Identification of hub genes and therapeutic siRNAs to develop novel adjunctive therapy for Duchenne muscular dystrophy

OBJECTIVE

Duchenne muscular dystrophy (DMD) is a devastating X-linked neuromuscular disorder caused by various defects in the dystrophin gene and still no universal therapy. This study aims to identify the hub genes unrelated to excessive immune response but responsible for DMD progression and explore therapeutic siRNAs, thereby providing a novel treatment.

METHODS

Top ten hub genes for DMD were identified from GSE38417 dataset by using GEO2R and PPI networks based on Cytoscape analysis. The hub genes unrelated to excessive immune response were identified by GeneCards, and their expression was further verified in mdx and C57 mice at 2 and 4 months (M) by (RT-q) PCR and western blotting. Therapeutic siRNAs were deemed as those that could normalize the expression of the validated hub genes in transfected C2C12 cells.

RESULTS

855 up-regulated and 324 down-regulated DEGs were screened from GSE38417 dataset. Five of the top 10 hub genes were considered as the candidate genes unrelated to excessive immune response, and three of these candidates were consistently and significantly up-regulated in mdx mice at 2 M and 4 M when compared with age-matched C57 mice, including Col1a2, Fbn1 and Fn1. Furthermore, the three validated up-regulated candidate genes can be significantly down-regulated by three rational designed siRNA (p < 0.0001), respectively.

CONCLUSION

COL1A2, FBN1 and FN1 may be novel biomarkers for DMD, and the siRNAs designed in our study were help to develop adjunctive therapy for Duchenne muscular dystrophy.

Scaffold Matcher: A CMA-ES based algorithm for identifying hotspot aligned peptidomimetic scaffolds

The design of protein interaction inhibitors is a promising approach to address aberrant protein interactions that cause disease. One strategy in designing inhibitors is to use peptidomimetic scaffolds that mimic the natural interaction interface. A central challenge in using peptidomimetics as protein interaction inhibitors, however, is determining how best the molecular scaffold aligns to the residues of the interface it is attempting to mimic. Here we present the Scaffold Matcher algorithm that aligns a given molecular scaffold onto hotspot residues from a protein interaction interface. To optimize the degrees of freedom of the molecular scaffold we implement the covariance matrix adaptation evolution strategy (CMA-ES), a state-of-the-art derivative-free optimization algorithm in Rosetta. To evaluate the performance of the CMA-ES, we used 26 peptides from the FlexPepDock Benchmark and compared with three other algorithms in Rosetta, specifically, Rosetta's default minimizer, a Monte Carlo protocol of small backbone perturbations, and a Genetic algorithm. We test the algorithms' performance on their ability to align a molecular scaffold to a series of hotspot residues (i.e., constraints) along native peptides. Of the 4 methods, CMA-ES was able to find the lowest energy conformation for all 26 benchmark peptides. Additionally, as a proof of concept, we apply the Scaffold Match algorithm with CMA-ES to align a peptidomimetic oligooxopiperazine scaffold to the hotspot residues of the substrate of the main protease of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Our implementation of CMA-ES into Rosetta allows for an alternative optimization method to be used on macromolecular modeling problems with rough energy landscapes. Finally, our Scaffold Matcher algorithm allows for the identification of initial conformations of interaction inhibitors that can be further designed and optimized as high-affinity reagents.

In vivo mapping of protein-protein interactions of schizophrenia risk factors generates an interconnected disease network

Genetic analyses of Schizophrenia (SCZ) patients have identified thousands of risk factors. In silico protein-protein interaction (PPI) network analysis has provided strong evidence that disrupted PPI networks underlie SCZ pathogenesis. In this study, we performed in vivo PPI analysis of several SCZ risk factors in the rodent brain. Using endogenous antibody immunoprecipitations coupled to mass spectrometry (MS) analysis, we constructed a SCZ network comprising 1612 unique PPI with a 5% FDR. Over 90% of the PPI were novel, reflecting the lack of previous PPI MS studies in brain tissue. Our SCZ PPI network was enriched with known SCZ risk factors, which supports the hypothesis that an accumulation of disturbances in selected PPI networks underlies SCZ. We used Stable Isotope Labeling in Mammals (SILAM) to quantitate phencyclidine (PCP) perturbations in the SCZ network and found that PCP weakened most PPI but also led to some enhanced or new PPI. These findings demonstrate that quantitating PPI in perturbed biological states can reveal alterations to network biology.

Jagged1 intracellular domain/SMAD3 complex transcriptionally regulates TWIST1 to drive glioma invasion

Jagged1 (JAG1) is a Notch ligand that correlates with tumor progression. Not limited to its function as a ligand, JAG1 can be cleaved, and its intracellular domain translocates to the nucleus, where it functions as a transcriptional cofactor. Previously, we showed that JAG1 intracellular domain (JICD1) forms a protein complex with DDX17/SMAD3/TGIF2. However, the molecular mechanisms underlying JICD1-mediated tumor aggressiveness remains unclear. Here, we demonstrate that JICD1 enhances the invasive phenotypes of glioblastoma cells by transcriptionally activating epithelial-to-mesenchymal transition (EMT)-related genes, especially TWIST1. The inhibition of TWIST1 reduced JICD1-driven tumor aggressiveness. Although SMAD3 is an important component of transforming growth factor (TGF)-β signaling, the JICD1/SMAD3 transcriptional complex was shown to govern brain tumor invasion independent of TGF-β signaling. Moreover, JICD1-TWIST1-MMP2 and MMP9 axes were significantly correlated with clinical outcome of glioblastoma patients. Collectively, we identified the JICD1/SMAD3-TWIST1 axis as a novel inducer of invasive phenotypes in cancer cells.

Annexin A2 Stabilizes Oncogenic JAG1 Intracellular Domain by Inhibiting Proteasomal Degradation in Glioblastoma Cells

Glioblastoma (GBM) is the most lethal brain cancer, causing inevitable deaths of patients owing to frequent relapses of cancer stem cells (CSCs). The significance of the NOTCH signaling pathway in CSCs has been well recognized; however, there is no NOTCH-selective treatment applicable to patients with GBM. We recently reported that Jagged1 (JAG1), a NOTCH ligand, drives a NOTCH receptor-independent signaling pathway via JAG1 intracellular domain (JICD1) as a crucial signal that renders CSC properties. Therefore, mechanisms regulating the JICD1 signaling pathway should be elucidated to further develop a selective therapeutic regimen. Here, we identified annexin A2 (ANXA2) as an essential modulator to stabilize intrinsically disordered JICD1. The binding of ANXA2 to JICD1 prevents the proteasomal degradation of JICD1 by heat shock protein-70/90 and carboxy-terminus of Hsc70 interacting protein E3 ligase. Furthermore, JICD1-driven propagation and tumor aggressiveness were inhibited by ANXA2 knockdown. Taken together, our findings show that ANXA2 maintains the function of the NOTCH receptor-independent JICD1 signaling pathway by stabilizing JICD1, and the targeted suppression of JICD1-driven CSC properties can be achieved by blocking its interaction with ANXA2.

