Expanding the number of 'druggable' targets: non-enzymes and protein-protein interactions

Therapeutic Target Identification and Drug Discovery Driven by Chemical Proteomics

Throughout the human lifespan, from conception to the end of life, small molecules have an intrinsic relationship with numerous physiological processes. The investigation into small-molecule targets holds significant implications for pharmacological discovery. The determination of the action sites of small molecules provide clarity into the pharmacodynamics and toxicological mechanisms of small-molecule drugs, assisting in the elucidation of drug off-target effects and resistance mechanisms. Consequently, innovative methods to study small-molecule targets have proliferated in recent years, with chemical proteomics standing out as a vanguard development in chemical biology in the post-genomic age. Chemical proteomics can non-selectively identify unknown targets of compounds within complex biological matrices, with both probe and non-probe modalities enabling effective target identification. This review attempts to summarize methods and illustrative examples of small-molecule target identification via chemical proteomics. It delves deeply into the interactions between small molecules and human biology to provide pivotal directions and strategies for the discovery and comprehension of novel pharmaceuticals, as well as to improve the evaluation of drug safety.

Distinct phases of cellular signaling revealed by time-resolved protein synthesis

The post-translational regulation of protein function is involved in most cellular processes. As such, synthetic biology tools that operate at this level provide opportunities for manipulating cellular states. Here we deploy proximity-triggered protein trans-splicing technology to enable the time-resolved synthesis of target proteins from premade parts. The modularity of the strategy allows for the addition or removal of various control elements as a function of the splicing reaction, in the process permitting the cellular location and/or activity state of starting materials and products to be differentiated. The approach is applied to a diverse set of proteins, including the kinase oncofusions breakpoint cluster region-Abelson (BCR-ABL) and DNAJ-PKAc where dynamic cellular phosphorylation events are dissected, revealing distinct phases of signaling and identifying molecular players connecting the oncofusion to cancer transformation as new therapeutic targets of cancer cells. We envision that the tools and control strategies developed herein will allow the activity of both naturally occurring and designer proteins to be harnessed for basic and applied research.

Long way up: rethink diseases in light of phase separation and phase transition

Biomolecular condensation, driven by multivalency, serves as a fundamental mechanism within cells, facilitating the formation of distinct compartments, including membraneless organelles that play essential roles in various cellular processes. Perturbations in the delicate equilibrium of condensation, whether resulting in gain or loss of phase separation, have robustly been associated with cellular dysfunction and physiological disorders. As ongoing research endeavors wholeheartedly embrace this newly acknowledged principle, a transformative shift is occurring in our comprehension of disease. Consequently, significant strides have been made in unraveling the profound relevance and potential causal connections between abnormal phase separation and various diseases. This comprehensive review presents compelling recent evidence that highlight the intricate associations between aberrant phase separation and neurodegenerative diseases, cancers, and infectious diseases. Additionally, we provide a succinct summary of current efforts and propose innovative solutions for the development of potential therapeutics to combat the pathological consequences attributed to aberrant phase separation.

DPI_CDF: druggable protein identifier using cascade deep forest

BACKGROUND

Drug targets in living beings perform pivotal roles in the discovery of potential drugs. Conventional wet-lab characterization of drug targets is although accurate but generally expensive, slow, and resource intensive. Therefore, computational methods are highly desirable as an alternative to expedite the large-scale identification of druggable proteins (DPs); however, the existing in silico predictor's performance is still not satisfactory.

METHODS

In this study, we developed a novel deep learning-based model DPI_CDF for predicting DPs based on protein sequence only. DPI_CDF utilizes evolutionary-based (i.e., histograms of oriented gradients for position-specific scoring matrix), physiochemical-based (i.e., component protein sequence representation), and compositional-based (i.e., normalized qualitative characteristic) properties of protein sequence to generate features. Then a hierarchical deep forest model fuses these three encoding schemes to build the proposed model DPI_CDF.

RESULTS

The empirical outcomes on 10-fold cross-validation demonstrate that the proposed model achieved 99.13 % accuracy and 0.982 of Matthew's-correlation-coefficient (MCC) on the training dataset. The generalization power of the trained model is further examined on an independent dataset and achieved 95.01% of maximum accuracy and 0.900 MCC. When compared to current state-of-the-art methods, DPI_CDF improves in terms of accuracy by 4.27% and 4.31% on training and testing datasets, respectively. We believe, DPI_CDF will support the research community to identify druggable proteins and escalate the drug discovery process.

AVAILABILITY

The benchmark datasets and source codes are available in GitHub: http://github.com/Muhammad-Arif-NUST/DPI_CDF .

AI-guided pipeline for protein-protein interaction drug discovery identifies a SARS-CoV-2 inhibitor

Protein-protein interactions (PPIs) offer great opportunities to expand the druggable proteome and therapeutically tackle various diseases, but remain challenging targets for drug discovery. Here, we provide a comprehensive pipeline that combines experimental and computational tools to identify and validate PPI targets and perform early-stage drug discovery. We have developed a machine learning approach that prioritizes interactions by analyzing quantitative data from binary PPI assays or AlphaFold-Multimer predictions. Using the quantitative assay LuTHy together with our machine learning algorithm, we identified high-confidence interactions among SARS-CoV-2 proteins for which we predicted three-dimensional structures using AlphaFold-Multimer. We employed VirtualFlow to target the contact interface of the NSP10-NSP16 SARS-CoV-2 methyltransferase complex by ultra-large virtual drug screening. Thereby, we identified a compound that binds to NSP10 and inhibits its interaction with NSP16, while also disrupting the methyltransferase activity of the complex, and SARS-CoV-2 replication. Overall, this pipeline will help to prioritize PPI targets to accelerate the discovery of early-stage drug candidates targeting protein complexes and pathways.

