 17 truncating mutations, 7 fusions, and 3 in-frame mutations were identified. Further, 17 genes and 19 miRNAs were found to be exclusively co-expressed and echinoderm microtubule-associated protein-like 4 (EML4) was identified as the most positively correlated gene (log odds ratio >3). The gene ontology and signaling pathways of the genes co-expressed with ALK in these six cancers were also identified. Our findings offer a basis for ALK as a prognostic biomarker and therapeutic target in cancers, which will potentially contribute to precision oncology and assist clinicians in identifying suitable treatment options.

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.

Designing of disruptor molecules to restrain the protein-protein interaction network of VANG1/SCRIB/NOS1AP using fragment-based drug discovery techniques

Governing protein-protein interaction networks are the cynosure of cell signaling and oncogenic networks. Multifarious processes when aligned with one another can result in a dysregulated output which can result in cancer progression. In the current research, one such network of proteins comprising VANG1/SCRIB/NOS1AP, which is responsible for cell migration, is targeted. The proteins are modeled using in-silico approaches, and the interaction is visualized utilizing protein-protein docking. Designing drugs for the convoluted protein network can serve as a challenging task that can be overcome by fragment-based drug designing, a recent game-changer in the computational drug discovery strategy for protein interaction networks. The model is exposed to the extraction of hotspots, also known as the restrained regions for small molecular hits. The hotspot regions are subjected to a library of generated fragments, which are then recombined and rejoined to develop small molecular disruptors of the macromolecular assemblage. Rapid screening methods using pharmacokinetic tools and 2D interaction studies resulted in four molecules that could serve the purpose of a disruptor. The final validation is executed by long-range simulations of 100 ns and exploring the stability of the complex using several parameters leading to the emergence of two novel molecules VNS003 and VNS005 that could be used as the disruptors of the protein assembly VANG1/SCRIB/NOS1AP. Also, the molecules were explored as single protein targets approbated via molecular docking and 100 ns molecular dynamics simulation. This concluded VNS003 as the most suitable inhibitor module capable of acting as a disruptor of a macromolecular assembly as well as acting on individual protein chains, thus leading to the primary hindrance in the formation of the protein interaction complex.

Multi-targeting of K-Ras domains and mutations by peptide and small molecule inhibitors

K-Ras activating mutations are significantly associated with tumor progression and aggressive metastatic behavior in various human cancers including pancreatic cancer. So far, despite a large number of concerted efforts, targeting of mutant-type K-Ras has not been successful. In this regard, we aimed to target this oncogene by a combinational approach consisting of small peptide and small molecule inhibitors. Based on a comprehensive analysis of structural and physicochemical properties of predominantly K-Ras mutants, an anti-cancer peptide library and a small molecule library were screened to simultaneously target oncogenic mutations and functional domains of mutant-type K-Ras located in the P-loop, switch I, and switch II regions. The selected peptide and small molecule showed notable binding affinities to their corresponding binding sites, and hindered the growth of tumor cells carrying K-Ras^G12D and K-Ras^G12C mutations. Of note, the expression of K-Ras downstream genes (i.e., CTNNB1, CCND1) was diminished in the treated Kras-positive cells. In conclusion, our combinational platform signifies a new potential for blockade of oncogenic K-Ras and thereby prevention of tumor progression and metastasis. However, further validations are still required regarding the in vitro and in vivo efficacy and safety of this approach.

K-Ras activating mutations are associated with tumor progression and aggressive metastatic behavior in cancers. We aimed to target this mutated protein as an oncogene with small peptides and small molecules. The selected peptide and small molecules by computational methods showed notable binding affinities to mutated and oncogenic K-Ras. Also, they hindered the proliferation of pancreatic tumor cells. These compounds diminished the expression of downstream genes to mutant K-Ras too. Our combinatorial approach introduces new candidates for blockade of oncogenic K-Ras which is observed in many types of cancer. The effect of these compounds should be validated by further in vitro and in vivo analysis.

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.

Computational Study on Potential Novel Anti-Ebola Virus Protein VP35 Natural Compounds

Ebola virus (EBOV) is one of the most lethal pathogens that can infect humans. The Ebola viral protein VP35 (EBOV VP35) inhibits host IFN-α/β production by interfering with host immune responses to viral invasion and is thus considered as a plausible drug target. The aim of this study was to identify potential novel lead compounds against EBOV VP35 using computational techniques in drug discovery. The 3D structure of the EBOV VP35 with PDB ID: 3FKE was used for molecular docking studies. An integrated library of 7675 African natural product was pre-filtered using ADMET risk, with a threshold of 7 and, as a result, 1470 ligands were obtained for the downstream molecular docking using AutoDock Vina, after an energy minimization of the protein via GROMACS. Five known inhibitors, namely, amodiaquine, chloroquine, gossypetin, taxifolin and EGCG were used as standard control compounds for this study. The area under the curve (AUC) value, evaluating the docking protocol obtained from the receiver operating characteristic (ROC) curve, generated was 0.72, which was considered to be acceptable. The four identified potential lead compounds of NANPDB4048, NANPDB2412, ZINC000095486250 and NANPDB2476 had binding affinities of −8.2, −8.2, −8.1 and −8.0 kcal/mol, respectively, and were predicted to possess desirable antiviral activity including the inhibition of RNA synthesis and membrane permeability, with the probable activity (Pa) being greater than the probable inactivity (Pi) values. The predicted anti-EBOV inhibition efficiency values (IC₅₀), found using a random forest classifier, ranged from 3.35 to 11.99 μM, while the Ki values ranged from 0.97 to 1.37 μM. The compounds NANPDB4048 and NANPDB2412 had the lowest binding energy of −8.2 kcal/mol, implying a higher binding affinity to EBOV VP35 which was greater than those of the known inhibitors. The compounds were predicted to possess a low toxicity risk and to possess reasonably good pharmacological profiles. Molecular dynamics (MD) simulations of the protein–ligand complexes, lasting 50 ns, and molecular mechanisms Poisson-Boltzmann surface area (MM-PBSA) calculations corroborated the binding affinities of the identified compounds and identified novel critical interacting residues. The antiviral potential of the molecules could be confirmed experimentally, while the scaffolds could be optimized for the design of future novel anti-EBOV chemotherapeutics.

