Structural biology and drug discovery for protein-protein interactions

Combinatorial Libraries of Bipodal Binders of the Insulin Receptor

The binding process of insulin to its transmembrane receptor entails a sophisticated interplay between two proteins, each possessing two binding sites. Given the difficulties associated with the use of insulin in the treatment of diabetes, despite its remarkable efficacy, there is interest in smaller and more stable compounds than the native hormone that would effectively activate the receptor. Our study adopts a strategy focused on synthesizing extensive combinatorial libraries of bipodal compounds consisting of two distinct peptides linked to a molecular scaffold. These constructs, evaluated in a resin bead-bound format, were designed to assess their binding to the insulin receptor. Despite notable nonspecific binding, our approach successfully generated and tested millions of compounds. Rigorous evaluations via flow cytometry and specific antibodies revealed peptide sequences with specific interactions at either receptor binding Site 1 or 2. Notably, these sequences bear similarity to peptides discovered through phage display by other researchers. This convergence of chemical and biological methods underscores nature's beauty, revealing general principles in peptide binding to the insulin receptor. Overall, our study deepens the understanding of molecular interactions in ligand binding to the insulin receptor, highlighting the challenges of targeting large proteins with small synthetic peptides.

A native mass spectrometry approach to qualitatively elucidate interfacial epitopes of transient protein-protein interactions

Native mass spectrometric analysis of TPR2A and GrpE with unpurified peptides derived from limited proteolysis of their respective PPI partners (HSP90 C-terminus and DnaK) facilitated efficient, qualitative identification of interfacial epitopes involved in transient PPI formation. Application of this approach can assist in elucidating interfaces of currently uncharacterised transient PPIs.

Leveraging Bidirectional Nature of Allostery To Inhibit Protein-Protein Interactions (PPIs): A Case Study of PCSK9-LDLR Interaction

The protein PCSK9 (proprotein convertase subtilisin/Kexin type 9) negatively regulates the recycling of LDLR (low-density lipoprotein receptor), leading to an elevated plasma level of LDL. Inhibition of PCSK9-LDLR interaction has emerged as a promising therapeutic strategy to manage hypercholesterolemia. However, the large interaction surface area between PCSK9 and LDLR makes it challenging to identify a small molecule competitive inhibitor. An alternative strategy would be to identify distal cryptic sites as targets for allosteric inhibitors that can remotely modulate PCSK9-LDLR interaction. Using several microseconds long molecular dynamics (MD) simulations, we demonstrate that on binding with LDLR, there is a significant conformational change (population shift) in a distal loop (residues 211-222) region of PCSK9. Consistent with the bidirectional nature of allostery, we establish a clear correlation between the loop conformation and the binding affinity with LDLR. Using a thermodynamic argument, we establish that the loop conformations predominantly present in the apo state of PCSK9 would have lower LDLR binding affinity, and they would be potential targets for designing allosteric inhibitors. We elucidate the molecular origin of the allosteric coupling between this loop and the LDLR binding interface in terms of the population shift in a set of salt bridges and hydrogen bonds. Overall, our work provides a general strategy toward identifying allosteric hotspots: compare the conformational ensemble of the receptor between the apo and bound states of the protein and identify distal conformational changes, if any. The inhibitors should be designed to bind and stabilize the apo-specific conformations.

GNNGL-PPI: multi-category prediction of protein-protein interactions using graph neural networks based on global graphs and local subgraphs

Most proteins exert their functions by interacting with other proteins, making the identification of protein-protein interactions (PPI) crucial for understanding biological activities, pathological mechanisms, and clinical therapies. Developing effective and reliable computational methods for predicting PPI can significantly reduce the time-consuming and labor-intensive associated traditional biological experiments. However, accurately identifying the specific categories of protein-protein interactions and improving the prediction accuracy of the computational methods remain dual challenges. To tackle these challenges, we proposed a novel graph neural network method called GNNGL-PPI for multi-category prediction of PPI based on global graphs and local subgraphs. GNNGL-PPI consisted of two main components: using Graph Isomorphism Network (GIN) to extract global graph features from PPI network graph, and employing GIN As Kernel (GIN-AK) to extract local subgraph features from the subgraphs of protein vertices. Additionally, considering the imbalanced distribution of samples in each category within the benchmark datasets, we introduced an Asymmetric Loss (ASL) function to further enhance the predictive performance of the method. Through evaluations on six benchmark test sets formed by three different dataset partitioning algorithms (Random, BFS, DFS), GNNGL-PPI outperformed the state-of-the-art multi-category prediction methods of PPI, as measured by the comprehensive performance evaluation metric F1-measure. Furthermore, interpretability analysis confirmed the effectiveness of GNNGL-PPI as a reliable multi-category prediction method for predicting protein-protein interactions.

Impact of DNA ligase 1 and IIIα interactions with APE1 and polβ on the efficiency of base excision repair pathway at the downstream steps

Base excision repair (BER) requires a tight coordination between the repair enzymes through protein-protein interactions and involves gap filling by DNA polymerase (pol) β and subsequent nick sealing by DNA ligase (LIG) 1 or LIGIIIα at the downstream steps. Apurinic/apyrimidinic-endonuclease 1 (APE1), by its exonuclease activity, proofreads 3' mismatches incorporated by polβ during BER. We previously reported that the interruptions in the functional interplay between polβ and the BER ligases result in faulty repair events. Yet, how the protein interactions of LIG1 and LIGIIIα could affect the repair pathway coordination during nick sealing at the final steps remains unknown. Here, we demonstrate that LIGIIIα interacts more tightly with polβ and APE1 than LIG1, and the N-terminal noncatalytic region of LIG1 as well as the catalytic core and BRCT domain of LIGIIIα mediate interactions with both proteins. Our results demonstrated less efficient nick sealing of polβ nucleotide insertion products in the absence of LIGIIIα zinc-finger domain and LIG1 N-terminal region. Furthermore, we showed a coordination between APE1 and LIG1/LIGIIIα during the removal of 3' mismatches from the nick repair intermediate on which both BER ligases can seal noncanonical ends or gap repair intermediate leading to products of single deletion mutagenesis. Overall results demonstrate the importance of functional coordination from gap filling by polβ coupled to nick sealing by LIG1/LIGIIIα in the presence of proofreading by APE1, which is mainly governed by protein-protein interactions and protein-DNA intermediate communications, to maintain repair efficiency at the downstream steps of the BER pathway.