Advances in RNA therapeutics for modulation of 'undruggable' targets

Over the past decades, drug discovery utilizing small pharmacological compounds, fragment-based therapeutics, and antibody therapy have significantly advanced treatment options for many human diseases. However, a major bottleneck has been that>70% of human proteins/genomic regions are 'undruggable' by the above-mentioned approaches. Many of these proteins constitute essential drug targets against complex multifactorial diseases like cancer, immunological disorders, and neurological diseases. Therefore, alternative approaches are required to target these proteins or genomic regions in human cells. RNA therapeutics is a promising approach for many of the traditionally 'undruggable' targets by utilizing methods such as antisense oligonucleotides, RNA interference, CRISPR/Cas-based genome editing, aptamers, and the development of mRNA therapeutics. In the following chapter, we will put emphasis on recent advancements utilizing these approaches against challenging drug targets, such as intranuclear proteins, intrinsically disordered proteins, untranslated genomic regions, and targets expressed in inaccessible tissues.

Assessing Squarates as Amine-Reactive Probes

Probes that covalently label protein targets facilitate the identification of ligand-binding sites. Lysine residues are prevalent in the proteome, making them attractive substrates for covalent probes. However, identifying electrophiles that undergo amine-specific, regioselective reactions with binding site lysine residues is challenging. Squarates can engage in two sequential conjugate addition-elimination reactions with amines. Nitrogen donation reduces the second reaction rate, making the mono squaramide a mild electrophile. We postulated that this mild electrophilicity would demand a longer residence time near the amine, affording higher selectivity for binding site lysines. Therefore, we compared the kinetics of squarate and monosquaramide amine substitution to alternative amine bioconjugation handles. The data revealed that N-hydroxy succinimidyl esters react 4 orders of magnitude faster, consistent with their labeling promiscuity. Squarate reactivity can be tuned by a substitution pattern. Electron-withdrawing groups on the vinylogous ester or amide increase reaction rates. Dithionosquarates react more rapidly than squarates, while vinylogous thioester analogs, dithiosquarates, react more slowly. We assessed squarate selectively using the UDP-sugar processing enzyme GlfT2 from Mycobacterium tuberculosis, which possesses 21 surface-exposed lysines. The reaction predominately modified one lysine proximal to a binding site to afford covalent inhibition. These findings demonstrate the selectivity of squaric esters and squaramides, which is a critical feature for affinity-based chemoproteomic probes.

Natural compounds as lactate dehydrogenase inhibitors: potential therapeutics for lactate dehydrogenase inhibitors-related diseases

Lactate dehydrogenase (LDH) is a crucial enzyme involved in energy metabolism and present in various cells throughout the body. Its diverse physiological functions encompass glycolysis, and its abnormal activity is associated with numerous diseases. Targeting LDH has emerged as a vital approach in drug discovery, leading to the identification of LDH inhibitors among natural compounds, such as polyphenols, alkaloids, and terpenoids. These compounds demonstrate therapeutic potential against LDH-related diseases, including anti-cancer effects. However, challenges concerning limited bioavailability, poor solubility, and potential toxicity must be addressed. Combining natural compounds with LDH inhibitors has led to promising outcomes in preclinical studies. This review highlights the promise of natural compounds as LDH inhibitors for treating cancer, cardiovascular, and neurodegenerative diseases.

DNA and RNA Molecules as a Foundation of Therapy Strategies for Treatment of Cardiovascular Diseases

There has always been a tendency of medicine to take an individualised approach to treating patients, but the most significant advances were achieved through the methods of molecular biology, where the nucleic acids are in the limelight. Decades of research of molecular biology resulted in setting medicine on a completely new platform. The most significant current research is related to the possibilities that DNA and RNA analyses can offer in terms of more precise diagnostics and more subtle stratification of patients in order to identify patients for specific therapy treatments. Additionally, principles of structure and functioning of nucleic acids have become a motive for creating entirely new therapy strategies and an innovative generation of drugs. All this also applies to cardiovascular diseases (CVDs) which are the leading cause of mortality in developed countries. This review considers the most up-to-date achievements related to the use of translatory potential of DNA and RNA in treatment of cardiovascular diseases, and considers the challenges and prospects in this field. The foundations which allow the use of translatory potential are also presented. The first part of this review focuses on the potential of the DNA variants which impact conventional therapies and on the DNA variants which are starting points for designing new pharmacotherapeutics. The second part of this review considers the translatory potential of non-coding RNA molecules which can be used to formulate new generations of therapeutics for CVDs.

Distinct phases of cellular signaling revealed by time-resolved protein synthesis

The post-translational regulation of protein function is involved in most cellular processes. As such, synthetic biology tools that operate at this level provide opportunities for manipulating cellular states. Here, we deploy a proximity-triggered protein trans-splicing technology to enable the time-resolved synthesis of target proteins from pre-made parts. The modularity of the strategy allows for the addition or removal of various control elements as a function of the splicing reaction, in the process permitting the cellular location and/or activity state of starting materials and products to be differentiated. The approach is applied to a diverse set of proteins, including the kinase oncofusions BCR/ABL and DNAJB1/PRKACA where dynamic cellular phosphorylation events are dissected, revealing distinct phases of signaling and identifying molecular players connecting the oncofusion to cancer transformation as novel therapeutic targets of cancer cells. We envision that the tools and control strategies developed herein will allow the activity of both naturally occurring and designer proteins to be harnessed for basic and applied research.