Analysis of physicochemical properties of protein-protein interaction modulators suggests stronger alignment with the "rule of five"

Despite the important roles played by protein-protein interactions (PPIs) in disease, they have been long considered as 'undruggable'. However, recent advances have suggested that PPIs are druggable but may not follow conventional rules of 'drug ability'. Here we explore which physicochemical parameters are essential for a PPI modulator to be a clinical drug by analysing the physicochemical properties of small-molecule PPI modulators in the market, in clinical trials, and published. Our analysis reveals that those compounds currently on the market have a larger range of values for most of the physicochemical parameters, whereas those in clinical trials fit much more stringently to standard drug-like parameters. This observation was particularly true for molecular weight, clog P and topological polar surface area, where aside from a few outliers, most of the compounds in clinical trials fit within standard drug-like parameters. This implies that the newer PPI modulators are more drug-like than those currently on the market, suggesting that designing new PPI-specific screening libraries should remain within standard drug-like parameters in order to obtain a clinical candidate. Taken together, our analysis has important implications for designing future drug discovery campaigns aimed at targeting PPIs.

Design and synthesis of a new orthogonally protected glutamic acid analog and its use in the preparation of high affinity polo-like kinase 1 polo-box domain - binding peptide macrocycles

Targeting protein - protein interactions (PPIs) has emerged as an important area of discovery for anticancer therapeutic development. In the case of phospho-dependent PPIs, such as the polo-like kinase 1 (Plk1) polo-box domain (PBD), a phosphorylated protein residue can provide high-affinity recognition and binding to target protein hot spots. Developing antagonists of the Plk1 PBD can be particularly challenging if one relies solely on interactions within and proximal to the phospho-binding pocket. Fortunately, the affinity of phospho-dependent PPI antagonists can be significantly enhanced by taking advantage of interactions in both the phospho-binding site and hidden "cryptic" pockets that may be revealed on ligand binding. In our current paper, we describe the design and synthesis of macrocyclic peptide mimetics directed against the Plk1 PBD, which are characterized by a new glutamic acid analog that simultaneously serves as a ring-closing junction that provides accesses to a cryptic binding pocket, while at the same time achieving proper orientation of a phosphothreonine (pT) residue for optimal interaction in the signature phospho-binding pocket. Macrocycles prepared with this new amino acid analog introduce additional hydrogen-bonding interactions not found in the open-chain linear parent peptide. It is noteworthy that this new glutamic acid-based amino acid analog represents the first example of extremely high affinity ligands where access to the cryptic pocket from the pT-2 position is made possible with a residue that is not based on histidine. The concepts employed in the design and synthesis of these new macrocyclic peptide mimetics should be useful for further studies directed against the Plk1 PBD and potentially for ligands directed against other PPI targets.

A Step toward NRF2-DNA Interaction Inhibitors by Fragment-Based NMR Methods

The NRF2 transcription factor is a key regulator in cellular oxidative stress response, and acts as a tumor suppressor. Aberrant activation of NRF2 has been implicated in promoting chemo-resistance, tumor growth, and metastasis by activating its downstream target genes. Hence, inhibition of NRF2 promises to be an attractive therapeutic strategy to suppress cell proliferation and enhance cell apoptosis in cancer. Direct targeting of NRF2 with small-molecules to discover protein-DNA interaction inhibitors is challenging as it is a largely intrinsically disordered protein. To discover molecules that bind to NRF2 at the DNA binding interface, we performed an NMR-based fragment screen against its DNA-binding domain. We discovered several weakly binding fragment hits that bind to a region overlapping with the DNA binding site. Using SAR by catalogue we developed an initial structure-activity relationship for the most interesting initial hit series. By combining NMR chemical shift perturbations and data-driven docking, binding poses which agreed with NMR information and the observed SAR were elucidated. The herein discovered NRF2 hits and proposed binding modes form the basis for future structure-based optimization campaigns on this important but to date 'undrugged' cancer driver.

