Emerging roles of allosteric modulators in the regulation of protein-protein interactions (PPIs): A new paradigm for PPI drug discovery

Targeting cyclin-dependent kinases: From pocket specificity to drug selectivity

The development of selective modulators of cyclin-dependent kinases (CDKs), a kinase family with numerous members and functional variations, is a significant preclinical challenge. Recent advancements in crystallography have revealed subtle differences in the highly conserved CDK pockets. Exploiting these differences has proven to be an effective strategy for achieving excellent drug selectivity. While previous reports briefly discussed the structural features that lead to selectivity in individual CDK members, attaining inhibitor selectivity requires consideration of not only the specific structures of the target CDK but also the features of off-target members. In this review, we summarize the structure-activity relationships (SARs) that influence selectivity in CDK drug development and analyze the pocket features that lead to selectivity using molecular-protein binding models. In addition, in recent years, novel CDK modulators have been developed, providing more avenues for achieving selectivity. These cases were also included. We hope that these efforts will assist in the development of novel CDK drugs.

HBCVTr: an end-to-end transformer with a deep neural network hybrid model for anti-HBV and HCV activity predictor from SMILES

Hepatitis B and C viruses (HBV and HCV) are significant causes of chronic liver diseases, with approximately 350 million infections globally. To accelerate the finding of effective treatment options, we introduce HBCVTr, a novel ligand-based drug design (LBDD) method for predicting the inhibitory activity of small molecules against HBV and HCV. HBCVTr employs a hybrid model consisting of double encoders of transformers and a deep neural network to learn the relationship between small molecules' simplified molecular-input line-entry system (SMILES) and their antiviral activity against HBV or HCV. The prediction accuracy of HBCVTr has surpassed baseline machine learning models and existing methods, with R-squared values of 0.641 and 0.721 for the HBV and HCV test sets, respectively. The trained models were successfully applied to virtual screening against 10 million compounds within 240 h, leading to the discovery of the top novel inhibitor candidates, including IJN04 for HBV and IJN12 and IJN19 for HCV. Molecular docking and dynamics simulations identified IJN04, IJN12, and IJN19 target proteins as the HBV core antigen, HCV NS5B RNA-dependent RNA polymerase, and HCV NS3/4A serine protease, respectively. Overall, HBCVTr offers a new and rapid drug discovery and development screening method targeting HBV and HCV.

Recent Discovery and Development of Inhibitors that Target CDK9 and Their Therapeutic Indications

CDK9 is a cyclin-dependent kinase that plays pivotal roles in multiple cellular functions including gene transcription, cell cycle regulation, DNA damage repair, and cellular differentiation. Targeting CDK9 is considered an attractive strategy for antitumor therapy, especially for leukemia and lymphoma. Several potent small molecule inhibitors, exemplified by TG02 (4), have progressed to clinical trials. However, many of them face challenges such as low clinical efficacy and multiple adverse reactions and may necessitate the exploration of novel strategies to lead to success in the clinic. In this perspective, we present a comprehensive overview of the structural characteristics, biological functions, and preclinical status of CDK9 inhibitors. Our focus extends to various types of inhibitors, including pan-inhibitors, selective inhibitors, dual-target inhibitors, degraders, PPI inhibitors, and natural products. The discussion encompasses chemical structures, structure-activity relationships (SARs), biological activities, selectivity, and therapeutic potential, providing detailed insight into the diverse landscape of CDK9 inhibitors.

Self-assembly of peptide nanomaterials at biointerfaces: molecular design and biomedical applications

Self-assembly is an important strategy for constructing ordered structures and complex functions in nature. Based on this, people can imitate nature and artificially construct functional materials with novel structures through the supermolecular self-assembly pathway of biological interfaces. Among the many assembly units, peptide molecular self-assembly has received widespread attention in recent years. In this review, we introduce the interactions (hydrophobic interaction, hydrogen bond, and electrostatic interaction) between peptide nanomaterials and biological interfaces, summarizing the latest advancements in multifunctional self-assembling peptide materials. We systematically demonstrate the assembly mechanisms of peptides at biological interfaces, such as proteins and cell membranes, while highlighting their application potential and challenges in fields like drug delivery, antibacterial strategies, and cancer therapy.

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.