Oligomerization of drug transporters: Forms, functions, and mechanisms

Drug transporters are essential players in the transmembrane transport of a wide variety of clinical drugs. The broad substrate spectra and versatile distribution pattern of these membrane proteins infer their pharmacological and clinical significance. With our accumulating knowledge on the three-dimensional structure of drug transporters, their oligomerization status has become a topic of intense study due to the possible functional roles carried out by such kind of post-translational modification (PTM). In-depth studies of oligomeric complexes formed among drug transporters as well as their interactions with other regulatory proteins can help us better understand the regulatory mechanisms of these membrane proteins, provide clues for the development of novel drugs, and improve the therapeutic efficacy. In this review, we describe different oligomerization forms as well as their structural basis of major drug transporters in the ATP-binding cassette and solute carrier superfamilies, summarize our current knowledge on the influence of oligomerization for protein expression level and transport function of these membrane proteins, and discuss the regulatory mechanisms of oligomerization. Finally, we highlight the challenges associated with the current oligomerization studies and propose some thoughts on the pharmaceutical application of this important drug transporter PTM.

Discovery of DNL343: A Potent, Selective, and Brain-Penetrant eIF2B Activator Designed for the Treatment of Neurodegenerative Diseases

Eukaryotic translation initiation factor 2B (eIF2B) is a key component of the integrated stress response (ISR), which regulates protein synthesis and stress granule formation in response to cellular insult. Modulation of the ISR has been proposed as a therapeutic strategy for treatment of neurodegenerative diseases such as vanishing white matter (VWM) disease and amyotrophic lateral sclerosis (ALS) based on its ability to improve cellular homeostasis and prevent neuronal degeneration. Herein, we report the small-molecule discovery campaign that identified potent, selective, and CNS-penetrant eIF2B activators using both structure- and ligand-based drug design. These discovery efforts culminated in the identification of DNL343, which demonstrated a desirable preclinical drug profile, including a long half-life and high oral bioavailability across preclinical species. DNL343 was progressed into clinical studies and is currently undergoing evaluation in late-stage clinical trials for ALS.

Impact of polβ/XRCC1 Interaction Variants on the Efficiency of Nick Sealing by DNA Ligase IIIα in the Base Excision Repair Pathway

Base excision repair (BER) requires a coordination from gap filling by DNA polymerase (pol) β to subsequent nick sealing by DNA ligase (LIG) IIIα at downstream steps of the repair pathway. X-ray cross-complementing protein 1 (XRCC1), a non-enzymatic scaffolding protein, forms repair complexes with polβ and LIGIIIα. Yet, the impact of the polβ mutations that affect XRCC1 interaction and protein stability on the repair pathway coordination during nick sealing by LIGIIIα remains unknown. Our results show that the polβ colon cancer-associated variant T304 exhibits a reduced interaction with XRCC1 and the mutations in the interaction interface of V303 loop (L301R/V303R/V306R) and at the lysine residues (K206A/K244A) that prevent ubiquitin-mediated degradation of the protein exhibit a diminished repair protein complex formation with XRCC1. Furthermore, we demonstrate no significant effect on gap and nick DNA binding affinity of wild-type polβ by these mutations. Finally, our results reveal that XRCC1 leads to an efficient channeling of nick repair products after nucleotide incorporation by polβ variants to LIGIIIα, which is compromised by the L301R/V303R/V306R and K206A/K244A mutations. Overall, our findings provide insight into how the mutations in the polβ/XRCC1 interface and the regions affecting protein stability could dictate accurate BER pathway coordination at the downstream steps involving nick sealing by LIGIIIα.

PICKLUSTER: a protein-interface clustering and analysis plug-in for UCSF ChimeraX

SUMMARY

The identification and characterization of interfaces in protein complexes is crucial for understanding the mechanisms of molecular recognition. These interfaces are also attractive targets for protein inhibition. However, targeting protein interfaces can be challenging for large interfaces that consist of multiple interacting regions. We present PICKLUSTER [Protein Interface C(K)luster]-a program for identifying "sub-interfaces" in protein-protein complexes using distance clustering. The division of the interface into smaller "sub-interfaces" offers a more focused approach for targeting protein-protein interfaces.

AVAILABILITY AND IMPLEMENTATION

PICKLUSTER is implemented as a plug-in for the molecular visualization program UCSF ChimeraX 1.4 and subsequent versions. It is freely available for download in the ChimeraX Toolshed and https://gitlab.com/topf-lab/pickluster.git.

Structure-Based Optimization of Covalent, Small-Molecule Stabilizers of the 14-3-3σ/ERα Protein-Protein Interaction from Nonselective Fragments

The stabilization of protein-protein interactions (PPIs) has emerged as a promising strategy in chemical biology and drug discovery. The identification of suitable starting points for stabilizing native PPIs and their subsequent elaboration into selective and potent molecular glues lacks structure-guided optimization strategies. We have previously identified a disulfide fragment that stabilized the hub protein 14-3-3σ bound to several of its clients, including ERα and C-RAF. Here, we show the structure-based optimization of the nonselective fragment toward selective and highly potent small-molecule stabilizers of the 14-3-3σ/ERα complex. The more elaborated molecular glues, for example, show no stabilization of 14-3-3σ/C-RAF up to 150 μM compound. Orthogonal biophysical assays, including mass spectrometry and fluorescence anisotropy, were used to establish structure-activity relationships. The binding modes of 37 compounds were elucidated with X-ray crystallography, which further assisted the concomitant structure-guided optimization. By targeting specific amino acids in the 14-3-3σ/ERα interface and locking the conformation with a spirocycle, the optimized covalent stabilizer 181 achieved potency, cooperativity, and selectivity similar to the natural product Fusicoccin-A. This case study showcases the value of addressing the structure, kinetics, and cooperativity for molecular glue development.

SGPPI: structure-aware prediction of protein-protein interactions in rigorous conditions with graph convolutional network

While deep learning (DL)-based models have emerged as powerful approaches to predict protein-protein interactions (PPIs), the reliance on explicit similarity measures (e.g. sequence similarity and network neighborhood) to known interacting proteins makes these methods ineffective in dealing with novel proteins. The advent of AlphaFold2 presents a significant opportunity and also a challenge to predict PPIs in a straightforward way based on monomer structures while controlling bias from protein sequences. In this work, we established Structure and Graph-based Predictions of Protein Interactions (SGPPI), a structure-based DL framework for predicting PPIs, using the graph convolutional network. In particular, SGPPI focused on protein patches on the protein-protein binding interfaces and extracted the structural, geometric and evolutionary features from the residue contact map to predict PPIs. We demonstrated that our model outperforms traditional machine learning methods and state-of-the-art DL-based methods using non-representation-bias benchmark datasets. Moreover, our model trained on human dataset can be reliably transferred to predict yeast PPIs, indicating that SGPPI can capture converging structural features of protein interactions across various species. The implementation of SGPPI is available at https://github.com/emerson106/SGPPI.