BRCT Domains: Structure, Functions, and Implications in Disease-New Therapeutic Targets for Innovative Drug Discovery against Infections

The search for new therapeutic targets and their implications in drug development remains an emerging scientific topic. BRCT-bearing proteins are found in Archaea, Bacteria, Eukarya, and viruses. They are traditionally involved in DNA repair, recombination, and cell cycle control. To carry out these functions, BRCT domains are able to interact with DNA and proteins. Moreover, such domains are also implicated in several pathogenic processes and malignancies including breast, ovarian, and lung cancer. Although these domains exhibit moderately conserved folding, their sequences show very low conservation. Interestingly, sequence variations among species are considered positive traits in the search for suitable therapeutic targets, since non-specific drug interactions might be reduced. These main characteristics of BRCT, as well as its critical implications in key biological processes in the cell, have prompted the study of these domains as therapeutic targets. This review explores the possible roles of BRCT domains as therapeutic targets for drug discovery. We describe their common structural features and relevant interactions and pathways, as well as their implications in pathologic processes. Drugs commonly used to target these domains are also presented. Finally, based on their structures, we describe new drug design possibilities using modern and innovative techniques.

AI-guided pipeline for protein-protein interaction drug discovery identifies a SARS-CoV-2 inhibitor

Protein-protein interactions (PPIs) offer great opportunities to expand the druggable proteome and therapeutically tackle various diseases, but remain challenging targets for drug discovery. Here, we provide a comprehensive pipeline that combines experimental and computational tools to identify and validate PPI targets and perform early-stage drug discovery. We have developed a machine learning approach that prioritizes interactions by analyzing quantitative data from binary PPI assays and AlphaFold-Multimer predictions. Using the quantitative assay LuTHy together with our machine learning algorithm, we identified high-confidence interactions among SARS-CoV-2 proteins for which we predicted three-dimensional structures using AlphaFold Multimer. We employed VirtualFlow to target the contact interface of the NSP10-NSP16 SARS-CoV-2 methyltransferase complex by ultra-large virtual drug screening. Thereby, we identified a compound that binds to NSP10 and inhibits its interaction with NSP16, while also disrupting the methyltransferase activity of the complex, and SARS-CoV-2 replication. Overall, this pipeline will help to prioritize PPI targets to accelerate the discovery of early-stage drug candidates targeting protein complexes and pathways.

Strategies for targeting RNA with small molecule drugs

INTRODUCTION

Historically, therapeutic treatment of disease has been restricted to targeting proteins. Of the approximately 20,000 translated human proteins, approximately 1600 are associated with diseases. Strikingly, less than 15% of disease-associated proteins are predicted or known to be 'druggable.' While the concept and narrative of protein druggability continue to evolve with the development of novel technological and pharmacological advances, most of the human proteome remains undrugged. Recent genomic studies indicate that less than 2% of the human genome encodes for proteins, and while as much as 75% of the genome is transcribed, RNA has largely been ignored as a druggable target for therapeutic interventions.

AREAS COVERED

This review delineates the theory and techniques involved in the development of small molecule inhibitors of RNAs from brute force, high-throughput screening technologies to de novo molecular design using computational machine and deep learning. We will also highlight the potential pitfalls and limitations of targeting RNA with small molecules.

EXPERT OPINION

Although significant advances have recently been made in developing systems to identify small molecule inhibitors of RNAs, many challenges remain. Focusing on RNA structure and ligand binding sites may help bring drugging RNA in line with traditional protein drug targeting.

Increased slow dynamics defines ligandability of BTB domains

Efficient determination of protein ligandability, or the propensity to bind small-molecules, would greatly facilitate drug development for novel targets. Ligandability is currently assessed using computational methods that typically consider the static structural properties of putative binding sites or by experimental fragment screening. Here, we evaluate ligandability of conserved BTB domains from the cancer-relevant proteins LRF, KAISO, and MIZ1. Using fragment screening, we discover that MIZ1 binds multiple ligands. However, no ligands are uncovered for the structurally related KAISO or LRF. To understand the principles governing ligand-binding by BTB domains, we perform comprehensive NMR-based dynamics studies and find that only the MIZ1 BTB domain exhibits backbone µs-ms time scale motions. Interestingly, residues with elevated dynamics correspond to the binding site of fragment hits and recently defined HUWE1 interaction site. Our data argue that examining protein dynamics using NMR can contribute to identification of cryptic binding sites, and may support prediction of the ligandability of novel challenging targets.

Pharmacological re-assessment of traditional medicinal plants-derived inhibitors as antidotes against snakebite envenoming: A critical review

ETHNOPHARMACOLOGICAL RELEVANCE

Traditional healers have used medicinal plants to treat snakebite envenomation worldwide; however, mostly without scientific validation. There have been many studies on the therapeutic potential of the natural products against snake envenomation.

AIM OF THE STUDY

This review has highlighted snake venom inhibitory activity of bioactive compounds and peptides from plants that have found a traditional use in treating snakebite envenomation. We have systematically reviewed the scenario of different phases of natural snake venom inhibitors characterization covering a period from 1994 until the present and critically analysed the lacuna of the studies if any, and further scope for their translation from bench to bedside.

MATERIALS AND METHODS

The medicinal plant-derived compounds used against snakebite therapy were reviewed from the available literature in public databases (Scopus, MEDLINE) from 1994 till 2020. The search words used were 'natural inhibitors against snakebite,' 'natural products as therapeutics against snakebite,' 'natural products as antidote against snake envenomation,' ' snake venom toxin natural inhibitors,' 'snake venom herbal inhibitors'. However, the scope of this review does not include computational (in silico) predictions without any wet laboratory validation and snake venom inhibitory activity of the crude plant extracts. In addition, we have also predicted the ADMET properties of the identified snake venom inhibitors to highlight their valuable pharmacokinetics for future clinical studies.

