Protein-protein interactions as targets for small-molecule therapeutics in cancer

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.

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

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

What Makes a Good Protein-Protein Interaction Stabilizer: Analysis and Application of the Dual-Binding Mechanism

Protein-protein interactions (PPIs) are essential for biological processes including immune reactions and diseases. Inhibition of PPIs by drug-like compounds is a common basis for therapeutic approaches. In many cases the flat interface of PP complexes prevents discovery of specific compound binding to cavities on one partner and PPI inhibition. However, frequently new pockets are formed at the PP interface that allow accommodation of stabilizers which is often as desirable as inhibition but a much less explored alternative strategy. Herein, we employ molecular dynamics simulations and pocket detection to investigate 18 known stabilizers and associated PP complexes. For most cases, we find that a dual-binding mechanism, a similar stabilizer interaction strength with each protein partner, is an important prerequisite for effective stabilization. A few stabilizers follow an allosteric mechanism by stabilizing the protein bound structure and/or increase the PPI indirectly. On 226 protein-protein complexes, we find in >75% of the cases interface cavities suitable for binding of drug-like compounds. We propose a computational compound identification workflow that exploits new PP interface cavities and optimizes the dual-binding mechanism and apply it to 5 PP complexes. Our study demonstrates a great potential for in silico PPI stabilizers discovery with a wide range of therapeutic applications.

Tether-free optogenetic control of insulin secretion using an upconversion nanoparticle-doped hydrogel platform

Glucagon-like peptide-1 (GLP-1), as a molecular therapeutic, induces glucose-dependent stimulation of insulin secretion, which has drawn significant attention in treating type II diabetes. However, it always suffers from hurdles such as short half-lives or instability. Thus, producing such therapeutics endogenously, as and when needed, is beneficial. Optogenetics-based production of GLP-1 offers an attractive alternative, wherein, the cell lines such as HEK293T can be genetically modified to bring the expression of the gene of interest under visible light control. However, the need for blue light for activation necessitates the implantation of invasive optical fibers owing to high tissue scattering and low depth of penetration through biological tissue at this wavelength. Here, we overcome this problem by proposing an upconversion nanoparticle (UCNP)-based system. HEK293T cells, rewired to produce GLP-1 under blue light illumination, were co-encapsulated with UCNPs in a hydrogel. The UCNPs act as near-infrared (NIR) to blue light nano-transducers, allowing deep penetration toward implementing a tether-free optogenetic gene expression platform. This platform is particularly powerful for thick gel implants (>3 mm) that cannot be illuminated throughout using a blue light source. Moreover, the GLP-1 produced in this platform was sufficient to increase insulin secretion in rat insulinoma cells, providing a powerful and controllable therapeutic tool for diabetes.

Protein-protein interaction and interference of carcinogenesis by supramolecular modifications

Protein-protein interactions (PPIs) are essential in normal biological processes, but they can become disrupted or imbalanced in cancer. Various technological advancements have led to an increase in the number of PPI inhibitors, which target hubs in cancer cell's protein networks. However, it remains difficult to develop PPI inhibitors with desired potency and specificity. Supramolecular chemistry has only lately become recognized as a promising method to modify protein activities. In this review, we highlight recent advances in the use of supramolecular modification approaches in cancer therapy. We make special note of efforts to apply supramolecular modifications, such as molecular tweezers, to targeting the nuclear export signal (NES), which can be used to attenuate signaling processes in carcinogenesis. Finally, we discuss the strengths and weaknesses of using supramolecular approaches to targeting PPIs.

ProtInteract: A deep learning framework for predicting protein-protein interactions

Proteins mainly perform their functions by interacting with other proteins. Protein-protein interactions underpin various biological activities such as metabolic cycles, signal transduction, and immune response. However, due to the sheer number of proteins, experimental methods for finding interacting and non-interacting protein pairs are time-consuming and costly. We therefore developed the ProtInteract framework to predict protein-protein interaction. ProtInteract comprises two components: first, a novel autoencoder architecture that encodes each protein's primary structure to a lower-dimensional vector while preserving its underlying sequence attributes. This leads to faster training of the second network, a deep convolutional neural network (CNN) that receives encoded proteins and predicts their interaction under three different scenarios. In each scenario, the deep CNN predicts the class of a given encoded protein pair. Each class indicates different ranges of confidence scores corresponding to the probability of whether a predicted interaction occurs or not. The proposed framework features significantly low computational complexity and relatively fast response. The contributions of this work are twofold. First, ProtInteract assimilates the protein's primary structure into a pseudo-time series. Therefore, we leverage the nature of the time series of proteins and their physicochemical properties to encode a protein's amino acid sequence into a lower-dimensional vector space. This approach enables extracting highly informative sequence attributes while reducing computational complexity. Second, the ProtInteract framework utilises this information to identify protein interactions with other proteins based on its amino acid configuration. Our results suggest that the proposed framework performs with high accuracy and efficiency in predicting protein-protein interactions.