Targeting SOST using a small-molecule compound retards breast cancer bone metastasis

BACKGROUND

Breast cancer metastasis to the bone can be exacerbated by osteoporosis, is associated with poor long-term survival, and has limited therapeutic options. Sclerostin (SOST) is an endogenous inhibitor of bone formation, and an attractive target for treatment of osteoporosis. However, it is unclear whether SOST can be used as a therapeutic target for bone metastases of breast cancer, and whether small molecule compounds that target SOST in breast cancer cells can inhibit breast cancer bone metastasis.

METHODS

SOST expression in 442 breast cancer tissues was characterized by immunohistochemistry and statistically analyzed for the association with breast cancer bone metastases. Bone metastatic breast cancer SCP2 cells were induced for SOST silencing or overexpression and their bone metastatic behaviors were tested in vitro and in vivo. To identify potential therapeutics, we screened inhibitors of the interaction of SOST with STAT3 from a small chemical molecule library and tested the inhibitory effects of one inhibitor on breast cancer growth and bone metastasis in vitro and in vivo.

RESULTS

We found that up-regulated SOST expression was associated with breast cancer bone metastases and worse survival of breast cancer patients. SOST silencing significantly reduced the bone metastatic capacity of SCP2 cells. SOST interacted with STAT3 to enhance the TGF-β/KRAS signaling, increasing both tumor growth and bone metastasis. Treatment with one lead candidate, S6, significantly inhibited the growth of breast-cancer organoids and bone metastasis in mice.

CONCLUSIONS

Our findings highlight a new class of potential therapeutics for treatment of bone metastasis in breast cancer.

Using the Cyclotide Scaffold for Targeting Biomolecular Interactions in Drug Development

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

Protein-protein interaction prediction with deep learning: A comprehensive review

Most proteins perform their biological function by interacting with themselves or other molecules. Thus, one may obtain biological insights into protein functions, disease prevalence, and therapy development by identifying protein-protein interactions (PPI). However, finding the interacting and non-interacting protein pairs through experimental approaches is labour-intensive and time-consuming, owing to the variety of proteins. Hence, protein-protein interaction and protein-ligand binding problems have drawn attention in the fields of bioinformatics and computer-aided drug discovery. Deep learning methods paved the way for scientists to predict the 3-D structure of proteins from genomes, predict the functions and attributes of a protein, and modify and design new proteins to provide desired functions. This review focuses on recent deep learning methods applied to problems including predicting protein functions, protein-protein interaction and their sites, protein-ligand binding, and protein design.

Application of In Silico Filtering and Isothermal Titration Calorimetry for the Discovery of Small Molecule Inhibitors of MDM2

The initial discovery phase of protein modulators, which consists of filtering molecular libraries and in vitro direct binding validation, is central in drug discovery. Thus, virtual screening of large molecular libraries, together with the evaluation of binding affinity by isothermal calorimetry, generates an efficient experimental setup. Herein, we applied virtual screening for discovering small molecule inhibitors of MDM2, a major negative regulator of the tumor suppressor p53, and thus a promising therapeutic target. A library of 20 million small molecules was screened against an averaged model derived from multiple structural conformations of MDM2 based on published structures. Selected molecules originating from the computational filtering were tested in vitro for their direct binding to MDM2 via isothermal titration calorimetry. Three new molecules, representing distinct chemical scaffolds, showed binding to MDM2. These were further evaluated by exploring structure-similar chemical analogues. Two scaffolds were further evaluated by de novo synthesis of molecules derived from the initial molecules that bound MDM2, one with a central oxoazetidine acetamide and one with benzene sulfonamide. Several molecules derived from these scaffolds increased wild-type p53 activity in MCF7 cancer cells. These set a basis for further chemical optimization and the development of new chemical entities as anticancer drugs.

Characterization and computational simulation of human Syx, a RhoGEF implicated in glioblastoma

Structural discovery of guanine nucleotide exchange factor (GEF) protein complexes is likely to become increasingly relevant with the development of new therapeutics targeting small GTPases and development of new classes of small molecules that inhibit protein‐protein interactions. Syx (also known as PLEKHG5 in humans) is a RhoA GEF implicated in the pathology of glioblastoma (GBM). Here we investigated protein expression and purification of ten different human Syx constructs and performed biophysical characterizations and computational studies that provide insights into why expression of this protein was previously intractable. We show that human Syx can be expressed and isolated and Syx is folded as observed by circular dichroism (CD) spectroscopy and actively binds to RhoA as determined by co‐elution during size exclusion chromatography (SEC). This characterization may provide critical insights into the expression and purification of other recalcitrant members of the large class of oncogenic—Diffuse B‐cell lymphoma (Dbl) homology GEF proteins. In addition, we performed detailed homology modeling and molecular dynamics simulations on the surface of a physiologically realistic membrane. These simulations reveal novel insights into GEF activity and allosteric modulation by the plekstrin homology (PH) domain. These newly revealed interactions between the GEF PH domain and the membrane embedded region of RhoA support previously unexplained experimental findings regarding the allosteric effects of the PH domain from numerous activity studies of Dbl homology GEF proteins. This work establishes new hypotheses for structural interactivity and allosteric signal modulation in Dbl homology RhoGEFs.

Computational Methods for Structure-Based Drug Design Through System Biology

The advances in computational chemistry and biology, computer science, structural biology, and molecular biology go in parallel with the rapid progress in target-based systems. This technique has become a powerful tool in medicinal chemistry for the identification of hit molecules. The recent developments in target-based systems have played a major role in the creation of libraries of compounds, and it has also been widely applied for the design of molecular docking methods. The main advantage of this method is that it hits the fragment that has the strongest binding, has relatively small size, and leads to better compounds in terms of pharmacokinetic properties when compared with virtual screening (VS) and high-throughput screening (HTS) hits. De novo design is an essential aspect of target-based systems and requires the synthesis of chemical to allow the design of promising compound.

