AlloFinder: a strategy for allosteric modulator discovery and allosterome analyses

An allosteric inhibitor targeting the STAT3 coiled-coil domain selectively suppresses proliferation of breast cancer

Signal transducer and activator of transcription 3 (STAT3) remains a challenging and attractive therapeutic target in cancer research. The coiled-coil domain (CCD) of STAT3 represents a novel site for targeted intervention, distinct from the Src-homology 2 domain, and plays a crucial role in regulating the earlier activation and biological function of STAT3 in cell proliferation, survival and invasion of breast cancer cells. We previously reported K116, N'-(1-(2,4-dihydroxyphenyl)ethylidene)thiophene-2-carbohydrazide, as a potent allosteric inhibitor specifically targeting the STAT3 CCD. This study aimed to investigate the antiproliferation effect of K116 on breast cancer cells in vitro and in vivo. The results showed that K116 inhibited the proliferation of breast cancer cell lines in a dose-dependent manner by reducing the phosphorylation of STAT3 Lyr705 and did not inhibit the proliferation of HGC-27 and A549 cells nor their STAT3 Lyr705 phosphorylation. Compared with Stattic (STAT3 SH2 inhibitor), K116 selectively inhibited the proliferation of breast cancer cells. Furthermore, K116 (20 μM) directly monitored STAT3 stabilization and engagement within MDA-MB-468 cells, without affecting STAT1, STAT5, and Akt1. K116 induced apoptosis and inhibited migration as well as pY705STAT3 nuclear translocation and transcriptional activity of STAT3. In addition, K116 (30 mg kg-1) markedly suppressed tumor growth and inhibited STAT3 activity in a 4T1 cell-derived murine breast cancer model. Overall, our results provided pharmacological evidence supporting future clinical investigation of K116 as a promising STAT3 CCD allosteric inhibitor for breast cancer treatment.

STAT3-controlled CHI3L1/SPP1 positive feedback loop demonstrates the spatial heterogeneity and immune characteristics of glioblastoma

Proneural-mesenchymal transition (PMT) is a phenotypic alteration and contributes to the malignant progression of glioblastoma (GBM). Macrophages, as a main infiltrating component of the tumor immune microenvironment (TIM), control the biological processes of PMT; however, the mechanisms driving this process remain largely unknown. Here, the overall landscape of tumor and nontumor cells was described by scMulti-omics technology. Then, we demonstrated that chitinase-3-like protein 1 (CHI3L1) played a critical role in maintaining mesenchymal (MES) status and reprogramming macrophage phenotype using C57BL/6 and NSG mice models derived from PN20 cells. Mechanistically, osteopontin (OPN)/ITGB1 maintained the activation of nuclear factor κB (NF-κB) and signal transducer and activator of transcription 3 (STAT3) pathways by establishing a positive feedback loop with the CHI3L1-STAT3 axis, resulting in PMT. CHI3L1 enhanced the phosphorylation, nuclear localization, and transcriptional activity of STAT3 via directly binding its coiled-coil domain (CCD). Importantly, we screened and validated that hygromycin B (HB), an inhibitor of the STAT3-CCD domain, disrupted the CHI3L1-STAT3 interaction, thereby reducing the tumor burden in vitro and in vivo.

A novel allosteric driver mutation of β-glucuronidase promotes head and neck squamous cell carcinoma progression through STT3B-mediated PD-L1 N-glycosylation

Head and neck squamous cell carcinoma (HNSCC) develops and advances because of the accumulation of somatic mutations located in orthosteric and allosteric areas. However, the biological effects of allosteric driver mutations during tumorigenesis are mostly unknown. Here, we mapped somatic mutations generated from 10 tumor-normal matched HNSCC samples into allosteric sites to prioritize the mutated allosteric proteins via whole-exome sequencing and AlloDriver, identifying the specific mutation H351Q in β-glucuronidase (GUSB), a lysosomal enzyme, as a novel allosteric driver mutation, which considerably encouraged HNSCC progression both in vitro and in vivo. Mechanistically, the allosteric mutation of H351Q remarkably attenuated protein trafficking from the endoplasmic reticulum (ER) to lysosomes, leading to ER retention, in which GUSB-H351Q facilitated the aberrant N-glycosylation of PD-L1 through increasing protein stability and mRNA transcripts of the STT3 oligosaccharyltransferase complex catalytic subunit B, an oligosaccharyltransferase complex. Moreover, GUSB-H351Q reshaped a more immunosuppressive microenvironment featuring increased infiltration of exhausted CD8+ T cells and remodeled tumor metabolism, characterized by increased activity of the purine metabolism pathways and pyruvic acid accumulation. This study provides a mechanism-driven approach to overcoming HNSCC progression and immune evasion and identifies novel druggable targets based on the presence of GUSB allosteric driver mutation.

Acidic Extracellular pH-Activated Allosteric DNA Nanodevice for Fluorescence Imaging of APE1 Activity in Tumor Cells

Allostery is a phenomenon where the binding of a ligand at one allosteric site influences the affinity for another ligand at an active site. Different from orthosteric regulation, it allows for more precise control of biomolecular activity and enhances the stability of the molecules. Inspired by allosteric regulation of natural molecules, we present a Y-shaped allosteric DNA nanodevice, termed YssAP, that was pH-responsive and functionalized with the AS1411 aptamer for accurate fluorescence imaging of human apurinic/apyrimidinic endonuclease (APE1) activity in tumor cells. With rational design, YssAP could not be cut by APE1, and Cy5 was in the proximity of BHQ2, leading to suppressed signal emission. In contrast, since acidic pH acted as an allosteric effector, YssAP underwent a conformational change into an activated DNA probe (YdsAP) at acidic extracellular pH. After entering the tumor cell via the specific recognition of AS1411 aptamer, the overexpressed APE1 in the tumor cell cut the AP site on YdsAP. Cy5 moved far away from BHQ2, resulting in a strong signal output. Compared with the direct construction of the APE1 substrate, allosteric DNA nanodevices have more accurate imaging effects, which can be precisely adjusted by changing the switching state. We anticipate that this strategy will be applied in the screening of APE1 inhibitors and precise tumor diagnosis.