Hotspot Identification and Drug Design of Protein-Protein Interaction Modulators Using the Fragment Molecular Orbital Method

Protein-protein interactions (PPIs) are essential for the function of many proteins. Aberrant PPIs have the potential to lead to disease, making PPIs promising targets for drug discovery. There are over 64,000 PPIs in the human interactome reference database; however, to date, very few PPI modulators have been approved for clinical use. Further development of PPI-specific therapeutics is highly dependent on the availability of structural data and the existence of reliable computational tools to explore the interface between two interacting proteins. The fragment molecular orbital (FMO) quantum mechanics method offers comprehensive and computationally inexpensive means of identifying the strength (in kcal/mol) and the chemical nature (electrostatic or hydrophobic) of the molecular interactions taking place at the protein-protein interface. We have integrated FMO and PPI exploration (FMO-PPI) to identify the residues that are critical for protein-protein binding (hotspots). To validate this approach, we have applied FMO-PPI to a dataset of protein-protein complexes representing several different protein subfamilies and obtained FMO-PPI results that are in agreement with published mutagenesis data. We observed that critical PPIs can be divided into three major categories: interactions between residues of two proteins (intermolecular), interactions between residues within the same protein (intramolecular), and interactions between residues of two proteins that are mediated by water molecules (water bridges). We extended our findings by demonstrating how this information obtained by FMO-PPI can be utilized to support the structure-based drug design of PPI modulators (SBDD-PPI).

Application of nanopore sensors for biomolecular interactions and drug discovery

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

Crystallographic mining of ASK1 regulators to unravel the intricate PPI interfaces for the discovery of small molecule

Protein seldom performs biological activities in isolation. Understanding the protein–protein interactions’ physical rewiring in response to pathological conditions or pathogen infection can help advance our comprehension of disease etiology, progression, and pathogenesis, which allow us to explore the alternate route to control the regulation of key target interactions, timely and effectively. Nonalcoholic steatohepatitis (NASH) is now a global public health problem exacerbated due to the lack of appropriate treatments. The most advanced anti-NASH lead compound (selonsertib) is withdrawn, though it is able to inhibit its target Apoptosis signal-regulating kinase 1 (ASK1) completely, indicating the necessity to explore alternate routes rather than complete inhibition. Understanding the interaction fingerprints of endogenous regulators at the molecular level that underpin disease formation and progression may spur the rationale of designing therapeutic strategies. Based on our analysis and thorough literature survey of the various key regulators and PTMs, the current review emphasizes PPI-based drug discovery’s relevance for NASH conditions. The lack of structural detail (interface sites) of ASK1 and its regulators makes it challenging to characterize the PPI interfaces. This review summarizes key regulators interaction fingerprinting of ASK1, which can be explored further to restore the homeostasis from its hyperactive states for therapeutics intervention against NASH.

Protein-Protein Binding Free Energy Predictions with the MM/PBSA Approach Complemented with the Gaussian-Based Method for Entropy Estimation

Here, we present a Gaussian-based method for estimation of protein-protein binding entropy to augment the molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) method for computational prediction of binding free energy (ΔG). The method is termed f5-MM/PBSA/E, where "E" stands for entropy and f5 for five adjustable parameters. The enthalpy components of ΔG (molecular mechanics, polar and non-polar solvation energies) are computed from a single implicit solvent generalized Born (GB) energy minimized structure of a protein-protein complex, while the binding entropy is computed using independently GB energy minimized unbound and bound structures. It should be emphasized that the f5-MM/PBSA/E method does not use snapshots, just energy minimized structures, and is thus very fast and computationally efficient. The method is trained and benchmarked in 5-fold validation test over a data set consisting of 46 protein-protein binding cases with experimentally determined dissociation constant K _d values. This data set has been used for benchmarking in recently published protein-protein binding studies that apply conventional MM/PBSA and MM/PBSA with an enhanced sampling method. The f5-MM/PBSA/E tested on the same data set achieves similar or better performance than these computationally demanding approaches, making it an excellent choice for high throughput protein-protein binding affinity prediction studies.

A Bionic-Homodimerization Strategy for Optimizing Modulators of Protein-Protein Interactions: From Statistical Mechanics Theory to Potential Clinical Translation

Emerging protein-protein interaction (PPI) modulators have brought out exciting ability as therapeutics in human diseases, but its clinical translation has been greatly hampered by the limited affinity. Inspired by the homodimerize structure of antibody, the homodimerization contributes hugely to generating the optimized affinity is conjectured. Herein, a statistical-mechanics-theory-guided method is established to quantize the affinity of ligands with different topologies through analyzing the change of enthalpy and the loss of translational and rotational entropies. A peptide modulator for p53-MDM2 termed CPAP is used to homodimerize connecting, and this simple homodimerization can significantly increase the affinity. To realize the cellular internalization and tumor accumulation, Dimer CPAP and Mono CPAP are nanoengineered into gold(I)-CPAP supermolecule by the aurophilic interaction-driven self-assembly. Nano-Dimer CPAP potently suppressed tumor growth in lung cancer allograft model and a patient-derived xenograft model in more action than Nano-Mono CPAP, while keeping a favorable drug safety profile. This work not only presents a physico-mechanical method for calculating the affinity of PPI modulators, but also provides a simple yet robust homodimerization strategy to optimize the affinity of PPI modulators.