Small-Molecule Inhibitors of the Coronavirus Spike: ACE2 Protein-Protein Interaction as Blockers of Viral Attachment and Entry for SARS-CoV-2

Inhibitors of the protein-protein interaction (PPI) between the SARS-CoV-2 spike protein and human ACE2 (hACE2), which acts as a ligand-receptor pair that initiates the viral attachment and cellular entry of this coronavirus causing the ongoing COVID-19 pandemic, are of considerable interest as potential antiviral agents. While blockade of such PPIs with small molecules is more challenging than that with antibodies, small-molecule inhibitors (SMIs) might offer alternatives that are less strain- and mutation-sensitive, suitable for oral or inhaled administration, and more controllable/less immunogenic. Here, we report the identification of SMIs of this PPI by screening our compound library focused around the chemical space of organic dyes. Among promising candidates identified, several dyes (Congo red, direct violet 1, Evans blue) and novel druglike compounds (DRI-C23041, DRI-C91005) inhibited the interaction of hACE2 with the spike proteins of SARS-CoV-2 as well as SARS-CoV with low micromolar activity in our cell-free ELISA-type assays (IC50's of 0.2-3.0 μM), whereas control compounds, such as sunset yellow FCF, chloroquine, and suramin, showed no activity. Protein thermal shift assays indicated that the SMIs of interest identified here bind SARS-CoV-2-S and not hACE2. While dyes seemed to be promiscuous inhibitors, DRI-C23041 showed some selectivity and inhibited the entry of two different SARS-CoV-2-S expressing pseudoviruses into hACE2-expressing cells in a concentration-dependent manner with low micromolar IC50's (6-7 μM). This provides proof-of-principle evidence for the feasibility of small-molecule inhibition of PPIs critical for SARS-CoV-2 attachment/entry and serves as a first guide in the search for SMI-based alternative antiviral therapies for the prevention and treatment of diseases caused by coronaviruses in general and COVID-19 in particular.

Small molecule probes for targeting autophagy

Autophagy is implicated in a wide range of (patho)physiological processes including maintenance of cellular homeostasis, neurodegenerative disorders, aging and cancer. As such, small molecule autophagy modulators are in great demand, both for their ability to act as tools to better understand this essential process and as potential therapeutics. Despite substantial advances in the field, major challenges remain in the development and comprehensive characterization of probes that are specific to autophagy. In this Review, we discuss recent developments in autophagy-modulating small molecules, including the specific challenges faced in the development of activators and inhibitors, and recommend guidelines for their use. Finally, we discuss the potential to hijack the process for targeted protein degradation, an area of great importance in chemical biology and drug discovery.

Emerging oncogene ATAD2: Signaling cascades and therapeutic initiatives

ATAD2 is a promising oncoprotein with tumor-promoting functions in many cancers. It is a valid cancer drug-target and a potential cancer-biomarker for multiple malignancies. As a cancer/testis antigen (CTA), ATAD2 could also be a probable candidate for immunotherapy. It is a unique CTA that belongs to both AAA+ ATPase and bromodomain family proteins. Since 2007, several research groups have been reported on the pleiotropic oncogenic functions of ATAD2 in diverse signaling pathways, including Rb/E2F-cMyc pathway, steroid hormone signaling pathway, p53 and p38-MAPK-mediated apoptotic pathway, AKT pathway, hedgehog signaling pathway, HIF1α signaling pathway, and Epithelial to Mesenchymal Transition (EMT) pathway in various cancers. In all these pathways, ATAD2 participates in chromatin dynamics, DNA replication, and gene transcription, demonstrating its role as an epigenetic reader and transcription factor or coactivator to promote tumorigenesis. However, despite the progress, an overall mechanism of ATAD2-mediated oncogenesis in diverse origin is elusive. In this review, we summarize the accumulated evidence to envision the overall ATAD2 signaling networks during carcinogenesis and highlight the area where missing links await further research. Besides, the structure-function aspect of ATAD2 is also discussed. Since the efforts have already been initiated to explore targeted drug molecules and RNA-based therapeutic alternatives against ATAD2, their potency and prospects have been elucidated. Together, we believe this is a well-rounded review on ATAD2, facilitating a new drift in ATAD2 research, essential for its clinical implication as a biomarker and/or cancer drug-target.

Multi-Omics Data Analysis of Gene Expressions and Alterations, Cancer-Associated Fibroblast and Immune Infiltrations, Reveals the Onco-Immune Prognostic Relevance of STAT3/CDK2/4/6 in Human Malignancies

Simple Summary

Signal transducer and activator of transcription 3 (STAT3)/Cyclin-dependent kinases are multifunctional proteins that play instrumental roles in carcinogenesis. However, the genetic alterations of the STAT3/CDK2/4/6 signaling axis and its role in predicting immune infiltration and immunotherapeutic response remain unclear. Here, we used in silico analyses of multi-Omics data to map out the role of epigenetic and genetic alterations of STAT3/CDK2/4/6 in tumor immune infiltrations, immunotherapy response, and prognosis of cancer patients. Our study collectively suggested that STAT3/CDK2/4/6 are important onco-immune signatures that contribute to tumor immune invasion, poor prognoses, and immune therapy failure. Our finding may be clinically useful in designing therapeutic strategies, prognosis assessment, and follow-up management in patients receiving immunotherapy in multiple cancers.