JAK/STAT3 signaling in cardiac fibrosis: a promising therapeutic target

Cardiac fibrosis is a serious health problem because it is a common pathological change in almost all forms of cardiovascular diseases. Cardiac fibrosis is characterized by the transdifferentiation of cardiac fibroblasts (CFs) into cardiac myofibroblasts and the excessive deposition of extracellular matrix (ECM) components produced by activated myofibroblasts, which leads to fibrotic scar formation and subsequent cardiac dysfunction. However, there are currently few effective therapeutic strategies protecting against fibrogenesis. This lack is largely because the molecular mechanisms of cardiac fibrosis remain unclear despite extensive research. The Janus kinase/signal transducer and activator of transcription (JAK/STAT) signaling cascade is an extensively present intracellular signal transduction pathway and can regulate a wide range of biological processes, including cell proliferation, migration, differentiation, apoptosis, and immune response. Various upstream mediators such as cytokines, growth factors and hormones can initiate signal transmission via this pathway and play corresponding regulatory roles. STAT3 is a crucial player of the JAK/STAT pathway and its activation is related to inflammation, malignant tumors and autoimmune illnesses. Recently, the JAK/STAT3 signaling has been in the spotlight for its role in the occurrence and development of cardiac fibrosis and its activation can promote the proliferation and activation of CFs and the production of ECM proteins, thus leading to cardiac fibrosis. In this manuscript, we discuss the structure, transactivation and regulation of the JAK/STAT3 signaling pathway and review recent progress on the role of this pathway in cardiac fibrosis. Moreover, we summarize the current challenges and opportunities of targeting the JAK/STAT3 signaling for the treatment of fibrosis. In summary, the information presented in this article is critical for comprehending the role of the JAK/STAT3 pathway in cardiac fibrosis, and will also contribute to future research aimed at the development of effective anti-fibrotic therapeutic strategies targeting the JAK/STAT3 signaling.

Protein dynamics underlying allosteric regulation

Allostery is the mechanism by which information and control are propagated in biomolecules. It regulates ligand binding, chemical reactions, and conformational changes. An increasing level of experimental resolution and control over allosteric mechanisms promises a deeper understanding of the molecular basis for life and powerful new therapeutics. In this review, we survey the literature for an up-to-date biological and theoretical understanding of protein allostery. By delineating five ways in which the energy landscape or the kinetics of a system may change to give rise to allostery, we aim to help the reader grasp its physical origins. To illustrate this framework, we examine three systems that display these forms of allostery: allosteric inhibitors of beta-lactamases, thermosensation of TRP channels, and the role of kinetic allostery in the function of kinases. Finally, we summarize the growing power of computational tools available to investigate the different forms of allostery presented in this review.

ASD2023: towards the integrating landscapes of allosteric knowledgebase

Allosteric regulation, induced by perturbations at an allosteric site topographically distinct from the orthosteric site, is one of the most direct and efficient ways to fine-tune macromolecular function. The Allosteric Database (ASD; accessible online at http://mdl.shsmu.edu.cn/ASD) has been systematically developed since 2009 to provide comprehensive information on allosteric regulation. In recent years, allostery has seen sustained growth and wide-ranging applications in life sciences, from basic research to new therapeutics development, while also elucidating emerging obstacles across allosteric research stages. To overcome these challenges and maintain high-quality data center services, novel features were curated in the ASD2023 update: (i) 66 589 potential allosteric sites, covering > 80% of the human proteome and constituting the human allosteric pocketome; (ii) 748 allosteric protein-protein interaction (PPI) modulators with clear mechanisms, aiding protein machine studies and PPI-targeted drug discovery; (iii) 'Allosteric Hit-to-Lead,' a pioneering dataset providing panoramic views from 87 well-defined allosteric hits to 6565 leads and (iv) 456 dualsteric modulators for exploring the simultaneous regulation of allosteric and orthosteric sites. Meanwhile, ASD2023 maintains a significant growth of foundational allosteric data. Based on these efforts, the allosteric knowledgebase is progressively evolving towards an integrated landscape, facilitating advancements in allosteric target identification, mechanistic exploration and drug discovery.

A Structure-Based Allosteric Modulator Design Paradigm

Importance: Allosteric drugs bound to topologically distal allosteric sites hold a substantial promise in modulating therapeutic targets deemed undruggable at their orthosteric sites. Traditionally, allosteric modulator discovery has predominantly relied on serendipitous high-throughput screening. Nevertheless, the landscape has undergone a transformative shift due to recent advancements in our understanding of allosteric modulation mechanisms, coupled with a significant increase in the accessibility of allosteric structural data. These factors have extensively promoted the development of various computational methodologies, especially for machine-learning approaches, to guide the rational design of structure-based allosteric modulators. Highlights: We here presented a comprehensive structure-based allosteric modulator design paradigm encompassing 3 critical stages: drug target acquisition, allosteric binding site, and modulator discovery. The recent advances in computational methods in each stage are encapsulated. Furthermore, we delve into analyzing the successes and obstacles encountered in the rational design of allosteric modulators. Conclusion: The structure-based allosteric modulator design paradigm holds immense potential for the rational design of allosteric modulators. We hope that this review would heighten awareness of the use of structure-based computational methodologies in advancing the field of allosteric drug discovery.

Progressive computational approaches to facilitate decryption of allosteric mechanism and drug discovery

Allostery is a ubiquitous biological phenomenon where perturbation at topologically distal areas of a protein serves as a trigger to fine-tune the orthosteric site and thus regulate protein function. The investigation of allosteric regulation greatly enhances our understanding of human diseases and broadens avenue for drug discovery. For decades, owing to the difficulty in allostery characterization through serendipitous experimental screening, researchers have developed several innovative computational approaches, which proves to accelerate the elucidation of allostery. Herein, we review the state-of-the-art advance of computational methodologies for allostery study, with particular emphasis on promising trends emerging over the past two years. We expect this review will outline the latest landscape of allostery study and inspire researchers to further facilitate this field.

Allosteric modulation of SHP2: Quest from known to unknown

Src homology-2 domain-containing protein tyrosine phosphatase-2 (SHP2) is a key regulatory factor in the cell cycle and its activating mutations play an important role in the development of various cancers, making it an important target for antitumor drugs. Due to the highly conserved amino acid sequence and positively charged nature of the active site of SHP2, it is difficult to discover inhibitors with high affinity for the catalytic site of SHP2 and sufficient cell permeability, making it considered an "undruggable" target. However, the discovery of allosteric regulation mechanisms provides new opportunities for transforming undruggable targets into druggable ones. Given the limitations of orthosteric inhibitors, SHP2 allosteric inhibitors have become a more selective and safer research direction. In this review, we elucidate the oncogenic mechanism of SHP2 and summarize the discovery methods of SHP2 allosteric inhibitors, providing new strategies for the design and improvement of SHP2 allosteric inhibitors.