Recent developments in the utility of saturated azaheterocycles in peptidomimetics

To a large extent, the physical and chemical properties of peptidomimetic molecules are dictated by the integrated heterocyclic scaffolds they contain. Heterocyclic moieties are introduced into a majority of peptide-mimicking molecules to modulate conformational flexibility, improve bioavailability, and fine-tune electronics, and in order to achieve potency similar to or better than that of the natural peptide ligand. This mini-review delineates recent developments, limited to the past five years, in the utility of selected saturated 3- to 6-membered heterocyclic moieties in peptidomimetic design. Also discussed is the chemistry involved in the synthesis of the azaheterocyclic scaffolds and the structural implications of the introduction of these azaheterocycles in peptide backbones as well as side chains of the peptide mimics.

Understanding the function and regulation of Sox2 for its therapeutic potential in breast cancer

Sox family of transcriptional factors play essential functions in development and are implicated in multiple clinical disorders, including cancer. Sox2 being their most prominent member and performing a critical role in reprogramming differentiated adult cells to an embryonic phenotype is frequently upregulated in multiple cancers. High Sox2 levels are detected in breast tumor tissues and correlate with a worse prognosis. In addition, Sox2 expression is connected with resistance to conventional anticancer therapy. Together, it can be said that inhibiting Sox2 expression can reduce the malignant features associated with breast cancer, including invasion, migration, proliferation, stemness, and chemoresistance. This review highlights the critical roles played by the Sox gene family members in initiating or suppressing breast tumor development, while primarily focusing on Sox2 and its role in breast tumor initiation, maintenance, and progression, elucidates the probable mechanisms that control its activity, and puts forward potential therapeutic strategies to inhibit its expression.

In silico Approaches for the Design and Optimization of Interfering Peptides Against Protein-Protein Interactions

Large contact surfaces of protein-protein interactions (PPIs) remain to be an ongoing issue in the discovery and design of small molecule modulators. Peptides are intrinsically capable of exploring larger surfaces, stable, and bioavailable, and therefore bear a high therapeutic value in the treatment of various diseases, including cancer, infectious diseases, and neurodegenerative diseases. Given these promising properties, a long way has been covered in the field of targeting PPIs via peptide design strategies. In silico tools have recently become an inevitable approach for the design and optimization of these interfering peptides. Various algorithms have been developed to scrutinize the PPI interfaces. Moreover, different databases and software tools have been created to predict the peptide structures and their interactions with target protein complexes. High-throughput screening of large peptide libraries against PPIs; "hotspot" identification; structure-based and off-structure approaches of peptide design; 3D peptide modeling; peptide optimization strategies like cyclization; and peptide binding energy evaluation are among the capabilities of in silico tools. In the present study, the most recent advances in the field of in silico approaches for the design of interfering peptides against PPIs will be reviewed. The future perspective of the field and its advantages and limitations will also be pinpointed.

Targeting protein-protein interaction interfaces in COVID-19 drug discovery

To date, the COVID-19 pandemic has claimed over 1 million human lives, infected another 50 million individuals and wreaked havoc on the global economy. The crisis has spurred the ongoing development of drugs targeting its etiological agent, the SARS-CoV-2. Targeting relevant protein-protein interaction interfaces (PPIIs) is a viable paradigm for the design of antiviral drugs and enriches the targetable chemical space by providing alternative targets for drug discovery. In this review, we will provide a comprehensive overview of the theory, methods and applications of PPII-targeted drug development towards COVID-19 based on recent literature. We will also highlight novel developments, such as the successful use of non-native protein-protein interactions as targets for antiviral drug screening. We hope that this review may serve as an entry point for those interested in applying PPIIs towards COVID-19 drug discovery and speed up drug development against the pandemic.

Scanning Protein Surfaces with DNA-Encoded Libraries

Understanding the ligandability of a target protein, defined as the capability of a protein to bind drug-like compounds on any site, can give important stimuli to drug-development projects. For instance, inhibition of protein-protein interactions usually depends on the identification of protein surface binders. DNA-encoded chemical libraries (DELs) allow scanning of protein surfaces with large chemical space. Encoded library selection screens uncovered several protein-protein interaction inhibitors and compounds binding to the surface of G protein-coupled receptors (GPCRs) and kinases. The protein surface-binding chemotypes from DELs are predominantly chemically modified and cyclized peptides, and functional small-molecule peptidomimetics. Peptoid libraries and structural peptidomimetics have been less studied in the DEL field, hinting at hitherto less populated chemical space and suggesting alternative library designs. Roughly a third of bioactive molecules evolved from smaller, target-focused libraries. They showcase the potential of encoded libraries to identify more potent molecules from weak, for example, fragment-like, starting points.

Design, Synthesis and Evaluation of New Bioactive Oxadiazole Derivatives as Anticancer Agents Targeting Bcl-2

A series of 2-(1H-indol-3-yl)-5-substituted-1,3,4-oxadiazoles, 4a-m, were designed, synthesized and tested in vitro as potential pro-apoptotic Bcl-2 inhibitory anticancer agents based on our previously reported hit compounds. Synthesis of the target 1,3,4-oxadiazoles was readily accomplished through a cyclization reaction of indole carboxylic acid hydrazide 2 with substituted carboxylic acid derivatives 3a-m in the presence of phosphorus oxychloride. New compounds 4a-m showed a range of IC₅₀ values concentrated in the low micromolar range selectively in Bcl-2 positive human cancer cell lines. The most potent candidate 4-trifluoromethyl substituted analogue 4j showed selective IC₅₀ values of 0.52-0.88 μM against Bcl-2 expressing cell lines with no inhibitory effects in the Bcl-2 negative cell line. Moreover, 4j showed binding that was two-fold more potent than the positive control gossypol in the Bcl-2 ELISA binding affinity assay. Molecular modeling studies helped to further rationalize anti-apoptotic Bcl-2 binding and identified compound 4j as a candidate with drug-like properties for further investigation as a selective Bcl-2 inhibitory anticancer agent.