Graph Neural Network Model for Prediction of Non-Small Cell Lung Cancer Lymph Node Metastasis Using Protein-Protein Interaction Network and 18F-FDG PET/CT Radiomics

The image texture features obtained from 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) images of non-small cell lung cancer (NSCLC) have revealed tumor heterogeneity. A combination of genomic data and radiomics may improve the prediction of tumor prognosis. This study aimed to predict NSCLC metastasis using a graph neural network (GNN) obtained by combining a protein-protein interaction (PPI) network based on gene expression data and image texture features. 18F-FDG PET/CT images and RNA sequencing data of 93 patients with NSCLC were acquired from The Cancer Imaging Archive. Image texture features were extracted from 18F-FDG PET/CT images and area under the curve receiver operating characteristic curve (AUC) of each image feature was calculated. Weighted gene co-expression network analysis (WGCNA) was used to construct gene modules, followed by functional enrichment analysis and identification of differentially expressed genes. The PPI of each gene module and genes belonging to metastasis-related processes were converted via a graph attention network. Images and genomic features were concatenated. The GNN model using PPI modules from WGCNA and metastasis-related functions combined with image texture features was evaluated quantitatively. Fifty-five image texture features were extracted from 18F-FDG PET/CT, and radiomic features were selected based on AUC (n = 10). Eighty-six gene modules were clustered by WGCNA. Genes (n = 19) enriched in the metastasis-related pathways were filtered using DEG analysis. The accuracy of the PPI network, derived from WGCNA modules and metastasis-related genes, improved from 0.4795 to 0.5830 (p < 2.75 × 10-12). Integrating PPI of four metastasis-related genes with 18F-FDG PET/CT image features in a GNN model elevated its accuracy over a without image feature model to 0.8545 (95% CI = 0.8401-0.8689, p-value < 0.02). This model demonstrated significant enhancement compared to the model using PPI and 18F-FDG PET/CT derived from WGCNA (p-value < 0.02), underscoring the critical role of metastasis-related genes in prediction model. The enhanced predictive capability of the lymph node metastasis prediction GNN model for NSCLC, achieved through the integration of comprehensive image features with genomic data, demonstrates promise for clinical implementation.

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.

Unraveling the therapeutic potential of Senna singueana phytochemicals to attenuate pancreatic cancer using protein-protein interactions, molecular docking, and MD simulation

Pancreatic cancer (PC) presents challenges due to limited treatment options and patients seek complementary therapies alongside conventional treatments to improve well-being. This study uses computational drug discovery approaches to find potential phytochemicals from S. singueana for PC treatment. Among the 38 phytochemicals screened from S. singueana, specific inhibitors against PC were selected. Protein-protein interaction (PPI) network analysis highlighted key targets with high degrees, including PTEN (8) and PTK2 (7) genes, along with their respective proteins 5BZX and 3BZ3, which were employed for molecular docking studies. 1-methylchrysene and 3-methyl-1,8,9-anthracenetriol showed strong binding affinities of - 9.2 and - 8.1 Kcal/mol, respectively. Molecular dynamics simulations lasting 300 ns assessed structural stability and interaction energy of compound-target dockings: 1-methylchrysene-PTEN and 3-methyl-1,8,9-anthracenetriol-PTK2. In molecular dynamics simulations, the 3-methyl-1,8,9-anthracenetriol-PTK2 complex showed lower RMSD, RMSF, radius of gyration, solvent-accessible surface area, and more hydrogen bonds than the 1-methylchrysene-PTEN complex. The 3-methyl-1,8,9-anthracenetriol-PTK2 complex exhibited significantly stronger binding with a binding free energy (ΔG) of - 21.92 kcal/mol compared to the less favourable ΔG of - 10.65 kcal/mol for the 1-methylchrysene-PTEN complex. The consistent and stable binding interaction observed in the 3-methyl-1,8,9-anthracenetriol-PTK2 complex highlights its potential as a potent inhibitor of Focal Adhesion Kinase 1. Consequently, it emerges as a promising lead compound for the development of pancreatic cancer therapeutics. Conversely, the fluctuations observed in the 1-methylchrysene-PTEN complex indicate a less stable binding interaction. This indicates the potential of 3-methyl-1,8,9-anthracenetriol as a primary candidate for pancreatic cancer treatment. These findings improve our grasp of S. singueana's multi-target effects and its promise in addressing pancreatic cancer. Nevertheless, additional in-vivo and in-vitro studies are required to validate their effectiveness and therapeutic potential.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-023-00179-9.