Gibbs Energy and Gene Expression Combined as a New Technique for Selecting Drug Targets for Inhibiting Specific Protein-Protein Interactions

One of the most important aspects of successful cancer therapy is the identification of a target protein for inhibition interaction. Conventionally, this consists of screening a panel of genes to assess which is mutated and then developing a small molecule to inhibit the interaction of two proteins or to simply inhibit a specific protein from all interactions. In previous work, we have proposed computational methods that analyze protein-protein networks using both topological approaches and thermodynamic quantification provided by Gibbs free energy. In order to make these approaches both easier to implement and free of arbitrary topological filtration criteria, in the present paper, we propose a modification of the topological-thermodynamic analysis, which focuses on the selection of the most thermodynamically stable proteins and their subnetwork interaction partners with the highest expression levels. We illustrate the implementation of the new approach with two specific cases, glioblastoma (glioma brain tumors) and chronic lymphatic leukoma (CLL), based on the publicly available patient-derived datasets. We also discuss how this can be used in clinical practice in connection with the availability of approved and investigational drugs.

Prophylactic Mitigation of Acute Graft versus Host Disease by Novel 2-(Pyrrolidin-1-ylmethyl)pyrrole-Based Stimulation-2 (ST2) Inhibitors

Hematopoietic cell transplantation (HCT) is a proven and potentially curable therapy for hematological malignancies and inherited hematological disease. The main risk of HCT is the development of graft versus host disease (GVHD) acquired in up to 50% of patients. Upregulation of soluble ST2 (sST2) is a key clinical biomarker for GVHD prognosis and was shown to be a potential therapeutic target for GVHD. Agents targeting sST2 to reduce the sST2 level after HCT have the potential to mitigate GVHD progression. Here, we report 32 (or XY52) as the lead ST2 inhibitor from our optimization campaign. XY52 had improved inhibitory activity and metabolic stability in vitro and in vivo. XY52 suppressed proinflammatory T-cell proliferation while increasing regulatory T cells in vitro. In a clinically relevant GVHD model, a 21-day prophylactic regimen of XY52 reduced plasma sST2 and IFN-γ levels and GVHD score and extended survival in mice. XY52 represented a significant improvement over our previous compound, iST2-1, and further optimization of XY52 is warranted. The small-molecule ST2 inhibitors can potentially be used as a biomarker-guided therapy for mitigating GVHD in future clinical applications.

Metallic micro-ring device for highly efficient large cargo delivery in mammalian cells using infrared light pulses

Uniform transfection of biomolecules into live cells with high delivery efficiency and cell viability is an immensely important area of biological research and has many biomedical applications. In the present study, we report highly efficient, uniform parallel intracellular delivery of small to very large biomolecules into diverse cell types using a titanium micro-ring (TMR) device activated by infrared (IR) light pulse. A TMR array device (2 cm × 2 cm) consists of a 10 μm outer diameter and 3 μm inner diameter for each micro-ring, and 10 μm interspacing between two micro-rings. Upon IR (1050 nm) pulse laser irradiation on the TMR device, photothermal cavitation bubbles are generated, disrupting the cell plasma membrane, and biomolecules are gently delivered into the cells by a simple diffusion process. This TMR device successfully delivered diverse types of small to very large biomolecules such as propidium iodide (PI; 668.4 Da) dye, dextran (3 kDa), small interfering RNA (13.3 kDa), enhanced green fluorescent protein expression plasmid DNA (6.2 kb), and β-galactosidase enzyme (465 kDa) into human cervical (SiHa), mouse fibroblast (L929), and mouse neural crest-derived (N2a) cancer cells. For smaller molecules (PI dye), delivery efficiency and cell viability were achieved at ∼96% and ∼97%, respectively, with a laser fluence of 21 mJ cm^-2 for 250 pulses. In contrast, ∼85% transfection efficiency and ∼90% cell viability were achieved for plasmid DNA with 45 mJ cm^-2 laser fluence for 250 pulses in SiHa cells. Moreover, the intracellular delivery of β-galactosidase enzyme was confirmed with confocal microscopy and flow cytometry analysis resulting in ∼83% co-staining of β-galactosidase enzyme and calcein AM. Based on these efficient deliveries of diverse types of biomolecules in different cell types, the device has the potential for cellular diagnostic and therapeutic applications.

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.

Bioinformatics and System Biology Techniques to Determine Biomolecular Signatures and Pathways of Prion Disorder

Prion disorder (PD) is caused by misfolding and the formation of clumps of proteins in the brain, notably Prion proteins resulting in a steady decrease in brain function. Early detection of PD is difficult due to its unpredictable nature, and diagnosis is limited regarding specificity and sensitivity. Considering the uncertainties, the current study used network-based integrative system biology approaches to reveal promising molecular biomarkers and therapeutic targets for PD. In this study, brain transcriptomics gene expression microarray datasets (GSE160208 and GSE124571) of human PD were evaluated and 35 differentially expressed genes (DEGs) were identified. By employing network-based protein-protein interaction (PPI) analysis on these DEGs, 10 central hub proteins, including SPP1, FKBP5, HPRT1, CDKN1A, BAG3, HSPB1, SYK, TNFRSF1A, PTPN6, and CD44, were identified. Employing bioinformatics approaches, a variety of transcription factors (EGR1, SSRP1, POLR2A, TARDP, and NR2F1) and miRNAs (hsa-mir-8485, hsa-mir-148b-3p, hsa-mir-4295, hsa-mir-26b-5p, and hsa-mir-16-5p) were predicted. EGR1 was found as the most imperative transcription factor (TF), and hsa-mir-16-5p and hsa-mir-148b-3p were found as the most crucial miRNAs targeted in PD. Finally, resveratrol and hypochlorous acid were predicted as possible therapeutic drugs for PD. This study could be helpful in better understanding of molecular systems and prospective pharmacological targets for developing effective PD treatments.

Structure-Activity relationship of 1-(Furan-2ylmethyl)Pyrrolidine-Based Stimulation-2 (ST2) inhibitors for treating graft versus host disease

An elevated plasma level of soluble ST2 (sST2) is a risk biomarker for graft-versus-host disease (GVHD) and death in patients receiving hematopoietic cell transplantation (HCT). sST2 functions as a trap for IL-33 and amplifies the pro-inflammatory type 1 and 17 response while suppressing the tolerogenic type 2 and regulatory T cells activation during GVHD development. We previously identified small-molecule ST2 inhibitors particularly iST2-1 that reduces plasma sST2 levels and improved survival in two animal models. Here, we reported the structure-activity relationship of the furanylmethylpyrrolidine-based ST2 inhibitors based on iST2-1. Based on the biochemical AlphaLISA assay, we improved the activity of iST2-1 by 6-fold (∼6 μM in IC50 values) in the inhibition of ST2/IL-33 and confirmed the activities of the compounds in a cellular reporter assay. To determine the inhibition of the alloreactivity in vitro, we used the mixed lymphocyte reaction assay to demonstrate that our ST2 inhibitors decreased CD4+ and CD8+ T cells proliferation and increased Treg population. The data presented in this work are critical to the development of ST2 inhibitors in future.

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.