Recent advances in the development of protein-protein interactions modulators: mechanisms and clinical trials

Protein-protein interactions (PPIs) have pivotal roles in life processes. The studies showed that aberrant PPIs are associated with various diseases, including cancer, infectious diseases, and neurodegenerative diseases. Therefore, targeting PPIs is a direction in treating diseases and an essential strategy for the development of new drugs. In the past few decades, the modulation of PPIs has been recognized as one of the most challenging drug discovery tasks. In recent years, some PPIs modulators have entered clinical studies, some of which been approved for marketing, indicating that the modulators targeting PPIs have broad prospects. Here, we summarize the recent advances in PPIs modulators, including small molecules, peptides, and antibodies, hoping to provide some guidance to the design of novel drugs targeting PPIs in the future.

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.

ImmtorLig_DB: repertoire of virtually screened small molecules against immune receptors to bolster host immunity

Host directed therapies to boost immunity against infection are gaining considerable impetus following the observation that use of antibiotics has become a continuous source for the emergence of drug resistant strains of pathogens. Receptors expressed by the cells of immune system play a cardinal role in initiating sequence of events necessary to ameliorate many morbid conditions. Although, ligands for the immune receptors are available; but their use is limited due to complex structure, synthesis and cost-effectiveness. Virtual screening (VS) is an integral part of chemoinformatics and computer-aided drug design (CADD) and aims to streamline the process of drug discovery. ImmtorLig_DB is a repertoire of 5000 novel small molecules, screened from ZINC database and ranked using structure based virtual screening (SBVS) against 25 immune receptors which play a pivotal role in defending and initiating the activation of immune system. Consequently, in the current study, small molecules were screened by docking on the essential domains present on the receptors expressed by cells of immune system. The screened molecules exhibited efficacious binding to immune receptors, and indicated a possibility of discovering novel small molecules. Other features of ImmtorLig_DB include information about availability, clustering analysis, and estimation of absorption, distribution, metabolism, and excretion (ADME) properties of the screened small molecules. Structural comparisons indicate that predicted small molecules may be considered novel. Further, this repertoire is available via a searchable graphical user interface (GUI) through http://bioinfo.imtech.res.in/bvs/immtor/ .

Osteopontin at the Crossroads of Inflammation and Tumor Progression

Complex interactions between tumor and host cells regulate systemic tumor dissemination, a process that begins early at the primary tumor site and goes on until tumor cells detach themselves from the tumor mass and start migrating into the blood or lymphatic vessels. Metastatic cells colonize the target organs and are capable of surviving and growing at distant sites. In this context, osteopontin (OPN) appears to be a key determinant of the crosstalk between cancer cells and the host microenvironment, which in turn modulates immune evasion. OPN is overexpressed in several human carcinomas and has been implicated in inflammation, tumor progression, and metastasis. Thus, it represents one of the most attracting targets for cancer therapy. Within the tumor mass, OPN is secreted in various forms either by the tumor itself or by stroma cells, and it can exert either pro- or antitumorigenic effects according to the cell type and tumor microenvironment. Thus, targeting OPN for therapeutic purposes needs to take into account the heterogeneous functions of the multiple OPN forms with regard to cancer formation and progression. In this review, we will describe the role of systemic, tumor-derived, and stroma-derived OPN, highlighting its pivotal role at the crossroads of inflammation and tumor progression.

PPIMpred: a web server for high-throughput screening of small molecules targeting protein-protein interaction

PPIMpred is a web server that allows high-throughput screening of small molecules for targeting specific protein-protein interactions, namely Mdm2/P53, Bcl2/Bak and c-Myc/Max. Three different kernels of support vector machine (SVM), namely, linear, polynomial and radial basis function (RBF), and two other machine learning techniques including Naive Bayes and Random Forest were used to train the models. A fivefold cross-validation technique was used to measure the performance of these classifiers. The RBF kernel of SVM outperformed and/or was comparable with all other methods with accuracy values of 83%, 79% and 90% for Mdm2/P53, Bcl2/Bak and c-Myc/Max, respectively. About 80% of the predicted SVM scores of training/testing datasets from Mdm2/P53 and Bcl2/Bak have significant IC50 values and docking scores. The proposed models achieved an accuracy of 66-90% with blind sets. The three mentioned (Mdm2/P53, Bcl2/Bak and c-Myc/Max) proposed models were screened in a large dataset of 265 242 small chemicals from National Cancer Institute open database. To further realize the robustness of this approach, hits with high and random SVM scores were used for molecular docking in AutoDock Vina wherein the molecules with high and random predicted SVM scores yielded moderately significant docking scores (p-values < 0.1). In addition to the above-mentioned classification scheme, this web server also allows users to get the structural and chemical similarities with known chemical modulators or drug-like molecules based on Tanimoto coefficient similarity search algorithm. PPIMpred is freely available at http://bicresources.jcbose.ac.in/ssaha4/PPIMpred/.