Small-molecule inhibitors that disrupt the MTDH-SND1 complex suppress breast cancer progression and metastasis

Metastatic breast cancer is a leading health burden worldwide. Previous studies have shown that metadherin (MTDH) promotes breast cancer initiation, metastasis and therapy resistance; however, the therapeutic potential of targeting MTDH remains largely unexplored. Here, we used genetically modified mice and demonstrate that genetic ablation of Mtdh inhibits breast cancer development through disrupting the interaction with staphylococcal nuclease domain-containing 1 (SND1), which is required to sustain breast cancer progression in established tumors. We performed a small-molecule compound screening to identify a class of specific inhibitors that disrupts the protein-protein interaction (PPI) between MTDH and SND1 and show that our lead candidate compounds C26-A2 and C26-A6 suppressed tumor growth and metastasis and enhanced chemotherapy sensitivity in preclinical models of triple-negative breast cancer (TNBC). Our results demonstrate a significant therapeutic potential in targeting the MTDH-SND1 complex and identify a new class of therapeutic agents for metastatic breast cancer.

Targeting protein-protein interactions in the DNA damage response pathways for cancer chemotherapy

Cellular DNA damage response (DDR) is an extensive signaling network that orchestrates DNA damage recognition, repair and avoidance, cell cycle progression and cell death. DDR alteration is a hallmark of cancer, with the deficiency in one DDR capability often compensated by a dependency on alternative pathways endowing cancer cells with survival and growth advantage. Targeting these DDR pathways has provided multiple opportunities for the development of cancer therapies. Traditional drug discovery has mainly focused on catalytic inhibitors that block enzyme active sites, which limits the number of potential drug targets within the DDR pathways. This review article describes the emerging approach to the development of cancer therapeutics targeting essential protein-protein interactions (PPIs) in the DDR network. The overall strategy for the structure-based design of small molecule PPI inhibitors is discussed, followed by an overview of the major DNA damage sensing, DNA repair, and DNA damage tolerance pathways with a specific focus on PPI targets for anti-cancer drug design. The existing small molecule inhibitors of DDR PPIs are summarized that selectively kill cancer cells and/or sensitize cancers to front-line genotoxic therapies, and a range of new PPI targets are proposed that may lead to the development of novel chemotherapeutics.

Disabling the Nuclear Translocalization of RelA/NF-κB by a Small Molecule Inhibits Triple-Negative Breast Cancer Growth

INTRODUCTION

Constitutive activation of NF-κB has been implicated as being contributive to cancer cell growth, drug resistance, and tumor recurrence in many cancers including breast cancer. Activation of NF-κB leads to nuclear translocation of RelA, a critical component of the NF-κB transcription factor complex, which subsequently binds to specific DNA sites and activates a multitude of genes involved in diverse cell functions. Studies show that triple-negative breast cancer (TNBC) cells possess constitutively active NF-κB and concomitantly have higher levels of nuclear localization of RelA than cytoplasmic RelA. This feature is considered to be associated with the response to chemotherapy. However, currently, there is no specific inhibitor to block nuclear translocation of RelA.

METHODS

A structure-based approach was used to develop a small-molecule inhibitor of RelA nuclear translocation. The interaction between this molecule and RelA was verified biophysically through isothermal titration calorimetry and microscale thermophoresis. TNBC cell lines MDA-MB-231 and MDA-MB-468 and a human TNBC xenograft model were used to verify in vitro and in vivo efficacy of the small molecule, respectively.

RESULTS

We found that the small molecule, CRL1101, bound specifically to RelA as indicated by the biophysical assays. Further, CRL1101 blocked RelA nuclear translocation in breast cancer cells in vitro, and markedly reduced breast tumor growth in a triple-negative breast cancer xenograft model.

CONCLUSION

Our study demonstrates that CRL1101 may lead to new NF-κB-targeted therapeutics for TNBC. Further, blocking of nuclear translocation of shuttling transcription factors may be a useful general strategy in cancer drug development.

Site-specific single point mutation by anthranilic acid in hIAPP8-37 enhances anti-amyloidogenic activity

Amylin or hIAPP, together with insulin, plays a significant role in glucose metabolism. However, it undergoes β-sheet rich amyloid formation associated with pancreatic β-cell dysfunction leading to type-2 diabetes (T2D). Recent studies suggest that restricting β-sheet formation in it may halt amyloid formation, which may limit the risk for the disease. Several peptide-based inhibitors have been reported to prevent aggregation. However, most of them have limitations, including low binding efficiency, active only at higher doses, poor solubility, and proteolytic degradation. Insertion of non-coded amino acids renders proteolytically stable peptides. We incorporated a structurally rigid β-amino acid, Anthranilic acid (Ant), at different sites within the central hydrophobic region of hIAPP and developed two singly mutated hIAPP_8–37 peptidomimetics. These peptidomimetics inhibited the amyloid formation of hIAPP substantially even at low concentration, as evident from in vitro ThT, CD, FT-IR, TEM, and Congo red staining birefringence results. These peptidomimetics also disrupted the preformed aggregates formed by hIAPP into non-toxic species. These β-amino acid-based peptidomimetics can be an attractive scaffold for therapeutic design towards T2D or other protein misfolding diseases.

β-Amino acid based peptidomimetics are attractive scaffolds for therapeutics design towards T2D. They prevent amyloid formation of hIAPP by forming non-fibrillar non-toxic aggregates.

Deep Learning for Protein-Protein Interaction Site Prediction

Protein-protein interactions (PPIs) are central to cellular functions. Experimental methods for predicting PPIs are well developed but are time and resource expensive and suffer from high false-positive error rates at scale. Computational prediction of PPIs is highly desirable for a mechanistic understanding of cellular processes and offers the potential to identify highly selective drug targets. In this chapter, details of developing a deep learning approach to predicting which residues in a protein are involved in forming a PPI-a task known as PPI site prediction-are outlined. The key decisions to be made in defining a supervised machine learning project in this domain are here highlighted. Alternative training regimes for deep learning models to address shortcomings in existing approaches and provide starting points for further research are discussed. This chapter is written to serve as a companion to developing deep learning approaches to protein-protein interaction site prediction, and an introduction to developing geometric deep learning projects operating on protein structure graphs.

