Allosteric Modulator Discovery: From Serendipity to Structure-Based Design

GETdb: A comprehensive database for genetic and evolutionary features of drug targets

The development of an innovative drug is complex and time-consuming, and the drug target identification is one of the critical steps in drug discovery process. Effective and accurate identification of drug targets can accelerate the drug development process. According to previous research, evolutionary and genetic information of genes has been found to facilitate the identification of approved drug targets. In addition, allosteric proteins have great potential as targets due to their structural diversity. However, this information that could facilitate target identification has not been collated in existing drug target databases. Here, we construct a comprehensive drug target database named Genetic and Evolutionary features of drug Targets database (GETdb, http://zhanglab.hzau.edu.cn/GETdb/page/index.jsp). This database not only integrates and standardizes data from dozens of commonly used drug and target databases, but also innovatively includes the genetic and evolutionary information of targets. Moreover, this database features an effective allosteric protein prediction model. GETdb contains approximately 4000 targets and over 29,000 drugs, and is a user-friendly database for searching, browsing and downloading data to facilitate the development of novel targets.

Prediction of Protein Allosteric Sites with Transfer Entropy and Spatial Neighbor-Based Evolutionary Information Learned by an Ensemble Model

Allostery is one of the most direct and efficient ways to regulate protein functions. The diverse allosteric sites make it possible to design allosteric modulators of differential selectivity and improved safety compared with those of orthosteric drugs targeting conserved orthosteric sites. Here, we develop an ensemble machine learning method AllosES to predict protein allosteric sites in which the new and effective features are utilized, including the entropy transfer-based dynamic property, secondary structure features, and our previously proposed spatial neighbor-based evolutionary information besides the traditional physicochemical properties. To overcome the class imbalance problem, the multiple grouping strategy is proposed, which is applied to feature selection and model construction. The ensemble model is constructed where multiple submodels are trained on multiple training subsets, respectively, and their results are then integrated to be the final output. AllosES achieves a prediction performance of 0.556 MCC on the independent test set D24, and additionally, AllosES can rank the real allosteric sites in the top three for 83.3/89.3% of allosteric proteins from the test set D24/D28, outperforming the state-of-the-art peer methods. The comprehensive results demonstrate that AllosES is a promising method for protein allosteric site prediction. The source code is available at https://github.com/ChunhuaLab/AllosES.

Secondary Sites of the C-type Lectin-Like Fold

C-type lectins are a large superfamily of proteins involved in a multitude of biological processes. In particular, their involvement in immunity and homeostasis has rendered them attractive targets for diverse therapeutic interventions. They share a characteristic C-type lectin-like domain whose adaptability enables them to bind a broad spectrum of ligands beyond the originally defined canonical Ca2+-dependent carbohydrate binding. Together with variable domain architecture and high-level conformational plasticity, this enables C-type lectins to meet diverse functional demands. Secondary sites provide another layer of regulation and are often intricately linked to functional diversity. Located remote from the canonical primary binding site, secondary sites can accommodate ligands with other physicochemical properties and alter protein dynamics, thus enhancing selectivity and enabling fine-tuning of the biological response. In this review, we outline the structural determinants allowing C-type lectins to perform a large variety of tasks and to accommodate the ligands associated with it. Using the six well-characterized Ca2+-dependent and Ca2+-independent C-type lectin receptors DC-SIGN, langerin, MGL, dectin-1, CLEC-2 and NKG2D as examples, we focus on the characteristics of non-canonical interactions and secondary sites and their potential use in drug discovery endeavors.

Discovery of novel positive allosteric modulators targeting GluN1/2A NMDARs as anti-stroke therapeutic agents

Excitotoxicity due to excessive activation of NMDARs is one of the main mechanisms of neuronal death during ischemic stroke. Previous studies have suggested that activation of either synaptic or extrasynaptic GluN2B-containing NMDARs results in neuronal damage, whereas activation of GluN2A-containing NMDARs promotes neuronal survival against ischemic insults. This study applied a systematic in silico, in vitro, and in vivo approach to the discovery of novel and potential GluN1/2A NMDAR positive allosteric modulators (PAMs). Ten compounds were obtained and identified as potential GluN1/2A PAMs by structure-based virtual screening and calcium imaging. The neuroprotective activity of the candidate compounds was demonstrated in vitro. Subsequently, compound 15 (aegeline) was tested further in the model of transient middle cerebral artery occlusion (tMCAO) in vivo, which significantly decreased cerebral infarction. The mechanism by which aegeline exerts its effect on allosteric modulation was revealed using molecular dynamics simulations. Finally, we found that the neuroprotective effect of aegeline was significantly correlated with the enhanced phosphorylation of cAMP response element-binding protein (CREB). Our study discovered the neuroprotective effect of aegeline as a novel PAM targeting GluN1/2A NMDAR, which provides a potential opportunity for the development of therapeutic agents for ischemic stroke.

G protein-coupled receptors (GPCRs): advances in structures, mechanisms, and drug discovery

G protein-coupled receptors (GPCRs), the largest family of human membrane proteins and an important class of drug targets, play a role in maintaining numerous physiological processes. Agonist or antagonist, orthosteric effects or allosteric effects, and biased signaling or balanced signaling, characterize the complexity of GPCR dynamic features. In this study, we first review the structural advancements, activation mechanisms, and functional diversity of GPCRs. We then focus on GPCR drug discovery by revealing the detailed drug-target interactions and the underlying mechanisms of orthosteric drugs approved by the US Food and Drug Administration in the past five years. Particularly, an up-to-date analysis is performed on available GPCR structures complexed with synthetic small-molecule allosteric modulators to elucidate key receptor-ligand interactions and allosteric mechanisms. Finally, we highlight how the widespread GPCR-druggable allosteric sites can guide structure- or mechanism-based drug design and propose prospects of designing bitopic ligands for the future therapeutic potential of targeting this receptor family.