Targeting kinase signaling pathways with constrained peptide scaffolds

Kinases are amongst the largest families in the human proteome and serve as critical mediators of a myriad of cell signaling pathways. Since altered kinase activity is implicated in a variety of pathological diseases, kinases have become a prominent class of proteins for targeted inhibition. Although numerous small molecule and antibody-based inhibitors have already received clinical approval, several challenges may still exist with these strategies including resistance, target selection, inhibitor potency and in vivo activity profiles. Constrained peptide inhibitors have emerged as an alternative strategy for kinase inhibition. Distinct from small molecule inhibitors, peptides can provide a large binding surface area that allows them to bind shallow protein surfaces rather than defined pockets within the target protein structure. By including chemical constraints within the peptide sequence, additional benefits can be bestowed onto the peptide scaffold such as improved target affinity and target selectivity, cell permeability and proteolytic resistance. In this review, we highlight examples of diverse chemistries that are being employed to constrain kinase-targeting peptide scaffolds and highlight their application to modulate kinase signaling as well as their potential clinical implications.

Murine ultrasound-guided transabdominal para-aortic injections of self-assembling type I collagen oligomers

Abdominal aortic aneurysms (AAAs) represent a potentially life-threatening condition that predominantly affects the infrarenal aorta. Several preclinical murine models that mimic the human condition have been developed and are now widely used to investigate AAA pathogenesis. Cell- or pharmaceutical-based therapeutics designed to prevent AAA expansion are currently being evaluated with these animal models, but more minimally invasive strategies for delivery could improve their clinical translation. The purpose of this study was to investigate the use of self-assembling type I collagen oligomers as an injectable therapeutic delivery vehicle in mice. Here we show the success and reliability of a para-aortic, ultrasound-guided technique for injecting quickly-polymerizing collagen oligomer solutions into mice to form a collagen-fibril matrix at body temperature. A commonly used infrarenal mouse AAA model was used to determine the target location of these collagen injections. Ultrasound-guided, closed-abdominal injections supported consistent delivery of collagen to the area surrounding the infrarenal abdominal aorta halfway between the right renal artery and aortic trifurcation into the iliac and tail arteries. This minimally invasive approach yielded outcomes similar to open-abdominal injections into the same region. Histological analysis on tissue removed on day 14 post-operatively showed minimal in vivo degradation of the self-assembled fibrillar collagen and the majority of implants experienced minimal inflammation and cell invasion, further confirming this material's potential as a method for delivering therapeutics. Finally, we showed that the typical length and position of this infrarenal AAA model was statistically similar to the length and targeted location of the injected collagen, increasing its feasibility as a localized therapeutic delivery vehicle. Future preclinical and clinical studies are needed to determine if specific therapeutics incorporated into the self-assembling type I collagen matrix described here can be delivered near the aorta and locally limit AAA expansion.

Protein-Protein Interactions (PPIs) as an Alternative to Targeting the ATP Binding Site of Kinase

Most of the developed kinase inhibitor drugs are ATP competitive and suffer from drawbacks such as off-target kinase activity, development of resistance due to mutation in the ATP binding pocket and unfavorable intellectual property situations. Besides the ATP binding pocket, protein kinases have binding sites that are involved in Protein-Protein Interactions (PPIs); these PPIs directly or indirectly regulate the protein kinase activity. Of recent, small molecule inhibitors of PPIs are emerging as an alternative to ATP competitive agents. Rational design of inhibitors for kinase PPIs could be carried out using molecular modeling techniques. In silico tools available for the prediction of hot spot residues and cavities at the PPI sites and the means to utilize this information for the identification of inhibitors are discussed. Moreover, in silico studies to target the Aurora B-INCENP PPI sites are discussed in context. Overall, this chapter provides detailed in silico strategies that are available to the researchers for carrying out structure-based drug design of PPI inhibitors.

Synthesis and evaluation of 5-(1H-indol-3-yl)-N-aryl-1,3,4-oxadiazol-2-amines as Bcl-2 inhibitory anticancer agents

A series of 5-(1H-indol-3-yl)-N-aryl-1,3,4-oxadiazol-2-amines 8a-j has been designed, synthesized and tested in vitro as potential pro-apoptotic Bcl-2-inhibitory anticancer agents based on our previous lead compound 8a. Synthesis of the target compounds was readily accomplished through a cyclisation reaction between indole-3-carboxylic acid hydrazide (5) and substituted isothiocyanates 6a-j, followed by oxidative cyclodesulfurization of the corresponding thiosemicarbazide 7a-j using 1,3-dibromo-5,5-dimethylhydantoin. Active compounds of the series 8a-j were found to have sub-micromolar IC₅₀ values selectively in Bcl-2 expressing human cancer cell lines; notably the 2-nitrophenyl analogue 8a was found to exhibit potent activity, and compounds 8a and 8e possessed comparable Bcl-2 binding affinity (ELISA assay) to the established natural product-based Bcl-2 inhibitor, gossypol. Molecular modeling studies helped to further rationalise anti-apoptotic Bcl-2 binding, and identified compounds 8a and 8e as candidates for further development as Bcl-2 inhibitory anticancer agents.

Evaluation of the anticancer properties of the predicted hBaxBH3-mimetic compound 2-hydroxy-3,5-dinitrobenzamide in a mammary carcinogenesis-induced rat model

Designing small molecular prototypes having potential to disrupt binding interfaces of pro-apoptotic–anti-apoptotic/BH3-only proteins is a promising strategy in cancer chemotherapy.