μMap Photoproximity Labeling Enables Small Molecule Binding Site Mapping

The characterization of ligand binding modes is a crucial step in the drug discovery process and is especially important in campaigns arising from phenotypic screening, where the protein target and binding mode are unknown at the outset. Elucidation of target binding regions is typically achieved by X-ray crystallography or photoaffinity labeling (PAL) approaches; yet, these methods present significant challenges. X-ray crystallography is a mainstay technique that has revolutionized drug discovery, but in many cases structural characterization is challenging or impossible. PAL has also enabled binding site mapping with peptide- and amino-acid-level resolution; however, the stoichiometric activation mode can lead to poor signal and coverage of the resident binding pocket. Additionally, each PAL probe can have its own fragmentation pattern, complicating the analysis by mass spectrometry. Here, we establish a robust and general photocatalytic approach toward the mapping of protein binding sites, which we define as identification of residues proximal to the ligand binding pocket. By utilizing a catalytic mode of activation, we obtain sets of labeled amino acids in the proximity of the target protein binding site. We use this methodology to map, in vitro, the binding sites of six protein targets, including several kinases and molecular glue targets, and furthermore to investigate the binding site of the STAT3 inhibitor MM-206, a ligand with no known crystal structure. Finally, we demonstrate the successful mapping of drug binding sites in live cells. These results establish μMap as a powerful method for the generation of amino-acid- and peptide-level target engagement data.

AlphaFold, allosteric, and orthosteric drug discovery: Ways forward

Drug discovery is arguably a highly challenging and significant interdisciplinary aim. The stunning success of the artificial intelligence-powered AlphaFold, whose latest version is buttressed by an innovative machine-learning approach that integrates physical and biological knowledge about protein structures, raised drug discovery hopes that unsurprisingly, have not come to bear. Even though accurate, the models are rigid, including the drug pockets. AlphaFold's mixed performance poses the question of how its power can be harnessed in drug discovery. Here we discuss possible ways of going forward wielding its strengths, while bearing in mind what AlphaFold can and cannot do. For kinases and receptors, an input enriched in active (ON) state models can better AlphaFold's chance of rational drug design success.

Machine learning and protein allostery

The fundamental biological importance and complexity of allosterically regulated proteins stem from their central role in signal transduction and cellular processes. Recently, machine-learning approaches have been developed and actively deployed to facilitate theoretical and experimental studies of protein dynamics and allosteric mechanisms. In this review, we survey recent developments in applications of machine-learning methods for studies of allosteric mechanisms, prediction of allosteric effects and allostery-related physicochemical properties, and allosteric protein engineering. We also review the applications of machine-learning strategies for characterization of allosteric mechanisms and drug design targeting SARS-CoV-2. Continuous development and task-specific adaptation of machine-learning methods for protein allosteric mechanisms will have an increasingly important role in bridging a wide spectrum of data-intensive experimental and theoretical technologies.

Fragment-based drug discovery supports drugging 'undruggable' protein-protein interactions

Protein-protein interactions (PPIs) have important roles in various cellular processes, but are commonly described as 'undruggable' therapeutic targets due to their large, flat, featureless interfaces. Fragment-based drug discovery (FBDD) has achieved great success in modulating PPIs, with more than ten compounds in clinical trials. Here, we highlight the progress of FBDD in modulating PPIs for therapeutic development. Targeting hot spots that have essential roles in both fragment binding and PPIs provides a shortcut for the development of PPI modulators via FBDD. We highlight successful cases of cracking the 'undruggable' problems of PPIs using fragment-based approaches. We also introduce new technologies and future trends. Thus, we hope that this review will provide useful guidance for drug discovery targeting PPIs.

Transcription factor-based biosensors for screening and dynamic regulation

Advances in synthetic biology and genetic engineering are bringing into the spotlight a wide range of bio-based applications that demand better sensing and control of biological behaviours. Transcription factor (TF)-based biosensors are promising tools that can be used to detect several types of chemical compounds and elicit a response according to the desired application. However, the wider use of this type of device is still hindered by several challenges, which can be addressed by increasing the current metabolite-activated transcription factor knowledge base, developing better methods to identify new transcription factors, and improving the overall workflow for the design of novel biosensor circuits. These improvements are particularly important in the bioproduction field, where researchers need better biosensor-based approaches for screening production-strains and precise dynamic regulation strategies. In this work, we summarize what is currently known about transcription factor-based biosensors, discuss recent experimental and computational approaches targeted at their modification and improvement, and suggest possible future research directions based on two applications: bioproduction screening and dynamic regulation of genetic circuits.

Specifics of Metabolite-Protein Interactions and Their Computational Analysis and Prediction

Computational approaches to the characterization and prediction of compound-protein interactions have a long research history and are well established, driven primarily by the needs of drug development. While, in principle, many of the computational methods developed in the context of drug development can also be applied directly to the investigation of metabolite-protein interactions, the interactions of metabolites with proteins (enzymes) are characterized by a number of particularities that result from their natural evolutionary origin and their biological and biochemical roles, as well as from a different problem setting when investigating them. In this review, these special aspects will be highlighted and recent research on them and developed computational approaches presented, along with available resources. They concern, among others, binding promiscuity, allostery, the role of posttranslational modifications, molecular steering and crowding effects, and metabolic conversion rate predictions. Recent breakthroughs in the field of protein structure prediction and newly developed machine learning techniques are being discussed as a tremendous opportunity for developing a more detailed molecular understanding of metabolism.

Allosteric regulation in STAT3 interdomains is mediated by a rigid core: SH2 domain regulation by CCD in D170A variant

Signal Transducer and Activator of Transcription 3 (STAT3) plays a crucial role in cancer development and thus is a viable target for cancer treatment. STAT3 functions as a dimer mediated by phosphorylation of the SRC-homology 2 (SH2) domain, a key target for therapeutic drugs. While great efforts have been employed towards the development of compounds that directly target the SH2 domain, no compound has yet been approved by the FDA due to a lack of specificity and pharmacologic efficacy. Studies have shown that allosteric regulation of SH2 via the coiled-coil domain (CCD) is an alternative drug design strategy. Several CCD effectors have been shown to modulate SH2 binding and affinity, and at the time of writing at least one drug candidate has entered phase I clinical trials. However, the mechanism for SH2 regulation via CCD is poorly understood. Here, we investigate structural and dynamic features of STAT3 and compare the wild type to the reduced function variant D170A in order to delineate mechanistic differences and propose allosteric pathways. Molecular dynamics simulations were employed to explore conformational space of STAT3 and the variant, followed by structural, conformation, and dynamic analysis. The trajectories explored show distinctive conformational changes in the SH2 domain for the D170A variant, indicating long range allosteric effects. Multiple analyses provide evidence for long range communication pathways between the two STAT3 domains, which seem to be mediated by a rigid core which connects the CCD and SH2 domains via the linker domain (LD) and transmits conformational changes through a network of short-range interactions. The proposed allosteric mechanism provides new insight into the understanding of intramolecular signaling in STAT3 and potential pharmaceutical control of STAT3 specificity and activity.