Adaptor-Specific Antibody Fragment Inhibitors for the Intracellular Modulation of p97 (VCP) Protein-Protein Interactions

Protein-protein interactions (PPIs) form complex networks to drive cellular signaling and cellular functions. Precise modulation of a target PPI helps explain the role of the PPI in cellular events and possesses therapeutic potential. For example, valosin-containing protein (VCP/p97) is a hub protein that interacts with more than 30 adaptor proteins involved in various cellular functions. However, the role of each p97 PPI during the relevant cellular event is underexplored. The development of small-molecule PPI modulators remains challenging due to a lack of grooves and pockets in the relatively large PPI interface and the fact that a common binding groove in p97 binds to multiple adaptors. Here, we report an antibody fragment-based modulator for the PPI between p97 and its adaptor protein NSFL1C (p47). We engineered these antibody modulators by phage display against the p97-interacting domain of p47 and minimizing binding to other p97 adaptors. The selected antibody fragment modulators specifically disrupt the intracellular p97/p47 interaction. The potential of this antibody platform to develop PPI inhibitors in therapeutic applications was demonstrated through the inhibition of Golgi reassembly, which requires the p97/p47 interaction. This study presents a unique approach to modulate specific intracellular PPIs using engineered antibody fragments, demonstrating a method to dissect the function of a PPI within a convoluted PPI network.

Disclosing the antitumour potential of the marine bromoditerpene sphaerococcenol A on distinct cancer cellular models

Nature has revealed to be a key source of innovative anticancer drugs. This study evaluated the antitumour potential of the marine bromoditerpene sphaerococcenol A on different cancer cellular models. Dose-response analyses (0.1-100 µM; 24 h) were accomplished in eight different tumour cell lines (A549, CACO-2, HCT-15, MCF-7, NCI-H226, PC-3, SH-SY5Y, SK-MEL-28). Deeper studies were conducted on MFC-7 cells, namely, determination of hydrogen peroxide (H₂O₂) levels and evaluation of apoptosis biomarkers (phosphatidylserine membrane translocation, mitochondrial dysfunction, Caspase-9 activity, and DNA changes). The ability of the compound to induce genotoxicity was verified in L929 fibroblasts. Sphaerococcenol A capacity to impact colorectal-cancer stem cells (CSCs) tumourspheres (HT29, HCT116, SW620) was evaluated by determining tumourspheres viability, number, and area, as well as the proteasome inhibitory activity. Sphaerococcenol A hepatoxicity was studied in AML12 hepatocytes. The compound exhibited cytotoxicity in all malignant cell lines (IC₅₀ ranging from 4.5 to 16.6 µM). MCF-7 cells viability loss was accompanied by H₂O₂ generation, mitochondrial dysfunction, Caspase-9 activation and DNA nuclear morphology changes. Furthermore, the compound displayed the lowest IC₅₀ on HT29-derived tumourspheres (0.70 µM), followed by HCT116 (1.77 µM) and SW620 (2.74 µM), impacting the HT29 tumoursphere formation by reducing their number and area. Finally, the compound displayed low cytotoxicity on AML12 hepatocytes without genotoxicity. Overall, sphaerococcenol A exhibits broad cytotoxic effects on different tumour cells, increasing H₂O₂ production and apoptosis. It also affects colorectal CSCs-enriched tumoursphere development. These data highlight the relevance to include sphaerococcenol A in further pharmacological studies aiming cancer treatments.

Unprotected peptide macrocyclization and stapling via a fluorine-thiol displacement reaction

We report the discovery of a facile peptide macrocyclization and stapling strategy based on a fluorine thiol displacement reaction (FTDR), which renders a class of peptide analogues with enhanced stability, affinity, cellular uptake, and inhibition of cancer cells. This approach enabled selective modification of the orthogonal fluoroacetamide side chains in unprotected peptides in the presence of intrinsic cysteines. The identified benzenedimethanethiol linker greatly promoted the alpha helicity of a variety of peptide substrates, as corroborated by molecular dynamics simulations. The cellular uptake of benzenedimethanethiol stapled peptides appeared to be universally enhanced compared to the classic ring-closing metathesis (RCM) stapled peptides. Pilot mechanism studies suggested that the uptake of FTDR-stapled peptides may involve multiple endocytosis pathways in a distinct pattern in comparison to peptides stapled by RCM. Consistent with the improved cell permeability, the FTDR-stapled lead Axin and p53 peptide analogues demonstrated enhanced inhibition of cancer cells over the RCM-stapled analogues and the unstapled peptides.

Strategies capable of stapling unprotected peptides in a straightforward, chemoselective, and clean manner, as well as promoting cellular uptake are of great interest. Here the authors report a peptide macrocyclization and stapling strategy which satisfies those criteria, based on a fluorine thiol displacement reaction.

Protein Interaction Network Reconstruction with a Structural Gated Attention Deep Model by Incorporating Network Structure Information

Protein-protein interactions (PPIs) provide a physical basis of molecular communications for a wide range of biological processes in living cells. Establishing the PPI network has become a fundamental but essential task for a better understanding of biological events and disease pathogenesis. Although many machine learning algorithms have been employed to predict PPIs, with only protein sequence information as the training features, these models suffer from low robustness and prediction accuracy. In this study, a new deep-learning-based framework named the Structural Gated Attention Deep (SGAD) model was proposed to improve the performance of PPI network reconstruction (PINR). The improved predictive performances were achieved by augmenting multiple protein sequence descriptors, the topological features and information flow of the PPI network, which were further implemented with a gating mechanism to improve its robustness to noise. On 11 independent test data sets and one combined data set, SGAD yielded area under the curve values of approximately 0.83-0.93, outperforming other models. Furthermore, the SGAD ensemble can learn more characteristics information on protein pairs through a two-layer neural network, serving as a powerful tool in the exploration of PPI biological space.

Probing GPCR Dimerization Using Peptides

G protein-coupled receptors (GPCRs) are the largest class of membrane proteins and the most common and extensively studied pharmacological target. Numerous studies over the last decade have confirmed that GPCRs do not only exist and function in their monomeric form but in fact, have the ability to form dimers or higher order oligomers with other GPCRs, as well as other classes of receptors. GPCR oligomers have become increasingly attractive to investigate as they have the ability to modulate the pharmacological responses of the receptors which in turn, could have important functional roles in diseases, such as cancer and several neurological & neuropsychiatric disorders. Despite the growing evidence in the field of GPCR oligomerisation, the lack of structural information, as well as targeting the 'undruggable' protein-protein interactions (PPIs) involved in these complexes, has presented difficulties. Outside the field of GPCRs, targeting PPIs has been widely studied, with a variety of techniques being investigated; from small-molecule inhibitors to disrupting peptides. In this review, we will demonstrate several physiologically relevant GPCR dimers and discuss an array of strategies and techniques that can be employed when targeting these complexes, as well as provide ideas for future development.

Insights on peptide topology in the computational design of protein ligands: the example of lysozyme binding peptides

Herein, we compared the ability of linear and cyclic peptides generated in silico to target different protein sites: internal pockets and solvent-exposed sites. We selected human lysozyme (HuL) as a model target protein combined with the computational evolution of linear and cyclic peptides. The sequence evolution of these peptides was based on the PARCE algorithm. The generated peptides were screened based on their aqueous solubility and HuL binding affinity. The latter was evaluated by means of scoring functions and atomistic molecular dynamics (MD) trajectories in water, which allowed prediction of the structural features of the protein-peptide complexes. The computational results demonstrated that cyclic peptides constitute the optimal choice for solvent exposed sites, while both linear and cyclic peptides are capable of targeting the HuL pocket effectively. The most promising binders found in silico were investigated experimentally by surface plasmon resonance (SPR), nuclear magnetic resonance (NMR), and electrospray ionization mass spectrometry (ESI-MS) techniques. All tested peptides displayed dissociation constants in the micromolar range, as assessed by SPR; however, both NMR and ESI-MS suggested multiple binding modes, at least for the pocket binding peptides. A detailed NMR analysis confirmed that both linear and cyclic pocket peptides correctly target the binding site they were designed for.