Multiple target drug cocktail design for attacking the core network markers of four cancers using ligand-based and structure-based virtual screening methods

BACKGROUND

Computer-aided drug design has a long history of being applied to discover new molecules to treat various cancers, but it has always been focused on single targets. The development of systems biology has let scientists reveal more hidden mechanisms of cancers, but attempts to apply systems biology to cancer therapies remain at preliminary stages. Our lab has successfully developed various systems biology models for several cancers. Based on these achievements, we present the first attempt to combine multiple-target therapy with systems biology.

METHODS

In our previous study, we identified 28 significant proteins--i.e., common core network markers--of four types of cancers as house-keeping proteins of these cancers. In this study, we ranked these proteins by summing their carcinogenesis relevance values (CRVs) across the four cancers, and then performed docking and pharmacophore modeling to do virtual screening on the NCI database for anti-cancer drugs. We also performed pathway analysis on these proteins using Panther and MetaCore to reveal more mechanisms of these cancer house-keeping proteins.

RESULTS

We designed several approaches to discover targets for multiple-target cocktail therapies. In the first one, we identified the top 20 drugs for each of the 28 cancer house-keeping proteins, and analyzed the docking pose to further understand the interaction mechanisms of these drugs. After screening for duplicates, we found that 13 of these drugs could target 11 proteins simultaneously. In the second approach, we chose the top 5 proteins with the highest summed CRVs and used them as the drug targets. We built a pharmacophore and applied it to do virtual screening against the Life-Chemical library for anti-cancer drugs. Based on these results, wet-lab bio-scientists could freely investigate combinations of these drugs for multiple-target therapy for cancers, in contrast to the traditional single target therapy.

CONCLUSIONS

Combination of systems biology with computer-aided drug design could help us develop novel drug cocktails with multiple targets. We believe this will enhance the efficiency of therapeutic practice and lead to new directions for cancer therapy.

Targeting protein-protein interactions in hematologic malignancies: still a challenge or a great opportunity for future therapies?

Over the past several years, there has been an increasing research effort focused on inhibition of protein-protein interactions (PPIs) to develop novel therapeutic approaches for cancer, including hematologic malignancies. These efforts have led to development of small molecule inhibitors of PPIs, some of which already advanced to the stage of clinical trials while others are at different stages of preclinical optimization, emphasizing PPIs as an emerging and attractive class of drug targets. Here, we review several examples of recently developed inhibitors of PPIs highly relevant to hematologic cancers. We address the existing skepticism about feasibility of targeting PPIs and emphasize potential therapeutic benefit from blocking PPIs in hematologic malignancies. We then use these examples to discuss the approaches for successful identification of PPI inhibitors and provide analysis of the protein-protein interfaces, with the goal to address 'druggability' of new PPIs relevant to hematology. We discuss lessons learned to improve the success of targeting new PPIs and evaluate prospects and limits of the research in this field. We conclude that not all PPIs are equally tractable for blocking by small molecules, and detailed analysis of PPI interfaces is critical for selection of those with the highest chance of success. Together, our analysis uncovers patterns that should help to advance drug discovery in hematologic malignancies by successful targeting of new PPIs.

TRB3 links insulin/IGF to tumour promotion by interacting with p62 and impeding autophagic/proteasomal degradations

High insulin/IGF is a biologic link between diabetes and cancers, but the underlying molecular mechanism remains unclear. Here we report a previously unrecognized tumour-promoting mechanism for stress protein TRB3, which mediates a reciprocal antagonism between autophagic and proteasomal degradation systems and connects insulin/IGF to malignant promotion. We find that several human cancers express higher TRB3 and phosphorylated insulin receptor substrate 1, which correlates negatively with patient's prognosis. TRB3 depletion protects against tumour-promoting actions of insulin/IGF and attenuates tumour initiation, growth and metastasis in mice. TRB3 interacts with autophagic receptor p62 and hinders p62 binding to LC3 and ubiquitinated substrates, which causes p62 deposition and suppresses autophagic/proteasomal degradation. Several tumour-promoting factors accumulate in cancer cells to support tumour metabolism, proliferation, invasion and metastasis. Interrupting TRB3/p62 interaction produces potent antitumour efficacies against tumour growth and metastasis. Our study opens possibility of targeting this interaction as a potential novel strategy against cancers with diabetes.

DARC 2.0: Improved Docking and Virtual Screening at Protein Interaction Sites

Over the past decade, protein-protein interactions have emerged as attractive but challenging targets for therapeutic intervention using small molecules. Due to the relatively flat surfaces that typify protein interaction sites, modern virtual screening tools developed for optimal performance against “traditional” protein targets perform less well when applied instead at protein interaction sites. Previously, we described a docking method specifically catered to the shallow binding modes characteristic of small-molecule inhibitors of protein interaction sites. This method, called DARC (Docking Approach using Ray Casting), operates by comparing the topography of the protein surface when “viewed” from a vantage point inside the protein against the topography of a bound ligand when “viewed” from the same vantage point. Here, we present five key enhancements to DARC. First, we use multiple vantage points to more accurately determine protein-ligand surface complementarity. Second, we describe a new scheme for rapidly determining optimal weights in the DARC scoring function. Third, we incorporate sampling of ligand conformers “on-the-fly” during docking. Fourth, we move beyond simple shape complementarity and introduce a term in the scoring function to capture electrostatic complementarity. Finally, we adjust the control flow in our GPU implementation of DARC to achieve greater speedup of these calculations. At each step of this study, we evaluate the performance of DARC in a “pose recapitulation” experiment: predicting the binding mode of 25 inhibitors each solved in complex with its distinct target protein (a protein interaction site). Whereas the previous version of DARC docked only one of these inhibitors to within 2 Å RMSD of its position in the crystal structure, the newer version achieves this level of accuracy for 12 of the 25 complexes, corresponding to a statistically significant performance improvement (p < 0.001). Collectively then, we find that the five enhancements described here – which together make up DARC 2.0 – lead to dramatically improved speed and performance relative to the original DARC method.

