Biophysical and computational fragment-based approaches to targeting protein-protein interactions: applications in structure-guided drug discovery

Impairing protein-protein interactions in an essential tRNA modification complex: An innovative antimicrobial strategy against Pseudomonas aeruginosa

Protein-protein interactions (PPIs) have been recognized as a promising target for the development of new drugs, as proved by the growing number of PPI modulators reaching clinical trials. In this context, peptides represent a valid alternative to small molecules, owing to their unique ability to mimic the target protein structure and interact with wider surface areas. Among the possible fields of interest, bacterial PPIs represent an attractive target to face the urgent necessity to fight antibiotic resistance. Growing attention has been paid to the YgjD/YeaZ/YjeE complex responsible for the essential t6A37 tRNA modification in bacteria. We previously identified an α-helix on the surface of Pseudomonas aeruginosa YeaZ, crucial for the YeaZ-YeaZ homodimer formation and the conserved YeaZ-YgjD interactions. Herein, we present our studies for impairing the PPIs involved in the formation of the YeaZ dimers through synthetic peptide derivatives of this helical moiety, both in vitro with purified components and on P. aeruginosa cells. Our results proved the possibility of targeting those PPIs which are usually essential for protein functioning and thus are refractory to mutational changes and antibiotic resistance development.

Competitive SPR chaser assay to study biomolecular interactions with very slow binding dissociation rate constant

Binding kinetics of drug and its target protein is crucial for the efficacy and safety of the drug. Using surface plasmon resonance (SPR) technology, we performed a competitive SPR chaser assay, a method to study biomolecular interactions with very slow dissociation rate constants (kd < 1E-4 s-1). This report described the principle and the experimental setup of the chaser assay, which involves using a competitive probe (chaser) to detect changes in target occupancy by a test molecule over time. We demonstrated the applicability of the chaser assay for both small and large molecules and compared the results with conventional SPR kinetic analysis and other methods. We suggest that the chaser assay is a useful and robust technique to characterize very tight biomolecular interactions, and that it can also be used to study cooperativity in ternary complex formation.

Stapled Peptides: An Innovative and Ultimate Future Drug Offering a Highly Powerful and Potent Therapeutic Alternative

Peptide-based therapeutics have traditionally faced challenges, including instability in the bloodstream and limited cell membrane permeability. However, recent advancements in α-helix stapled peptide modification techniques have rekindled interest in their efficacy. Notably, these developments ensure a highly effective method for improving peptide stability and enhancing cell membrane penetration. Particularly in the realm of antimicrobial peptides (AMPs), the application of stapled peptide techniques has significantly increased peptide stability and has been successfully applied to many peptides. Furthermore, constraining the secondary structure of peptides has also been proven to enhance their biological activity. In this review, the entire process through which hydrocarbon-stapled antimicrobial peptides attain improved drug-like properties is examined. First, the essential secondary structural elements required for their activity as drugs are validated, specific residues are identified using alanine scanning, and stapling techniques are strategically incorporated at precise locations. Additionally, the mechanisms by which these structure-based stapled peptides function as AMPs are explored, providing a comprehensive and engaging discussion.

Studying protein-protein interactions: Latest and most popular approaches

PPIs, or protein-protein interactions, are essential for many biological processes. According to the findings, abnormal PPIs have been linked to several diseases, such as cancer and infectious and neurological disorders. Consequently, focusing on PPIs is a path toward disease treatment and a crucial tool for producing novel medications. Many methods exist to investigate PPIs, including low- and high-throughput studies. Since many PPIs have been discovered using in vitro and in vivo experimental approaches, the use of computational methods to predict PPIs has grown due to the expanding scale of PPI data and the intrinsic complexity of interacting mechanisms. Recognizing PPI networks offers a systematic means of predicting protein functions, and pathways that are included. These investigations can help uncover the underlying molecular mechanisms of complex phenotypes and clarify the biological processes related to health and diseases. Therefore, our goal in this study is to provide an overview of the latest and most popular approaches for investigating PPIs. We also overview some important clinical approaches based on the PPIs and how these interactions can be targeted.

Accessing Active Fragments for Drug Discovery Utilising Nitroreductase Biocatalysis

Biocatalysis has played a limited role in the early stages of drug discovery. This is often attributed to the limited substrate scope of enzymes not affording access to vast areas of novel chemical space. Here, we have shown a promiscuous nitroreductase enzyme (NR-55) can be used to produce a panel of functionalised anilines from a diverse panel of aryl nitro starting materials. After screening on analytical scale, we show that sixteen substrates could be scaled to 1 mmol scale, with several poly-functional anilines afforded with ease under the standard conditions. The aniline products were also screened for activity against several cell lines of interest, with modest activity observed for one compound. This study demonstrates the potential for nitroreductase biocatalysis to provide access to functional fragments under benign conditions.