An allosteric mechanism for potent inhibition of SARS-CoV-2 main proteinase

The main proteinase (Mpro) of SARS-CoV-2 plays a critical role in cleaving viral polyproteins into functional proteins required for viral replication and assembly, making it a prime drug target for COVID-19. It is well known that noncompetitive inhibition offers potential therapeutic options for treating COVID-19, which can effectively reduce the likelihood of cross-reactivity with other proteins and increase the selectivity of the drug. Therefore, the discovery of allosteric sites of Mpro has both scientific and practical significance. In this study, we explored the binding characteristics and inhibiting process of Mpro activity by two recently reported allosteric inhibitors, pelitinib and AT7519 which were obtained by the X-ray screening experiments, to probe the allosteric mechanism via molecular dynamic (MD) simulations. We found that pelitinib and AT7519 can stably bind to Mpro far from the active site. The binding affinity is estimated to be -24.37 ± 4.14 and - 26.96 ± 4.05 kcal/mol for pelitinib and AT7519, respectively, which is considerably stable compared with orthosteric drugs. Furthermore, the strong binding caused clear changes in the catalytic site of Mpro, thus decreasing the substrate accessibility. The community network analysis also validated that pelitinib and AT7519 strengthened intra- and inter-domain communication of Mpro dimer, resulting in a rigid Mpro, which could negatively impact substrate binding. In summary, our findings provide the detailed working mechanism for the two experimentally observed allosteric sites of Mpro. These allosteric sites greatly enhance the 'druggability' of Mpro and represent attractive targets for the development of new Mpro inhibitors.

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.

Precision Oncology Comes of Age: Designing Best-in-Class Small Molecules by Integrating Two Decades of Advances in Chemistry, Target Biology, and Data Science

Small-molecule drugs have enabled the practice of precision oncology for genetically defined patient populations since the first approval of imatinib in 2001. Scientific and technology advances over this 20-year period have driven the evolution of cancer biology, medicinal chemistry, and data science. Collectively, these advances provide tools to more consistently design best-in-class small-molecule drugs against known, previously undruggable, and novel cancer targets. The integration of these tools and their customization in the hands of skilled drug hunters will be necessary to enable the discovery of transformational therapies for patients across a wider spectrum of cancers.

SIGNIFICANCE

Target-centric small-molecule drug discovery necessitates the consideration of multiple approaches to identify chemical matter that can be optimized into drug candidates. To do this successfully and consistently, drug hunters require a comprehensive toolbox to avoid following the "law of instrument" or Maslow's hammer concept where only one tool is applied regardless of the requirements of the task. Combining our ever-increasing understanding of cancer and cancer targets with the technological advances in drug discovery described below will accelerate the next generation of small-molecule drugs in oncology.

Elastic network models and molecular dynamic simulations reveal the molecular basis of allosteric regulation in ubiquitin-specific protease 7 (USP7)

Ubiquitin-specific protease 7 (USP7) is one of the most abundant deubiquitinases and plays an important role in various malignant tumors. However, the molecular mechanisms underlying USP7's structures, dynamics, and biological significance are yet to be investigated. In this study, we constructed the full-length models of USP7 in both the extended and compact state, and applied elastic network models (ENM), molecular dynamics (MD) simulations, perturbation response scanning (PRS) analysis, residue interaction networks as well as allosteric pocket prediction to investigate allosteric dynamics in USP7. Our analysis of intrinsic and conformational dynamics revealed that the structural transition between the two states is characterized by global clamp motions, during which the catalytic domain (CD) and UBL4-5 domain exhibit strong negative correlations. The PRS analysis, combined with the analysis of disease mutations and post-translational modifications (PTMs) further highlighted the allosteric potential of the two domains. The residue interaction network based on MD simulations captured an allosteric communication path which starts at CD domain and ends at UBL4-5 domain. Moreover, we identified a pocket at the TRAF-CD interface as a high-potential allosteric site for USP7. Overall, our studies not only provide molecular insights into the conformational changes of USP7, but also aid in the design of allosteric modulators that target USP7.

Molecular Dynamics Simulations Combined with Markov Model to Explore the Effect of Allosteric Inhibitor Binding on Bromodomain-Containing Protein 4

Bromodomain-Containing Protein 4 (BRD4) can play an important role in gene transcriptional regulation of tumor development and survival by participating in histone modification epigenetic mechanism. Although it has been reported that novel allosteric inhibitors such as ZL0590 have a high affinity with target protein BRD4 and good efficacy, their inhibitory mechanism has not been studied further. The aim of this study was to reveal the inhibition mechanism of allosteric inhibitor ZL0590 on Free-BRD4 and BRD4 binding MS436 (orthosteric inhibitor) by molecular dynamics simulation combined with a Markov model. Our results showed that BRD4-ZL0590 led to α-helices formation of 100-105 compared with Free-BRD4; the combination of MS436 caused residues 30-40 and 95-105 to form α-helices, while the combination of allosteric inhibitors untangled the α-helices formed by the MS436. The results of Markov flux analysis showed that the binding process of inhibitors mainly involved changes in the degree of α-helices at ZA loop. The binding of ZL0590 reduced the distance between ZA loop and BC loop, blocked the conformation at the active site, and inhibited the binding of MS436. After the allosteric inhibitor binding, the MS436 that could normally penetrate into the interior of the pocket was floating on the edge of the active pocket and did not continue to penetrate into the active pocket as expected. In summary, we provide a theoretical basis for the inhibition mechanism of ZL0590 against BRD4, which can be used as a reference for improving the development of drug targets for cancer therapy.