The role of HTS in drug discovery at the University of Michigan

High throughput screening (HTS) is an integral part of a highly collaborative approach to drug discovery at the University of Michigan. The HTS lab is one of four core centers that provide services to identify, produce, screen and follow-up on biomedical targets for faculty. Key features of this system are: protein cloning and purification, protein crystallography, small molecule and siRNA HTS, medicinal chemistry and pharmacokinetics. Therapeutic areas that have been targeted include anti-bacterial, metabolic, neurodegenerative, cardiovascular, anti-cancer and anti-viral. The centers work in a coordinated, interactive environment to affordably provide academic investigators with the technology, informatics and expertise necessary for successful drug discovery. This review provides an overview of these centers at the University of Michigan, along with case examples of successful collaborations with faculty.

Self-similarity of human protein interaction networks: a novel strategy of distinguishing proteins

The successful determination of reliable protein interaction networks (PINs) in several species in the post-genomic era has hitherto facilitated the quest to understanding systems and structural properties of such networks. It is envisaged that a clearer understanding of their intrinsic topological properties would elucidate evolutionary and biological topography of organisms. This, in turn, may inform the understanding of diseases' aetiology. By analysing sub-networks that are induced in various layers identified by zones defined as distance from central proteins, we show that zones of human PINs display self-similarity patterns. What is observed at a global level is repeated at lower levels of inducement. Furthermore, it is observed that these levels of strength point to refinement and specialisations in these layers. This may point to the fact that various levels of representations in the self-similarity phenomenon offer a way of measuring and distinguishing the importance of proteins in the network. To consolidate our findings, we have also considered a gene co-expression network and a class of gene regulatory networks in the same framework. In all cases, the phenomenon is significantly evident. In particular, the truly unbiased regulatory networks show finer level of articulation of self-similarity.

Insights into the molecular mechanisms of action of bioportides: a strategy to target protein-protein interactions

Cell-penetrating peptides (CPPs) are reliable vehicles for the target-selective intracellular delivery of therapeutic agents. The identification and application of numerous intrinsically bioactive CPPs, now designated as bioportides, is further endorsement of the tremendous clinical potential of CPP technologies. The refinement of proteomimetic bioportides, particularly sequences that mimic cationic α-helical domains involved in protein-protein interactions (PPIs), provides tremendous opportunities to modulate this emergent drug modality in a clinical setting. Thus, a number of CPP-based constructs are currently undergoing clinical trials as human therapeutics, with a particular focus upon anti-cancer agents. A well-characterised array of synthetic modifications, compatible with modern solid-phase synthesis, can be utilised to improve the biophysical and pharmacological properties of bioportides and so achieve cell-and tissue-selective targeting in vivo. Moreover, considering the recent successful development of stapled α-helical peptides as anti-cancer agents, we hypothesise that similar structural modifications are applicable to the design of bioportides that more effectively modulate the many interactomes known to underlie human diseases. Thus, we propose that stapled-helical bioportides could satisfy all of the clinical requirements for metabolically stable, intrinsically cell-permeable agents capable of regulating discrete PPIs by a dominant negative mode of action with minimal toxicity.

Structure-based computational approaches for small-molecule modulation of protein-protein interactions

Three-dimensional structures of proteins offer an opportunity for the rational design of small molecules to modulate protein-protein interactions. The presence of a well-defined binding pocket on the surface of protein complexes, particularly at their interface, can be used for docking-based virtual screening of chemical libraries. Several approaches have been developed to identify binding pockets that are implemented in programs such as SiteMap, fpocket, and FTSite. These programs enable the scoring of these pockets to determine whether they are suitable to accommodate high-affinity small molecules. Virtual screening of commercial or combinatorial libraries can be carried out to enrich these libraries and select compounds for further experimental validation. In virtual screening, a compound library is docked to the target protein. The resulting structures are scored and ranked for the selection and experimental validation of top candidates. Molecular docking has been implemented in a number of computer programs such as AutoDock Vina. We select a set of protein-protein interactions that have been successfully inhibited with small molecules in the past. Several computer programs are applied to identify pockets on the surface, and molecular docking is conducted in an attempt to reproduce the binding pose of the inhibitors. The results highlight the strengths and limitations of computational methods for the design of PPI inhibitors.