Stabilized cyclic peptides as modulators of protein-protein interactions: promising strategies and biological evaluation

Protein-protein interactions (PPIs) control many essential biological pathways which are often misregulated in disease. As such, selective PPI modulators are desirable to unravel complex functions of PPIs and thus expand the repertoire of therapeutic targets. However, the large size and relative flatness of PPI interfaces make them challenging molecular targets for conventional drug modalities, rendering most PPIs "undruggable". Therefore, there is a growing need to discover innovative molecules that are able to modulate crucial PPIs. Peptides are ideal candidates to deliver such therapeutics attributed to their ability to closely mimic structural features of protein interfaces. However, their inherently poor proteolysis resistance and cell permeability inevitably hamper their biomedical applications. The introduction of a constraint (i.e., peptide cyclization) to stabilize peptides' secondary structure is a promising strategy to address this problem as witnessed by the rapid development of cyclic peptide drugs in the past two decades. Here, we comprehensively review the recent progress on stabilized cyclic peptides in targeting challenging PPIs. Technological advancements and emerging chemical approaches for stabilizing active peptide conformations are categorized in terms of α-helix stapling, β-hairpin mimetics and macrocyclization. To discover potent and selective ligands, cyclic peptide library technologies were updated based on genetic, biochemical or synthetic methodologies. Moreover, several advances to improve the permeability and oral bioavailability of biologically active cyclic peptides enable the de novo development of cyclic peptide ligands with pharmacological properties. In summary, the development of cyclic peptide-based PPI modulators carries tremendous promise for the next generation of therapeutic agents to target historically "intractable" PPI systems.

Improved prediction of protein-protein interactions by a modified strategy using three conventional docking software in combination

Proteins play a crucial role in many biological processes, where their interaction with other proteins are integral. Abnormal protein-protein interactions (PPIs) have been linked to various diseases including cancer, and thus targeting PPIs holds promise for drug development. However, experimental confirmation of the peculiarities of PPIs is challenging due to their dynamic and transient nature. As a complement to experimental technologies, multiple computational molecular docking (MD) methods have been developed to predict the structures of protein-protein complexes and their dynamics, still requiring further improvements in several issues. Here, we report an improved MD method, namely three-software docking (3SD), by employing three popular protein-peptide docking software (CABS-dock, HPEPDOCK, and HADDOCK) in combination to ensure constant quality for most targets. We validated our 3SD performance in known protein-peptide interactions (PpIs). We also enhanced MD performance in proteins having intrinsically disordered regions (IDRs) by applying the modified 3SD strategy, the three-software docking after removing random coiled IDR (3SD-RR), to the comparable crystal PpI structures. At the end, we applied 3SD-RR to the AlphaFold2-predicted receptors, yielding an efficient prediction of PpI pose with high relevance to the experimental data regardless of the presence of IDRs or the availability of receptor structures. Our study provides an improved solution to the challenges in studying PPIs through computational docking and has the potential to contribute to PPIs-targeted drug discovery. SIGNIFICANCE STATEMENT: Protein-protein interactions (PPIs) are integral to life, and abnormal PPIs are associated with diseases such as cancer. Studying protein-peptide interactions (PpIs) is challenging due to their dynamic and transient nature. Here we developed improved docking methods (3SD and 3SD-RR) to predict the PpI poses, ensuring constant quality in most targets and also addressing issues like intrinsically disordered regions (IDRs) and artificial intelligence-predicted structures. Our study provides an improved solution to the challenges in studying PpIs through computational docking and has the potential to contribute to PPIs-targeted drug discovery.

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

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

Targeting MCL-1 protein to treat cancer: opportunities and challenges

Evading apoptosis has been linked to tumor development and chemoresistance. One mechanism for this evasion is the overexpression of prosurvival B-cell lymphoma-2 (BCL-2) family proteins, which gives cancer cells a survival advantage. Mcl-1, a member of the BCL-2 family, is among the most frequently amplified genes in cancer. Targeting myeloid cell leukemia-1 (MCL-1) protein is a successful strategy to induce apoptosis and overcome tumor resistance to chemotherapy and targeted therapy. Various strategies to inhibit the antiapoptotic activity of MCL-1 protein, including transcription, translation, and the degradation of MCL-1 protein, have been tested. Neutralizing MCL-1's function by targeting its interactions with other proteins via BCL-2 interacting mediator (BIM)S2A has been shown to be an equally effective approach. Encouraged by the design of venetoclax and its efficacy in chronic lymphocytic leukemia, scientists have developed other BCL-2 homology (BH3) mimetics-particularly MCL-1 inhibitors (MCL-1i)-that are currently in clinical trials for various cancers. While extensive reviews of MCL-1i are available, critical analyses focusing on the challenges of MCL-1i and their optimization are lacking. In this review, we discuss the current knowledge regarding clinically relevant MCL-1i and focus on predictive biomarkers of response, mechanisms of resistance, major issues associated with use of MCL-1i, and the future use of and maximization of the benefits from these agents.

Stretching Peptides to Generate Small Molecule β-Strand Mimics

Advances in the modulation of protein-protein interactions (PPIs) enable both characterization of PPI networks that govern diseases and design of therapeutics and probes. The shallow protein surfaces that dominate PPIs are challenging to target using standard methods, and approaches for accessing extended backbone structures are limited. Here, we incorporate a rigid, linear, diyne brace between side chains at the i to i+2 positions to generate a family of low-molecular-weight, extended-backbone peptide macrocycles. NMR and density functional theory studies show that these stretched peptides adopt stable, rigid conformations in solution and can be tuned to explore extended peptide conformational space. The diyne brace is formed in excellent conversions (>95%) and amenable to high-throughput synthesis. The minimalist structure-inducing tripeptide core (<300 Da) is amenable to further synthetic elaboration. Diyne-braced inhibitors of bacterial type 1 signal peptidase demonstrate the utility of the technique.

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.

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

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

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.