Multipodal insulin mimetics built on adamantane or proline scaffolds

Multi-orthogonal molecular scaffolds can be applied as core structures of bioactive compounds. Here, we prepared four tri-orthogonal scaffolds based on adamantane or proline skeletons. The scaffolds were used for the solid-phase synthesis of model insulin mimetics bearing two different peptides on the scaffolds. We found that adamantane-derived compounds bind to the insulin receptor more effectively (K_d value of 0.5 μM) than proline-derived compounds (K_d values of 15-38 μM) bearing the same peptides. Molecular dynamics simulations suggest that spacers between peptides and central scaffolds can provide greater flexibility that can contribute to increased binding affinity. Molecular modeling showed possible binding modes of mimetics to the insulin receptor. Our data show that the structure of the central scaffold and flexibility of attached peptides in this type of compound are important and that different scaffolds should be considered when designing peptide hormone mimetics.

Polyglutamine spinocerebellar ataxias: emerging therapeutic targets

INTRODUCTION

Six of the most frequent dominantly inherited spinocerebellar ataxias (SCAs) worldwide - SCA1, SCA2, SCA3, SCA6, SCA7, and SCA17 - are caused by an expansion of a polyglutamine (polyQ) tract in the corresponding proteins. While the identification of the causative mutation has advanced knowledge on the pathogenesis of polyQ SCAs, effective therapeutics able to mitigate the severe clinical manifestation of these highly incapacitating disorders are not yet available.

AREAS COVERED

This review provides a comprehensive and critical perspective on well-established and emerging therapeutic targets for polyQ SCAs; it aims to inspire prospective drug discovery efforts.

EXPERT OPINION

The landscape of polyQ SCAs therapeutic targets and strategies includes (1) the mutant genes and proteins themselves, (2) enhancement of endogenous protein quality control responses, (3) abnormal protein-protein interactions of the mutant proteins, (4) disturbed neuronal function, (5) mitochondrial function, energy availability and oxidative stress, and (6) glial dysfunction, growth factor or hormone imbalances. Challenges include gaining a clearer definition of therapeutic targets for the drugs in clinical development, the discovery of novel drug-like molecules for challenging key targets, and the attainment of a stronger translation of preclinical findings to the clinic.

Design, synthesis and biological evaluation of anticholinesterase peptides: Fragment-based vs. template-based peptide design

The prevalence of Alzheimer's disease (AD) has become a substantial global concern. Approved AChE inhibitors have been used for symptomatic treatment of AD. Binding of amyloid β (Aβ) to the peripheral anionic site of AChE facilitates the formation of Aβ plaques. Blocking this proposed protein-protein interaction by inhibition of the peripheral anionic site of AChE, in addition to increasing the level of ACh, reduces the Aβ aggregation and might qualify to slow down the progression of disease besides the palliative treatment. Targeting protein-protein interactions consider as one of the most challenging issues in the realm of drug design in which peptides have potentials to excel in. In the present study, we applied two virtual fragment-based and template-based approaches to design peptidic inhibitors of the PAS of AChE. Based on the in silico studies, high scored peptides p2 (WTWYGYWVW) and p10 (NHRMLTRRY) obtained from fragment-based and template-based design respectively. Regarding in vitro results, p2 (IC₅₀ = 16 ± 3.2 μM) and p10 (IC₅₀ = 23.6 ± 4.9 μM) showed significant AChE inhibitory effects. The molecular mechanism of inhibition studied by Lineweaver-Burk plots was mixed inhibition for both peptides. The in vitro results conformed to the in silico results and showed that both peptides occupied the CAS and PAS of AChE. The comparison of two peptide-design approaches revealed that the fragment-based design had more chemical diversity and showed priority to the template-based design. According to the obtained results, peptidic inhibitors of AChE designed by the proposed fragment-based approach might be more efficient in comparison to traditional approaches.

Structure-based discovery and development of metabotropic glutamate receptor 5 negative allosteric modulators

The metabotropic glutamate (mGlu) receptors are a family of eight class C G protein-coupled receptors (GPCRs) which modulate cell signaling and synaptic transmission to the major excitatory neurotransmitter l-glutamate (l-glutamic acid). Due to their role in modulating glutamate response, their widespread distribution in the central nervous system (CNS) and some evidence of dysregulation in disease, the mGlu receptors have become attractive pharmacological targets. As the orthosteric (glutamate) binding site is highly conserved across the eight mGlu receptors, it is difficult not only to generate ligands with subtype selectivity but, due to the nature of the binding site, with suitable drug-like properties to allow oral bioavailability and CNS penetration. Selective pharmacological targeting of a single receptor subtype can be achieved by targeting alternative (allosteric) binding sites. The nature of the allosteric binding pockets allows ligands to be developed that have good physical chemical properties as evidenced by several allosteric modulators of mGlu receptors entering clinical trials. The first negative allosteric modulators of the metabotropic glutamate 5 (mGlu₅) receptor were discovered from high throughput screening activities. An alternative approach to drug discovery is to use structural knowledge to enable structure-based drug design (SBDD), which allows the design of molecules in a more rational, rather than empirical, fashion. Here we will describe the process of SBDD in the discovery of the mGlu₅ negative allosteric modulator HTL0014242 and describe how knowledge of receptor structure can also be used to gain insights into the receptor activation mechanisms.

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.

Probing the Druggablility on the Interface of the Protein-Protein Interaction and Its Allosteric Regulation Mechanism on the Drug Screening for the CXCR4 Homodimer

Modulating protein–protein interactions (PPIs) with small drug-like molecules targeting it exhibits great promise in modern drug discovery. G protein-coupled receptors (GPCRs) are the largest family of targeted proteins and could form dimers in living biological cells through PPIs. However, compared to drug development of the orthosteric site, there has been lack of investigations on the druggability of the PPI interface for GPCRs and its functional implication on experiments. Thus, in order to address these issues, we constructed a novel computational strategy, which involved in molecular dynamics simulation, virtual screening and protein structure network (PSN), to study one representative GPCR homodimer (CXCR4). One druggable pocket was identified in the PPI interface and one small molecule targeting it was screened, which could strengthen PPI mainly through hydrophobic interaction between the benzene rings of the PPI molecule and TM4 of the receptor. The PSN results further reveals that the PPI molecule could increase the number of the allosteric regulation pathways between the druggable pocket of the dimer interface to the orthostatic site for the subunit A but only play minor role for the other subunit B, leading to the asymmetric change in the volume of the binding pockets for the two subunits (increase for the subunit A and minor change for the subunit B). Consequently, the screening performance of the subunit A to the antagonists is enhanced while the subunit B is unchanged nearly, implying that the PPI molecule may be beneficial to enhance the drug efficacies of the antagonists. In addition, one main regulation pathway with the highest frequency was identified for the subunit A, which consists of Trp195^5.34–Tyr190^ECL2–Val196^5.35–Gln200^5.39–Asp262^6.58–Cys28^N-term, revealing their importance in the allosteric regulation from the PPI molecule. The observations from the work could provide valuable information for the development of the PPI drug-like molecule for GPCRs.