RESULTS

The therapeutic application of plant-derived natural inhibitors to treat snakebite envenomation as an auxiliary to antivenom therapy has been gaining significant momentum. Pharmacological reassessment of the natural compounds derived from traditional medicinal plants has demonstrated inhibition of the principal toxic enzymes of snake venoms at various extents to curb the lethal and/or deleterious effects of venomous snakebite. Nevertheless, such molecules are yet to be commercialized for clinical application in the treatment of snakebite. There are many obstacles in the marketability of the plant-derived natural products as snake envenomation antidote and strategies must be explored for the translation of these compounds from drug candidates to their clinical application.

CONCLUSION

In order to minimize the adverse implications of snake envenomation, strategies must be developed for the smooth transition of these plant-derived small molecule inhibitors from bench to bedside. In this article we have presented an inclusive review and have critically analysed natural products for their therapeutic potential against snake envenomation, and have proposed a road map for use of natural products as antidote against snakebite.

Disc and Actin Associated Protein 1 influences attachment in the intestinal parasite Giardia lamblia

The deep-branching eukaryote Giardia lamblia is an extracellular parasite that attaches to the host intestine via a microtubule-based structure called the ventral disc. Control of attachment is mediated in part by the movement of two regions of the ventral disc that either permit or exclude the passage of fluid under the disc. Several known disc-associated proteins (DAPs) contribute to disc structure and function, but no force-generating protein has been identified among them. We recently identified several Giardia actin (GlActin) interacting proteins at the ventral disc, which could potentially employ actin polymerization for force generation and disc conformational changes. One of these proteins, Disc and Actin Associated Protein 1 (DAAP1), is highly enriched at the two regions of the disc previously shown to be important for fluid flow during attachment. In this study, we investigate the role of both GlActin and DAAP1 in ventral disc morphology and function. We confirmed interaction between GlActin and DAAP1 through coimmunoprecipitation, and used immunofluorescence to localize both proteins throughout the cell cycle and during trophozoite attachment. Similar to other DAPs, the association of DAAP1 with the disc is stable, except during cell division when the disc disassembles. Depletion of GlActin by translation-blocking antisense morpholinos resulted in both impaired attachment and defects in the ventral disc, indicating that GlActin contributes to disc-mediated attachment. Depletion of DAAP1 through CRISPR interference resulted in intact discs but impaired attachment, gating, and flow under the disc. As attachment is essential for infection, elucidation of these and other molecular mediators is a promising area for development of new therapeutics against a ubiquitous parasite.

Designing strategies of small-molecule compounds for modulating non-coding RNAs in cancer therapy

Non-coding RNAs (ncRNAs) have been defined as a class of RNA molecules transcribed from the genome but not encoding proteins, such as microRNAs, long non-coding RNAs, Circular RNAs, and Piwi-interacting RNAs. Accumulating evidence has recently been revealing that ncRNAs become potential druggable targets for regulation of several small-molecule compounds, based on their complex spatial structures and biological functions in cancer therapy. Thus, in this review, we focus on summarizing some new emerging designing strategies, such as high-throughput screening approach, small-molecule microarray approach, structure-based designing approach, phenotypic screening approach, fragment-based designing approach, and pharmacological validation approach. Based on the above-mentioned approaches, a series of representative small-molecule compounds, including Bisphenol-A, Mitoxantrone and Enoxacin have been demonstrated to modulate or selectively target ncRNAs in different types of human cancers. Collectively, these inspiring findings would provide a clue on developing more novel avenues for pharmacological modulations of ncRNAs with small-molecule drugs for future cancer therapeutics.

The applications of deep learning algorithms on in silico druggable proteins identification

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We developed the first deep learning-based druggable protein classifier for fast and accurate identification of potential druggable proteins.

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Experimental results on a standard dataset demonstrate that the prediction performance of deep learning model is comparable to those of existing methods.

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We visualized the representations of druggable proteins learned by deep learning models, which helps us understand how they work.

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Our analysis reconfirms that the attention mechanism is especially useful for explaining deep learning models.

Introduction

The top priority in drug development is to identify novel and effective drug targets. In vitro assays are frequently used for this purpose; however, traditional experimental approaches are insufficient for large-scale exploration of novel drug targets, as they are expensive, time-consuming and laborious. Therefore, computational methods have emerged in recent decades as an alternative to aid experimental drug discovery studies by developing sophisticated predictive models to estimate unknown drugs/compounds and their targets. The recent success of deep learning (DL) techniques in machine learning and artificial intelligence has further attracted a great deal of attention in the biomedicine field, including computational drug discovery.

Objectives

This study focuses on the practical applications of deep learning algorithms for predicting druggable proteins and proposes a powerful predictor for fast and accurate identification of potential drug targets.

Methods

Using a gold-standard dataset, we explored several typical protein features and different deep learning algorithms and evaluated their performance in a comprehensive way. We provide an overview of the entire experimental process, including protein features and descriptors, neural network architectures, libraries and toolkits for deep learning modelling, performance evaluation metrics, model interpretation and visualization.

Results

Experimental results show that the hybrid model (architecture: CNN-RNN (BiLSTM) + DNN; feature: dictionary encoding + DC_TC_CTD) performed better than the other models on the benchmark dataset. This hybrid model was able to achieve 90.0% accuracy and 0.800 MCC on the test dataset and 84.8% and 0.703 on a nonredundant independent test dataset, which is comparable to those of existing methods.

Conclusion

We developed the first deep learning-based classifier for fast and accurate identification of potential druggable proteins. We hope that this study will be helpful for future researchers who would like to use deep learning techniques to develop relevant predictive models.