Identification of the Allosteric Binding Site for Thiazolopyrimidine on the C-Type Lectin Langerin

Langerin is a mammalian C-type lectin expressed on Langerhans cells in the skin. As an innate immune cell receptor, Langerin is involved in coordinating innate and adaptive immune responses against various incoming threats. We have previously reported a series of thiazolopyrimidines as murine Langerin ligands. Prompted by the observation that its human homologue exhibits different binding specificities for these small molecules, we report here our investigations to define their exact binding site. By using structural comparison and molecular dynamics simulations, we showed that the nonconserved short loops have a high degree of conformational flexibility between the human and murine homologues. Sequence analysis and mutational studies indicated that a pair of residues are essential for the recognition of the thiazolopyrimidines. Taking solvent paramagnetic relaxation enhancement NMR studies together with a series of peptides occupying the same site, we could define the cleft between the short and long loops as the allosteric binding site for these aromatic heterocycles.

Allosteric Antagonism of the Pregnane X Receptor (PXR): Current-State-of-the-Art and Prediction of Novel Allosteric Sites

The pregnane X receptor (PXR, NR1I2) is a xenobiotic-activated transcription factor with high levels of expression in the liver. It not only plays a key role in drug metabolism and elimination, but also promotes tumor growth, drug resistance, and metabolic diseases. It has been proposed as a therapeutic target for type II diabetes, metabolic syndrome, and inflammatory bowel disease, and PXR antagonists have recently been considered as a therapy for colon cancer. There are currently no PXR antagonists that can be used in a clinical setting. Nevertheless, due to the large and complex ligand-binding pocket (LBP) of the PXR, it is challenging to discover PXR antagonists at the orthosteric site. Alternative ligand binding sites of the PXR have also been proposed and are currently being studied. Recently, the AF-2 allosteric binding site of the PXR has been identified, with several compounds modulating the site discovered. Herein, we aimed to summarize our current knowledge of allosteric modulation of the PXR as well as our attempt to unlock novel allosteric sites. We describe the novel binding function 3 (BF-3) site of PXR, which is also common for other nuclear receptors. In addition, we also mention a novel allosteric site III based on in silico prediction. The identified allosteric sites of the PXR provide new insights into the development of safe and efficient allosteric modulators of the PXR receptor. We therefore propose that novel PXR allosteric sites might be promising targets for treating chronic metabolic diseases and some cancers.

SHF Acts as a Novel Tumor Suppressor in Glioblastoma Multiforme by Disrupting STAT3 Dimerization

Sustained activation of signal transducer and activator of transcription 3 (STAT3) is a critical contributor in tumorigenesis and chemoresistance, thus making it an attractive cancer therapeutic target. Here, SH2 domain-containing adapter protein F (SHF) is identified as a tumor suppressor in glioblastoma Multiforme (GBM) and its negative regulation of STAT3 activity is characterized. Mechanically, SHF selectively binds and inhibits acetylated STAT3 dimerization without affecting STAT3 phosphorylation or acetylation. Additionally, by blocking STAT3-DNMT1 (DNA Methyltransferase 1) interaction, SHF relieves methylation of tumor suppressor genes. The SH2 domain is documented to be essential for SHF's actions on STAT3, and almost entirely replaces the functions of SHF on STAT3 independently. Moreover, the peptide C16 a peptide derived from the STAT3-binding sites of SHF inhibits STAT3 dimerization and STAT3/DNMT1 interaction, and achieves remarkable growth inhibition in GBM cells in vitro and in vivo. These findings strongly identify targeting of the SHF/STAT3 interaction as a promising strategy for developing an optimal STAT3 inhibitor and provide early evidence of the potential clinical efficacy of STAT3 inhibitors such as C16 in GBM.

Identification and Inhibition of the Druggable Allosteric Site of SARS-CoV-2 NSP10/NSP16 Methyltransferase through Computational Approaches

Since its emergence in early 2019, the respiratory infectious virus, SARS-CoV-2, has ravaged the health of millions of people globally and has affected almost every sphere of life. Many efforts are being made to combat the COVID-19 pandemic’s emerging and recurrent waves caused by its evolving and more infectious variants. As a result, novel and unexpected targets for SARS-CoV-2 have been considered for drug discovery. 2′-O-Methyltransferase (nsp10/nsp16) is a significant and appealing target in the SARS-CoV-2 life cycle because it protects viral RNA from the host degradative enzymes via a cap formation process. In this work, we propose prospective allosteric inhibitors that target the allosteric site, SARS-CoV-2 MTase. Four drug libraries containing ~119,483 compounds were screened against the allosteric site of SARS-CoV-2 MTase identified in our research. The identified best compounds exhibited robust molecular interactions and alloscore-score rankings with the allosteric site of SARS-CoV-2 MTase. Moreover, to further assess the dynamic stability of these compounds (CHEMBL2229121, ZINC000009464451, SPECS AK-91811684151, NCI-ID = 715319), a 100 ns molecular dynamics simulation, along with its holo-form, was performed to provide insights on the dynamic nature of these allosteric inhibitors at the allosteric site of the SARS-CoV-2 MTase. Additionally, investigations of MM-GBSA binding free energies revealed a good perspective for these allosteric inhibitor–enzyme complexes, indicating their robust antagonistic action on SARS-CoV-2 (nsp10/nsp16) methyltransferase. We conclude that these allosteric repressive agents should be further evaluated through investigational assessments in order to combat the proliferation of SARS-CoV-2.

Mapping the binding sites of challenging drug targets

An increasing number of medically important proteins are challenging drug targets because their binding sites are too shallow or too polar, are cryptic and thus not detectable without a bound ligand or located in a protein-protein interface. While such proteins may not bind druglike small molecules with sufficiently high affinity, they are frequently druggable using novel therapeutic modalities. The need for such modalities can be determined by experimental or computational fragment based methods. Computational mapping by mixed solvent molecular dynamics simulations or the FTMap server can be used to determine binding hot spots. The strength and location of the hot spots provide very useful information for selecting potentially successful approaches to drug discovery.

Targeting allosteric regulation of cancer metabolism

Metabolic reprogramming is observed across all cancer types. Indeed, the success of many classic chemotherapies stems from their targeting of cancer metabolism. Contemporary research in this area has refined our understanding of tumor-specific metabolic mechanisms and has revealed strategies for exploiting these vulnerabilities selectively. Based on this growing understanding, new small-molecule tools and drugs have been developed to study and target tumor metabolism. Here, we highlight allosteric modulation of metabolic enzymes as an attractive mechanism of action for small molecules that target metabolic enzymes. We then discuss the mechanistic insights garnered from their application in cancer studies and highlight the achievements of this approach in targeting cancer metabolism. Finally, we discuss technological advances in drug discovery for allosteric modulators of enzyme activity.