Next-Generation Genome-Scale Metabolic Modeling through Integration of Regulatory Mechanisms

Genome-scale metabolic models (GEMs) are powerful tools for understanding metabolism from a systems-level perspective. However, GEMs in their most basic form fail to account for cellular regulation. A diverse set of mechanisms regulate cellular metabolism, enabling organisms to respond to a wide range of conditions. This limitation of GEMs has prompted the development of new methods to integrate regulatory mechanisms, thereby enhancing the predictive capabilities and broadening the scope of GEMs. Here, we cover integrative models encompassing six types of regulatory mechanisms: transcriptional regulatory networks (TRNs), post-translational modifications (PTMs), epigenetics, protein-protein interactions and protein stability (PPIs/PS), allostery, and signaling networks. We discuss 22 integrative GEM modeling methods and how these have been used to simulate metabolic regulation during normal and pathological conditions. While these advances have been remarkable, there remains a need for comprehensive and widespread integration of regulatory constraints into GEMs. We conclude by discussing challenges in constructing GEMs with regulation and highlight areas that need to be addressed for the successful modeling of metabolic regulation. Next-generation integrative GEMs that incorporate multiple regulatory mechanisms and their crosstalk will be invaluable for discovering cell-type and disease-specific metabolic control mechanisms.

Targeting a Cryptic Pocket in a Protein-Protein Contact by Disulfide-Induced Rupture of a Homodimeric Interface

Interference with protein-protein interfaces represents an attractive as well as challenging option for therapeutic intervention and drug design. The enzyme tRNA-guanine transglycosylase, a target to fight Shigellosis, is only functional as a homodimer. Although we previously produced monomeric variants by site-directed mutagenesis, we only crystallized the functional dimer, simply because upon crystallization the local protein concentration increases and favors formation of the dimer interface, which represents an optimal and highly stable packing of the protein in the solid state. Unfortunately, this prevents access to structural information about the interface geometry in its monomeric state and complicates the development of modulators that can interfere with and prevent dimer formation. Here, we report on a cysteine-containing protein variant in which, under oxidizing conditions, a disulfide linkage is formed. This reinforces a novel packing geometry of the enzyme. In this captured quasi-monomeric state, the monomer units arrange in a completely different way and, thus, expose a loop-helix motif, originally embedded into the old interface, now to the surface. The motif adopts a geometry incompatible with the original dimer formation. Via the soaking of fragments into the crystals, we identified several hits accommodating a cryptic binding site next to the loop-helix motif and modulated its structural features. Our study demonstrates the druggability of the interface by breaking up the homodimeric protein using an introduced disulfide cross-link. By rational concepts, we increased the potency of these fragments to a level where we confirmed their binding by NMR to a nondisulfide-linked TGT variant. The idea of intermediately introducing a disulfide linkage may serve as a general concept of how to transform a homodimer interface into a quasi-monomeric state and give access to essential structural and design information.

TRP Channels Interactome as a Novel Therapeutic Target in Breast Cancer

Breast cancer is one of the most frequent cancer types worldwide and the first cause of cancer-related deaths in women. Although significant therapeutic advances have been achieved with drugs such as tamoxifen and trastuzumab, breast cancer still caused 627,000 deaths in 2018. Since cancer is a multifactorial disease, it has become necessary to develop new molecular therapies that can target several relevant cellular processes at once. Ion channels are versatile regulators of several physiological- and pathophysiological-related mechanisms, including cancer-relevant processes such as tumor progression, apoptosis inhibition, proliferation, migration, invasion, and chemoresistance. Ion channels are the main regulators of cellular functions, conducting ions selectively through a pore-forming structure located in the plasma membrane, protein–protein interactions one of their main regulatory mechanisms. Among the different ion channel families, the Transient Receptor Potential (TRP) family stands out in the context of breast cancer since several members have been proposed as prognostic markers in this pathology. However, only a few approaches exist to block their specific activity during tumoral progress. In this article, we describe several TRP channels that have been involved in breast cancer progress with a particular focus on their binding partners that have also been described as drivers of breast cancer progression. Here, we propose disrupting these interactions as attractive and potential new therapeutic targets for treating this neoplastic disease.

Exploring the Binding Interaction of Raf Kinase Inhibitory Protein With the N-Terminal of C-Raf Through Molecular Docking and Molecular Dynamics Simulation

Protein-protein interactions are indispensable physiological processes regulating several biological functions. Despite the availability of structural information on protein-protein complexes, deciphering their complex topology remains an outstanding challenge. Raf kinase inhibitory protein (RKIP) has gained substantial attention as a favorable molecular target for numerous pathologies including cancer and Alzheimer’s disease. RKIP interferes with the RAF/MEK/ERK signaling cascade by endogenously binding with C-Raf (Raf-1 kinase) and preventing its activation. In the current investigation, the binding of RKIP with C-Raf was explored by knowledge-based protein-protein docking web-servers including HADDOCK and ZDOCK and a consensus binding mode of C-Raf/RKIP structural complex was obtained. Molecular dynamics (MD) simulations were further performed in an explicit solvent to sample the conformations for when RKIP binds to C-Raf. Some of the conserved interface residues were mutated to alanine, phenylalanine and leucine and the impact of mutations was estimated by additional MD simulations and MM/PBSA analysis for the wild-type (WT) and constructed mutant complexes. Substantial decrease in binding free energy was observed for the mutant complexes as compared to the binding free energy of WT C-Raf/RKIP structural complex. Furthermore, a considerable increase in average backbone root mean square deviation and fluctuation was perceived for the mutant complexes. Moreover, per-residue energy contribution analysis of the equilibrated simulation trajectory by HawkDock and ANCHOR web-servers was conducted to characterize the key residues for the complex formation. One residue each from C-Raf (Arg398) and RKIP (Lys80) were identified as the druggable “hot spots” constituting the core of the binding interface and corroborated by additional long-time scale (300 ns) MD simulation of Arg398Ala mutant complex. A notable conformational change in Arg398Ala mutant occurred near the mutation site as compared to the equilibrated C-Raf/RKIP native state conformation and an essential hydrogen bonding interaction was lost. The thirteen binding sites assimilated from the overall analysis were mapped onto the complex as surface and divided into active and allosteric binding sites, depending on their location at the interface. The acquired information on the predicted 3D structural complex and the detected sites aid as promising targets in designing novel inhibitors to block the C-Raf/RKIP interaction.