Abstract

Signal transducer and activator of transcription 3 (STAT3)/Cyclin-dependent kinases are multifunctional proteins that play an important implicative role in cancer initiations, progression, drug resistance, and metastasis, and has been extensively explored in cancer therapy. However, the genetic alterations of STAT3/CDK2/4/6 and its role in predicting immune infiltration and immunotherapeutic response are yet to be well exploited. In this study, we use in silico methods to analyze differential expression, prognostic value, genetic and epigenetic alterations, association with tumor-infiltrating immune cells, and cancer-associated fibroblast (CAF) infiltrations of STAT3/CDK2/4/6 in multiple cancer types. Our results revealed that the expression of STAT3/CDK2/4/6 was altered in various cancers and is associated with poor overall and disease-free survival of the cohorts. Moreover, genetic alterations in STAT3/CDK2/4/6 co-occurred with a number of other genetic alterations and are associated with poorer prognoses of the cohorts. The protein-protein interaction (PPI) network analysis suggests CDK2/4/6/STAT3 may directly interact with factors that promote tumorigenesis and immune response. We found that STAT3/CDK2/4/6 expressions were associated with infiltrations of CAF and the various immune cells in multiple cancers and it’s associated with poor response to immunotherapy. Collectively, our study suggested that STAT3/CDK2/4/6 are important onco-immune signatures that play central roles in tumor immune invasion, poor prognoses and, immune therapy response. Findings from the present study may therefore be clinically useful in prognosis assessment and follow-up management of immunotherapy.

The iPPI-DB initiative: A Community-centered database of Protein-Protein Interaction modulators

MOTIVATION

One avenue to address the paucity of clinically testable targets is to reinvestigate the druggable genome by tackling complicated types of targets such as Protein-Protein Interactions (PPIs). Given the challenge to target those interfaces with small chemical compounds, it has become clear that learning from successful examples of PPI modulation is a powerful strategy. Freely-accessible databases of PPI modulators that provide the community with tractable chemical and pharmacological data, as well as powerful tools to query them, are therefore essential to stimulate new drug discovery projects on PPI targets.

RESULTS

Here, we present the new version iPPI-DB, our manually curated database of PPI modulators. In this completely redesigned version of the database, we introduce a new web interface relying on crowdsourcing for the maintenance of the database. This interface was created to enable community contributions, whereby external experts can suggest new database entries. Moreover, the data model, the graphical interface, and the tools to query the database have been completely modernized and improved. We added new PPI modulators, new PPI targets, and extended our focus to stabilizers of PPIs as well.

AVAILABILITY AND IMPLEMENTATION

The iPPI-DB server is available at https://ippidb.pasteur.fr The source code for this server is available at https://gitlab.pasteur.fr/ippidb/ippidb-web/ and is distributed under GPL licence (http://www.gnu.org/licences/gpl). Queries can be shared through persistent links according to the FAIR data standards. Data can be downloaded from the website as csv files.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Explicit Treatment of Non-Michaelis-Menten and Atypical Kinetics in Early Drug Discovery*

Biological systems are highly regulated. They are also highly resistant to sudden perturbations enabling them to maintain the dynamic equilibrium essential to sustain life. This robustness is conferred by regulatory mechanisms that influence the activity of enzymes/proteins within their cellular context to adapt to changing environmental conditions. However, the initial rules governing the study of enzyme kinetics were mostly tested and implemented for cytosolic enzyme systems that were easy to isolate and/or recombinantly express. Moreover, these enzymes lacked complex regulatory modalities. Now, with academic labs and pharmaceutical companies turning their attention to more-complex systems (for instance, multiprotein complexes, oligomeric assemblies, membrane proteins and post-translationally modified proteins), the initial axioms defined by Michaelis-Menten (MM) kinetics are rendered inadequate, and the development of a new kind of kinetic analysis to study these systems is required. This review strives to present an overview of enzyme kinetic mechanisms that are atypical and, oftentimes, do not conform to the classical MM kinetics. Further, it presents initial ideas on the design and analysis of experiments in early drug-discovery for such systems, to enable effective screening and characterisation of small-molecule inhibitors with desirable physiological outcomes.

Discovery of 5,5'-Methylenedi-2,3-Cresotic Acid as a Potent Inhibitor of the Chemotactic Activity of the HMGB1·CXCL12 Heterocomplex Using Virtual Screening and NMR Validation

HMGB1 is a key molecule that both triggers and sustains inflammation following infection or injury, and is involved in a large number of pathologies, including cancer. HMGB1 participates in the recruitment of inflammatory cells, forming a heterocomplex with the chemokine CXCL12 (HMGB1·CXCL12), thereby activating the G-protein coupled receptor CXCR4. Thus, identification of molecules that disrupt this heterocomplex can offer novel pharmacological opportunities to treat inflammation-related diseases. To identify new HMGB1·CXCL12 inhibitors we have performed a study on the ligandability of the single HMG boxes of HMGB1 followed by a virtual screening campaign on both HMG boxes using Zbc Drugs and three different docking programs (Glide, AutoDock Vina, and AutoDock 4.2.6). The best poses in terms of scoring functions, visual inspection, and predicted ADME properties were further filtered according to a pharmacophore model based on known HMGB1 binders and clustered according to their structures. Eight compounds representative of the clusters were tested for HMGB1 binding by NMR. We identified 5,5'-methylenedi-2,3-cresotic acid (2a) as a binder of both HMGB1 and CXCL12; 2a also targets the HMGB1·CXCL12 heterocomplex. In cell migration assays 2a inhibited the chemotactic activity of HMGB1·CXCL12 with IC50 in the subnanomolar range, the best documented up to now. These results pave the way for future structure activity relationship studies to optimize the pharmacological targeting of HMGB1·CXCL12 for anti-inflammatory purposes.