Investigation of an Allosteric Deoxyhypusine Synthase Inhibitor in P. falciparum

The treatment of a variety of protozoal infections, in particular those causing disabling human diseases, is still hampered by a lack of drugs or increasing resistance to registered drugs. However, in recent years, remarkable progress has been achieved to combat neglected tropical diseases by sequencing the parasites’ genomes or the validation of new targets in the parasites by novel genetic manipulation techniques, leading to loss of function. The novel amino acid hypusine is a posttranslational modification (PTM) that occurs in eukaryotic initiation factor 5A (EIF5A) at a specific lysine residue. This modification occurs by two steps catalyzed by deoxyhypusine synthase (dhs) and deoxyhypusine hydroxylase (DOHH) enzymes. dhs from Plasmodium has been validated as a druggable target by small molecules and reverse genetics. Recently, the synthesis of a series of human dhs inhibitors led to 6-bromo-N-(1H-indol-4yl)-1-benzothiophene-2-carboxamide, a potent allosteric inhibitor with an IC50 value of 0.062 µM. We investigated this allosteric dhs inhibitor in Plasmodium. In vitro P. falciparum growth assays showed weak inhibition activity, with IC50 values of 46.1 µM for the Dd2 strain and 51.5 µM for the 3D7 strain, respectively. The antimalarial activity could not be attributed to the targeting of the Pfdhs gene, as shown by chemogenomic profiling with transgenically modified P. falciparum lines. Moreover, in dose-dependent enzymatic assays with purified recombinant P. falciparum dhs protein, only 45% inhibition was observed at an inhibitor dose of 0.4 µM. These data are in agreement with a homology-modeled Pfdhs, suggesting significant structural differences in the allosteric site between the human and parasite enzymes. Virtual screening of the allosteric database identified candidate ligand binding to novel binding pockets identified in P. falciparum dhs, which might foster the development of parasite-specific inhibitors.

Novel inhibitors of the STAT3 signaling pathway: an updated patent review (2014-present)

INTRODUCTION

STAT3 is a critical transcription factor that transmits signals from the cell surface to the nucleus, thus influencing the transcriptional regulation of some oncogenes. The inhibition of the activation of STAT3 is considered a promising strategy for cancer therapy. Numerous STAT3 inhibitors bearing different scaffolds have been reported to date, with a few of them having been considered in clinical trials.

AREAS COVERED

This review summarizes the advances on STAT3 inhibitors with different structural skeletons, focusing on the structure-activity relationships in the related patent literature published from 2014 to date.

EXPERT OPINION

Since the X-ray crystal structure of STAT3β homo dimer bound to DNA was solved in 1998, the development of STAT3 inhibitors has gone through a boom in recent years. However, none of them have been approved for marketing, probably due to the complex biological functions of the STAT3 signaling pathway, including its character and the poor drug-like physicochemical properties of its inhibitors. Nonetheless, targeting STAT3 continues to be an exciting field for the development of anti-tumor agents along with the emergence of new STAT3 inhibitors with unique mechanisms of action.

The Impact of the Secondary Binding Pocket on the Pharmacology of Class A GPCRs

G-protein coupled receptors (GPCRs) are considered important therapeutic targets due to their pathophysiological significance and pharmacological relevance. Class A receptors represent the largest group of GPCRs that gives the highest number of validated drug targets. Endogenous ligands bind to the orthosteric binding pocket (OBP) embedded in the intrahelical space of the receptor. During the last 10 years, however, it has been turned out that in many receptors there is secondary binding pocket (SBP) located in the extracellular vestibule that is much less conserved. In some cases, it serves as a stable allosteric site harbouring allosteric ligands that modulate the pharmacology of orthosteric binders. In other cases it is used by bitopic compounds occupying both the OBP and SBP. In these terms, SBP binding moieties might influence the pharmacology of the bitopic ligands. Together with others, our research group showed that SBP binders contribute significantly to the affinity, selectivity, functional activity, functional selectivity and binding kinetics of bitopic ligands. Based on these observations we developed a structure-based protocol for designing bitopic compounds with desired pharmacological profile.

Allosteric Site of ACE-2 as a Drug Target for COVID-19

The coronavirus disease 2019 (COVID-19) pandemic has a significant impact on healthcare systems and our lives. Vaccines against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) provide protection against SARS-CoV-2. However, mutations in the viral genome are common, raising concerns about the effectiveness of existing vaccines for SARS-CoV-2. The receptor-binding domain (RBD) of SARS-CoV-2 uses angiotensin-converting enzyme-2 (ACE-2) as a gateway to enter host cells. Therefore, the ACE-2-RBD interaction may be targeted by antiviral drugs. In this context, allosteric modulation of ACE-2 may offer a promising approach. It may lead to allosteric inhibition of the interaction between ACE-2 and SARS-CoV-2.

Allosteric binding on nuclear receptors: Insights on screening of non-competitive endocrine-disrupting chemicals

Endocrine-disrupting chemicals (EDCs) can compete with endogenous hormones and bind to the orthosteric site of nuclear receptors (NRs), affecting normal endocrine system function and causing severe symptoms. Recently, a series of pharmaceuticals and personal care products (PPCPs) have been discovered to bind to the allosteric sites of NRs and induce similar effects. However, it remains unclear how diverse EDCs work in this new way. Therefore, we have systematically summarized the allosteric sites and underlying mechanisms based on existing studies, mainly regarding drugs belonging to the PPCP class. Advanced methods, classified as structural biology, biochemistry and computational simulation, together with their advantages and hurdles for allosteric site recognition and mechanism insight have also been described. Furthermore, we have highlighted two available strategies for virtual screening of numerous EDCs, relying on the structural features of allosteric sites and lead compounds, respectively. We aim to provide reliable theoretical and technical support for a broader view of various allosteric interactions between EDCs and NRs, and to drive high-throughput and accurate screening of potential EDCs with non-competitive effects.

Hidden allosteric sites and De-Novo drug design

INTRODUCTION

Hidden allosteric sites are not visible in apo-crystal structures, but they may be visible in holo-structures when a certain ligand binds and maintains the ligand intended conformation. Several computational and experimental techniques have been used to investigate these hidden sites but identifying them remains a challenge.

AREAS COVERED

This review provides a summary of the many theoretical approaches for predicting hidden allosteric sites in disease-related proteins. Furthermore, promising cases have been thoroughly examined to reveal the hidden allosteric site and its modulator.

EXPERT OPINION

In the recent past, with the development in scientific techniques and bioinformatics tools, the number of drug targets for complex human diseases has significantly increased but unfortunately most of these targets are undruggable due to several reasons. Alternative strategies such as finding cryptic (hidden) allosteric sites are an attractive approach for exploitation of the discovery of new targets. These hidden sites are difficult to recognize compared to allosteric sites, mainly due to a lack of visibility in the crystal structure. In our opinion, after many years of development, MD simulations are finally becoming successful for obtaining a detailed molecular description of drug-target interaction.