Design, synthesis, and biological evaluation of novel 4,4'-bipyridine derivatives acting as CDK9-Cyclin T1 protein-protein interaction inhibitors against triple-negative breast cancer

Cyclin-dependent kinase 9 (CDK9) is directly related to tumor development in triple-negative breast cancer (TNBC) patients. Increased CDK9 is significantly associated with poor patient prognosis, while inhibiting CDK9-Cyclin T1 protein-protein interaction has recently been demonstrated as a new approach to TNBC treatment. Herein, we synthesized a novel class of 4,4'-bipyridine derivatives as potential CDK9-Cyclin T1 PPI inhibitors against TNBC. The represented compound B19 was found to be an excellent and selective CDK9-Cyclin T1 PPI inhibitor with good potency against TNBC cell lines while exhibiting lower toxicity in normal human cell lines than the positive compound I-CDK9. Notably, compound B19 showed good pharmacokinetic properties and excellent antitumor activity against TNBC (4T1) allografts in mice with a therapeutic index of more than 42 (TGI4T1(12.5 mg/kg,i.p.) = 63.1% vs. LD50 = 537 mg/kg). Moreover, the administration of B19 in combination with the PARP inhibitor Olaparib results in a significant increase of the antitumor activity in MDA-MB-231 cells relative to that of either single agent. To our knowledge, B19 is the first reported non-metal organic compound that acts as a selective CDK9-Cyclin T1 PPI inhibitor with in vivo antitumor activity, and it may be alone and in combination with PARP inhibitor Olaparib for TNBC therapy.

Allosteric inhibition of myosin by phenamacril: a synergistic mechanism revealed by computational and experimental approaches

BACKGROUND

Myosin plays a crucial role in cellular processes, while its dysfunction can lead to organismal malfunction. Phenamacril (PHA), a highly species-specific and non-competitive inhibitor of myosin I (FgMyoI) from Fusarium graminearum, has been identified as an effective fungicide for controlling plant diseases caused by partial Fusarium pathogens, such as wheat scab and rice bakanae. However, the molecular basis of its action is still unclear.

RESULTS

This study used multiple computational approaches first to elucidate the allosteric inhibition mechanism of FgMyoI by PHA at the atomistic level. The results indicated the increase of adenosine triphosphate (ATP) binding affinity upon PHA binding, which might impede the release of hydrolysis products. Furthermore, simulations revealed a broadened outer cleft and a significantly more flexible interface for actin binding, accompanied by a decrease in signaling transduction from the catalytic center to the actin-binding interface. These various effects might work together to disrupt the actomyosin cycle and hinder the ability of motor to generate force. Our experimental results further confirmed that PHA reduces the enzymatic activity of myosin and its binding with actin.

CONCLUSION

Therefore, our findings demonstrated that PHA might suppress the function of myosin through a synergistic mechanism, providing new insights into myosin allostery and offering new avenues for drug/fungicide discovery targeting myosin. © 2023 Society of Chemical Industry.

Small molecules targeting protein-protein interactions for cancer therapy

Protein-protein interactions (PPIs) are fundamental to many biological processes that play an important role in the occurrence and development of a variety of diseases. Targeting the interaction between tumour-related proteins with emerging small molecule drugs has become an attractive approach for treatment of human diseases, especially tumours. Encouragingly, selective PPI-based therapeutic agents have been rapidly advancing over the past decade, providing promising perspectives for novel therapies for patients with cancer. In this review we comprehensively clarify the discovery and development of small molecule modulators of PPIs from multiple aspects, focusing on PPIs in disease, drug design and discovery strategies, structure-activity relationships, inherent dilemmas, and future directions.

Characterization of allosteric modulators that disrupt androgen receptor co-activator protein-protein interactions to alter transactivation-Drug leads for metastatic castration resistant prostate cancer

Three series of compounds were prioritized from a high content screening campaign that identified molecules that blocked dihydrotestosterone (DHT) induced formation of Androgen Receptor (AR) protein-protein interactions (PPIs) with the Transcriptional Intermediary Factor 2 (TIF2) coactivator and also disrupted preformed AR-TIF2 PPI complexes; the hydrobenzo-oxazepins (S1), thiadiazol-5-piperidine-carboxamides (S2), and phenyl-methyl-indoles (S3). Compounds from these series inhibited AR PPIs with TIF2 and SRC-1, another p160 coactivator, in mammalian 2-hybrid assays and blocked transcriptional activation in reporter assays driven by full length AR or AR-V7 splice variants. Compounds inhibited the growth of five prostate cancer cell lines, with many exhibiting differential cytotoxicity towards AR positive cell lines. Representative compounds from the 3 series substantially reduced both endogenous and DHT-enhanced expression and secretion of the prostate specific antigen (PSA) cancer biomarker in the C4-2 castration resistant prostate cancer (CRPC) cell line. The comparatively weak activities of series compounds in the H3-DHT and/or TIF2 box 3 LXXLL-peptide binding assays to the recombinant ligand binding domain of AR suggest that direct antagonism at the orthosteric ligand binding site or AF-2 surface respectively are unlikely mechanisms of action. Cellular enhanced thermal stability assays (CETSA) indicated that compounds engaged AR and reduced the maximum efficacy and right shifted the EC50 of DHT-enhanced AR thermal stabilization consistent with the effects of negative allosteric modulators. Molecular docking of potent representative hits from each series to AR structures suggest that S1-1 and S2-6 engage a novel binding pocket (BP-1) adjacent to the orthosteric ligand binding site, while S3-11 occupies the AR binding function 3 (BF-3) allosteric pocket. Hit binding poses indicate spaces and residues adjacent to the BP-1 and BF-3 pockets that will be exploited in future medicinal chemistry optimization studies. Small molecule allosteric modulators that prevent/disrupt AR PPIs with coactivators like TIF2 to alter transcriptional activation in the presence of orthosteric agonists might evade the resistance mechanisms to existing prostate cancer drugs and provide novel starting points for medicinal chemistry lead optimization and future development into therapies for metastatic CRPC.