Therapeutics-how to treat phase separation-associated diseases

Liquid-liquid phase separation has drawn attention as many neurodegeneration or cancer-associated proteins are able to form liquid membraneless compartments (condensates) by liquid-liquid phase separation. Furthermore, there is rapidly growing evidence that disease-associated mutation or post-translational modification of these proteins causes aberrant location, composition or physical properties of the condensates. It is ambiguous whether aberrant condensates are always causative in disease mechanisms, however they are likely promising potential targets for therapeutics. The conceptual framework of liquid-liquid phase separation provides opportunities for novel therapeutic approaches. This review summarises how the extensive recent advances in understanding control of nucleation, growth and composition of condensates by protein post-translational modification has revealed many possibilities for intervention by conventional small molecule enzyme inhibitors. This includes the first proof-of-concept examples. However, understanding membraneless organelle formation as a physical chemistry process also highlights possible physicochemical mechanisms of intervention. There is huge demand for innovation in drug development, especially for challenging diseases of old age including neurodegeneration and cancer. The conceptual framework of liquid-liquid phase separation provides a new paradigm for thinking about modulating protein function and is very different from enzyme lock-and-key or structured binding site concepts and presents new opportunities for innovation.

Rational Design of Constrained Peptides as Protein Interface Inhibitors

The lack of progress in developing targeted therapeutics directed at protein–protein complexes has been due to the absence of well-defined ligand-binding pockets and the extensive intermolecular contacts at the protein–protein interface. Our laboratory has developed approaches to dissect protein–protein complexes focusing on the superfamilies of erbB and tumor necrosis factor (TNF) receptors by the combined use of structural biology and computational biology to facilitate small molecule development. We present a perspective on the development and application of peptide inhibitors as well as immunoadhesins to cell surface receptors performed in our laboratory.

Molecular dynamics analysis of a flexible loop at the binding interface of the SARS-CoV-2 spike protein receptor-binding domain

Since the identification of the SARS-CoV-2 virus as the causative agent of the current COVID-19 pandemic, considerable effort has been spent characterizing the interaction between the Spike protein receptor-binding domain (RBD) and the human angiotensin converting enzyme 2 (ACE2) receptor. This has provided a detailed picture of the end point structure of the RBD-ACE2 binding event, but what remains to be elucidated is the conformation and dynamics of the RBD prior to its interaction with ACE2. In this work, we utilize molecular dynamics simulations to probe the flexibility and conformational ensemble of the unbound state of the receptor-binding domain from SARS-CoV-2 and SARS-CoV. We have found that the unbound RBD has a localized region of dynamic flexibility in Loop 3 and that mutations identified during the COVID-19 pandemic in Loop 3 do not affect this flexibility. We use a loop-modeling protocol to generate and simulate novel conformations of the CoV2-RBD Loop 3 region that sample conformational space beyond the ACE2 bound crystal structure. This has allowed for the identification of interesting substates of the unbound RBD that are lower energy than the ACE2-bound conformation, and that block key residues along the ACE2 binding interface. These novel unbound substates may represent new targets for therapeutic design.

Fragment-based drug design targeting syntenin PDZ2 domain involved in exosomal release and tumour spread

Syntenin stimulates exosome production and its expression is upregulated in many cancers and implicated in the spread of metastatic tumor. These effects are supported by syntenin PDZ domains interacting with syndecans. We therefore aimed to develop, through a fragment-based drug design approach, novel inhibitors targeting syntenin-syndecan interactions. We describe here the optimization of a fragment, 'hit' C58, identified by in vitro screening of a PDZ-focused fragment library, which binds specifically to the syntenin-PDZ2 domain at the same binding site as the syndecan-2 peptide. X-ray crystallographic structures and computational docking were used to guide our optimization process and lead to compounds 45 and 57 (IC₅₀ = 33 μM and 47 μM; respectively), two representatives of syntenin-syndecan interactions inhibitors, that selectively affect the syntenin-exosome release. These findings demonstrate that it is possible to identify small molecules inhibiting syntenin-syndecan interaction and exosome release that may be useful for cancer therapy.

De novo macrocyclic peptides dissect energy coupling of a heterodimeric ABC transporter by multimode allosteric inhibition

ATP-binding cassette (ABC) transporters constitute the largest family of primary active transporters involved in a multitude of physiological processes and human diseases. Despite considerable efforts, it remains unclear how ABC transporters harness the chemical energy of ATP to drive substrate transport across cell membranes. Here, by random nonstandard peptide integrated discovery (RaPID), we leveraged combinatorial macrocyclic peptides that target a heterodimeric ABC transport complex and explore fundamental principles of the substrate translocation cycle. High-affinity peptidic macrocycles bind conformationally selective and display potent multimode inhibitory effects. The macrocycles block the transporter either before or after unidirectional substrate export along a single conformational switch induced by ATP binding. Our study reveals mechanistic principles of ATP binding, conformational switching, and energy transduction for substrate transport of ABC export systems. We highlight the potential of de novo macrocycles as effective inhibitors for membrane proteins implicated in multidrug resistance, providing avenues for the next generation of pharmaceuticals.

Target-templated de novo design of macrocyclic d-/l-peptides: discovery of drug-like inhibitors of PD-1

Peptides are a rapidly growing class of therapeutics with various advantages over traditional small molecules, especially for targeting difficult protein-protein interactions. However, current structure-based methods are largely limited to natural peptides and are not suitable for designing bioactive cyclic topologies that go beyond natural l-amino acids. Here, we report a generalizable framework that exploits the computational power of Rosetta, in terms of large-scale backbone sampling, side-chain composition and energy scoring, to design heterochiral cyclic peptides that bind to a protein surface of interest. To showcase the applicability of our approach, we developed two new inhibitors (PD-i3 and PD-i6) of programmed cell death 1 (PD-1), a key immune checkpoint in oncology. A comprehensive biophysical evaluation was performed to assess their binding to PD-1 as well as their blocking effect on the endogenous PD-1/PD-L1 interaction. Finally, NMR elucidation of their in-solution structures confirmed our de novo design approach.