Targeting RAS phosphorylation in cancer therapy: Mechanisms and modulators

RAS, a member of the small GTPase family, functions as a binary switch by shifting between inactive GDP-loaded and active GTP-loaded state. RAS gain-of-function mutations are one of the leading causes in human oncogenesis, accounting for ∼19% of the global cancer burden. As a well-recognized target in malignancy, RAS has been intensively studied in the past decades. Despite the sustained efforts, many failures occurred in the earlier exploration and resulted in an ‘undruggable’ feature of RAS proteins. Phosphorylation at several residues has been recently determined as regulators for wild-type and mutated RAS proteins. Therefore, the development of RAS inhibitors directly targeting the RAS mutants or towards upstream regulatory kinases supplies a novel direction for tackling the anti-RAS difficulties. A better understanding of RAS phosphorylation can contribute to future therapeutic strategies. In this review, we comprehensively summarized the current advances in RAS phosphorylation and provided mechanistic insights into the signaling transduction of associated pathways. Importantly, the preclinical and clinical success in developing anti-RAS drugs targeting the upstream kinases and potential directions of harnessing allostery to target RAS phosphorylation sites were also discussed.

Emerging role of signal transducer and activator of transcription 3 (STAT3) in pituitary adenomas

Pituitary adenomas are benign tumours that can cause an individual various clinical manifestations including tumour mass effects and/or the diverse effects of abnormal pituitary hormone secretion. Given the morbidity and limited treatment options for pituitary adenomas, there is a need for better biomarkers and treatment options. One molecule that is of specific interest is the signal transducer and activator of transcription 3 (STAT3), a transcription factor that plays a critical role in mediating cytokine-induced changes in gene expression. In addition, STAT3 controls cell proliferation by regulating mitochondrial activity. Not only does activation of STAT3 play a crucial role in tumorigenesis, including pituitary tumorigenesis, but a number of studies also demonstrate pharmacological STAT3 inhibition as a promising treatment approach for many types of tumours, including pituitary tumours. This review will focus on the role of STAT3 in different pituitary adenomas, in particular, growth hormone-producing adenomas and null cell adenomas. Furthermore, how STAT3 is involved in the cell proliferation and hormone regulation in pituitary adenomas and its potential role as a molecular therapeutic target in pituitary adenomas will be summarized.

SCOUR: a stepwise machine learning framework for predicting metabolite-dependent regulatory interactions

BACKGROUND

The topology of metabolic networks is both well-studied and remarkably well-conserved across many species. The regulation of these networks, however, is much more poorly characterized, though it is known to be divergent across organisms-two characteristics that make it difficult to model metabolic networks accurately. While many computational methods have been built to unravel transcriptional regulation, there have been few approaches developed for systems-scale analysis and study of metabolic regulation. Here, we present a stepwise machine learning framework that applies established algorithms to identify regulatory interactions in metabolic systems based on metabolic data: stepwise classification of unknown regulation, or SCOUR.

RESULTS

We evaluated our framework on both noiseless and noisy data, using several models of varying sizes and topologies to show that our approach is generalizable. We found that, when testing on data under the most realistic conditions (low sampling frequency and high noise), SCOUR could identify reaction fluxes controlled only by the concentration of a single metabolite (its primary substrate) with high accuracy. The positive predictive value (PPV) for identifying reactions controlled by the concentration of two metabolites ranged from 32 to 88% for noiseless data, 9.2 to 49% for either low sampling frequency/low noise or high sampling frequency/high noise data, and 6.6-27% for low sampling frequency/high noise data, with results typically sufficiently high for lab validation to be a practical endeavor. While the PPVs for reactions controlled by three metabolites were lower, they were still in most cases significantly better than random classification.

CONCLUSIONS

SCOUR uses a novel approach to synthetically generate the training data needed to identify regulators of reaction fluxes in a given metabolic system, enabling metabolomics and fluxomics data to be leveraged for regulatory structure inference. By identifying and triaging the most likely candidate regulatory interactions, SCOUR can drastically reduce the amount of time needed to identify and experimentally validate metabolic regulatory interactions. As high-throughput experimental methods for testing these interactions are further developed, SCOUR will provide critical impact in the development of predictive metabolic models in new organisms and pathways.

Untangling Dual-Targeting Therapeutic Mechanism of Epidermal Growth Factor Receptor (EGFR) Based on Reversed Allosteric Communication

Dual-targeting therapeutics by coadministration of allosteric and orthosteric drugs is drawing increased attention as a revolutionary strategy for overcoming the drug-resistance problems. It was further observed that the occupation of orthosteric sites by therapeutics agents has the potential to enhance allosteric ligand binding, which leads to improved potency of allosteric drugs. Epidermal growth factor receptor (EGFR), as one of the most critical anti-cancer targets belonging to the receptor tyrosine kinase family, represents a quintessential example. It was revealed that osimertinib, an ATP-competitive covalent EGFR inhibitor, remarkably enhanced the affinity of a recently developed allosteric inhibitor JBJ-04-125-02 for EGFRL858R/T790M. Here, we utilized extensive large-scale molecular dynamics simulations and the reversed allosteric communication to untangle the detailed molecular underpinning, in which occupation of osimertinib at the orthosteric site altered the overall conformational ensemble of EGFR mutant and reshaped the allosteric site via long-distance signaling. A unique intermediate state resembling the active conformation was identified, which was further stabilized by osimertinib loading. Based on the allosteric communication pathway, we predicted a novel allosteric site positioned around K867, E868, H893, and K960 within the intermediate state. Its correlation with the orthosteric site was validated by both structural and energetic analysis, and its low sequence conservation indicated the potential for selective targeting across the human kinome. Together, these findings not only provided a mechanistic basis for future clinical application of the dual-targeting therapeutics, but also explored an innovative perception of allosteric inhibition of tyrosine kinase signaling.

Exploring the Allosteric Territory of Protein Function

While the pervasiveness of allostery in proteins is commonly accepted, we further show the generic nature of allosteric mechanisms by analyzing here transmembrane ion-channel viroporin 3a and RNA-dependent RNA polymerase (RdRp) from SARS-CoV-2 along with metabolic enzymes isocitrate dehydrogenase 1 (IDH1) and fumarate hydratase (FH) implicated in cancers. Using the previously developed structure-based statistical mechanical model of allostery (SBSMMA), we share our experience in analyzing the allosteric signaling, predicting latent allosteric sites, inducing and tuning targeted allosteric response, and exploring the allosteric effects of mutations. This, yet incomplete list of phenomenology, forms a complex and unique allosteric territory of protein function, which should be thoroughly explored. We propose a generic computational framework, which not only allows one to obtain a comprehensive allosteric control over proteins but also provides an opportunity to approach the fragment-based design of allosteric effectors and drug candidates. The advantages of allosteric drugs over traditional orthosteric compounds, complemented by the emerging role of the allosteric effects of mutations in the expansion of the cancer mutational landscape and in the increased mutability of viral proteins, leave no choice besides further extensive studies of allosteric mechanisms and their biomedical implications.