PPI Modulators of E6 as Potential Targeted Therapeutics for Cervical Cancer: Progress and Challenges in Targeting E6

Advanced cervical cancer is primarily managed using cytotoxic therapies, despite evidence of limited efficacy and known toxicity. There is a current lack of alternative therapeutics to treat the disease more effectively. As such, there have been more research endeavors to develop targeted therapies directed at oncogenic host cellular targets over the past 4 decades, but thus far, only marginal gains in survival have been realized. The E6 oncoprotein, a protein of human papillomavirus origin that functionally inactivates various cellular antitumor proteins through protein–protein interactions (PPIs), represents an alternative target and intriguing opportunity to identify novel and potentially effective therapies to treat cervical cancer. Published research has reported a number of peptide and small-molecule modulators targeting the PPIs of E6 in various cell-based models. However, the reported compounds have rarely been well characterized in animal or human subjects. This indicates that while notable progress has been made in targeting E6, more extensive research is needed to accelerate the optimization of leads. In this review, we summarize the current knowledge and understanding of specific E6 PPI inhibition, the progress and challenges being faced, and potential approaches that can be utilized to identify novel and potent PPI inhibitors for cervical cancer treatment.

Construction of diverse peptide structural architectures via chemoselective peptide ligation

Herein, we report the development of a facile synthetic strategy for constructing diverse peptide structural architectures via chemoselective peptide ligation. The key advancement involved is to utilize the benzofuran moiety as the peptide salicylaldehyde ester surrogate, and Dap-Ser/Lys-Ser dipeptide as the hydroxyl amino functionality, which could be successfully introduced at the side chain of peptides enabling peptide ligation. With this method, the side chain-to-side chain cyclic peptide, branched/bridged peptides, tailed cyclic peptides and multi-cyclic peptides have been designed and successfully synthesized with native peptidic linkages at the ligation sites. This strategy has provided an alternative strategic opportunity for synthetic peptide development. It also serves as an inspiration for the structural design of PPI inhibitors with new modalities.

Effect of Stapling on the Thermodynamics of mdm2-p53 Binding

Protein-protein interaction (PPI) is one of the key regulatory features driving biomolecular processes and hence is targeted for designing therapeutics against diseases. Small peptides are a new and emerging class of therapeutics owing to their high specificity and low toxicity. For achieving efficient targeting of the PPI, amino acid side chains are often stapled together, resulting in the rigidification of these peptides. Exploring the scope of these peptides demands a comprehensive understanding of their working principle. In this work, two stapled p53 peptides have been considered to delineate their binding mechanism with mdm2 using computational approaches. The addition of stapling agent protects the secondary structure of the peptides even in the case of thermal and chemical denaturation. Although the introduction of a stapling agent increases the hydrophobicity of the peptide, the enthalpic stabilization decreases. This is overcome by the lowering of the entropic penalty, and the overall binding affinity improves. The mechanistic insights into the benefit of peptide stapling can be adopted for further improvement of peptide therapeutics.

Converting peptides into drugs targeting intracellular protein-protein interactions

Peptides are gaining increasing attention as therapeutics to target intracellular protein-protein interactions that are involved in disease progression. In this review, we discuss how peptides that are able to bind and inhibit a therapeutic target can be translated into drug leads. We discuss the advantages of using peptides as therapeutics to target intracellular protein-protein interactions, chemical strategies to generate macrocyclic peptides that are resistant to proteolytic enzymes, high-throughput screening approaches to identify peptides that have high affinity for therapeutic targets, strategies that permit these peptides to cross cell membranes and so reach intracellular targets, and the importance of investigating their mode-of-action in guiding the development of novel therapeutics.

Molecular basis for protein-protein interactions

This minireview provides an overview on the current knowledge of protein-protein interactions, common characterisation methods to characterise them, and their role in protein complex formation with some examples. A deep understanding of protein-protein interactions and their molecular interactions is important for a number of applications, including drug design. Protein-protein interactions and their discovery are thus an interesting avenue for understanding how protein complexes, which make up the majority of proteins, work.

PROBselect: accurate prediction of protein-binding residues from proteins sequences via dynamic predictor selection

Motivation

Knowledge of protein-binding residues (PBRs) improves our understanding of protein−protein interactions, contributes to the prediction of protein functions and facilitates protein−protein docking calculations. While many sequence-based predictors of PBRs were published, they offer modest levels of predictive performance and most of them cross-predict residues that interact with other partners. One unexplored option to improve the predictive quality is to design consensus predictors that combine results produced by multiple methods.

Results

We empirically investigate predictive performance of a representative set of nine predictors of PBRs. We report substantial differences in predictive quality when these methods are used to predict individual proteins, which contrast with the dataset-level benchmarks that are currently used to assess and compare these methods. Our analysis provides new insights for the cross-prediction concern, dissects complementarity between predictors and demonstrates that predictive performance of the top methods depends on unique characteristics of the input protein sequence. Using these insights, we developed PROBselect, first-of-its-kind consensus predictor of PBRs. Our design is based on the dynamic predictor selection at the protein level, where the selection relies on regression-based models that accurately estimate predictive performance of selected predictors directly from the sequence. Empirical assessment using a low-similarity test dataset shows that PROBselect provides significantly improved predictive quality when compared with the current predictors and conventional consensuses that combine residue-level predictions. Moreover, PROBselect informs the users about the expected predictive quality for the prediction generated from a given input protein.

Availability and implementation

PROBselect is available at http://bioinformatics.csu.edu.cn/PROBselect/home/index.

Supplementary information

Supplementary data are available at Bioinformatics online.

Cholesterol-Rich Microdomains Contribute to PAR1 Signaling in Platelets Despite a Weak Localization of the Receptor in These Microdomains

Platelet protease-activated receptor 1 (PAR1) is a cell surface G-protein-coupled receptor (GPCR) that acts as a thrombin receptor promoting platelet aggregation. Targeting the PAR1 pathway by vorapaxar, a PAR1 antagonist, leads to a reduction in ischemic events in cardiovascular patients with a history of myocardial infarction or with peripheral arterial disease. In platelets, specialized microdomains highly enriched in cholesterol act as modulators of the activity of several GPCRs and play a pivotal role in the signaling pathway. However, their involvement in platelet PAR1 function remains incompletely characterized. In this context, we aimed to investigate whether activation of PAR1 in human platelets requires its localization in the membrane cholesterol-rich microdomains. Using confocal microscopy, biochemical isolation, and proteomics approaches, we found that PAR1 was not localized in cholesterol-rich microdomains in resting platelets, and only a small fraction of the receptor relocated to the microdomains following its activation. Vorapaxar treatment increased the level of PAR1 at the platelet surface, possibly by reducing its endocytosis, while its colocalization with cholesterol-rich microdomains remained weak. Consistent with a cholesterol-dependent activation of Akt and p38 MAP kinase in thrombin receptor-activating peptide (TRAP)-activated platelets, the proteomic data of cholesterol-rich microdomains isolated from TRAP-activated platelets showed the recruitment of proteins contributing to these signaling pathways. In conclusion, contrary to endothelial cells, we found that PAR1 was only weakly present in cholesterol-rich microdomains in human platelets but used these microdomains for efficient activation of downstream signaling pathways following TRAP activation.