Combining structural and coevolution information to unveil allosteric sites

Understanding allosteric regulation in biomolecules is of great interest to pharmaceutical research and computational methods emerged during the last decades to characterize allosteric coupling. However, the prediction of allosteric sites in a protein structure remains a challenging task. Here, we integrate local binding site information, coevolutionary information, and information on dynamic allostery into a structure-based three-parameter model to identify potentially hidden allosteric sites in ensembles of protein structures with orthosteric ligands. When tested on five allosteric proteins (LFA-1, p38-α, GR, MAT2A, and BCKDK), the model successfully ranked all known allosteric pockets in the top three positions. Finally, we identified a novel druggable site in MAT2A confirmed by X-ray crystallography and SPR and a hitherto unknown druggable allosteric site in BCKDK validated by biochemical and X-ray crystallography analyses. Our model can be applied in drug discovery to identify allosteric pockets.

Targeting Arginine Methyltransferase PRMT5 for Cancer Therapy: Updated Progress and Novel Strategies

As a predominant type II protein arginine methyltransferase, PRMT5 plays critical roles in various normal cellular processes by catalyzing the mono- and symmetrical dimethylation of a wide range of histone and nonhistone substrates. Clinical studies have revealed that high expression of PRMT5 is observed in different solid tumors and hematological malignancies and is closely associated with cancer initiation and progression. Accordingly, PRMT5 is becoming a promising anticancer target and has received great attention in both the pharmaceutical industry and the academic community. In this Perspective, we comprehensively summarize recent advances in the development of first-generation PRMT5 enzymatic inhibitors and highlight novel strategies targeting PRMT5 in the past 5 years. We also discuss the challenges and opportunities of PRMT5 inhibition, with the aim of shedding light on future PRMT5 drug discovery.

Structure-Activity Relationship Study of the High-Affinity Neuropeptide Y4 Receptor Positive Allosteric Modulator VU0506013

Positive allosteric modulators targeting the Y₄ receptor (Y₄R), a G protein-coupled receptor (GPCR) involved in the regulation of satiety, offer great potential in anti-obesity research. In this study, we selected 603 compounds by using quantitative structure-activity relationship (QSAR) models and tested them in high-throughput screening (HTS). Here, the novel positive allosteric modulator (PAM) VU0506013 was identified, which exhibits nanomolar affinity and pronounced selectivity toward the Y₄R in engineered cell lines and mouse descending colon mucosa natively expressing the Y₄R. Based on this lead structure, we conducted a systematic SAR study in two regions of the scaffold and presented a series of 27 analogues with modifications in the N- and C-terminal heterocycles of the molecule to obtain insight into functionally relevant positions. By mutagenesis and computational docking, we present a potential binding mode of VU0506013 in the transmembrane core of the Y₄R. VU0506013 presents a promising scaffold for developing in vivo tools to move toward anti-obesity drug research focused on the Y₄R.

Advances in the allostery of angiotensin II type 1 receptor

Angiotensin II type 1 receptor (AT1R) is a promising therapeutic target for cardiovascular diseases. Compared with orthosteric ligands, allosteric modulators attract considerable attention for drug development due to their unique advantages of high selectivity and safety. However, no allosteric modulators of AT1R have been applied in clinical trials up to now. Except for the classical allosteric modulators of AT1R such as antibody, peptides and amino acids, cholesterol and biased allosteric modulators, there are non-classical allosteric modes including the ligand-independent allosteric mode, and allosteric mode of biased agonists and dimers. In addition, finding the allosteric pockets based on AT1R conformational change and interaction interface of dimers are the future of drug design. In this review, we summarize the different allosteric mode of AT1R, with a view to contribute to the development and utilization of drugs targeting AT1R allostery.

Factor XIa Inhibitors in Anticoagulation Therapy: Recent Advances and Perspectives

Factor XIa (FXIa) in the intrinsic pathway of the coagulation process has been proven to be an effective and safe target for anticoagulant discovery with limited or no bleeding. Numerous small-molecule FXIa inhibitors (SMFIs) with various scaffolds have been identified in the early stages of drug discovery. They have served as the foundation for the recent discovery of additional promising SMFIs with improved potency, selectivity, and pharmacokinetic profiles, some of which have entered clinical trials for the treatment of thrombosis. After reviewing the coagulation process and structure of FXIa, this perspective discusses the rational or structure-based design, discovery, structure-activity relationships, and development of SMFIs disclosed in recent years. Strategies for identifying more selective and druggable SMFIs are provided, paving the way for the design and discovery of more useful SMFIs for anticoagulation therapy.

Computational approaches for the design of modulators targeting protein-protein interactions

BACKGROUND

Protein-protein interactions (PPIs) are intriguing targets for designing novel small-molecule inhibitors. The role of PPIs in various infectious and neurodegenerative disorders makes them potential therapeutic targets . Despite being portrayed as undruggable targets, due to their flat surfaces, disorderedness, and lack of grooves. Recent progresses in computational biology have led researchers to reconsider PPIs in drug discovery.

AREAS COVERED

In this review, we introduce in-silico methods used to identify PPI interfaces and present an in-depth overview of various computational methodologies that are successfully applied to annotate the PPIs. We also discuss several successful case studies that use computational tools to understand PPIs modulation and their key roles in various physiological processes.

EXPERT OPINION

Computational methods face challenges due to the inherent flexibility of proteins, which makes them expensive, and result in the use of rigid models. This problem becomes more significant in PPIs due to their flexible and flat interfaces. Computational methods like molecular dynamics (MD) simulation and machine learning can integrate the chemical structure data into biochemical and can be used for target identification and modulation. These computational methodologies have been crucial in understanding the structure of PPIs, designing PPI modulators, discovering new drug targets, and predicting treatment outcomes.