TEAD-YAP Interaction Inhibitors and MDM2 Binders from DNA-Encoded Indole-Focused Ugi Peptidomimetics

DNA-encoded combinatorial synthesis provides efficient and dense coverage of chemical space around privileged molecular structures. The indole side chain of tryptophan plays a prominent role in key, or "hot spot", regions of protein-protein interactions. A DNA-encoded combinatorial peptoid library was designed based on the Ugi four-component reaction by employing tryptophan-mimetic indole side chains to probe the surface of target proteins. Several peptoids were synthesized on a chemically stable hexathymidine adapter oligonucleotide "hexT", encoded by DNA sequences, and substituted by azide-alkyne cycloaddition to yield a library of 8112 molecules. Selection experiments for the tumor-relevant proteins MDM2 and TEAD4 yielded MDM2 binders and a novel class of TEAD-YAP interaction inhibitors that perturbed the expression of a gene under the control of these Hippo pathway effectors.

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.

In-silico design of a potential inhibitor of SARS-CoV-2 S protein

The SARS-CoV-2 virus has caused a pandemic and is public health emergency of international concern. As of now, no registered therapies are available for treatment of coronavirus infection. The viral infection depends on the attachment of spike (S) glycoprotein to human cell receptor angiotensin-converting enzyme 2 (ACE2). We have designed a protein inhibitor (ΔABP-D25Y) targeting S protein using computational approach. The inhibitor consists of two α helical peptides homologues to protease domain (PD) of ACE2. Docking studies and molecular dynamic simulation revealed that the inhibitor binds exclusively at the ACE2 binding site of S protein. The computed binding affinity of the inhibitor is higher than the ACE2 and thus will likely out compete ACE2 for binding to S protein. Hence, the proposed inhibitor ΔABP-D25Y could be a potential blocker of S protein and receptor binding domain (RBD) attachment.

Application of Fragment-Based Drug Discovery to Versatile Targets

Fragment-based drug discovery (FBDD) is a powerful method to develop potent small-molecule compounds starting from fragments binding weakly to targets. As FBDD exhibits several advantages over high-throughput screening campaigns, it becomes an attractive strategy in target-based drug discovery. Many potent compounds/inhibitors of diverse targets have been developed using this approach. Methods used in fragment screening and understanding fragment-binding modes are critical in FBDD. This review elucidates fragment libraries, methods utilized in fragment identification/confirmation, strategies applied in growing the identified fragments into drug-like lead compounds, and applications of FBDD to different targets. As FBDD can be readily carried out through different biophysical and computer-based methods, it will play more important roles in drug discovery.

Refinement of pairwise potentials via logistic regression to score protein-protein interactions

Protein-protein interactions (PPIs) are ubiquitous and functionally of great importance in biological systems. Hence, the accurate prediction of PPIs by protein-protein docking and scoring tools is highly desirable in order to characterize their structure and biological function. Ab initio docking protocols are divided into the sampling of docking poses to produce at least one near-native structure, and then to evaluate the vast candidate structures by scoring. Concurrent development in both sampling and scoring is crucial for the deployment of protein-protein docking software. In the present work, we apply a machine learning model on pairwise potentials to refine the task of protein quaternary structure native structure detection among decoys. A decoy set was featurized using the Knowledge and Empirical Combined Scoring Algorithm 2 (KECSA2) pairwise potential. The highly unbalanced decoy set was then balanced using a comparison concept between native and decoy structures. The resultant comparison descriptors were used to train a logistic regression (LR) classifier. The LR model yielded the optimal performance for native detection among decoys compared with conventional scoring functions, while exhibiting lesser performance for the detection of low root mean square deviation decoy structures. Its deployment on an independent benchmark set confirms that the scoring function performs competitively relative to other scoring functions. The scripts used are available at https://github.com/TanemuraKiyoto/PPI-native-detection-via-LR.

Discovery of Protein-Protein Interaction Inhibitors by Integrating Protein Engineering and Chemical Screening Platforms

Protein-protein interactions (PPIs) govern intracellular life, and identification of PPI inhibitors is challenging. Roadblocks in assay development stemming from weak binding affinities of natural PPIs impede progress in this field. We postulated that enhancing binding affinity of natural PPIs via protein engineering will aid assay development and hit discovery. This proof-of-principle study targets PPI between linear ubiquitin chains and NEMO UBAN domain, which activates NF-κB signaling. Using phage display, we generated ubiquitin variants that bind to the functional UBAN epitope with high affinity, act as competitive inhibitors, and structurally maintain the existing PPI interface. When utilized in assay development, variants enable generation of robust cell-based assays for chemical screening. Top compounds identified using this approach directly bind to UBAN and dampen NF-κB signaling. This study illustrates advantages of integrating protein engineering and chemical screening in hit identification, a development that we anticipate will have wide application in drug discovery.