Challenges in Discovering Drugs That Target the Protein-Protein Interactions of Disordered Proteins

Protein–protein interactions (PPIs) outnumber proteins and are crucial to many fundamental processes; in consequence, PPIs are associated with several pathological conditions including neurodegeneration and modulating them by drugs constitutes a potentially major class of therapy. Classically, however, the discovery of small molecules for use as drugs entails targeting individual proteins rather than targeting PPIs. This is largely because discovering small molecules to modulate PPIs has been seen as extremely challenging. Here, we review the difficulties and limitations of strategies to discover drugs that target PPIs directly or indirectly, taking as examples the disordered proteins involved in neurodegenerative diseases.

Intracellular Delivery of Antibodies for Selective Cell Signaling Interference

Many intracellular signaling events remain poorly characterized due to a general lack of tools to interfere with "undruggable" targets. Antibodies have the potential to elucidate intracellular mechanisms via targeted disruption of cell signaling cascades because of their ability to bind to a target with high specificity and affinity. However, due to their size and chemical composition, antibodies cannot innately cross the cell membrane, and thus access to the cytosol with these macromolecules has been limited. Here, we describe strategies for accessing the intracellular space with recombinant antibodies mediated by cationic lipid nanoparticles to selectively disrupt intracellular signaling events. Together, our results demonstrate the use of recombinantly produced antibodies, delivered at concentrations of 10 nM, to selectively interfere with signaling driven by a single posttranslational modification. Efficient intracellular delivery of engineered antibodies opens up possibilities for modulation of previously "undruggable" targets, including for potential therapeutic applications.

Discovery of Novel Small Molecule Inhibitors Disrupting the PCSK9-LDLR Interaction

Proprotein convertase subtilisin kexin 9 (PCSK9) has been identified as a reliable therapeutic target for hypercholesterolemia and coronary artery heart diseases since the monoclonal antibodies of PCSK9 have launched. Disrupting the protein-protein interaction (PPI) between PCSK9 and the low-density lipoprotein receptor (LDLR) has been considered as a promising approach for developing PCSK9 inhibitors. However, PPIs have been traditionally considered difficult to target by small molecules since the PPI surface is usually large, flat, featureless, and without a "pocket" or "groove" for ligand binding. The PCSK9-LDLR PPI interface is such a typical case. In this study, a potential binding pocket was generated on the PCSK9-LDLR PPI surface of PCSK9 through induced-fit docking. On the basis of this induced binding pocket, virtual screening, molecular dynamics (MD) simulation, and biological evaluations have been applied for the identification of novel small molecule inhibitors of PCSK9-LDLR PPI. Among the selected compounds, compound 13 exhibited certain PCSK9-LDLR PPI inhibitory activity (IC₅₀: 7.57 ± 1.40 μM). The direct binding affinity between 13 and PCSK9 was determined with a K_D value of 2.50 ± 0.73 μM. The LDLR uptake function could be also restored to a certain extent by 13 in HepG2 cells. This well-characterized hit compound will facilitate the further development of novel small molecule inhibitors of PCSK9-LDLR PPI.

Evolution of biophysical tools for quantitative protein interactions and drug discovery

With millions of signalling events occurring simultaneously, cells process a continuous flux of information. The genesis, processing, and regulation of information are dictated by a huge network of protein interactions. This is proven by the fact that alterations in the levels of proteins, single amino acid changes, post-translational modifications, protein products arising out of gene fusions alter the interaction landscape leading to diseases such as congenital disorders, deleterious syndromes like cancer, and crippling diseases like the neurodegenerative disorders which are often fatal. Needless to say, there is an immense effort to understand the biophysical basis of such direct interactions between any two proteins, the structure, domains, and sequence motifs involved in tethering them, their spatio-temporal regulation in cells, the structure of the network, and their eventual manipulation for intervention in diseases. In this chapter, we will deliberate on a few techniques that allow us to dissect the thermodynamic and kinetic aspects of protein interaction, how innovation has rendered some of the traditional techniques applicable for rapid analysis of multiple samples using small amounts of material. These advances coupled with automation are catching up with the genome-wide or proteome-wide studies aimed at identifying new therapeutic targets. The chapter will also summarize how some of these techniques are suited either in the standalone mode or in combination with other biophysical techniques for the drug discovery process.

Discovery of novel immunopharmacological ligands targeting the IL-17 inflammatory pathway

Interleukin 17 (IL-17) is a proinflammatory cytokine that acts as an immune checkpoint for several autoimmune diseases. Therapeutic neutralizing antibodies that target this cytokine have demonstrated clinical efficacy in psoriasis. However, biologics have limitations such as their high cost and their lack of oral bioavailability. Thus, it is necessary to expand the therapeutic options for this IL-17A/IL-17RA pathway, applying novel drug discovery methods to find effective small molecules. In this work, we combined biophysical and cell-based assays with structure-based docking to find novel ligands that target this pathway. First, a virtual screening of our chemical library of 60000 compounds was used to identify 67 potential ligands of IL-17A and IL-17RA. We developed a biophysical label-free binding assay to determine interactions with the extracellular domain of IL-17RA. Two molecules (CBG040591 and CBG060392) with quinazolinone and pyrrolidinedione chemical scaffolds, respectively, were confirmed as ligands of IL-17RA with micromolar affinity. The anti-inflammatory activity of these ligands as cytokine-release inhibitors was evaluated in human keratinocytes. Both ligands inhibited the release of chemokines mediated by IL-17A, with an IC₅₀ of 20.9 ± 12.6 μM and 23.6 ± 11.8 μM for CCL20 and an IC₅₀ of 26.7 ± 13.1 μM and 45.3 ± 13.0 μM for CXCL8. Hence, they blocked IL-17A proinflammatory activity, which is consistent with the inhibition of the signalling of the IL-17A receptor by ligand CBG060392. Therefore, we identified two novel immunopharmacological ligands targeting the IL-17A/IL-17RA pathway with antiinflammatory efficacy that can be promising tools for a drug discovery program for psoriasis.