Identification of novel allosteric binding sites and multi-targeted allosteric inhibitors of receptor and non-receptor tyrosine kinases using a computational approach

EGFR1, VEGFR2, Bcr-Abl and Src kinases are key drug targets in non-small cell lung cancer (NSCLC), bladder cancer, pancreatic cancer, CML, ALL, colorectal cancer, etc. The available drugs targeting these kinases have limited therapeutic efficacy due to novel mutations resulting in drug resistance and toxicity, as they target ATP binding site. Allosteric drugs have shown promising results in overcoming drug resistance, but the discovery of allosteric drugs is challenging. The allosteric binding pockets are difficult to predict, as they are generally associated with high energy conformations and regulate protein function in yet unknown mechanisms. In addition, the discovery of drugs using conventional methods takes long time and goes through several challenges, putting the lives of many cancer patients at risk. Therefore, the aim of the present work was to apply the most successful, drug repurposing approach in combination with computational methods to identify kinase inhibitors targeting novel allosteric sites on protein structure and assess their potential multi-kinase binding affinity. Multiple crystal structures belonging to EGFR1, VEGFR2, Bcr-Abl and Src tyrosine kinases were selected, including mutated, inhibitor bound and allosteric conformations to identify potential leads, close to physiological conditions. Interestingly the potential inhibitors identified were peptides. The drugs identified in this study could be used in therapy as a single multi-kinase inhibitor or in a combination of single kinase inhibitors after experimental validation. In addition, we have also identified new hot spots that are likely to be druggable allosteric sites for drug discovery of kinase-specific drugs in the future.Communicated by Ramaswamy H. Sarma.

Analyzing In Silico the Relationship Between the Activation of the Edema Factor and Its Interaction With Calmodulin

Molecular dynamics (MD) simulations have been recorded on the complex between the edema factor (EF) of Bacilllus anthracis and calmodulin (CaM), starting from a structure with the orthosteric inhibitor adefovir bound in the EF catalytic site. The starting structure has been destabilized by alternately suppressing different co-factors, such as adefovir ligand or ions, revealing several long-distance correlations between the conformation of CaM, the geometry of the CaM/EF interface, the enzymatic site and the overall organization of the complex. An allosteric communication between CaM/EF interface and the EF catalytic site, highlighted by these correlations, was confirmed by several bioinformatics approaches from the literature. A network of hydrogen bonds and stacking interactions extending from the helix V of of CaM, and the residues of the switches A, B and C, and connecting to catalytic site residues, is a plausible candidate for the mediation of allosteric communication. The greatest variability in volume between the different MD conditions was also found for cavities present at the EF/CaM interface and in the EF catalytic site. The similarity between the predictions from literature and the volume variability might introduce the volume variability as new descriptor of allostery.

Discovery of cryptic allosteric sites using reversed allosteric communication by a combined computational and experimental strategy

Allostery, which is one of the most direct and efficient methods to fine-tune protein functions, has gained increasing recognition in drug discovery. However, there are several challenges associated with the identification of allosteric sites, which is the fundamental cornerstone of drug design. Previous studies on allosteric site predictions have focused on communication signals propagating from the allosteric sites to the orthosteric sites. However, recent biochemical studies have revealed that allosteric coupling is bidirectional and that orthosteric perturbations can modulate allosteric sites through reversed allosteric communication. Here, we proposed a new framework for the prediction of allosteric sites based on reversed allosteric communication using a combination of computational and experimental strategies (molecular dynamics simulations, Markov state models, and site-directed mutagenesis). The desirable performance of our approach was demonstrated by predicting the known allosteric site of the small molecule MDL-801 in nicotinamide dinucleotide (NAD⁺)-dependent protein lysine deacetylase sirtuin 6 (Sirt6). A potential novel cryptic allosteric site located around the L116, R119, and S120 residues within the dynamic ensemble of Sirt6 was identified. The allosteric effect of the predicted site was further quantified and validated using both computational and experimental approaches. This study proposed a state-of-the-art computational pipeline for detecting allosteric sites based on reversed allosteric communication. This method enabled the identification of a previously uncharacterized potential cryptic allosteric site on Sirt6, which provides a starting point for allosteric drug design that can aid the identification of candidate pockets in other therapeutic targets.

Using reversed allosteric communication, we performed MD simulations, MSMs, and mutagenesis experiments, to discover allosteric sites. It reproduced the known allosteric site for MDL-801 on Sirt6 and uncovered a novel cryptic allosteric Pocket X.

LncRNA-encoded polypeptide ASRPS inhibits triple-negative breast cancer angiogenesis

In this study, Wang et al. demonstrate that lncRNA-encoded polypeptide ASRPS is down-regulated in TNBC. ASRPS regulates angiogenesis and may serve as a novel prognostic marker and therapeutic target for TNBC.

Triple-negative breast cancer (TNBC) is a subtype of breast cancer (BC) with the most aggressive phenotype and poor overall survival. Using bioinformatics tools, we identified LINC00908 encoding a 60–aa polypeptide and differentially expressed in TNBC tissues. We named this endogenously expressed polypeptide ASRPS (a small regulatory peptide of STAT3). ASRPS expression was down-regulated in TNBCs and associated with poor overall survival. We showed that LINC00908 was directly regulated by ERα, which was responsible for the differential down-regulation of LINC00908 in TNBCs. ASRPS directly bound to STAT3 through the coiled coil domain (CCD) and down-regulated STAT3 phosphorylation, which led to reduced expression of VEGF. In human endothelial cells, a mouse xenograft breast cancer model, and a mouse spontaneous BC model, ASRPS expression reduced angiogenesis. In a mouse xenograft breast cancer model, down-regulation of ASRPS promoted tumor growth, and ASRPS acted as an antitumor peptide. We presented strong evidence that LINC00908-encoded polypeptide ASRPS represented a TNBC-specific target for treatment.

AlloSigMA 2: paving the way to designing allosteric effectors and to exploring allosteric effects of mutations

The AlloSigMA 2 server provides an interactive platform for exploring the allosteric signaling caused by ligand binding and/or mutations, for analyzing the allosteric effects of mutations and for detecting potential cancer drivers and pathogenic nsSNPs. It can also be used for searching latent allosteric sites and for computationally designing allosteric effectors for these sites with required agonist/antagonist activity. The server is based on the implementation of the Structure-Based Statistical Mechanical Model of Allostery (SBSMMA), which allows one to evaluate the allosteric free energy as a result of the perturbation at per-residue resolution. The Allosteric Signaling Map (ASM) providing a comprehensive residue-by-residue allosteric control over the protein activity can be obtained for any structure of interest. The Allosteric Probing Map (APM), in turn, allows one to perform the fragment-based-like computational design experiment aimed at finding leads for potential allosteric effectors. The server can be instrumental in elucidating of allosteric mechanisms and actions of allosteric mutations, and in the efforts on design of new elements of allosteric control. The server is freely available at: http://allosigma.bii.a-star.edu.sg.