Allosteric regulation and inhibition of protein kinases

The human genome encodes more than 500 different protein kinases: signaling enzymes with tightly regulated activity. Enzymatic activity within the conserved kinase domain is influenced by numerous regulatory inputs including the binding of regulatory domains, substrates, and the effect of post-translational modifications such as autophosphorylation. Integration of these diverse inputs occurs via allosteric sites that relate signals via networks of amino acid residues to the active site and ensures controlled phosphorylation of kinase substrates. Here, we review mechanisms of allosteric regulation of protein kinases and recent advances in the field.

Targeting Death-Associated Protein Kinases for Treatment of Human Diseases: Recent Advances and Future Directions

The death-associated protein kinase (DAPK) family is a member of the calcium/calmodulin-regulated serine/threonine protein kinase family, and studies have shown that its role, as its name suggests, is mainly to regulate cell death. The DAPK family comprises five members, including DAPK1, DAPK2, DAPK3, DRAK1 and DRAK2, which show high homology in the common N-terminal kinase domain but differ in the extra-catalytic domain. Notably, previous research has suggested that the DAPK family plays an essential role in both the development and regulation of human diseases. However, only a few small-molecule inhibitors have been reported. In this Perspective, we mainly discuss the structure, biological function, and role of DAPKs in diseases and the currently discovered small-molecule inhibitors, providing valuable information for the development of the DAPK field.

Recent applications of computational methods to allosteric drug discovery

Interest in exploiting allosteric sites for the development of new therapeutics has grown considerably over the last two decades. The chief driving force behind the interest in allostery for drug discovery stems from the fact that in comparison to orthosteric sites, allosteric sites are less conserved across a protein family, thereby offering greater opportunity for selectivity and ultimately tolerability. While there is significant overlap between structure-based drug design for orthosteric and allosteric sites, allosteric sites offer additional challenges mostly involving the need to better understand protein flexibility and its relationship to protein function. Here we examine the extent to which structure-based drug design is impacting allosteric drug design by highlighting several targets across a variety of target classes.

Allosteric Hotspots in the Main Protease of SARS-CoV-2

Inhibiting the main protease of SARS-CoV-2 is of great interest in tackling the COVID-19 pandemic caused by the virus. Most efforts have been centred on inhibiting the binding site of the enzyme. However, considering allosteric sites, distant from the active or orthosteric site, broadens the search space for drug candidates and confers the advantages of allosteric drug targeting. Here, we report the allosteric communication pathways in the main protease dimer by using two novel fully atomistic graph-theoretical methods: Bond-to-bond propensity, which has been previously successful in identifying allosteric sites in extensive benchmark data sets without a priori knowledge, and Markov transient analysis, which has previously aided in finding novel drug targets in catalytic protein families. Using statistical bootstrapping, we score the highest ranking sites against random sites at similar distances, and we identify four statistically significant putative allosteric sites as good candidates for alternative drug targeting.

Computer-Aided Drug Design Boosts RAS Inhibitor Discovery

The Rat Sarcoma (RAS) family (NRAS, HRAS, and KRAS) is endowed with GTPase activity to regulate various signaling pathways in ubiquitous animal cells. As proto-oncogenes, RAS mutations can maintain activation, leading to the growth and proliferation of abnormal cells and the development of a variety of human cancers. For the fight against tumors, the discovery of RAS-targeted drugs is of high significance. On the one hand, the structural properties of the RAS protein make it difficult to find inhibitors specifically targeted to it. On the other hand, targeting other molecules in the RAS signaling pathway often leads to severe tissue toxicities due to the lack of disease specificity. However, computer-aided drug design (CADD) can help solve the above problems. As an interdisciplinary approach that combines computational biology with medicinal chemistry, CADD has brought a variety of advances and numerous benefits to drug design, such as the rapid identification of new targets and discovery of new drugs. Based on an overview of RAS features and the history of inhibitor discovery, this review provides insight into the application of mainstream CADD methods to RAS drug design.

The protein kinase CK1: Inhibition, activation, and possible allosteric modulation

Protein kinases play a vital role in biology and deregulation of kinases is implicated in numerous diseases ranging from cancer to neurodegenerative diseases, making them a major target class for the pharmaceutical industry. However, the high degree of conservation that exists between ATP-binding sites among kinases makes it difficult for current inhibitors to be highly specific. In the context of neurodegeneration, several groups including ours, have linked different kinases such as CK1 and Alzheimer’s disease for example. Strictly CK1-isoform specific regulators do not exist and known CK1 inhibitors are inhibiting the enzymatic activity, targeting the ATP-binding site. Here we review compounds known to target CK1, as well as other inhibitory types that could benefit CK1. We introduce the DNA-encoded library (DEL) technology that might represent an interesting approach to uncover allosteric modulators instead of ATP competitors. Such a strategy, taking into account known allosteric inhibitors and mechanisms, might help designing modulators that are more specific towards a specific kinase, and in the case of CK1, toward specific isoforms.

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.