In silico identification of small molecule modulators for disruption of Hsp90-Cdc37 protein-protein interaction interface for cancer therapeutic application

The protein-protein interactions (PPIs) in the biological systems are important to maintain a number of cellular processes. Several disorders including cancer may be developed due to dysfunction in the assembly of PPI networks. Hence, targeting intracellular PPIs can be considered as a crucial drug target for cancer therapy. Among the enormous and diverse group of cancer-enabling PPIs, the Hsp90-Cdc37 is prominent for cancer therapeutic development. The successful inhibition of Hsp90-Cdc37 PPI interface can be an important therapeutic option for cancer management. In the current study, a set of more than sixty thousand compounds belong to four databases were screened through a multi-steps molecular docking study in Glide against the Hsp90-Cdc37 interaction interface. The Glide-score and Prime-MM-GBSA based binding free energy of DCZ3112, standard Hsp90-Cdc37 inhibitor were found to be -6.96 and -40.46 kcal/mol, respectively. The above two parameters were used as cut-off score to reduce the chemical space from all successfully docked molecules. Furthermore, the in-silico pharmacokinetics parameters, common-feature pharmacophore analyses and the molecular binding interactions were used to wipe out the inactive molecules. Finally, four molecules were found to be important to modulate the Hsp90-Cdc37 interface. The potentiality of the final four molecules was checked through several drug-likeness characteristics. The molecular dynamics (MD) simulation study explained that all four molecules retained inside the interface of Hsp90-Cdc37. The binding free energy of each molecule obtained from the MD simulation trajectory was clearly explained the strong affection towards the Hsp90-Cdc37. Hence, the proposed molecule might be crucial for successful inhibition of the Hsp90-Cdc37 interface.Communicated by Ramaswamy H. Sarma.

Exploring Proteomic Drug Targets, Therapeutic Strategies and Protein - Protein Interactions in Cancer: Mechanistic View

Protein-Protein Interactions (PPIs) drive major signalling cascades and play critical role in cell proliferation, apoptosis, angiogenesis and trafficking. Deregulated PPIs are implicated in multiple malignancies and represent the critical targets for treating cancer. Herein, we discuss the key protein-protein interacting domains implicated in cancer notably PDZ, SH2, SH3, LIM, PTB, SAM and PH. These domains are present in numerous enzymes/kinases, growth factors, transcription factors, adaptor proteins, receptors and scaffolding proteins and thus represent essential sites for targeting cancer. This review explores the candidature of various proteins involved in cellular trafficking (small GTPases, molecular motors, matrix-degrading enzymes, integrin), transcription (p53, cMyc), signalling (membrane receptor proteins), angiogenesis (VEGFs) and apoptosis (BCL-2family), which could possibly serve as targets for developing effective anti-cancer regimen. Interactions between Ras/Raf; X-linked inhibitor of apoptosis protein (XIAP)/second mitochondria-derived activator of caspases (Smac/DIABLO); Frizzled (FRZ)/Dishevelled (DVL) protein; beta-catenin/T Cell Factor (TCF) have also been studied as prospective anticancer targets. Efficacy of diverse molecules/ drugs targeting such PPIs although evaluated in various animal models/cell lines, there is an essential need for human-based clinical trials. Therapeutic strategies like the use of biologicals, high throughput screening (HTS) and fragment-based technology could play an imperative role in designing cancer therapeutics. Moreover, bioinformatic/computational strategies based on genome sequence, protein sequence/structure and domain data could serve as competent tools for predicting PPIs. Exploring hot spots in proteomic networks represents another approach for developing targetspecific therapeutics. Overall, this review lays emphasis on a productive amalgamation of proteomics, genomics, biochemistry, and molecular dynamics for successful treatment of cancer.

Infrared Pulse Laser-Activated Highly Efficient Intracellular Delivery Using Titanium Microdish Device

We report infrared (IR) pulse laser-activated highly efficient parallel intracellular delivery by using an array of titanium microdish (TMD) device. Upon IR laser pulse irradiation, a two-dimensional array of TMD device generated photothermal cavitation bubbles to disrupt the cell membrane surface and create transient membrane pores to deliver biomolecules into cells by a simple diffusion process. We successfully delivered the dyes and different sizes of dextran in different cell types with variations of laser pulses. Our platform has the ability to transfect more than a million cells in a parallel fashion within a minute. The best results were achieved for SiHa cells with a delivery efficiency of 96% and a cell viability of around 98% for propidium iodide dye using 600 pulses, whereas a delivery efficiency of 98% and a cell viability of 100% were obtained for dextran 3000 MW delivery using 700 pulses. For dextran 10,000 MW, the delivery efficiency was 92% and the cell viability was 98%, respectively. The device is compact, easy-to-use, and potentially applicable for cellular therapy and diagnostic purposes.

The Molecular Basis of JAZ-MYC Coupling, a Protein-Protein Interface Essential for Plant Response to Stressors

The jasmonic acid (JA) signaling pathway is one of the primary mechanisms that allow plants to respond to a variety of biotic and abiotic stressors. Within this pathway, the JAZ repressor proteins and the basic helix-loop-helix (bHLH) transcription factor MYC3 play a critical role. JA is a volatile organic compound with an essential role in plant immunity. The increase in the concentration of JA leads to the decoupling of the JAZ repressor proteins and the bHLH transcription factor MYC3 causing the induction of genes of interest. The primary goal of this study was to identify the molecular basis of JAZ-MYC coupling. For this purpose, we modeled and validated 12 JAZ-MYC3 3D in silico structures and developed a molecular dynamics/machine learning pipeline to obtain two outcomes. First, we calculated the average free binding energy of JAZ-MYC3 complexes, which was predicted to be -10.94 +/-2.67 kJ/mol. Second, we predicted which ones should be the interface residues that make the predominant contribution to the free energy of binding (molecular hotspots). The predicted protein hotspots matched a conserved linear motif SL••FL•••R, which may have a crucial role during MYC3 recognition of JAZ proteins. As a proof of concept, we tested, both in silico and in vitro, the importance of this motif on PEAPOD (PPD) proteins, which also belong to the TIFY protein family, like the JAZ proteins, but cannot bind to MYC3. By mutating these proteins to match the SL••FL•••R motif, we could force PPDs to bind the MYC3 transcription factor. Taken together, modeling protein-protein interactions and using machine learning will help to find essential motifs and molecular mechanisms in the JA pathway.

The disruption of protein-protein interactions as a therapeutic strategy for prostate cancer

Prostate cancer (PCa) is one of the most common male-specific cancers worldwide, with high morbidity and mortality rates associated with advanced disease stages. The current treatment options of PCa are prostatectomy, hormonal therapy, chemotherapy or radiotherapy, the selection of which is usually dependent upon the stage of the disease. The development of PCa to a castration-resistant phenotype (CRPC) is associated with a more severe prognosis requiring the development of a new and effective therapy. Protein-protein interactions (PPIs) have been recognised as an emerging drug modality and targeting PPIs is a promising therapeutic approach for several diseases, including cancer. The efficacy of several compounds in which target PPIs and consequently impair disease progression were validated in phase I/II clinical trials for different types of cancer. In PCa, various small molecules and peptides proved successful in inhibiting important PPIs, mainly associated with the androgen receptor (AR), Bcl-2 family proteins, and kinases/phosphatases, thus impairing the growth of PCa cells in vitro. Moreover, a majority of these compounds require further validation in vivo and, preferably, in clinical trials. In addition, several other PPIs associated with PCa progression have been identified and now require experimental validation as potential therapeutic loci. In conclusion, we consider the disruption of PPIs to be a promising though challenging therapeutic strategy for PCa. Agents which modulate PPIs might be employed as a monotherapy or as an adjunct to classical chemotherapeutics to overcome drug resistance and improve efficacy. The discovery of new PPIs with important roles in disease progression, and of novel optimized strategies to target them are major challenges for the scientific and pharmacological communities.