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.

Binding of excipients is a poor predictor for aggregation kinetics of biopharmaceutical proteins

One of the major challenges in formulation development of biopharmaceuticals is improving long-term storage stability, which is often achieved by addition of excipients to the final formulation. Finding the optimal excipient for a given protein is usually done using a trial-and-error approach, due to the lack of general understanding of how excipients work for a particular protein. Previously, preferential interactions (binding or exclusion) of excipients with proteins were postulated as a mechanism explaining diversity in the stabilisation effects. Weak preferential binding is however difficult to quantify experimentally, and the question remains whether the formulation process should seek excipients which preferentially bind with proteins, or not. Here, we apply solution NMR spectroscopy to comprehensively evaluate protein-excipient interactions between therapeutically relevant proteins and commonly used excipients. Additionally, we evaluate the effect of excipients on thermal and colloidal protein stability, on aggregation kinetics and protein storage stability at elevated temperatures. We show that there is a weak negative correlation between the strength of protein-excipient interactions and effect on enhancing protein thermal stability. We found that the overall protein-excipient binding per se can be a poor criterion for choosing excipients enhancing formulation stability. Experiments on a diverse set of excipients and test proteins reveal that while excipients affect all of the different aspects of protein stability, the effects are very much protein specific, and care must be taken to avoid apparent generalisations if a smaller dataset is being used.

The design and development of covalent protein-protein interaction inhibitors for cancer treatment

Protein-protein interactions (PPIs) are central to a variety of biological processes, and their dysfunction is implicated in the pathogenesis of a range of human diseases, including cancer. Hence, the inhibition of PPIs has attracted significant attention in drug discovery. Covalent inhibitors have been reported to achieve high efficiency through forming covalent bonds with cysteine or other nucleophilic residues in the target protein. Evidence suggests that there is a reduced risk for the development of drug resistance against covalent drugs, which is a major challenge in areas such as oncology and infectious diseases. Recent improvements in structural biology and chemical reactivity have enabled the design and development of potent and selective covalent PPI inhibitors. In this review, we will highlight the design and development of therapeutic agents targeting PPIs for cancer therapy.

Interaction Energetics and Druggability of the Protein-Protein Interaction between Kelch-like ECH-Associated Protein 1 (KEAP1) and Nuclear Factor Erythroid 2 Like 2 (Nrf2)

Development of small molecule inhibitors of protein-protein interactions (PPIs) is hampered by our poor understanding of the druggability of PPI target sites. Here, we describe the combined application of alanine-scanning mutagenesis, fragment screening, and FTMap computational hot spot mapping to evaluate the energetics and druggability of the highly charged PPI interface between Kelch-like ECH-associated protein 1 (KEAP1) and nuclear factor erythroid 2 like 2 (Nrf2), an important drug target. FTMap identifies four binding energy hot spots at the active site. Only two of these are exploited by Nrf2, which alanine scanning of both proteins shows to bind primarily through E79 and E82 interacting with KEAP1 residues S363, R380, R415, R483, and S508. We identify fragment hits and obtain X-ray complex structures for three fragments via crystal soaking using a new crystal form of KEAP1. Combining these results provides a comprehensive and quantitative picture of the origins of binding energy at the interface. Our findings additionally reveal non-native interactions that might be exploited in the design of uncharged synthetic ligands to occupy the same site on KEAP1 that has evolved to bind the highly charged DEETGE binding loop of Nrf2. These include π-stacking with KEAP1 Y525 and interactions at an FTMap-identified hot spot deep in the binding site. Finally, we discuss how the complementary information provided by alanine-scanning mutagenesis, fragment screening, and computational hot spot mapping can be integrated to more comprehensively evaluate PPI druggability.

Disorders of FZ-CRD; insights towards FZ-CRD folding and therapeutic landscape

The ER is hub for protein folding. Proteins that harbor a Frizzled cysteine-rich domain (FZ-CRD) possess 10 conserved cysteine motifs held by a unique disulfide bridge pattern which attains a correct fold in the ER. Little is known about implications of disease-causing missense mutations within FZ-CRD families. Mutations in FZ-CRD of Frizzled class receptor 4 (FZD4) and Muscle, skeletal, receptor tyrosine kinase (MuSK) and Receptor tyrosine kinase-like orphan receptor 2 (ROR2) cause Familial Exudative Vitreoretinopathy (FEVR), Congenital Myasthenic Syndrome (CMS), and Robinow Syndrome (RS) respectively. We highlight reported pathogenic inherited missense mutations in FZ-CRD of FZD4, MuSK and ROR2 which misfold, and traffic abnormally in the ER, with ER-associated degradation (ERAD) as a common pathogenic mechanism for disease. Our review shows that all studied FZ-CRD mutants of RS, FEVR and CMS result in misfolded proteins and/or partially misfolded proteins with an ERAD fate, thus we coin them as “disorders of FZ-CRD”. Abnormal trafficking was demonstrated in 17 of 29 mutants studied; 16 mutants were within and/or surrounding the FZ-CRD with two mutants distant from FZ-CRD. These ER-retained mutants were improperly N-glycosylated confirming ER-localization. FZD4 and MuSK mutants were tagged with polyubiquitin chains confirming targeting for proteasomal degradation. Investigating the cellular and molecular mechanisms of these mutations is important since misfolded protein and ER-targeted therapies are in development. The P344R-MuSK kinase mutant showed around 50% of its in-vitro autophosphorylation activity and P344R-MuSK increased two-fold on proteasome inhibition. M105T-FZD4, C204Y-FZD4, and P344R-MuSK mutants are thermosensitive and therefore, might benefit from extending the investigation to a larger number of chemical chaperones and/or proteasome inhibitors. Nonetheless, FZ-CRD ER-lipidation it less characterized in the literature and recent structural data sheds light on the importance of lipidation in protein glycosylation, proper folding, and ER trafficking. Current treatment strategies in-place for the conformational disease landscape is highlighted. From this review, we envision that disorders of FZ-CRD might be receptive to therapies that target FZ-CRD misfolding, regulation of fatty acids, and/or ER therapies; thus paving the way for a newly explored paradigm to treat different diseases with common defects.