Understanding gilteritinib resistance to FLT3-F691L mutation through an integrated computational strategy

FMS-like tyrosine kinase 3 (FLT3) serves as an important drug target for acute myeloid leukemia (AML), and gene mutations of FLT3 have been closely associated with AML patients with an incidence rate of ~ 30%. However, the mechanism of the clinically relevant F691L gatekeeper mutation conferred resistance to the drug gilteritinib remained poorly understood. In this study, multiple microsecond molecular dynamics (MD) simulations, end-point free energy calculations, and dynamic correlated and network analyses were performed to investigate the molecular basis of gilteritinib resistance to the FLT3-F691L mutation. The simulations revealed that the resistant mutation largely induced the conformational changes of the activation loop (A-loop), the phosphate-binding loop, and the helix αC of the FLT3 protein. The binding abilities of the gilteritinib to the wild-type and the F691L mutant were different through the binding free energy prediction. The simulation results further indicated that the driving force to determine the binding affinity of gilteritinib was derived from the differences in the energy terms of electrostatic and van der Waals interactions. Moreover, the per-residue free energy decomposition suggested that the four residues (Phe803, Gly831, Leu832, and Ala833) located at the A-loop of FLT3 had a significant impact on the binding affinity of gilteritinib to the F691L mutant. This study may provide useful information for the design of novel FLT3 inhibitors specially targeting the F691L gatekeeper mutant.

Mechanistic Insights into the Long-range Allosteric Regulation of KRAS Via Neurofibromatosis Type 1 (NF1) Scaffold Upon SPRED1 Loading

Allosteric regulation is the most direct and efficient way of regulating protein function, wherein proteins transmit the perturbations at one site to another distinct functional site. Deciphering the mechanism of allosteric regulation is of vital importance for the comprehension of both physiological and pathological events in vivo as well as the rational allosteric drug design. However, it remains challenging to elucidate dominant allosteric signal transduction pathways, especially for large and multi-component protein machineries where long-range allosteric regulation exits. One of the quintessential examples having long-range allosteric regulation is the ternary complex, SPRED1-RAS-neurofibromin type 1 (NF1, a RAS GTPase-activating protein), in which SPRED1 facilitates RAS-GTP hydrolysis by interacting with NF1 at a distal, allosteric site from the RAS binding site. To address the underlying mechanism, we performed extensive Gaussian accelerated molecular dynamics simulations and Markov state model analysis of KRAS-NF1 complex in the presence and absence of SPRED1. Our findings suggested that SPRED1 loading allosterically enhanced KRAS-NF1 binding, but hindered conformational transformation of the NF1 catalytic center for RAS hydrolysis. Moreover, we unveiled the possible allosteric pathways upon SPRED1 binding through difference contact network analysis. This study not only provided an in-depth mechanistic insight into the allosteric regulation of KRAS by SPRED1, but also shed light on the investigation of long-range allosteric regulation among complex macromolecular systems.

Prediction of Protein Allosteric Signalling Pathways and Functional Residues Through Paths of Optimised Propensity

Allostery commonly refers to the mechanism that regulates protein activity through the binding of a molecule at a different, usually distal, site from the orthosteric site. The omnipresence of allosteric regulation in nature and its potential for drug design and screening render the study of allostery invaluable. Nevertheless, challenges remain as few computational methods are available to effectively predict allosteric sites, identify signalling pathways involved in allostery, or to aid with the design of suitable molecules targeting such sites. Recently, bond-to-bond propensity analysis has been shown successful at identifying allosteric sites for a large and diverse group of proteins from knowledge of the orthosteric sites and its ligands alone by using network analysis applied to energy-weighted atomistic protein graphs. To address the identification of signalling pathways, we propose here a method to compute and score paths of optimised propensity that link the orthosteric site with the identified allosteric sites, and identifies crucial residues that contribute to those paths. We showcase the approach with three well-studied allosteric proteins: h-Ras, caspase-1, and 3-phosphoinositide-dependent kinase-1 (PDK1). Key residues in both orthosteric and allosteric sites were identified and showed agreement with experimental results, and pivotal signalling residues along the pathway were also revealed, thus providing alternative targets for drug design. By using the computed path scores, we were also able to differentiate the activity of different allosteric modulators.

The Role of Conformational Dynamics and Allostery in the Control of Distinct Efficacies of Agonists to the Glucocorticoid Receptor

Glucocorticoid receptor (GR) regulates various cellular functions. Given its broad influence on metabolic activities, it has been the target of drug discovery for decades. However, how drugs induce conformational changes in GR has remained elusive. Herein, we used five GR agonists (dex, AZ938, pred, cor, and dibC) with different efficacies to investigate which aspect of the ligand induced the differences in efficacy. We performed molecular dynamics simulations on the five systems (dex-, AZ938-, pred-, cor-, and dibC-bound systems) and observed a distinct discrepancy in the conformation of the cofactor TIF2. Moreover, we discovered ligand-induced differences regarding the level of conformational changes posed by the binding of cofactor TIF2 and identified a pair of essential residues D590 and T39. We further found a positive correlation between the efficacies of ligands and the interaction of the two binding pockets’ domains, where D590 and T739 were involved, implying their significance in the participation of allosteric communication. Using community network analysis, two essential communities containing D590 and T739 were identified with their connectivity correlating to the efficacy of ligands. The potential communication pathways between these two residues were revealed. These results revealed the underlying mechanism of allosteric communication between the ligand-binding and cofactor-binding pockets and identified a pair of important residues in the allosteric communication pathway, which can serve as a guide for future drug discovery.