Critical protein-protein interactions within the CARMA1-BCL10-MALT1 complex: Take-home points for the cell biologist

The CBM complex, which is composed of the proteins CARMA1, BCL10, and MALT1, serves multiple pivotal roles as a mediator of T-cell receptor and B-cell receptor-dependent NF-κB induction and lymphocyte activation. CARMA1, BCL10, and MALT1 are each proto-oncoproteins and dysregulation of CBM signaling, as a result of somatic gain-of-function mutation or chromosomal translocation, is a hallmark of multiple lymphoid malignancies including Activated B-cell Diffuse Large B-cell Lymphoma. Moreover, loss-of-function as well as gain-of-function germline mutations in CBM complex proteins have been associated with a range of immune dysregulation syndromes. A wealth of detailed structural information has become available over the past decade through meticulous interrogation of the interactions between CBM components. Here, we review key findings regarding the biochemical nature of these protein-protein interactions which have ultimately led the field to a sophisticated understanding of how these proteins assemble into high-order filamentous CBM complexes. To date, approaches to therapeutic inhibition of the CBM complex for the treatment of lymphoid malignancy and/or auto-immunity have focused on blocking MALT1 protease function. We also review key studies relating to the structural impact of MALT1 protease inhibitors on key protein-protein interactions.

Methodologies for Backbone Macrocyclic Peptide Synthesis Compatible With Screening Technologies

Backbone macrocyclic structures are often found in diverse bioactive peptides and contribute to greater conformational rigidity, peptidase resistance, and potential membrane permeability compared to their linear counterparts. Therefore, such peptide scaffolds are an attractive platform for drug-discovery endeavors. Recent advances in synthetic methods for backbone macrocyclic peptides have enabled the discovery of novel peptide drug candidates against diverse targets. Here, we overview recent technical advancements in the synthetic methods including 1) enzymatic synthesis, 2) chemical synthesis, 3) split-intein circular ligation of peptides and proteins (SICLOPPS), and 4) in vitro translation system combined with genetic code reprogramming. We also discuss screening methodologies compatible with those synthetic methodologies, such as one-beads one-compound (OBOC) screening compatible with the synthetic method 2, cell-based assay compatible with 3, limiting-dilution PCR and mRNA display compatible with 4.

Small molecule inhibits T-cell acute lymphoblastic leukaemia oncogenic interaction through conformational modulation of LMO2

Ectopic expression in T-cell precursors of LIM only protein 2 (LMO2), a key factor in hematopoietic development, has been linked to the onset of T-cell acute lymphoblastic leukaemia (T-ALL). In the T-ALL context, LMO2 drives oncogenic progression through binding to erythroid-specific transcription factor SCL/TAL1 and sequestration of E-protein transcription factors, normally required for T-cell differentiation. A key requirement for the formation of this oncogenic protein-protein interaction (PPI) is the conformational flexibility of LMO2. Here we identify a small molecule inhibitor of the SCL-LMO2 PPI, which hinders the interaction in vitro through direct binding to LMO2. Biophysical analysis demonstrates that this inhibitor acts through a mechanism of conformational modulation of LMO2. Importantly, this work has led to the identification of a small molecule inhibitor of the SCL-LMO2 PPI, which can provide a starting point for the development of new agents for the treatment of T-ALL. These results suggest that similar approaches, based on the modulation of protein conformation by small molecules, might be used for therapeutic targeting of other oncogenic PPIs.

A high-affinity, bivalent PDZ domain inhibitor complexes PICK1 to alleviate neuropathic pain

Maladaptive plasticity involving increased expression of AMPA-type glutamate receptors is involved in several pathologies, including neuropathic pain, but direct inhibition of AMPARs is associated with side effects. As an alternative, we developed a cell-permeable, high-affinity (~2 nM) peptide inhibitor, Tat-P₄ -(C5)₂ , of the PDZ domain protein PICK1 to interfere with increased AMPAR expression. The affinity is obtained partly from the Tat peptide and partly from the bivalency of the PDZ motif, engaging PDZ domains from two separate PICK1 dimers to form a tetrameric complex. Bivalent Tat-P₄ -(C5)₂ disrupts PICK1 interaction with membrane proteins on supported cell membrane sheets and reduce the interaction of AMPARs with PICK1 and AMPA-receptor surface expression in vivo. Moreover, Tat-P₄ -(C5)₂ administration reduces spinal cord transmission and alleviates mechanical hyperalgesia in the spared nerve injury model of neuropathic pain. Taken together, our data reveal Tat-P₄ -(C5)₂ as a novel promising lead for neuropathic pain treatment and expand the therapeutic potential of bivalent inhibitors to non-tandem protein-protein interaction domains.