Evolution of In Silico Strategies for Protein-Protein Interaction Drug Discovery

The advent of advanced molecular modeling software, big data analytics, and high-speed processing units has led to the exponential evolution of modern drug discovery and better insights into complex biological processes and disease networks. This has progressively steered current research interests to understanding protein-protein interaction (PPI) systems that are related to a number of relevant diseases, such as cancer, neurological illnesses, metabolic disorders, etc. However, targeting PPIs are challenging due to their "undruggable" binding interfaces. In this review, we focus on the current obstacles that impede PPI drug discovery, and how recent discoveries and advances in in silico approaches can alleviate these barriers to expedite the search for potential leads, as shown in several exemplary studies. We will also discuss about currently available information on PPI compounds and systems, along with their usefulness in molecular modeling. Finally, we conclude by presenting the limits of in silico application in drug discovery and offer a perspective in the field of computer-aided PPI drug discovery.

Targeting Allostery with Avatars to Design Inhibitors Assessed by Cell Activity: Dissecting MRE11 Endo- and Exonuclease Activities

For inhibitor design, as in most research, the best system is question dependent. We suggest structurally defined allostery to design specific inhibitors that target regions beyond active sites. We choose systems allowing efficient quality structures with conformational changes as optimal for structure-based design to optimize inhibitors. We maintain that evolutionarily related targets logically provide molecular avatars, where this Sanskrit term for descent includes ideas of functional relationships and of being a physical embodiment of the target's essential features without requiring high sequence identity. Appropriate biochemical and cell assays provide quantitative measurements, and for biomedical impacts, any inhibitor's activity should be validated in human cells. Specificity is effectively shown empirically by testing if mutations blocking target activity remove cellular inhibitor impact. We propose this approach to be superior to experiments testing for lack of cross-reactivity among possible related enzymes, which is a challenging negative experiment. As an exemplary avatar system for protein and DNA allosteric conformational controls, we focus here on developing separation-of-function inhibitors for meiotic recombination 11 nuclease activities. This was achieved not by targeting the active site but rather by geometrically impacting loop motifs analogously to ribosome antibiotics. These loops are neighboring the dimer interface and active site act in sculpting dsDNA and ssDNA into catalytically competent complexes. One of our design constraints is to preserve DNA substrate binding to geometrically block competing enzymes and pathways from the damaged site. We validate our allosteric approach to controlling outcomes in human cells by reversing the radiation sensitivity and genomic instability in BRCA mutant cells.

HTS by NMR for the Identification of Potent and Selective Inhibitors of Metalloenzymes

We have recently proposed a novel drug discovery approach based on biophysical screening of focused positional scanning libraries in which each element of the library contained a common binding moiety for the given target or class of targets. In this Letter, we report on the implementation of this approach to target metal containing proteins. In our implementation, we first derived a focused positional scanning combinatorial library of peptide mimetics (of approximately 100,000 compounds) in which each element of the library contained the metal-chelating moiety hydroxamic acid at the C-terminal. Screening of this library by nuclear magnetic resonance spectroscopy in solution allowed the identification of a novel and selective compound series targeting MMP-12. The data supported that our general approach, perhaps applied using other metal chelating agents or other initial binding fragments, may result very effective in deriving novel and selective agents against metalloenzyme.

Assessing the performance of the MM/PBSA and MM/GBSA methods. 6. Capability to predict protein-protein binding free energies and re-rank binding poses generated by protein-protein docking

Understanding protein-protein interactions (PPIs) is quite important to elucidate crucial biological processes and even design compounds that interfere with PPIs with pharmaceutical significance. Protein-protein docking can afford the atomic structural details of protein-protein complexes, but the accurate prediction of the three-dimensional structures for protein-protein systems is still notoriously difficult due in part to the lack of an ideal scoring function for protein-protein docking. Compared with most scoring functions used in protein-protein docking, the Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) and Molecular Mechanics/Poisson Boltzmann Surface Area (MM/PBSA) methodologies are more theoretically rigorous, but their overall performance for the predictions of binding affinities and binding poses for protein-protein systems has not been systematically evaluated. In this study, we first evaluated the performance of MM/PBSA and MM/GBSA to predict the binding affinities for 46 protein-protein complexes. On the whole, different force fields, solvation models, and interior dielectric constants have obvious impacts on the prediction accuracy of MM/GBSA and MM/PBSA. The MM/GBSA calculations based on the ff02 force field, the GB model developed by Onufriev et al. and a low interior dielectric constant (εin = 1) yield the best correlation between the predicted binding affinities and the experimental data (rp = -0.647), which is better than MM/PBSA (rp = -0.523) and a number of empirical scoring functions used in protein-protein docking (rp = -0.141 to -0.529). Then, we examined the capability of MM/GBSA to identify the possible near-native binding structures from the decoys generated by ZDOCK for 43 protein-protein systems. The results illustrate that the MM/GBSA rescoring has better capability to distinguish the correct binding structures from the decoys than the ZDOCK scoring. Besides, the optimal interior dielectric constant of MM/GBSA for re-ranking docking poses may be determined by analyzing the characteristics of protein-protein binding interfaces. Considering the relatively high prediction accuracy and low computational cost, MM/GBSA may be a good choice for predicting the binding affinities and identifying correct binding structures for protein-protein systems.