Insights into the Allosteric Effect of SENP1 Q597A Mutation on the Hydrolytic Reaction of SUMO1 via an Integrated Computational Study

Small ubiquitin-related modifier (SUMO)-specific protease 1 (SENP1) is a cysteine protease that catalyzes the cleavage of the C-terminus of SUMO1 for the processing of SUMO precursors and deSUMOylation of target proteins. SENP1 is considered to be a promising target for the treatment of hepatocellular carcinoma (HCC) and prostate cancer. SENP1 Gln597 is located at the unstructured loop connecting the helices α4 to α5. The Q597A mutation of SENP1 allosterically disrupts the hydrolytic reaction of SUMO1 through an unknown mechanism. Here, extensive multiple replicates of microsecond molecular dynamics (MD) simulations, coupled with principal component analysis, dynamic cross-correlation analysis, community network analysis, and binding free energy calculations, were performed to elucidate the detailed mechanism. Our MD simulations showed that the Q597A mutation induced marked dynamic conformational changes in SENP1, especially in the unstructured loop connecting the helices α4 to α5 which the mutation site occupies. Moreover, the Q597A mutation caused conformational changes to catalytic Cys603 and His533 at the active site, which might impair the catalytic activity of SENP1 in processing SUMO1. Moreover, binding free energy calculations revealed that the Q597A mutation had a minor effect on the binding affinity of SUMO1 to SENP1. Together, these results may broaden our understanding of the allosteric modulation of the SENP1−SUMO1 complex.

Learning about allosteric drugs and ways to design them

While the accelerating quest for precision medicine requires new individually targeting and selective drugs, and the ability to work with so-called undruggable targets, the realm of allosteric drugs meeting this need remains largely uncharted. Generalizing the observations on two major drug targets with widely observed inherent allostery, GPCRs and kinases, we describe and discuss basic allosteric modes of action that are universally applicable in all types of structures and functions. Using examples of Class A GPCRs and CMGC protein kinases, we show how Allosteric Signalling and Probing Fingerprints can be used to identify potential allosteric sites and reveal effector-leads that may serve as a starting point for the development of allosteric drugs targeting these regulatory sites. A set of distinct characteristics of allosteric ligands was established, which highlights the versatility of their design and make them advantageous before their orthosteric counterparts in personalized medicine. We argue that rational design of allosteric drugs should begin with the search for latent sites or design of non-natural binding sites followed by fragment-based design of allosteric ligands and by the mutual adjustment of the site-ligand pair in order to achieve required effects. On the basis of the perturbative nature and reversibility of allosteric communication, we propose a generic protocol for computational design of allosteric effectors, enabling also the allosteric tuning of biologics, in obtaining allosteric control over protein functions.

Exploring the kinase-inhibitor fragment interaction space facilitates the discovery of kinase inhibitor overcoming resistance by mutations

Protein kinases play crucial roles in many cellular signaling processes, making them become important targets for drug discovery. But drug resistance mediated by mutation puts a barrier to the therapeutic effect of kinase inhibitors. Fragment-based drug discovery has been successfully applied to overcome such resistance. However, the complicate kinase-inhibitor fragment interaction and fragment-to-lead process seriously limit the efficiency of kinase inhibitor discovery against resistance caused by mutation. Here, we constructed a comprehensive web platform KinaFrag for the fragment-based kinase inhibitor discovery to overcome resistance. The kinase-inhibitor fragment space was investigated from 7783 crystal kinase-inhibitor fragment complexes, and the structural requirements of kinase subpockets were analyzed. The core fragment-based virtual screening workflow towards specific subpockets was developed to generate new kinase inhibitors. A series of tropomyosin receptor kinase (TRK) inhibitors were designed, and the most potent compound YT9 exhibits up to 70-fold activity improvement than marketed drugs larotrectinib and selitrectinib against G595R, G667C and F589L mutations of TRKA. YT9 shows promising antiproliferative against tumor cells in vitro and effectively inhibits tumor growth in vivo for wild type TRK and TRK mutants. Our results illustrate the great potential of KinaFrag in the kinase inhibitor discovery to combat resistance mediated by mutation. KinaFrag is freely available at http://chemyang.ccnu.edu.cn/ccb/database/KinaFrag/.

Delineating the conformational landscape and intrinsic properties of the angiotensin II type 2 receptor using a computational study

As a key regulator for the renin-angiotensin system, a class A G protein-coupled receptor (GPCR), AngII type 2 receptor (AT₂R), plays a pivotal role in the homeostasis of the cardiovascular system. Compared with other GPCRs, AT₂R has a unique antagonist-bound conformation and its mechanism is still an enigma. Here, we applied combined dynamic and evolutional approaches to investigate the conformational space and intrinsic properties of AT₂R. With molecular dynamic simulations, Markov State Models, and statistics coupled analysis, we captured the conformational landscape of AT₂R and identified its uniquity from both dynamical and evolutional viewpoints. A cryptic pocket was also discovered in the intermediate state during conformation transitions. These findings offer a deeper understanding of the AT₂R mechanism at an atomic level and provide hints for the design of novel AT₂R modulators.

An Evolutionary Conservation and Druggability Analysis of Enzymes Belonging to the Bacterial Shikimate Pathway

Enzymes belonging to the shikimate pathway have long been considered promising targets for antibacterial drugs because they have no counterpart in mammals and are essential for bacterial growth and virulence. However, despite decades of research, there are currently no clinically relevant antibacterial drugs targeting any of these enzymes, and there are legitimate concerns about whether they are sufficiently druggable, i.e., whether they can be adequately modulated by small and potent drug-like molecules. In the present work, in silico analyses combining evolutionary conservation and druggability are performed to determine whether these enzymes are candidates for broad-spectrum antibacterial therapy. The results presented here indicate that the substrate-binding sites of most enzymes in this pathway are suitable drug targets because of their reasonable conservation and druggability scores. An exception was the substrate-binding site of 3-deoxy-D-arabino-heptulosonate-7-phosphate synthase, which was found to be undruggable because of its high content of charged residues and extremely high overall polarity. Although the presented study was designed from the perspective of broad-spectrum antibacterial drug development, this workflow can be readily applied to any antimicrobial target analysis, whether narrow- or broad-spectrum. Moreover, this research also contributes to a deeper understanding of these enzymes and provides valuable insights into their properties.