Fragment screening targeting Ebola virus nucleoprotein C-terminal domain identifies lead candidates

The Ebola Virus is a causative agent of viral hemorrhagic fever outbreaks and a potential global health risk. The outbreak in West Africa (2013-2016) led to 11,000+ deaths and 30,000+ Ebola infected individuals. The current outbreak in the Democratic Republic of Congo (DRC) with 3000+ confirmed cases and 2000+ deaths attributed to Ebola virus infections provides a reminder that innovative countermeasures are still needed. Ebola virus encodes 7 open reading frames (ORFs). Of these, the nucleocapsid protein (eNP) encoded by the first ORF plays many significant roles, including a role in viral RNA synthesis. Here we describe efforts to target the C-terminal domain of eNP (eNP-CTD) that contains highly conserved residues 641-739 as a pan-Ebola antiviral target. Interactions of eNP-CTD with Ebola Viral Protein 30 (eVP30) and Viral Protein 40 (eVP40) have been shown to be crucial for viral RNA synthesis, virion formation, and virion transport. We used nuclear magnetic response (NMR)-based methods to screened the eNP-CTD against a fragment library. Perturbations of 1D ¹H NMR spectra identified of 48 of the 439 compounds screened as potential eNP CTD interactors. Subsequent analysis of these compounds to measure chemical shift perturbations in 2D ¹H,¹⁵N NMR spectra of ¹⁵N-labeled protein identified six with low millimolar affinities. All six perturbed an area consisting mainly of residues at or near the extreme C-terminus that we named "Site 1" while three other sites were perturbed by other compounds. Our findings here demonstrate the potential utility of eNP as a target, several fragment hits, and provide an experimental pipeline to validate viral-viral interactions as potential panfiloviral inhibitor targets.

Design of Drug-Like Protein-Protein Interaction Stabilizers Guided By Chelation-Controlled Bioactive Conformation Stabilization

Protein-protein interactions (PPIs) of 14-3-3 proteins are a model system for studying PPI stabilization. The complex natural product Fusicoccin A stabilizes many 14-3-3 PPIs but is not amenable for use in SAR studies, motivating the search for more drug-like chemical matter. However, drug-like 14-3-3 PPI stabilizers enabling such studies have remained elusive. An X-ray crystal structure of a PPI in complex with an extremely low potency stabilizer uncovered an unexpected non-protein interacting, ligand-chelated Mg²⁺ leading to the discovery of metal-ion-dependent 14-3-3 PPI stabilization potency. This originates from a novel chelation-controlled bioactive conformation stabilization effect. Metal chelation has been associated with pan-assay interference compounds (PAINS) and frequent hitter behavior, but chelation can evidently also lead to true potency gains and find use as a medicinal chemistry strategy to guide compound optimization. To demonstrate this, we exploited the effect to design the first potent, selective, and drug-like 14-3-3 PPI stabilizers.

Aromatic clusters in protein-protein and protein-drug complexes

Aromatic rings are important residues for biological interactions and appear to a large extent as part of protein-drug and protein-protein interactions. They are relevant for both protein stability and molecular recognition processes due to their natural occurrence in aromatic aminoacids (Trp, Phe, Tyr and His) as well as in designed drugs since they are believed to contribute to optimizing both affinity and specificity of drug-like molecules. Despite the mentioned relevance, the impact of aromatic clusters on protein-protein and protein-drug complexes is still poorly characterized, especially in those that go beyond a dimer. In this work, we studied protein-drug and protein-protein complexes and systematically analyzed the presence and structure of their aromatic clusters. Our results show that aromatic clusters are highly prevalent in both protein-protein and protein-drug complexes, and suggest that protein-protein aromatic clusters have idealized interactions, probably because they were optimized by evolution, as compared to protein-drug clusters that were manually designed. Interestingly, the configuration, solvent accessibility and secondary structure of aromatic residues in protein-drug complexes shed light on the relation between these properties and compound affinity, allowing researchers to better design new molecules.

Novel and selective inactivators of Triosephosphate isomerase with anti-trematode activity

Trematode infections such as schistosomiasis and fascioliasis cause significant morbidity in an estimated 250 million people worldwide and the associated agricultural losses are estimated at more than US$ 6 billion per year. Current chemotherapy is limited. Triosephosphate isomerase (TIM), an enzyme of the glycolytic pathway, has emerged as a useful drug target in many parasites, including Fasciola hepatica TIM (FhTIM). We identified 21 novel compounds that selectively inhibit this enzyme. Using microscale thermophoresis we explored the interaction between target and compounds and identified a potent interaction between the sulfonyl-1,2,4-thiadiazole (compound 187) and FhTIM, which showed an IC₅₀ of 5 µM and a K_d of 66 nM. In only 4 hours, this compound killed the juvenile form of F. hepatica with an IC₅₀ of 3 µM, better than the reference drug triclabendazole (TCZ). Interestingly, we discovered in vitro inhibition of FhTIM by TCZ, with an IC₅₀ of 7 µM suggesting a previously uncharacterized role of FhTIM in the mechanism of action of this drug. Compound 187 was also active against various developmental stages of Schistosoma mansoni. The low toxicity in vitro in different cell types and lack of acute toxicity in mice was demonstrated for this compound, as was demonstrated the efficacy of 187in vivo in F. hepatica infected mice. Finally, we obtained the first crystal structure of FhTIM at 1.9 Å resolution which allows us using docking to suggest a mechanism of interaction between compound 187 and TIM. In conclusion, we describe a promising drug candidate to control neglected trematode infections in human and animal health.

Medicinal Effect, In Silico Bioactivity Prediction, and Pharmaceutical Formulation of Ageratum conyzoides L.: A Review

Goat weed (Ageratum conyzoides L.), or bandotan in Indonesia, is an herbaceous plant that broadly grows up in both subtropical as well as tropical areas. This herb contains many phytoconstituents which have many benefits in different aspects. The essential oil contains phytochemicals such as phenol, phenolic ester, and coumarin, whereas many compounds can been identified in the whole part such as terpenoid, steroid, chromene, pyrrolizidine alkaloid, and flavonoid. Empirically, this herb has been used as an antihemorrhagic, antiseptic, antileprosy, and wound-healing agent. This article reviews the potency of the herb in medication according to the chemical substances being deposited, which are collected from numerous studies, followed by its in silico bioactivity prediction as well as its pharmaceutical dosage form formulation.

Fundamental physical and chemical concepts behind “drug-likeness” and “natural product-likeness”

The discovery of a drug is known to be quite cumbersome, both in terms of the microscopic fundamental research behind it and the industrial scale manufacturing process. A major concern in drug discovery is the acceleration of the process and cost reduction. The fact that clinical trials cannot be accelerated, therefore, emphasizes the need to accelerate the strategies for identifying lead compounds at an early stage. We, herein, focus on the definition of what would be regarded as a “drug-like” molecule and a “lead-like” one. In particular, “drug-likeness” is referred to as resemblance to existing drugs, whereas “lead-likeness” is characterized by the similarity with structural and physicochemical properties of a “lead”compound, i.e. a reference compound or a starting point for further drug development. It is now well known that a huge proportion of the drug discovery is inspired or derived from natural products (NPs), which have larger complexity as well as size when compared with synthetic compounds. Therefore, similar definitions of “drug-likeness” and “lead-likeness” cannot be applied for the NP-likeness. Rather, there is the dire need to define and explain NP-likeness in regard to chemical structure. An attempt has been made here to give an overview of the general concepts associated with NP discovery, and to provide the foundational basis for defining a molecule as a “drug”, a “lead” or a “natural compound.”