Deciphering the regulatory genome of Escherichia coli, one hundred promoters at a time

Advances in DNA sequencing have revolutionized our ability to read genomes. However, even in the most well-studied of organisms, the bacterium Escherichia coli, for ≈65% of promoters we remain ignorant of their regulation. Until we crack this regulatory Rosetta Stone, efforts to read and write genomes will remain haphazard. We introduce a new method, Reg-Seq, that links massively parallel reporter assays with mass spectrometry to produce a base pair resolution dissection of more than a E. coli promoters in 12 growth conditions. We demonstrate that the method recapitulates known regulatory information. Then, we examine regulatory architectures for more than 80 promoters which previously had no known regulatory information. In many cases, we also identify which transcription factors mediate their regulation. This method clears a path for highly multiplexed investigations of the regulatory genome of model organisms, with the potential of moving to an array of microbes of ecological and medical relevance.

STAT3, the Challenge for Chemotherapeutic and Radiotherapeutic Efficacy

Chemoradiotherapy is one of the most effective and extensively used strategies for cancer treatment. Signal transducer and activator of transcription 3 (STAT3) regulates vital biological processes, such as cell proliferation and cell growth. It is constitutively activated in various cancers and limits the application of chemoradiotherapy. Accumulating evidence suggests that STAT3 regulates resistance to chemotherapy and radiotherapy and thereby impairs therapeutic efficacy by mediating its feedback loop and several target genes. The alternative splicing product STAT3β is often identified as a dominant-negative regulator, but it enhances sensitivity to chemotherapy and offers a new and challenging approach to reverse therapeutic resistance. We focus here on exploring the role of STAT3 in resistance to receptor tyrosine kinase (RTK) inhibitors and radiotherapy, outlining the potential of targeting STAT3 to overcome chemo(radio)resistance for improving clinical outcomes, and evaluating the importance of STAT3β as a potential therapeutic approach to overcomes chemo(radio)resistance. In this review, we discuss some new insights into the effect of STAT3 and its subtype STAT3β on chemoradiotherapy sensitivity, and we explore how these insights influence clinical treatment and drug development for cancer.

Unraveling allosteric landscapes of allosterome with ASD

Allosteric regulation is one of the most direct and efficient ways to fine-tune protein function; it is induced by the binding of a ligand at an allosteric site that is topographically distinct from an orthosteric site. The Allosteric Database (ASD, available online at http://mdl.shsmu.edu.cn/ASD) was developed ten years ago to provide comprehensive information related to allosteric regulation. In recent years, allosteric regulation has received great attention in biological research, bioengineering, and drug discovery, leading to the emergence of entire allosteric landscapes as allosteromes. To facilitate research from the perspective of the allosterome, in ASD 2019, novel features were curated as follows: (i) >10 000 potential allosteric sites of human proteins were deposited for allosteric drug discovery; (ii) 7 human allosterome maps, including protease and ion channel maps, were built to reveal allosteric evolution within families; (iii) 1312 somatic missense mutations at allosteric sites were collected from patient samples from 33 cancer types and (iv) 1493 pharmacophores extracted from allosteric sites were provided for modulator screening. Over the past ten years, the ASD has become a central resource for studying allosteric regulation and will play more important roles in both target identification and allosteric drug discovery in the future.

AlloMAPS: allosteric mutation analysis and polymorphism of signaling database

AlloMAPS database provides data on the causality and energetics of allosteric communication obtained with the structure-based statistical mechanical model of allostery (SBSMMA). The database contains data on allosteric signaling in three sets of proteins and protein chains: (i) 46 proteins with comprehensively annotated functional and allosteric sites; (ii) 1908 protein chains from PDBselect set of chains with low (<25%) sequence identity; (iii) 33 proteins with more than 50 known pathological SNPs in each molecule. In addition to energetics of allosteric signaling between known functional and regulatory sites, allosteric modulation caused by the binding to these sites, by SNPs, and by mutations designated by the user can be explored. Allosteric Signaling Maps (ASMs), which are produced via the exhaustive computational scanning for stabilizing and destabilizing mutations and for the modulation range caused by the sequence position are available for each protein/protein chain in the database. We propose to use this database for evaluating the effects of allosteric signaling in the search for latent regulatory sites and in the design of allosteric sites and effectors. The database is freely available at: http://allomaps.bii.a-star.edu.sg.

Binding Interactions of Ergotamine and Dihydroergotamine to 5-Hydroxytryptamine Receptor 1B (5-HT1b) Using Molecular Dynamics Simulations and Dynamic Network Analysis

Ergotamine (ERG) and dihydroergotamine (DHE), common migraine drugs, have small structural differences but lead to clinically important distinctions in their pharmacological profiles. For example, DHE is less potent than ERG by about 10-fold at the 5-hydroxytrptamine receptor 1B (5-HT_1B). Although the high-resolution crystal structures of the 5-HT_1B receptor with both ligands have been solved, the high similarity between these two complex structures does not sufficiently explain their activity differences and the activation mechanism of the receptor. Hence, an examination of the dynamic motion of both drugs with the receptor is required. In this study, we ran a total of 6.0 μs molecular dynamics simulations on each system. Our simulation data show the subtle variations between the two systems in terms of the ligand-receptor interactions and receptor secondary structures. More importantly, the ligand and protein root-mean-square fluctuations (RMSFs) for the two systems were distinct, with ERG having a trend of lower RMSF values, indicating it to be bound tighter to 5-HT_1B with less fluctuations. The molecular mechanism-general born surface area (MM-GBSA) binding energies illustrate this further, proving ERG has an overall stronger MM-GBSA binding energy. Analysis of several different microswitches has shown that the 5-HT_1B-ERG complex is in a more active conformation state than 5-HT_1B-DHE, which is further supported by the dynamic network model, with reference to mutagenesis data with the critical nodes and the first three low-energy modes from the normal mode analysis. We also identify Trp327^6.48 and Phe331^6.52 as key residues involved in the active state 5-HT_1B for both ligands. Using the detailed dynamic information from our analysis, we made predictions for possible modifications to DHE and ERG that yielded five derivatives that might have more favorable binding energies and reduced structural fluctuations.

Combining Allosteric and Orthosteric Drugs to Overcome Drug Resistance

Historically, most drugs target protein orthosteric sites. The gradual emergence of resistance hampers their therapeutic effectiveness, posing a challenge to drug development. Coadministration of allosteric and orthosteric drugs provides a revolutionary strategy to circumvent drug resistance, as drugs targeting the topologically distinct allosteric sites can restore or even enhance the efficacy of orthosteric drugs. Here, we comprehensively review the latest successful examples of such combination treatments against drug resistance, with a focus on their modes of action and the underlying structural mechanisms. Our work supplies an innovative insight into such promising methodology against the recalcitrant drug resistance conundrum and will be instructive for future clinical therapeutics.