Discovery and characterization of highly potent and selective allosteric USP7 inhibitors

Given the importance of ubiquitin-specific protease 7 (USP7) in oncogenic pathways, identification of USP7 inhibitors has attracted considerable interest. Despite substantial efforts, however, the development of validated deubiquitinase (DUB) inhibitors that exhibit drug-like properties and a well-defined mechanism of action has proven particularly challenging. In this article, we describe the identification, optimization and detailed characterization of highly potent (IC₅₀ < 10 nM), selective USP7 inhibitors together with their less active, enantiomeric counterparts. We also disclose, for the first time, co-crystal structures of a human DUB enzyme complexed with small-molecule inhibitors, which reveal a previously undisclosed allosteric binding site. Finally, we report the identification of cancer cell lines hypersensitive to USP7 inhibition (EC₅₀ < 30 nM) and demonstrate equal or superior activity in these cell models compared to clinically relevant MDM2 antagonists. Overall, these findings demonstrate the tractability and druggability of DUBs, and provide important tools for additional target validation studies.

A computational protocol for the discovery of lead molecules targeting DNA unique to pathogens

With the rapid emergence of drug resistant pathogens, it has become imperative to develop alternative medications as well as find new drug targets to overcome this crisis. Hence, this has become prime focus of several academic laboratories and pharmaceutical companies. Here, we report a computational protocol for identifying unique DNA sequence(s) in the pathogen which is absent in human and related non-pathogenic strains of the microbe. In order to use the unique sequence as drug target, the protocol, in the second step, uses virtual screening against a million compound library to identify candidate small molecules which can bind to these unique DNA targets in the pathogen only. Theoretically the molecules identified after screening should not bind to human DNA. This methodology is demonstrated on Mycobacterium tuberculosis H37Rv, wherein a new octamer sequence present only in H37Rv has been identified and a few candidate small molecules as potential drug have been proposed. Being fast and cost effective, this protocol could be of importance in generating new potential drug candidates against infectious organisms for further experimental studies. This methodology is freely available at http://www.scfbio-iitd.res.in/PSDDF/.

Hot-spot identification on a broad class of proteins and RNA suggest unifying principles of molecular recognition

Chemically diverse fragments tend to collectively bind at localized sites on proteins, which is a cornerstone of fragment-based techniques. A central question is how general are these strategies for predicting a wide variety of molecular interactions such as small molecule-protein, protein-protein and protein-nucleic acid for both experimental and computational methods. To address this issue, we recently proposed three governing principles, (1) accurate prediction of fragment-macromolecule binding free energy, (2) accurate prediction of water-macromolecule binding free energy, and (3) locating sites on a macromolecule that have high affinity for a diversity of fragments and low affinity for water. To test the generality of these concepts we used the computational technique of Simulated Annealing of Chemical Potential to design one small fragment to break the RecA-RecA protein-protein interaction and three fragments that inhibit peptide-deformylase via water-mediated multi-body interactions. Experiments confirm the predictions that 6-hydroxydopamine potently inhibits RecA and that PDF inhibition quantitatively tracks the water-mediated binding predictions. Additionally, the principles correctly predict the essential bound waters in HIV Protease, the surprisingly extensive binding site of elastase, the pinpoint location of electron transfer in dihydrofolate reductase, the HIV TAT-TAR protein-RNA interactions, and the MDM2-MDM4 differential binding to p53. The experimental confirmations of highly non-obvious predictions combined with the precise characterization of a broad range of known phenomena lend strong support to the generality of fragment-based methods for characterizing molecular recognition.

Genomes, structural biology and drug discovery: combating the impacts of mutations in genetic disease and antibiotic resistance

For over four decades structural biology has been used to understand the mechanisms of disease, and structure-guided approaches have demonstrated clearly that they can contribute to many aspects of early drug discovery, both computationally and experimentally. Structure can also inform our understanding of impacts of mutations in human genetic diseases and drug resistance in cancers and infectious diseases. We discuss the ways that structural insights might be useful in both repurposing off-licence drugs and guide the design of new molecules that might be less susceptible to drug resistance in the future.

Biophysics in drug discovery: impact, challenges and opportunities

Over the past 25 years, biophysical technologies such as X-ray crystallography, nuclear magnetic resonance spectroscopy, surface plasmon resonance spectroscopy and isothermal titration calorimetry have become key components of drug discovery platforms in many pharmaceutical companies and academic laboratories. There have been great improvements in the speed, sensitivity and range of possible measurements, providing high-resolution mechanistic, kinetic, thermodynamic and structural information on compound-target interactions. This Review provides a framework to understand this evolution by describing the key biophysical methods, the information they can provide and the ways in which they can be applied at different stages of the drug discovery process. We also discuss the challenges for current technologies and future opportunities to use biophysical methods to solve drug discovery problems.