Protein-protein complexes as targets for drug discovery against infectious diseases

Antibiotics are therapeutic agents against bacterial infections, however, the emergence of multiple and extremely drug-resistant microbes (Multi-Drug Resistant and Extremely Drug-Resistant) are compromising the effectiveness of the currently available treatment options. The drug resistance is not a novel crisis, the current pace of drug discovery has failed to compete with the growth of MDR and XDR pathogenic strains and therefore, it is highly central to find out novel antimicrobial drugs with unique mechanisms of action which may reduce the burden of MDR and XDR pathogenic strains. Protein-protein interactions (PPIs) are involved in a countless of the physiological and cellular phenomena and have become an attractive target to treat the diseases. Therefore, targeting PPIs in infectious agents may offer a completely novel strategy of intervention to develop anti-infective drugs that may combat the ever-increasing rate of drug resistant strains. This chapter describes how small molecule candidate inhibitors that are capable of disrupting the PPIs in pathogenic microbes and it could be an alternative lead discovery strategy to obtain novel antibiotics. Over the last three decades, there has been increasing efforts focused on the manipulation of PPIs in order to develop novel therapeutic interventions. The diversity and complexity of such a complex and highly dynamic systems pose many challenges in targeting PPIs by drug-like molecules with necessary selectivity and potency. Traditional and novel drug discovery strategies have provided tools for designing and assessing PPI inhibitors against infectious diseases.

Aminoquinoline-Rhodium(II) Conjugates as Src-Family SH3 Ligands

High-affinity, selective ligands are sought for a variety of biomolecules but are particularly difficult to generate in the protein-protein interaction space. Rhodium(II) conjugates provide a structure-based approach to improved affinity and specificity for targeting protein-protein interactions such as SH3 domains. In this study of small-molecule-rhodium conjugates, we report a potent ligand 4b (K _d of 27 nM) for the Lyn SH3 domain, based on an aminoquinoline fragment. The results demonstrate robust affinity gains possible from even modest small-molecule leads through cooperative inorganic-organic binding, based on specific histidine interactions. A docking study sheds light on the structural basis of binding and supports a previously proposed binding model.

Discovery and optimization of covalent Bcl-xL antagonists

Over the last ten years, targeted covalent inhibition has become a key discipline within medicinal chemistry research, most notably in the development of oncology therapeutics. One area where this approach is underrepresented, however, is in targeting protein-protein interactions. This is primarily because these hydrophobic interfaces lack appropriately located cysteine residues to allow for standard conjugate addition chemistry. Herein, we report our development of the first covalent inhibitors of the antiapoptotic protein B-cell lymphoma extra-large (Bcl-xL), utilizing a sulfonyl fluoride (SF) warhead to selectively covalently modify tyrosine 101 of the BH3 domain-binding groove. These compounds display time-dependent inhibition in a biochemical assay and are cellularly active (U266B1). In addition, compound 7 was further elaborated to generate a chemical-biology probe molecule, which may find utility in understanding the intricacies of Bcl-xL biology.

Proteomics and Leishmaniasis: Potential Clinical Applications

Leishmaniases are diseases caused by protozoan parasites of the genus Leishmania. They are endemic in 98 countries, affect around 12 million people worldwide and may present several distinct clinical forms. Unfortunately, there are only a few drugs available for treatment of leishmaniasis, which are toxic and not always effective. Different parasite species and different clinical forms require optimization of the treatment or more specific therapies, which are not available. The emergence of resistance is also a matter of concern. Besides, diagnosis can sometimes be complicated due to atypical manifestations and associations with other pathologies. In this review, proteomic data are presented and discussed in terms of their application in important issues in leishmaniasis such as parasite resistance to chemotherapy, diagnosis of active disease in patients and dogs, markers for different clinical forms, identification of virulence factors, and their potential use in vaccination. It is shown that proteomics has contributed to the discovery of potential biomarkers for prognosis, diagnosis, therapeutics, monitoring of disease progression, treatment follow-up and identification of vaccine candidates for specific diseases. However, the authors believe its capabilities have not yet been fully explored for routine clinical analysis for several reasons, which will be presented in this review.

Identification of biomarkers associated with progression and prognosis in bladder cancer via co-expression analysis

BACKGROUND

Bladder cancer is one of the most common genitourinary malignancies, with a high rate of recurrence and progression. The prognosis for patients with bladder cancer, especially muscle-invasive bladder cancer, remains poor despite systemic therapy.

OBJECTIVE

To explore the underlying disease mechanisms and identify more effective biomarkers for bladder cancer.

METHODS

Weighted gene co-expression network analysis (WGCNA) and protein-protein interaction (PPI) network analysis were applied to identify hub genes correlated with the bladder cancer progression. Survival analyses were then conducted to identify potential biomarkers correlated with the prognosis of bladder cancer. Finally, validation and analysis of these potential biomarkers were conducted by a series of bioinformatics analyses.

RESULTS

Based on the results of weighted gene co-expression network analysis and protein-protein interaction network analysis, ten hub genes closely correlated with bladder cancer progression were identified in the relevant module. Survival analyses of these genes indicated that elevated expressions of six potential biomarkers (COL3A1, FN1, COL5A1, FBN1, COL6A1 and THBS2) were significantly associated with a worse overall survival. Furthermore, these 6 potential biomarkers were validated in association with the progression of bladder cancer. Bladder cancer samples with higher expression of these genes were most significantly enriched in gene set associated with ECM-receptor interaction.

CONCLUSIONS

This study identified several biomarkers associated with bladder cancer progression and prognosis. As novel findings, these may have important clinical implications for diagnosis, treatment and prognosis prediction.

Structural features of methionine aminopeptidase2-active core peptide essential for binding with S100A4

Methionine aminopeptidase 2 (MetAP2) is one of the effector proteins of S100A4, a metastasis-associated calcium-binding protein. This interaction is involved in angiogenesis. The region of MetAP2 that interacts with S100A4 includes amino acids 170 to 208. A peptide corresponding to this region, named as NBD, has potent anti-angiogenic activity and suppresses tumor growth in a xenograft cancer model. However, the binding mode of NBD to S100A4 was totally unknown. Here we describe our analysis of the relationship between the inhibitory activity and the structure of NBD, which adopts a characteristic helix-turn-helix structure as shown by X-ray crystallographic analysis, and peptide fragments of NBD. We conducted physicochemical analyses of the interaction between S100A4 and the peptides, including surface plasmon resonance, microscale thermophoresis, and circular dichroism, and performed docking/molecular dynamics simulations. Active peptides had stable secondary structures, whereas inactive peptides had a little secondary structure. A computational analysis of the interaction mechanism led to the design of a peptide smaller than NBD, NBD-ΔN10, that possessed inhibitory activity. Our study provides a strategy for design for a specific peptide inhibitor against S100A4 that can be applied to the discovery of inhibitors of other protein-protein interactions.