PROM1 and PROM2 expression differentially modulates clinical prognosis of cancer: a multiomics analysis

Prominin 1 (PROM1) is considered a biomarker for cancer stem cells, although its biological role is unclear. Prominin 2 (PROM2) has also been associated with certain cancers. However, the prognostic value of PROM1 and PROM2 in cancer is controversial. Here, we performed a systematic data analysis to examine whether prominins can function as prognostic markers in human cancers. The expression of prominins was assessed and their prognostic value in human cancers was determined using univariate and multivariate survival analyses, via various online platforms. We selected a group of prominent functional protein partners of prominins by protein-protein interaction analysis. Subsequently, we investigated the relationship between mutations and copy number alterations in prominin genes and various types of cancers. Furthermore, we identified genes that correlated with PROM1 and PROM2 in certain cancers, based on their levels of expression. Gene ontology and pathway analyses were performed to assess the effect of these correlated genes on various cancers. We observed that PROM1 was frequently overexpressed in esophageal, liver, and ovarian cancers and its expression was negatively associated with prognosis, whereas PROM2 overexpression was associated with poor overall survival in lung and ovarian cancers. Based on the varying characteristics of prominins, we conclude that PROM1 and PROM2 expression differentially modulates the clinical outcomes of cancers.

Molecular Simulation of Oncostatin M and Receptor (OSM-OSMR) Interaction as a Potential Therapeutic Target for Inflammatory Bowel Disease

Therapeutics targeting cytokines such as the oncostatin M (OSM)-mediated inflammation represent a potential strategy for the treatment of inflammatory bowel disease (IBD). Despite the investigation of the specific role of the interactions between OSM and the receptor (OSMR) in IBD pathogenesis, the 3D structure of the OSM–OSMR complex remains elusive. In this work, the interaction mode between OSM and OSMR at atomic level was predicted by computational simulation approach. The interaction domain of the OSMR was built with the homology modeling method. The near-native structure of the OSM–OSMR complex was obtained by docking, and long-time scale molecular dynamics (MD) simulation in an explicit solvent was further performed to sample the conformations when OSM binds to the OSMR. After getting the equilibrated states of the simulation system, per-residue energy contribution was calculated to characterize the important residues for the OSM–OSMR complex formation. Based on these important residues, eight residues (OSM: Arg100, Leu103, Phe160, and Gln161; OSMR: Tyr214, Ser223, Asp262, and Trp267) were identified as the “hot spots” through computational alanine mutagenesis analysis and verified by additional MD simulation of R100A (one of the identified “hotspots”) mutant. Moreover, six cavities were detected at the OSM–OSMR interface through the FTMap analysis, and they were suggested as important binding sites. The predicted 3D structure of the OSM–OSMR complex and the identified “hot spots” constituting the core of the binding interface provide helpful information in understanding the OSM–OSMR interactions, and the detected sites serve as promising targets in designing small molecules to block the interactions.

Overcoming cisplatin resistance by targeting the MTDH-PTEN interaction in ovarian cancer with sera derived from rats exposed to Guizhi Fuling wan extract

Background

The well-known traditional Chinese herbal formula Guizhi Fuling Wan (GFW) was recently reported to improve the curative effects of chemotherapy for ovarian cancer with few clinical side effects. The present study aimed to investigate the reversal mechanism of sera derived from rats exposed to Guizhi Fuling Wan extract (GFWE) in cisplatin-resistant human ovarian cancer SKOV3/DDP cells; the proteins examined included phosphatase and tensin homolog (PTEN) and metadherin (MTDH), and the possible protein interaction between PTEN and MTDH was explored.

Methods

GFWE was administered to healthy Wistar rats, and the sera were collected after five days. The PubMed and CNKI databases were searched for literature on the bioactive blood components in the sera. The systemsDock website was used to predict potential PTEN/MTDH interactions with the compounds. RT-qPCR, western blotting, and immunofluorescence analyses were used to analyze the mRNA and protein levels of MTDH and PTEN. Laser confocal microscopy and coimmunoprecipitation (co-IP) were used to analyze the colocalization and interaction between MTDH and PTEN.

Results

Sixteen bioactive compounds were identified in GFWE sera after searching the PubMed and CNKI databases. The systemsDock website predicted the potential PTEN/MTDH interactions with the compounds. RT-qPCR, western blotting, and immunofluorescence analyses showed decreased MTDH expression and increased PTEN expression in the sera. Laser confocal microscopy images and coimmunoprecipitation (co-IP) analyses demonstrated that a colocalization and interaction occurred between MTDH and PTEN, and the addition of the sera changed the interaction status.

Conclusions

GFWE restored sensitivity to cisplatin by inhibiting MTDH expression, inducing PTEN expression, and improving the interaction between MTDH and PTEN in SKOV3/DDP cells, and these proteins and their interaction may serve as potential targets for cancer treatment. The sera may represent a new source of anticancer compounds that could help to manage chemoresistance more efficiently and safely.

Design, synthesis, and biological evaluation of novel substituted thiourea derivatives as potential anticancer agents for NSCLC by blocking K-Ras protein-effectors interactions

Mutation of the proto-oncogene K-Ras is one of the most common molecular mechanisms in non-small cell lung cancer. Many drugs for treating lung cancer have been developed, however, due to clinical observed K-Ras mutations, corresponding chemotherapy and targeted therapy for such mutation are not efficient enough. In this study, on the basis of the crystal structure of K-Ras, 21 analogues (TKR01–TKR21) containing urea or thiourea were rationally designed, which can effectively inhibit the lung cancer cell A549 growth. The designing of these compounds was based on the structure of K-Ras protein, and the related groups were replaced by bioisosteres to improve the affinity and selectivity. Biological testing revealed that compound TKR15 could significantly inhibit the proliferation of A549 cell with IC₅₀ of 0.21 µM. Docking analysis showed that the TKR15 can effectively bind to the hydrophobic cavity and form a hydrogen bond with the Glu37. In addition, through flow apoptosis assay and immunofluorescence staining assay, it confirmed that this compound can inhibit A549 cell proliferation with the mechanism of blocking K-Ras^G12V protein and effector proteins interactions through the apoptotic pathway. In conclusion, our studies in finding novel potent compound (TKR15) with confirmed mechanism showed great potential for further optimisation and other medicinal chemistry relevant studies.