Multivalent Ligands with Tailor-Made Anion Binding Motif as Stabilizers of Protein-Protein Interactions

Modulation of protein-protein interactions (PPIs) is essential for understanding and tuning biologically relevant processes. Although inhibitors for PPIs are widely used, the field still lacks the targeted design of stabilizers. Here, we report unnatural stabilizers based on the combination of multivalency effects and the artificial building block guanidiniocarbonylpyrrol (GCP), an arginine mimetic. Unlike other GCP-based ligands that modulate PPIs in different protein targets, only a tetrameric design shows potent activity as stabilizer of the 14-3-3ζ/C-Raf and 14-3-3ζ/Tau complexes in the low-micromolar range. This evidences the role of multivalency for achieving higher specificity in the modulation of PPIs.

Quantum Molecular Dynamics, Topological, Group Theoretical and Graph Theoretical Studies of Protein-Protein Interactions

BACKGROUND

Protein-protein interactions (PPIs) are becoming increasingly important as PPIs form the basis of multiple aggregation-related diseases such as cancer, Creutzfeldt-Jakob, and Alzheimer's diseases. This mini-review presents hybrid quantum molecular dynamics, quantum chemical, topological, group theoretical, graph theoretical, and docking studies of PPIs. We also show how these theoretical studies facilitate the discovery of some PPI inhibitors of therapeutic importance.

OBJECTIVE

The objective of this review is to present hybrid quantum molecular dynamics, quantum chemical, topological, group theoretical, graph theoretical, and docking studies of PPIs. We also show how these theoretical studies enable the discovery of some PPI inhibitors of therapeutic importance.

METHODS

This article presents a detailed survey of hybrid quantum dynamics that combines classical and quantum MD for PPIs. The article also surveys various developments pertinent to topological, graph theoretical, group theoretical and docking studies of PPIs and highlight how the methods facilitate the discovery of some PPI inhibitors of therapeutic importance.

RESULTS

It is shown that it is important to include higher-level quantum chemical computations for accurate computations of free energies and electrostatics of PPIs and Drugs with PPIs, and thus techniques that combine classical MD tools with quantum MD are preferred choices. Topological, graph theoretical and group theoretical techniques are shown to be important in studying large network of PPIs comprised of over 100,000 proteins where quantum chemical and other techniques are not feasible. Hence, multiple techniques are needed for PPIs.

CONCLUSION

Drug discovery and our understanding of complex PPIs require multifaceted techniques that involve several disciplines such as quantum chemistry, topology, graph theory, knot theory and group theory, thus demonstrating a compelling need for a multi-disciplinary approach to the problem.

An inter-subunit protein-peptide interface that stabilizes the specific activity and oligomerization of the AAA+ chaperone Reptin

Reptin is a member of the AAA+ superfamily whose members can exist in equilibrium between monomeric apo forms and ligand bound hexamers. Inter-subunit protein-protein interfaces that stabilize Reptin in its oligomeric state are not well-defined. A self-peptide binding assay identified a protein-peptide interface mapping to an inter-subunit "rim" of the hexamer bridged by Tyrosine-340. A Y340A mutation reduced ADP-dependent oligomer formation using a gel filtration assay, suggesting that Y340 forms a dominant oligomer stabilizing side chain. The monomeric Reptin^Y340A mutant protein exhibited increased activity to its partner protein AGR2 in an ELISA assay, further suggesting that hexamer formation can preclude certain protein interactions. Hydrogen-deuterium exchange mass spectrometry (HDX-MS) demonstrated that the Y340A mutation attenuated deuterium suppression of Reptin in this motif in the presence of ligand. By contrast, the tyrosine motif of Reptin interacts with a shallower pocket in the hetero-oligomeric structure containing Pontin and HDX-MS revealed no obvious role of the Y340 side chain in stabilizing the Reptin-Pontin oligomer. Molecular dynamic simulations (MDS) rationalized how the Y340A mutation impacts upon a normally stabilizing inter-subunit amino acid contact. MDS also revealed how the D299N mutation can, by contrast, remove oligomer de-stabilizing contacts. These data suggest that the Reptin interactome can be regulated by a ligand dependent equilibrium between monomeric and hexameric forms through a hydrophobic inter-subunit protein-protein interaction motif bridged by Tyrosine-340. SIGNIFICANCE: Discovering dynamic protein-protein interactions is a fundamental aim of research in the life sciences. An emerging view of protein-protein interactions in higher eukaryotes is that they are driven by small linear polypeptide sequences; the linear motif. We report on the use of linear-peptide motif screens to discover a relatively high affinity peptide-protein interaction for the AAA+ and pro-oncogenic protein Reptin. This peptide interaction site was shown to form a 'hot-spot' protein-protein interaction site, and validated to be important for ligand-induced oligomerization of the Reptin protein. These biochemical data provide a foundation to understand how single point mutations in Reptin can impact on its oligomerization and protein-protein interaction landscape.

The Potential of the Cyclotide Scaffold for Drug Development

Cyclotides are a novel class of micro-proteins (≈30-40 residues long) with a unique topology containing a head-to-tail cyclized backbone structure further stabilized by three disulfide bonds that form a cystine knot. This unique molecular framework makes them exceptionally stable to physical, chemical, and biological degradation compared to linear peptides of similar size. The cyclotides are also highly tolerant to sequence variability, aside from the conserved residues forming the cystine knot, and are orally bioavailable and able to cross cellular membranes to modulate intracellular protein-protein interactions (PPIs), both in vitro and in vivo. These unique properties make them ideal scaffolds for many biotechnological applications, including drug discovery. This review provides an overview of the properties of cyclotides and their potential for the development of novel peptide-based therapeutics. The selective disruption of PPIs still remains a very challenging task, as the interacting surfaces are relatively large and flat. The use of the cell-permeable highly constrained polypeptide molecular frameworks, such as the cyclotide scaffold, has shown great promise, as it provides unique pharmacological properties. The use of molecular techniques, such as epitope grafting, and molecular evolution have shown to be highly effective for the selection of bioactive cyclotides. However, despite successes in employing cyclotides to target PPIs, some of the challenges to move them into the clinic still remain.