Systematic Targeting of Protein-Protein Interactions

Over the past decade, protein-protein interactions (PPIs) have gone from being neglected as 'undruggable' to being considered attractive targets for the development of therapeutics. Recent advances in computational analysis, fragment-based screening, and molecular design have revealed promising strategies to address the basic molecular recognition challenge: how to target large protein surfaces with specificity. Several systematic and complementary workflows have been developed to yield successful inhibitors of PPIs. Here we review the major contemporary approaches utilized for the discovery of inhibitors and focus on a structure-based workflow, from the selection of a biological target to design.

Impact of germline and somatic missense variations on drug binding sites

Advancements in next-generation sequencing (NGS) technologies are generating a vast amount of data. This exacerbates the current challenge of translating NGS data into actionable clinical interpretations. We have comprehensively combined germline and somatic nonsynonymous single-nucleotide variations (nsSNVs) that affect drug binding sites in order to investigate their prevalence. The integrated data thus generated in conjunction with exome or whole-genome sequencing can be used to identify patients who may not respond to a specific drug because of alterations in drug binding efficacy due to nsSNVs in the target protein's gene. To identify the nsSNVs that may affect drug binding, protein–drug complex structures were retrieved from Protein Data Bank (PDB) followed by identification of amino acids in the protein–drug binding sites using an occluded surface method. Then, the germline and somatic mutations were mapped to these amino acids to identify which of these alter protein–drug binding sites. Using this method we identified 12 993 amino acid–drug binding sites across 253 unique proteins bound to 235 unique drugs. The integration of amino acid–drug binding sites data with both germline and somatic nsSNVs data sets revealed 3133 nsSNVs affecting amino acid–drug binding sites. In addition, a comprehensive drug target discovery was conducted based on protein structure similarity and conservation of amino acid–drug binding sites. Using this method, 81 paralogs were identified that could serve as alternative drug targets. In addition, non-human mammalian proteins bound to drugs were used to identify 142 homologs in humans that can potentially bind to drugs. In the current protein–drug pairs that contain somatic mutations within their binding site, we identified 85 proteins with significant differential gene expression changes associated with specific cancer types. Information on protein–drug binding predicted drug target proteins and prevalence of both somatic and germline nsSNVs that disrupt these binding sites can provide valuable knowledge for personalized medicine treatment. A web portal is available where nsSNVs from individual patient can be checked by scanning against DrugVar to determine whether any of the SNVs affect the binding of any drug in the database.

Fast assessment of structural models of ion channels based on their predicted current-voltage characteristics

Computational prediction of protein structures is a difficult task, which involves fast and accurate evaluation of candidate model structures. We propose to enhance single-model quality assessment with a functionality evaluation phase for proteins whose quantitative functional characteristics are known. In particular, this idea can be applied to evaluation of structural models of ion channels, whose main function - conducting ions - can be quantitatively measured with the patch-clamp technique providing the current-voltage characteristics. The study was performed on a set of KcsA channel models obtained from complete and incomplete contact maps. A fast continuous electrodiffusion model was used for calculating the current-voltage characteristics of structural models. We found that the computed charge selectivity and total current were sensitive to structural and electrostatic quality of models. In practical terms, we show that evaluating predicted conductance values is an appropriate method to eliminate models with an occluded pore or with multiple erroneously created pores. Moreover, filtering models on the basis of their predicted charge selectivity results in a substantial enrichment of the candidate set in highly accurate models. Tests on three other ion channels indicate that, in addition to being a proof of the concept, our function-oriented single-model quality assessment method can be directly applied to evaluation of structural models of some classes of protein channels. Finally, our work raises an important question whether a computational validation of functionality should be included in the evaluation process of structural models, whenever possible.

Identification of Non-nucleoside Human Ribonucleotide Reductase Modulators

Ribonucleotide reductase (RR) catalyzes the rate-limiting step of dNTP synthesis and is an established cancer target. Drugs targeting RR are mainly nucleoside in nature. In this study, we sought to identify non-nucleoside small-molecule inhibitors of RR. Using virtual screening, binding affinity, inhibition, and cell toxicity, we have discovered a class of small molecules that alter the equilibrium of inactive hexamers of RR, leading to its inhibition. Several unique chemical categories, including a phthalimide derivative, show micromolar IC50s and KDs while demonstrating cytotoxicity. A crystal structure of an active phthalimide binding at the targeted interface supports the noncompetitive mode of inhibition determined by kinetic studies. Furthermore, the phthalimide shifts the equilibrium from dimer to hexamer. Together, these data identify several novel non-nucleoside inhibitors of human RR which act by stabilizing the inactive form of the enzyme.

Lessons from Hot Spot Analysis for Fragment-Based Drug Discovery

Analysis of binding energy hot spots at protein surfaces can provide crucial insights into the prospects for successful application of fragment-based drug discovery (FBDD), and whether a fragment hit can be advanced into a high-affinity, drug-like ligand. The key factor is the strength of the top ranking hot spot, and how well a given fragment complements it. We show that published data are sufficient to provide a sophisticated and quantitative understanding of how hot spots derive from a protein 3D structure, and how their strength, number, and spatial arrangement govern the potential for a surface site to bind to fragment-sized and larger ligands. This improved understanding provides important guidance for the effective application of FBDD in drug discovery.

Drug leads for interactive protein targets with unknown structure

The disruption of protein-protein interfaces (PPIs) remains a challenge in drug discovery. The problem becomes daunting when the structure of the target protein is unknown and is even further complicated when the interface is susceptible to disruptive phosphorylation. Based solely on protein sequence and information about phosphorylation-susceptible sites within the PPI, a new technology has been developed to identify drug leads to inhibit protein associations. Here we reveal this technology and contrast it with current structure-based technologies for the generation of drug leads. The novel technology is illustrated by a patented invention to treat heart failure. The success of this technology shows that it is possible to generate drug leads in the absence of target structure.