Revealing new therapeutic opportunities through drug target prediction: a class imbalance-tolerant machine learning approach

MOTIVATION

In silico drug target prediction provides valuable information for drug repurposing, understanding of side effects as well as expansion of the druggable genome. In particular, discovery of actionable drug targets is critical to developing targeted therapies for diseases.

RESULTS

Here, we develop a robust method for drug target prediction by leveraging a class imbalance-tolerant machine learning framework with a novel training scheme. We incorporate novel features, including drug-gene phenotype similarity and gene expression profile similarity that capture information orthogonal to other features. We show that our classifier achieves robust performance and is able to predict gene targets for new drugs as well as drugs that potentially target unexplored genes. By providing newly predicted drug-target associations, we uncover novel opportunities of drug repurposing that may benefit cancer treatment through action on either known drug targets or currently undrugged genes.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

An Oculus to Profile and Probe Target Engagement In Vivo: How T-REX Was Born and Its Evolution into G-REX

Here we provide a personal account of innovation and design principles underpinning a method to interrogate precision electrophile signaling that has come to be known as "REX technologies". This Account is framed in the context of trying to improve methods of target mining and understanding of individual target-ligand engagement by a specific natural electrophile and the ramifications of this labeling event in cells and organisms. We start by explaining from a practical standpoint why gleaning such understanding is critical: we are constantly assailed by a battery of electrophilic molecules that exist as a consequence of diet, food preparation, ineluctable endogenous metabolic processes, and potentially disease. The resulting molecules, which are detectable in the body, appear to be able to modify function of specific proteins. Aside from potentially being biologically relevant in their own right, these labeling events are essentially identical to protein-covalent drug interactions. Thus, on what proteins and even in what ways a covalent drug will work can be understood through the eyes of natural electrophiles; extending this logic leads to the postulate that target identification of specific electrophiles can inform on drug design. However, when we entered this field, there was no way to interrogate how a specific labeling event impacted a specific protein in an unperturbed cell. Methods to evaluate stoichiometry of labeling, and even chemospecificity of a specific phenotype were limited. There were further no generally accepted ways to study electrophile signaling that did not hugely disturb physiology.We developed T-REX, a method to study single-protein-specific electrophile engagement, to interrogate how single-protein electrophile labeling shapes pathway flux. Using T-REX, we discovered that labeling of several proteins by a specific electrophile, even at low occupancy, leads to biologically relevant signaling outputs. Further experimentation using T-REX showed that in some instances, single-protein isoforms were electrophile responsive against other isoforms, such as Akt3. Selective electrophile-labeling of Akt3 elicited inhibition of Akt-pathway flux in cells and in zebrafish embryos. Using these data, we rationally designed a molecule to selectively target Akt3. This was a fusion of the naturally derived electrophile and an isoform-nonspecific, reversible Akt inhibitor in phase-II trials, MK-2206. The resulting molecule was a selective inhibitor of Akt3 and was shown to fare better than MK-2206 in breast cancer xenograft mouse models. Recently, we have also developed a means to screen electrophile sensors that is unbiased and uses a precise burst of electrophiles. Using this method, dubbed G-REX, in conjunction with T-REX, we discovered new DNA-damage response upregulation pathways orchestrated by simple natural electrophiles. We thus emphasize how deriving a quantitative understanding of electrophile signaling that is linked to thorough and precise mechanistic studies can open doors to numerous medicinally and biologically relevant insights, from gleaning better understanding of target engagement and target mining to rational design of targeted covalent medicines.

Pharmacological inhibition of syntenin PDZ2 domain impairs breast cancer cell activities and exosome loading with syndecan and EpCAM cargo

Exosomes support cell-to-cell communication in physiology and disease, including cancer. We currently lack tools, such as small chemicals, capable of modifying exosome composition and activity in a specific manner. Building on our previous understanding of how syntenin, and its PDZ partner syndecan (SDC), impact on exosome composition we optimized a small chemical compound targeting the PDZ2 domain of syntenin. In vitro , in tests on MCF-7 breast carcinoma cells, this compound is non-toxic and impairs cell proliferation, migration and primary sphere formation. It does not affect the size or the number of secreted particles, yet it decreases the amounts of exosomal syntenin, ALIX and SDC4 while leaving other exosomal markers unaffected. Interestingly, it also blocks the sorting of EpCAM, a bona fide target used for carcinoma exosome immunocapture. Our study highlights the first characterization of a small pharmacological inhibitor of the syntenin-exosomal pathway, of potential interest for exosome research and oncology.

Application of Advanced Technologies in Natural Product Research: A Review with Special Emphasis on ADMET Profiling

The successful conversion of natural products (NPs) into lead compounds and novel pharmacophores has emboldened the researchers to harness the drug discovery process with a lot more enthusiasm. However, forfeit of bioactive NPs resulting from an overabundance of metabolites and their wide dynamic range have created the bottleneck in NP researches. Similarly, the existence of multidimensional challenges, including the evaluation of pharmacokinetics, pharmacodynamics, and safety parameters, has been a concerning issue. Advancement of technology has brought the evolution of traditional natural product researches into the computer-based assessment exhibiting pretentious remarks about their efficiency in drug discovery. The early attention to the quality of the NPs may reduce the attrition rate of drug candidates by parallel assessment of ADMET profiling. This article reviews the status, challenges, opportunities, and integration of advanced technologies in natural product research. Indeed, emphasis will be laid on the current and futuristic direction towards the application of newer technologies in early-stage ADMET profiling of bioactive moieties from the natural sources. It can be expected that combinatorial approaches in ADMET profiling will fortify the natural product-based drug discovery in the near future.