Stapled Peptides Inhibitors: A New Window for Target Drug Discovery

Protein-protein interaction (PPI) is a hot topic in clinical research as protein networking has a major impact in human disease. Such PPIs are potential drugs targets, leading to the need to inhibit/block specific PPIs. While small molecule inhibitors have had some success and reached clinical trials, they have generally failed to address the flat and large nature of PPI surfaces. As a result, larger biologics were developed for PPI surfaces and they have successfully targeted PPIs located outside the cell. However, biologics have low bioavailability and cannot reach intracellular targets. A novel class -hydrocarbon-stapled α-helical peptides that are synthetic mini-proteins locked into their bioactive structure through site-specific introduction of a chemical linker- has shown promise. Stapled peptides show an ability to inhibit intracellular PPIs that previously have been intractable with traditional small molecule or biologics, suggesting that they offer a novel therapeutic modality. In this review, we highlight what stapling adds to natural-mimicking peptides, describe the revolution of synthetic chemistry techniques and how current drug discovery approaches have been adapted to stabilize active peptide conformations, including ring-closing metathesis (RCM), lactamisation, cycloadditions and reversible reactions. We provide an overview on the available stapled peptide high-resolution structures in the protein data bank, with four selected structures discussed in details due to remarkable interactions of their staple with the target surface. We believe that stapled peptides are promising drug candidates and open the doors for peptide therapeutics to reach currently "undruggable" space.

Using backbone-cyclized Cys-rich polypeptides as molecular scaffolds to target protein-protein interactions

The use of disulfide-rich backbone-cyclized polypeptides, as molecular scaffolds to design a new generation of bioimaging tools and drugs that are potent and specific, and thus might have fewer side effects than traditional small-molecule drugs, is gaining increasing interest among the scientific and in the pharmaceutical industries. Highly constrained macrocyclic polypeptides are exceptionally more stable to chemical, thermal and biological degradation and show better biological activity when compared with their linear counterparts. Many of these relatively new scaffolds have been also found to be highly tolerant to sequence variability, aside from the conserved residues forming the disulfide bonds, able to cross cellular membranes and modulate intracellular protein-protein interactions both in vitro and in vivo These properties make them ideal tools for many biotechnological applications. The present study provides an overview of the new developments on the use of several disulfide-rich backbone-cyclized polypeptides, including cyclotides, θ-defensins and sunflower trypsin inhibitor peptides, in the development of novel bioimaging reagents and therapeutic leads.

A multi-column plate adapter provides an economical and versatile high-throughput protein purification system

Protein purification is essential in the study of protein structure and function, and the development of novel therapeutics. Many studies require purifying multiple proteins at once, increasing the demand for improved purification methods. We hypothesized that multiple chromatography columns could be interfaced with a multi-well collection plate for rapid and convenient protein purification without the need of expensive instrumentation. As such, we developed a multi-column plate adapter (MCPA), which provides an economical yet versatile and time efficient, high-throughput protein purification system. The MCPA system simultaneously purified milligrams of different proteins under gravity or under vacuum for faster purification. The MCPA handles up to twenty-four 12 mL columns and multiple MCPA's in sequence allow milligram-scale purification of 96 different samples with relative ease. We also used the MCPA system for large scale affinity purification of four proteins, providing sufficient yields and purity for protein crystallization and biophysical characterization. The MCPA system is ideal for optimizing resin type and volume or any other purification parameter by customizing individual columns during the same purification. The high-throughput and versatile nature of this system should prove to be useful in obtaining adequate amounts of protein for subsequent analyses in any laboratory setting.

Niclosamide is a potential therapeutic for familial adenomatosis polyposis by disrupting Axin-GSK3 interaction

The epithelial-mesenchymal transition (EMT) is implicated in tumorigenesis and cancer progression, and canonical Wnt signaling tightly controls Snail, a key transcriptional repressor of EMT. While the suppression of canonical Wnt signaling and EMT comprises an attractive therapeutic strategy, molecular targets for small molecules reverting Wnt and EMT have not been widely studied. Meanwhile, the anti-helminthic niclosamide has been identified as a potent inhibitor of many oncogenic signaling pathways although its molecular targets have not yet been clearly identified. In this study, we show that niclosamide directly targets Axin-GSK3 interaction, at least in part, resulting in suppression of Wnt/Snail-mediated EMT. In vitro and in vivo, disruption of Axin-GSK3 complex by niclosamide induces mesenchymal to epithelial reversion at nM concentrations, accompanied with suppression of the tumorigenic potential of colon cancer. Niclosamide treatment successfully attenuates Snail abundance while increasing E-cadherin abundance in xenograft tumor. Notably, oral administration of niclosamide significantly suppressed adenoma formation in an APC-MIN mice model, indicating that niclosamide is an effective therapeutic for familial adenomatosis polyposis (FAP) patients. In this study, we identified a novel target to control the canonical Wnt pathway and Snail-mediated EMT program, and discovered a repositioned therapeutics for FAP patients.

Macrocyclic α helical peptide therapeutic modality: A perspective of learnings and challenges

Macrocyclic α-helical peptides have emerged as a compelling new therapeutic modality to tackle targets confined to the intracellular compartment. Within the scope of hydrocarbon-stapling there has been significant progress to date, including the first stapled α-helical peptide to enter into clinical trials. The principal design concept of stapled α-helical peptides is to mimic a cognate (protein) ligand relative to binding its target via an α-helical interface. However, it was the proclivity of such stapled α-helical peptides to exhibit cell permeability and proteolytic stability that underscored their promise as unique macrocyclic peptide drugs for intracellular targets. This perspective highlights key learnings as well as challenges in basic research with respect to structure-based design, innovative chemistry, cell permeability and proteolytic stability that are essential to fulfill the promise of stapled α-helical peptide drug development.