A script to highlight hydrophobicity and charge on protein surfaces

The composition of protein surfaces determines both affinity and specificity of protein-protein interactions. Matching of hydrophobic contacts and charged groups on both sites of the interface are crucial to ensure specificity. Here, we propose a highlighting scheme, YRB, which highlights both hydrophobicity and charge in protein structures. YRB highlighting visualizes hydrophobicity by highlighting all carbon atoms that are not bound to nitrogen and oxygen atoms. The charged oxygens of glutamate and aspartate are highlighted red and the charged nitrogens of arginine and lysine are highlighted blue. For a set of representative examples, we demonstrate that YRB highlighting intuitively visualizes segments on protein surfaces that contribute to specificity in protein-protein interfaces, including Hsp90/co-chaperone complexes, the SNARE complex and a transmembrane domain. We provide YRB highlighting in form of a script that runs using the software PyMOL.

State-of-the-art strategies for targeting protein-protein interactions by small-molecule inhibitors

Targeting protein-protein interactions (PPIs) has emerged as a viable approach in modern drug discovery. However, the identification of small molecules enabling us to effectively interrupt their interactions presents significant challenges. In the recent past, significant advances have been made in the development of new biological and chemical strategies to facilitate the discovery process of small-molecule PPI inhibitors. This review aims to highlight the state-of-the-art technologies and the achievements made recently in this field. The "hot spots" of PPIs have been proved to be critical for small molecules to bind. Three strategies including screening, designing, and synthetic approaches have been explored for discovering PPI inhibitors by targeting the "hot spots". Although the classic high throughput screening approach can be used, fragment screening, fragment-based drug design and newly improved virtual screening are demonstrated to be more effective in the discovery of PPI inhibitors. In addition to screening approaches, design strategies including anchor-based and small molecule mimetics of secondary structures involved in PPIs have become powerful tools as well. Finally, constructing new chemically spaced libraries with high diversity and complexity is becoming an important area of interest for PPI inhibitors. The successful cases from the recent five year studies are used to illustrate how these approaches are implemented to uncover and optimize small molecule PPI inhibitors and notably some of them have become promising therapeutics.

Exploring the chemical space of the lysine-binding pocket of the first kringle domain of hepatocyte growth factor/scatter factor (HGF/SF) yields a new class of inhibitors of HGF/SF-MET binding

The growth/motility factor hepatocyte growth factor/scatter factor (HGF/SF) and its receptor, the tyrosine kinase MET, constitute a signalling system essential for embryogenesis and for tissue/organ regeneration in post-natal life. HGF/SF-MET signalling, however, also plays a key role in the onset of metastasis of a large number of human tumours. Both HGF/SF and MET are high molecular weight proteins that bury an extensive interface upon complex formation and thus constitute a challenging target for the development of low molecular weight inhibitors. Here we have used surface plasmon resonance (SPR), nuclear magnetic resonance (NMR) and X-ray crystallography to screen a diverse fragment library of 1338 members as well as a range of piperazine-like compounds. Several small molecules were found to bind in the lysine-binding pocket of the kringle 1 domain of HGF/SF and its truncated splice variant NK1. We have defined the binding mode of these compounds, explored their biological activity and we show that selected fragments inhibit MET downstream signalling. Thus we demonstrate that targeting the lysine-binding pocket of NK1 is an effective strategy to generate MET receptor antagonists and we offer proof of concept that the HGF/SF-MET interface may be successfully targeted with small molecules. These studies have broad implications for the development of HGF/SF-MET therapeutics and cancer treatment.

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

Protein-protein interactions (PPIs) underlie the majority of biological processes, signaling, and disease. Approaches to modulate PPIs with small molecules have therefore attracted increasing interest over the past decade. However, there are a number of challenges inherent in developing small-molecule PPI inhibitors that have prevented these approaches from reaching their full potential. From target validation to small-molecule screening and lead optimization, identifying therapeutically relevant PPIs that can be successfully modulated by small molecules is not a simple task. Following the recent review by Arkin et al., which summarized the lessons learnt from prior successes, we focus in this article on the specific challenges of developing PPI inhibitors and detail the recent advances in chemistry, biology, and computation that facilitate overcoming them. We conclude by providing a perspective on the field and outlining four innovations that we see as key enabling steps for successful development of small-molecule inhibitors targeting PPIs.

Differential binding of prohibitin-2 to estrogen receptor α and to drug-resistant ERα mutants

Endocrine resistance is one of the most challenging problems in estrogen receptor alpha (ERα)-positive breast cancer. The transcriptional activity of ERα is controlled by several coregulators, including prohibitin-2 (PHB2). Because of its ability to repress the transcriptional activity of activated ERα, PHB2 is a promising antiproliferative agent. In this study, were analyzed the interaction of PHB2 with ERα and three mutants (Y537S, D538G, and E380Q) that are frequently associated with a lack of sensitivity to hormonal treatments, to help advance novel drug discovery. PHB2 bound to ERα wild-type (WT), Y537S, and D538G, but did not bind to E380Q. The binding thermodynamics of Y537S and D538G to PHB2 were favorably altered entropically compared with those of WT to PHB2. Our results show that PHB2 binds to the ligand binding domain of ERα with a conformational change in the helix 12 of ERα.