Design and biological evaluation of phenyl imidazole analogs as hedgehog signaling pathway inhibitors

The hedgehog (Hh) signaling pathway is involved in diverse aspects of cellular events. Aberrant activation of Hh signaling pathway drives oncogenic transformation for a wide range of cancers, and it is therefore a promising target in cancer therapy. In the principle of association and ring-opening, we designed and synthesized a series of Hh signaling pathway inhibitors with phenyl imidazole scaffold, which were biologically evaluated in Gli-Luc reporter assay. Compound 25 was identified to possess high potency with nanomolar IC₅₀ , and moreover, it preserved the inhibition against wild-type and drug-resistant Smo-overexpressing cells. A molecular modeling study of compound 25 expounded its binding mode to Smo receptor, providing a basis for the further structural modification of phenyl imidazole analogs.

How We Think about Targeting RNA with Small Molecules

RNA offers nearly unlimited potential as a target for small molecule chemical probes and lead medicines. Many RNAs fold into structures that can be selectively targeted with small molecules. This Perspective discusses molecular recognition of RNA by small molecules and highlights key enabling technologies and properties of bioactive interactions. Sequence-based design of ligands targeting RNA has established rules for affecting RNA targets and provided a potentially general platform for the discovery of bioactive small molecules. The RNA targets that contain preferred small molecule binding sites can be identified from sequence, allowing identification of off-targets and prediction of bioactive interactions by nature of ligand recognition of functional sites. Small molecule targeted degradation of RNA targets (ribonuclease-targeted chimeras, RIBOTACs) and direct cleavage by small molecules have also been developed. These growing technologies suggest that the time is right to provide small molecule chemical probes to target functionally relevant RNAs throughout the human transcriptome.

Targeting the cryptic sites: NMR-based strategy to improve protein druggability by controlling the conformational equilibrium

Cryptic ligand binding sites, which are not evident in the unligated structures, are beneficial in tackling with difficult but attractive drug targets, such as protein-protein interactions (PPIs). However, cryptic sites have thus far not been rationally pursued in the early stages of drug development. Here, we demonstrated by nuclear magnetic resonance that the cryptic site in Bcl-xL exists in a conformational equilibrium between the open and closed conformations under the unligated condition. While the fraction of the open conformation in the unligated wild-type Bcl-xL is estimated to be low, F143W mutation that is distal from the ligand binding site can substantially elevate the population. The F143W mutant showed a higher hit rate in a phage-display peptide screening, and the hit peptide bound to the cryptic site of the wild-type Bcl-xL. Therefore, by controlling the conformational equilibrium in the cryptic site, the opportunity to identify a PPI inhibitor could be improved.

Target-Directed Approaches for Screening Small Molecules against RNA Targets

RNA molecules have a variety of cellular functions that can drive disease pathologies. They are without a doubt one of the most intriguing yet controversial small-molecule drug targets. The ability to widely target RNA with small molecules could be revolutionary, once the right tools, assays, and targets are selected, thereby defining which biomolecules are targetable and what constitutes drug-like small molecules. Indeed, approaches developed over the past 5-10 years have changed the face of small molecule-RNA targeting by addressing historic concerns regarding affinity, selectivity, and structural dynamics. Presently, selective RNA-protein complex stabilizing drugs such as branaplam and risdiplam are in clinical trials for the modulation of SMN2 splicing, compounds identified from phenotypic screens with serendipitous outcomes. Fully developing RNA as a druggable target will require a target engagement-driven approach, and evolving chemical collections will be important for the industrial development of this class of target. In this review we discuss target-directed approaches that can be used to identify RNA-binding compounds and the chemical knowledge we have today of small-molecule RNA binders.

Selective inhibition of STAT3 signaling using monobodies targeting the coiled-coil and N-terminal domains

The transcription factor STAT3 is frequently activated in human solid and hematological malignancies and remains a challenging therapeutic target with no approved drugs to date. Here, we develop synthetic antibody mimetics, termed monobodies, to interfere with STAT3 signaling. These monobodies are highly selective for STAT3 and bind with nanomolar affinity to the N-terminal and coiled-coil domains. Interactome analysis detects no significant binding to other STATs or additional off-target proteins, confirming their exquisite specificity. Intracellular expression of monobodies fused to VHL, an E3 ubiquitin ligase substrate receptor, results in degradation of endogenous STAT3. The crystal structure of STAT3 in complex with monobody MS3-6 reveals bending of the coiled-coil domain, resulting in diminished DNA binding and nuclear translocation. MS3-6 expression strongly inhibits STAT3-dependent transcriptional activation and disrupts STAT3 interaction with the IL-22 receptor. Therefore, our study establishes innovative tools to interfere with STAT3 signaling by different molecular mechanisms.

STAT3 is an attractive therapeutic target but its homology with other STAT proteins complicates the development of selective inhibitors. Here, the authors develop monobodies with high affinity and selectivity for STAT3 and show that they can interfere with cellular STAT3 activity.

A FRET-based high-throughput screening platform for the discovery of chemical probes targeting the scaffolding functions of human tankyrases

Tankyrases catalyse poly-ADP-ribosylation of their binding partners and the modification serves as a signal for the subsequent proteasomal degradation of these proteins. Tankyrases thereby regulate the turnover of many proteins involved in multiple and diverse cellular processes, such as mitotic spindle formation, telomere homeostasis and Wnt/β-catenin signalling. In recent years, tankyrases have become attractive targets for the development of inhibitors as potential therapeutics against cancer and fibrosis. Further, it has become clear that tankyrases are not only enzymes, but also act as scaffolding proteins forming large cellular signalling complexes. While many potent and selective tankyrase inhibitors of the poly-ADP-ribosylation function exist, the inhibition of tankyrase scaffolding functions remains scarcely explored. In this work we present a robust, simple and cost-effective high-throughput screening platform based on FRET for the discovery of small molecule probes targeting the protein–protein interactions of tankyrases. Validatory screening with the platform led to the identification of two compounds with modest binding affinity to the tankyrase 2 ARC4 domain, demonstrating the applicability of this approach. The platform will facilitate identification of small molecules binding to tankyrase ARC or SAM domains and help to advance a structure-guided development of improved chemical probes targeting tankyrase oligomerization and substrate protein interactions.