Homo- and Heterodimerization of Proteins in Cell Signaling: Inhibition and Drug Design

Protein dimerization controls many physiological processes in the body. Proteins form homo-, hetero-, or oligomerization in the cellular environment to regulate the cellular processes. Any deregulation of these processes may result in a disease state. Protein-protein interactions (PPIs) can be inhibited by antibodies, small molecules, or peptides, and inhibition of PPI has therapeutic value. PPI drug discovery research has steadily increased in the last decade, and a few PPI inhibitors have already reached the pharmaceutical market. Several PPI inhibitors are in clinical trials. With advancements in structural and molecular biology methods, several methods are now available to study protein homo- and heterodimerization and their inhibition by drug-like molecules. Recently developed methods to study PPI such as proximity ligation assay and enzyme-fragment complementation assay that detect the PPI in the cellular environment are described with examples. At present, the methods used to design PPI inhibitors can be classified into three major groups: (1) structure-based drug design, (2) high-throughput screening, and (3) fragment-based drug design. In this chapter, we have described some of the experimental methods to study PPIs and their inhibition. Examples of homo- and heterodimers of proteins, their structural and functional aspects, and some of the inhibitors that have clinical importance are discussed. The design of PPI inhibitors of epidermal growth factor receptor heterodimers and CD2-CD58 is discussed in detail.

Current NMR Techniques for Structure-Based Drug Discovery

A variety of nuclear magnetic resonance (NMR) applications have been developed for structure-based drug discovery (SBDD). NMR provides many advantages over other methods, such as the ability to directly observe chemical compounds and target biomolecules, and to be used for ligand-based and protein-based approaches. NMR can also provide important information about the interactions in a protein-ligand complex, such as structure, dynamics, and affinity, even when the interaction is too weak to be detected by ELISA or fluorescence resonance energy transfer (FRET)-based high-throughput screening (HTS) or to be crystalized. In this study, we reviewed current NMR techniques. We focused on recent progress in NMR measurement and sample preparation techniques that have expanded the potential of NMR-based SBDD, such as fluorine NMR (¹⁹F-NMR) screening, structure modeling of weak complexes, and site-specific isotope labeling of challenging targets.

Affimer proteins inhibit immune complex binding to FcγRIIIa with high specificity through competitive and allosteric modes of action

Protein-protein interactions are essential for the control of cellular functions and are critical for regulation of the immune system. One example is the binding of Fc regions of IgG to the Fc gamma receptors (FcγRs). High sequence identity (98%) between the genes encoding FcγRIIIa (expressed on macrophages and natural killer cells) and FcγRIIIb (expressed on neutrophils) has prevented the development of monospecific agents against these therapeutic targets. We now report the identification of FcγRIIIa-specific artificial binding proteins called "Affimer" that block IgG binding and abrogate FcγRIIIa-mediated downstream effector functions in macrophages, namely TNF release and phagocytosis. Cocrystal structures and molecular dynamics simulations have revealed the structural basis of this specificity for two Affimer proteins: One binds directly to the Fc binding site, whereas the other acts allosterically.

Multiple spatially related pharmacophores define small molecule inhibitors of OLIG2 in glioblastoma

Transcription factors (TFs) are a major class of protein signaling molecules that play key cellular roles in cancers such as the highly lethal brain cancer—glioblastoma (GBM). However, the development of specific TF inhibitors has proved difficult owing to expansive protein-protein interfaces and the absence of hydrophobic pockets. We uniquely defined the dimerization surface as an expansive parental pharmacophore comprised of several regional daughter pharmacophores. We targeted the OLIG2 TF which is essential for GBM survival and growth, we hypothesized that small molecules able to fit each subpharmacophore would inhibit OLIG2 activation. The most active compound was OLIG2 selective, it entered the brain, and it exhibited potent anti-GBM activity in cell-based assays and in pre-clinical mouse orthotopic models. These data suggest that (1) our multiple pharmacophore approach warrants further investigation, and (2) our most potent compounds merit detailed pharmacodynamic, biophysical, and mechanistic characterization for potential preclinical development as GBM therapeutics.

Data driven flexible backbone protein design

Protein design remains an important problem in computational structural biology. Current computational protein design methods largely use physics-based methods, which make use of information from a single protein structure. This is despite the fact that multiple structures of many protein folds are now readily available in the PDB. While ensemble protein design methods can use multiple protein structures, they treat each structure independently. Here, we introduce a flexible backbone strategy, FlexiBaL-GP, which learns global protein backbone movements directly from multiple protein structures. FlexiBaL-GP uses the machine learning method of Gaussian Process Latent Variable Models to learn a lower dimensional representation of the protein coordinates that best represent backbone movements. These learned backbone movements are used to explore alternative protein backbones, while engineering a protein within a parallel tempered MCMC framework. Using the human ubiquitin-USP21 complex as a model we demonstrate that our design strategy outperforms current strategies for the interface design task of identifying tight binding ubiquitin variants for USP21.

Peptide-Directed Binding for the Discovery of Modulators of α-Helix-Mediated Protein-Protein Interactions: Proof-of-Concept Studies with the Apoptosis Regulator Mcl-1

Targeting PPIs with small molecules can be challenging owing to large, hydrophobic binding surfaces. Herein, we describe a strategy that exploits selective α-helical PPIs, transferring these characteristics to small molecules. The proof of concept is demonstrated with the apoptosis regulator Mcl-1, commonly exploited by cancers to avoid cell death. Peptide-directed binding uses few synthetic transformations, requires the production of a small number of compounds, and generates a high percentage of hits. In this example, about 50 % of the small molecules prepared showed an IC50 value of less than 100 μm, and approximately 25 % had IC50 values below 1 μm to Mcl-1. Compounds show selectivity for Mcl-1 over other anti-apoptotic proteins, possess cytotoxicity to cancer cell lines, and induce hallmarks of apoptosis. This approach represents a novel and economic process for the rapid discovery of new α-helical PPI modulators.

Targeting the Architecture of Deregulated Protein Complexes in Cancer

The architectures of central signaling hubs are precisely organized by static and dynamic protein-protein interactions (PPIs). Upon deregulation, these PPI platforms are capable to propagate or initiate pathophysiological signaling events. This causes the acquisition of molecular features contributing to the etiology or progression of many diseases, including cancer, where deregulated molecular interactions of signaling proteins have been best studied. The reasons for PPI-dependent reprogramming of cancer-initiating cells are manifold; in many cases, mutations perturb PPIs, enzyme activities, protein abundance, or protein localization. Consequently, the pharmaceutical targeting of PPIs promises to be of remarkable therapeutic value. For this review we have selected three key players of oncogenic signaling which are differently affected by PPI deregulation: two (the small G proteins of the RAS family and the transcription factor MYC) are considered "undruggable" using classical drug discovery approaches and in the case of the third protein discussed here, PKA, standard kinase inhibitors, may be unsuitable in the clinic. These circumstances require alternative strategies, which may lie in pharmaceutical drug interference of critical PPIs accountable for oncogenic signaling.