Multifunctional biosensor based on localized surface plasmon resonance for monitoring small molecule-protein interaction

We report an optical sensor based on localized surface plasmon resonance (LSPR) to study small-molecule protein interaction combining high sensitivity refractive index sensing for quantitative binding information and subsequent conformation-sensitive plasmon-activated circular dichroism spectroscopy. The interaction of α-amylase and a small-size molecule (PGG, pentagalloyl glucose) was log concentration-dependent from 0.5 to 154 μM. In situ tests were additionally successfully applied to the analysis of real wine samples. These studies demonstrate that LSPR sensors to monitor small molecule–protein interactions in real time and in situ, which is a great advance within technological platforms for drug discovery.

Core and specific network markers of carcinogenesis from multiple cancer samples

Cancer is the leading cause of death worldwide and is generally caused by mutations in multiple proteins or the dysregulation of pathways. Understanding the causes and the underlying carcinogenic mechanisms can help fight this disease. In this study, a systems biology approach was used to construct the protein-protein interaction (PPI) networks of four cancers and the non-cancers by their corresponding microarray data, PPI modeling and database-mining. By comparing PPI networks between cancer and non-cancer samples to find significant proteins with large PPI changes during carcinogenesis process, core and specific network markers were identified by the intersection and difference of significant proteins, respectively, with carcinogenesis relevance values (CRVs) for each cancer. A total of 28 significant proteins were identified as core network markers in the carcinogenesis of four types of cancer, two of which are novel cancer-related proteins (e.g., UBC and PSMA3). Moreover, seven crucial common pathways were found among these cancers based on their core network markers, and some specific pathways were particularly prominent based on the specific network markers of different cancers (e.g., the RIG-I-like receptor pathway in bladder cancer, the proteasome pathway and TCR pathway in liver cancer, and the HR pathway in lung cancer). Additional validation of these network markers using the literature and new tested datasets could strengthen our findings and confirm the proposed method. From these core and specific network markers, we could not only gain an insight into crucial common and specific pathways in the carcinogenesis, but also obtain a high promising PPI target for cancer therapy.

Drug-Like Protein-Protein Interaction Modulators: Challenges and Opportunities for Drug Discovery and Chemical Biology

[Formula: see text] Fundamental processes in living cells are largely controlled by macromolecular interactions and among them, protein-protein interactions (PPIs) have a critical role while their dysregulations can contribute to the pathogenesis of numerous diseases. Although PPIs were considered as attractive pharmaceutical targets already some years ago, they have been thus far largely unexploited for therapeutic interventions with low molecular weight compounds. Several limiting factors, from technological hurdles to conceptual barriers, are known, which, taken together, explain why research in this area has been relatively slow. However, this last decade, the scientific community has challenged the dogma and became more enthusiastic about the modulation of PPIs with small drug-like molecules. In fact, several success stories were reported both, at the preclinical and clinical stages. In this review article, written for the 2014 International Summer School in Chemoinformatics (Strasbourg, France), we discuss in silico tools (essentially post 2012) and databases that can assist the design of low molecular weight PPI modulators (these tools can be found at www.vls3d.com). We first introduce the field of protein-protein interaction research, discuss key challenges and comment recently reported in silico packages, protocols and databases dedicated to PPIs. Then, we illustrate how in silico methods can be used and combined with experimental work to identify PPI modulators.

Challenges and opportunities in targeting the menin-MLL interaction

Menin is an essential co-factor of oncogenic MLL fusion proteins and the menin-MLL interaction is critical for development of acute leukemia in vivo. Targeting the menin-MLL interaction with small molecules represents an attractive strategy to develop new anticancer agents. Recent developments, including determination of menin crystal structure and development of potent small molecule and peptidomimetic inhibitors, demonstrate the feasibility of targeting the menin-MLL interaction. On the other hand, biochemical and structural studies revealed that MLL binds to menin in a complex bivalent mode engaging two MLL motifs, and therefore inhibition of this protein-protein interaction represents a challenge. This review summarizes the most recent achievements in targeting the menin-MLL interaction as well as discusses potential benefits of blocking menin in cancer.

Development and validation of a high-content bimolecular fluorescence complementation assay for small-molecule inhibitors of HIV-1 Nef dimerization

Nef is a human immunodeficiency virus 1 (HIV-1) accessory factor essential for viral pathogenesis and AIDS progression. Many Nef functions require dimerization, and small molecules that block Nef dimerization may represent antiretroviral drug leads. Here we describe a cell-based assay for Nef dimerization inhibitors based on bimolecular fluorescence complementation (BiFC). Nef was fused to nonfluorescent, complementary fragments of yellow fluorescent protein (YFP) and coexpressed in the same cell population. Dimerization of Nef resulted in juxtaposition of the YFP fragments and reconstitution of the fluorophore. For automation, the Nef-YFP fusion proteins plus a monomeric red fluorescent protein (mRFP) reporter were expressed from a single vector, separated by picornavirus "2A" linker peptides to permit equivalent translation of all three proteins. Validation studies revealed a critical role for gating on the mRFP-positive subpopulation of transfected cells, as well as use of the mRFP signal to normalize the Nef-BiFC signal. Nef-BiFC/mRFP ratios resulting from cells expressing wild-type versus dimerization-defective Nef were very clearly separated, with Z factors consistently in the 0.6 to 0.7 range. A fully automated pilot screen of the National Cancer Institute Diversity Set III identified several hit compounds that reproducibly blocked Nef dimerization in the low micromolar range. This BiFC-based assay has the potential to identify cell-active small molecules that directly interfere with Nef dimerization and function.