Targeting epigenetic protein-protein interactions with small-molecule inhibitors

Epigenetic protein-protein interactions (PPIs) play essential roles in regulating gene expression, and their dysregulations have been implicated in many diseases. These PPIs are comprised of reader domains recognizing post-translational modifications on histone proteins, and of scaffolding proteins that maintain integrities of epigenetic complexes. Targeting PPIs have become focuses for development of small-molecule inhibitors and anticancer therapeutics. Here we summarize efforts to develop small-molecule inhibitors targeting common epigenetic PPI domains. Potent small molecules have been reported for many domains, yet small domains that recognize methylated lysine side chains on histones are challenging in inhibitor development. We posit that the development of potent inhibitors for difficult-to-prosecute epigenetic PPIs may be achieved by interdisciplinary approaches and extensive explorations of chemical space.

Creating and screening natural product libraries

Covering: up to 2020The National Cancer Institute of the United States (NCI) has initiated a Cancer Moonshot program entitled the NCI Program for Natural Product Discovery. As part of this effort, the NCI is producing a library of 1 000 000 partially purified natural product fractions which are being plated into 384-well plates and provided to the research community free of charge. As the first 326 000 of these fractions have now been made available, this review seeks to describe the general methods used to collect organisms, extract those organisms, and create a prefractionated library. Importantly, this review also details both cell-based and cell-free bioassay methods and the adaptations necessary to those methods to productively screen natural product libraries. Finally, this review briefly describes post-screen dereplication and compound purification and scale up procedures which can efficiently identify active compounds and produce sufficient quantities of natural products for further pre-clinical development.

Domain-mediated interactions for protein subfamily identification

Within a protein family, proteins with the same domain often exhibit different cellular functions, despite the shared evolutionary history and molecular function of the domain. We hypothesized that domain-mediated interactions (DMIs) may categorize a protein family into subfamilies because the diversified functions of a single domain often depend on interacting partners of domains. Here we systematically identified DMI subfamilies, in which proteins share domains with DMI partners, as well as with various functional and physical interaction networks in individual species. In humans, DMI subfamily members are associated with similar diseases, including cancers, and are frequently co-associated with the same diseases. DMI information relates to the functional and evolutionary subdivisions of human kinases. In yeast, DMI subfamilies contain proteins with similar phenotypic outcomes from specific chemical treatments. Therefore, the systematic investigation here provides insights into the diverse functions of subfamilies derived from a protein family with a link-centric approach and suggests a useful resource for annotating the functions and phenotypic outcomes of proteins.

Sequence-Derived Markers of Drug Targets and Potentially Druggable Human Proteins

Recent research shows that majority of the druggable human proteome is yet to be annotated and explored. Accurate identification of these unexplored druggable proteins would facilitate development, screening, repurposing, and repositioning of drugs, as well as prediction of new drug–protein interactions. We contrast the current drug targets against the datasets of non-druggable and possibly druggable proteins to formulate markers that could be used to identify druggable proteins. We focus on the markers that can be extracted from protein sequences or names/identifiers to ensure that they can be applied across the entire human proteome. These markers quantify key features covered in the past works (topological features of PPIs, cellular functions, and subcellular locations) and several novel factors (intrinsic disorder, residue-level conservation, alternative splicing isoforms, domains, and sequence-derived solvent accessibility). We find that the possibly druggable proteins have significantly higher abundance of alternative splicing isoforms, relatively large number of domains, higher degree of centrality in the protein-protein interaction networks, and lower numbers of conserved and surface residues, when compared with the non-druggable proteins. We show that the current drug targets and possibly druggable proteins share involvement in the catalytic and signaling functions. However, unlike the drug targets, the possibly druggable proteins participate in the metabolic and biosynthesis processes, are enriched in the intrinsic disorder, interact with proteins and nucleic acids, and are localized across the cell. To sum up, we formulate several markers that can help with finding novel druggable human proteins and provide interesting insights into the cellular functions and subcellular locations of the current drug targets and potentially druggable proteins.

Inducing the Degradation of Disease-Related Proteins Using Heterobifunctional Molecules

Current drug development strategies that target either enzymatic or receptor proteins for which specific small molecule ligands can be designed for modulation, result in a large portion of the proteome being overlooked as undruggable. The recruitment of natural degradation cascades for targeted protein removal using heterobifunctional molecules (or degraders) provides a likely avenue to expand the druggable proteome. In this review, we discuss the use of this drug development strategy in relation to degradation cascade-recruiting mechanisms and successfully targeted disease-related proteins. Essential characteristics to be considered in degrader design are deliberated upon and future development challenges mentioned.

On the discovery of a potential survivin inhibitor combining computational tools and cytotoxicity studies

Survivin protein is a metalloprotein member of the inhibitors of apoptosis proteins family, involved in the regulation of programmed cell death. Due to the recent development of antitumor therapies having survivin as molecular target, several strategies to interfere with the expression or function of survivin have emerged. This work describes the discovery of a new potential inhibitor of survivin function using a computer-aided drug design approach. Structure-based virtual screening and molecular dynamic simulations were carried out to select two compounds as possible inhibitors. According to the binding energy, possible ligand localization is in a cavity, close to dimerization interface. Next, cell-based assays were performed on three cell lines: two with tumor phenotype and over-expression of survivin, and another with normal phenotype and low expression of survivin. One of the selected compounds shows a selectively antitumor effect on panel cell lines suggesting that the compound effect could be correlated with the survivin expression.