Understanding the P-Loop Conformation in the Determination of Inhibitor Selectivity Toward the Hepatocellular Carcinoma-Associated Dark Kinase STK17B

As a member of the death-associated protein kinase family of serine/threonine kinases, the STK17B has been associated with diverse diseases such as hepatocellular carcinoma. However, the conformational dynamics of the phosphate-binding loop (P-loop) in the determination of inhibitor selectivity profile to the STK17B are less understood. Here, a multi-microsecond length molecular dynamics (MD) simulation of STK17B in the three different states (ligand-free, ADP-bound, and ligand-bound states) was carried out to uncover the conformational plasticity of the P-loop. Together with the analyses of principal component analysis, cross-correlation and generalized correlation motions, secondary structural analysis, and community network analysis, the conformational dynamics of the P-loop in the different states were revealed, in which the P-loop flipped into the ADP-binding site upon the inhibitor binding and interacted with the inhibitor and the C-lobe, strengthened the communication between the N- and C-lobes. These resulting interactions contributed to inhibitor selectivity profile to the STK17B. Our results may advance our understanding of kinase inhibitor selectivity and offer possible implications for the design of highly selective inhibitors for other protein kinases.

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.

Autopromotion of K-Ras4B Feedback Activation Through an SOS-Mediated Long-Range Allosteric Effect

The Ras-specific guanine nucleotide exchange factors Son of Sevenless (SOS) regulates Ras activation by converting inactive GDP-bound to active GTP-bound states. The catalytic activity of Ras is further allosterically regulated by GTP−Ras bound to a distal site through a positive feedback loop. To address the mechanism underlying the long-range allosteric activation of the catalytic K-Ras4B by an additional allosteric GTP–Ras through SOS, we employed molecular dynamics simulation of the K-Ras4B^G13D•SOS^cat complex with and without an allosteric GTP-bound K-Ras4B^G13D. We found that the binding of an allosteric GTP−K-Ras4B^G13D enhanced the affinity between the catalytic K-Ras4B^G13D and SOS^cat, forming a more stable conformational state. The peeling away of the switch I from the nucleotide binding site facilitated the dissociation of GDP, thereby contributing to the increased nucleotide exchange rate. The community networks further showed stronger edge connection upon allosteric GTP−K-Ras4B^G13D binding, which represented an increased interaction between catalytic K-Ras4B^G13D and SOS^cat. Moreover, GTP−K-Ras4B^G13D binding transmitted allosteric signaling pathways though the Cdc25 domain of SOS that enhanced the allosteric regulatory from the K-Ras4B^G13D allosteric site to the catalytic site. This study may provide an in-depth mechanism for abnormal activation and allosteric regulation of K-Ras4B^G13D.

Eltrombopag as an Allosteric Inhibitor of the METTL3-14 Complex Affecting the m6A Methylation of RNA in Acute Myeloid Leukemia Cells

N⁶A-methyladenosine (m⁶A) post-transcriptional modification, the most abundant internal RNA modification, is catalyzed by the METTL3-14 methyltransferase complex. Recently, attention has been drawn to the METTL3-14 complex regarding its significant roles in the pathogenesis of acute myeloid leukemia (AML), attracting the potential of novel therapeutic targets for the disease. Herein, we report the identification and characterization of eltrombopag as a selective allosteric inhibitor of the METTL3-14 complex. Eltrombopag exhibited selective inhibitory activity in the most active catalytic form of the METTL3-14 complex by direct binding, and the mechanism of inhibition was confirmed as a noncompetitive inhibition by interacting at a putative allosteric binding site in METTL3, which was predicted by cavity search and molecular docking studies. At a cellular level, eltrombopag displayed anti-proliferative effects in the relevant AML cell line, MOLM-13, in correlation with a reduction in m⁶A levels. Molecular mechanism studies of eltrombopag using m⁶A-seq analysis provided further evidence of its cellular function by determining the hypomethylation of leukemogenic genes in eltrombopag-treated MOLM-13 cells and the overlapping of the pattern with those of METTL3-knockdown MOLM-13 cells. In conclusion, eltrombopag was first disclosed as a functional METTL3-14 allosteric inhibitor in AML cells, which could be utilized for the further development of novel anti-AML therapy.

Principles of Kinase Allosteric Inhibition and Pocket Validation

With several marketed drugs, allosteric inhibition of kinases has translated to pharmacological effects and clinical benefits comparable to those from orthosteric inhibition. However, despite much effort over more than 20 years, the number of kinase targets associated with FDA-approved allosteric drugs is limited, suggesting the challenges in identifying and validating allosteric inhibitors. Here we review the principles of allosteric inhibition, summarize the discovery of allosteric MEK1/2 and BCR-ABL1 inhibitors, and discuss the approaches to screening and demonstrating the functional activity of allosteric pocket ligands.

Targeting key proteins involved in transcriptional regulation for cancer therapy: Current strategies and future prospective

The key proteins involved in transcriptional regulation play convergent roles in cellular homeostasis, and their dysfunction mediates aberrant gene expressions that underline the hallmarks of tumorigenesis. As tumor progression is dependent on such abnormal regulation of transcription, it is important to discover novel chemical entities as antitumor drugs that target key tumor-associated proteins involved in transcriptional regulation. Despite most key proteins (especially transcription factors) involved in transcriptional regulation are historically recognized as undruggable targets, multiple targeting approaches at diverse levels of transcriptional regulation, such as epigenetic intervention, inhibition of DNA-binding of transcriptional factors, and inhibition of the protein-protein interactions (PPIs), have been established in preclinically or clinically studies. In addition, several new approaches have recently been described, such as targeting proteasomal degradation and eliciting synthetic lethality. This review will emphasize on accentuating these developing therapeutic approaches and provide a thorough conspectus of the drug development to target key proteins involved in transcriptional regulation and their impact on future oncotherapy.