Outwitting an Old Neglected Nemesis: A Review on Leveraging Integrated Data-Driven Approaches to Aid in Unraveling of Leishmanicides of Therapeutic Potential

The global prevalence of leishmaniasis has increased with skyrocketed mortality in the past decade. The causative agent of leishmaniasis is Leishmania species, which infects populations in almost all the continents. Prevailing treatment regimens are consistently inefficient with reported side effects, toxicity and drug resistance. This review complements existing ones by discussing the current state of treatment options, therapeutic bottlenecks including chemoresistance and toxicity, as well as drug targets. It further highlights innovative applications of nanotherapeutics-based formulations, inhibitory potential of leishmanicides, anti-microbial peptides and organometallic compounds on leishmanial species. Moreover, it provides essential insights into recent machine learning-based models that have been used to predict novel leishmanicides and also discusses other new models that could be adopted to develop fast, efficient, robust and novel algorithms to aid in unraveling the next generation of anti-leishmanial drugs. A plethora of enriched functional genomic, proteomic, structural biology, high throughput bioassay and drug-related datasets are currently warehoused in both general and leishmania-specific databases. The warehoused datasets are essential inputs for training and testing algorithms to augment the prediction of biotherapeutic entities. In addition, we demonstrate how pharmacoinformatics techniques including ligand-, structure- and pharmacophore-based virtual screening approaches have been utilized to screen ligand libraries against both modeled and experimentally solved 3D structures of essential drug targets. In the era of data-driven decision-making, we believe that highlighting intricately linked topical issues relevant to leishmanial drug discovery offers a one-stop-shop opportunity to decipher critical literature with the potential to unlock implicit breakthroughs.

A Multi-Omics Analysis of Bone Morphogenetic Protein 5 (BMP5) mRNA Expression and Clinical Prognostic Outcomes in Different Cancers Using Bioinformatics Approaches

Cumulative studies have provided controversial evidence for the prognostic values of bone morphogenetic protein 5 (BMP5) in different types of cancers such as colon, breast, lung, bladder, and ovarian cancer. To address the inconsistent correlation of BMP5 expression with patient survival and molecular function of BMP5 in relation to cancer progression, we performed a systematic study to determine whether BMP5 could be used as a prognostic marker in human cancers. BMP5 expression and prognostic values were assessed using different bioinformatics tools such as ONCOMINE, GENT, TCGA, GEPIA, UALCAN, PrognoScan, PROGgene V2 server, and Kaplan-Meier Plotter. In addition, we used cBioPortal database for the identification and analysis of BMP5 mutations, copy number alterations, altered expression, and protein-protein interaction (PPI). We found that BMP5 is frequently down-regulated in our queried cancer types. Use of prognostic analysis showed negative association of BMP5 down-regulation with four types of cancer except for ovarian cancer. The highest mutation was found in the R321*/Q amino acid of BMP5 corresponding to colorectal and breast cancer whereas the alteration frequency was higher in lung squamous carcinoma datasets (>4%). In PPI analysis, we found 31 protein partners of BMP5, among which 11 showed significant co-expression (p-value < 0.001, log odds ratio > 1). Pathway analysis of differentially co-expressed genes with BMP5 in breast, lung, colon, bladder and ovarian cancers revealed the BMP5-correlated pathways. Collectively, this data-driven study demonstrates the correlation of BMP5 expression with patient survival and identifies the involvement of BMP5 pathways that may serve as targets of a novel biomarker for various types of cancers in human.

Development of a Novel Cell-Permeable Protein-Protein Interaction Inhibitor for the Polo-box Domain of Polo-like Kinase 1

Polo-like kinase 1 (PLK1) is a key regulator of mitosis and a recognized drug target for cancer therapy. Inhibiting the polo-box domain of PLK1 offers potential advantages of increased selectivity and subsequently reduced toxicity compared with targeting the kinase domain. However, many if not all existing polo-box domain inhibitors have been shown to be unsuitable for further development. In this paper, we describe a novel compound series, which inhibits the protein-protein interactions of PLK1 via the polo-box domain. We combine high throughput screening with molecular modeling and computer-aided design, synthetic chemistry, and cell biology to address some of the common problems with protein-protein interaction inhibitors, such as solubility and potency. We use molecular modeling to improve the solubility of a hit series with initially poor physicochemical properties, enabling biophysical and biochemical characterization. We isolate and characterize enantiomers to improve potency and demonstrate on-target activity in both cell-free and cell-based assays, entirely consistent with the proposed binding model. The resulting compound series represents a promising starting point for further progression along the drug discovery pipeline and a new tool compound to study kinase-independent PLK functions.