Mechanistic Insights Into Co-Administration of Allosteric and Orthosteric Drugs to Overcome Drug-Resistance in T315I BCR-ABL1

Chronic myeloid leukemia (CML) is a myeloproliferative neoplasm, driven by the BCR-ABL1 fusion oncoprotein. The discovery of orthosteric BCR-ABL1 tyrosine kinase inhibitors (TKIs) targeting its active ATP-binding pocket, such as first-generation Imatinib and second-generation Nilotinib (NIL), has profoundly revolutionized the therapeutic landscape of CML. However, currently targeted therapeutics still face considerable challenges with the inevitable emergence of drug-resistant mutations within BCR-ABL1. One of the most common resistant mutations in BCR-ABL1 is the T315I gatekeeper mutation, which confers resistance to most current TKIs in use. To resolve such conundrum, co-administration of orthosteric TKIs and allosteric drugs offers a novel paradigm to tackle drug resistance. Remarkably, previous studies have confirmed that the dual targeting BCR-ABL1 utilizing orthosteric TKI NIL and allosteric inhibitor ABL001 resulted in eradication of the CML xenograft tumors, exhibiting promising therapeutic potential. Previous studies have demonstrated the cooperated mechanism of two drugs. However, the conformational landscapes of synergistic effects remain unclear, hampering future efforts in optimizations and improvements. Hence, extensive large-scale molecular dynamics (MD) simulations of wide type (WT), WT-NIL, T315I, T315I-NIL, T315I-ABL001 and T315I-ABL001-NIL systems were carried out in an attempt to address such question. Simulation data revealed that the dynamic landscape of NIL-bound BCR-ABL1 was significantly reshaped upon ABL001 binding, as it shifted from an active conformation towards an inactive conformation. The community network of allosteric signaling was analyzed to elucidate the atomistic overview of allosteric regulation within BCR-ABL1. Moreover, binding free energy analysis unveiled that the affinity of NIL to BCR-ABL1 increased by the induction of ABL001, which led to its favorable binding and the release of drug resistance. The findings uncovered the in-depth structural mechanisms underpinning dual-targeting towards T315I BCR-ABL1 to overcome its drug resistance and will offer guidance for the rational design of next generations of BCR-ABL1 modulators and future combinatory therapeutic regimens.

Oncological drug discovery: AI meets structure-based computational research

The integration of machine learning and structure-based methods has proven valuable in the past as a way to prioritize targets and compounds in early drug discovery. In oncological research, these methods can be highly beneficial in addressing the diversity of neoplastic diseases portrayed by the different hallmarks of cancer. Here, we review six use case scenarios for integrated computational methods, namely driver prediction, computational mutagenesis, (off)-target prediction, binding site prediction, virtual screening, and allosteric modulation analysis. We address the heterogeneity of integration approaches and individual methods, while acknowledging their current limitations and highlighting their potential to bring drugs for personalized oncological therapies to the market faster.

Elucidation of the conformational dynamics and assembly of Argonaute−RNA complexes by distinct yet coordinated actions of the supplementary microRNA

Argonaute (AGO) proteins, the core of RNA-induced silencing complex, are guided by microRNAs (miRNAs) to recognize target RNA for repression. The miRNA–target RNA recognition forms initially through pairing at the seed region while the additional supplementary pairing can enhance target recognition and compensate for seed mismatch. The extension of miRNA lengths can strengthen the target affinity when pairing both in the seed and supplementary regions. However, the mechanism underlying the effect of the supplementary pairing on the conformational dynamics and the assembly of AGO–RNA complex remains poorly understood. To address this, we performed large-scale molecular dynamics simulations of AGO–RNA complexes with different pairing patterns and miRNA lengths. The results reveal that the additional supplementary pairing can not only strengthen the interaction between miRNA and target RNA, but also induce the increased plasticity of the PAZ domain and enhance the domain connectivity among the PAZ, PIWI, N domains of the AGO protein. The strong community network between these domains tightens the mouth of the supplementary chamber of AGO protein, which prevents the escape of target RNA from the complex and shields it from solvent water attack. Importantly, the inner stronger matching pairs between the miRNA and target RNA can compensate for weaker mismatches at the edge of supplementary region. These findings provide guidance for the design of miRNA mimics and anti-miRNAs for both clinical and experimental use and open the way for further engineering of AGO proteins as a new tool in the field of gene regulation.

Targeting a cryptic allosteric site of SIRT6 with small-molecule inhibitors that inhibit the migration of pancreatic cancer cells

SIRT6 belongs to the conserved NAD⁺-dependent deacetylase superfamily and mediates multiple biological and pathological processes. Targeting SIRT6 by allosteric modulators represents a novel direction for therapeutics, which can overcome the selectivity problem caused by the structural similarity of orthosteric sites among deacetylases. Here, developing a reversed allosteric strategy AlloReverse, we identified a cryptic allosteric site, Pocket Z, which was only induced by the bi-directional allosteric signal triggered upon orthosteric binding of NAD⁺. Based on Pocket Z, we discovered an SIRT6 allosteric inhibitor named JYQ-42. JYQ-42 selectively targets SIRT6 among other histone deacetylases and effectively inhibits SIRT6 deacetylation, with an IC₅₀ of 2.33 μmol/L. JYQ-42 significantly suppresses SIRT6-mediated cancer cell migration and pro-inflammatory cytokine production. JYQ-42, to our knowledge, is the most potent and selective allosteric SIRT6 inhibitor. This study provides a novel strategy for allosteric drug design and will help in the challenging development of therapeutic agents that can selectively bind SIRT6.