Sequence-based drug design as a concept in computational drug design

Drug development based on target proteins has been a successful approach in recent decades. However, the conventional structure-based drug design (SBDD) pipeline is a complex, human-engineered process with multiple independently optimized steps. Here, we propose a sequence-to-drug concept for computational drug design based on protein sequence information by end-to-end differentiable learning. We validate this concept in three stages. First, we design TransformerCPI2.0 as a core tool for the concept, which demonstrates generalization ability across proteins and compounds. Second, we interpret the binding knowledge that TransformerCPI2.0 learned. Finally, we use TransformerCPI2.0 to discover new hits for challenging drug targets, and identify new target for an existing drug based on an inverse application of the concept. Overall, this proof-of-concept study shows that the sequence-to-drug concept adds a perspective on drug design. It can serve as an alternative method to SBDD, particularly for proteins that do not yet have high-quality 3D structures available.

Effects of Homogeneous and Heterogeneous Crowding on Translational Diffusion of Rigid Bovine Serum Albumin and Disordered Alfa-Casein

Intracellular environment includes proteins, sugars, and nucleic acids interacting in restricted media. In the cytoplasm, the excluded volume effect takes up to 40% of the volume available for occupation by macromolecules. In this work, we tested several approaches modeling crowded solutions for protein diffusion. We experimentally showed how the protein diffusion deviates from conventional Brownian motion in artificial conditions modeling the alteration of medium viscosity and rigid spatial obstacles. The studied tracer proteins were globular bovine serum albumin and intrinsically disordered α-casein. Using the pulsed field gradient NMR, we investigated the translational diffusion of protein probes of different structures in homogeneous (glycerol) and heterogeneous (PEG 300/PEG 6000/PEG 40,000) solutions as a function of crowder concentration. Our results showed fundamentally different effects of homogeneous and heterogeneous crowded environments on protein self-diffusion. In addition, the applied "tracer on lattice" model showed that smaller crowding obstacles (PEG 300 and PEG 6000) create a dense net of restrictions noticeably hindering diffusing protein probes, whereas the large-sized PEG 40,000 creates a "less restricted" environment for the diffusive motion of protein molecules.

p-Chloropropynyl Phenylalanine, a Versatile Non-Canonical Amino Acid for Co-Translational Peptide Macrocyclization and Side Chain Diversification

Macrocyclization has proven to be a beneficial strategy to improve upon some of the disadvantages of peptides as therapeutics. Nevertheless, many peptide cyclization strategies are not compatible with in vitro display technologies like mRNA display. Here we describe the novel amino acid p-chloropropynyl phenylalanine (pCPF). pCPF is a substrate for a mutant phenylalanyl-tRNA synthetase and its introduction into peptides via in vitro translation leads to spontaneous peptide macrocyclization in the presence of peptides containing cysteine. Macrocyclization occurs efficiently with a wide variety of ring sizes. Moreover, pCPF can be reacted with thiols after charging onto tRNA, enabling the testing of diverse ncAAs in translation. The versatility of pCPF should facilitate downstream studies of translation and enable the creation of novel macrocyclic peptide libraries.

Metadynamics simulations of ligands binding to protein surfaces: a novel tool for rational drug design

Structure-based drug design protocols may encounter difficulties to investigate poses when the biomolecular targets do not exhibit typical binding pockets. In this study, by providing two concrete examples from our labs, we suggest that the combination of metadynamics free energy methods (validated against affinity measurements), along with experimental structural information (by X-ray crystallography and NMR), can help to identify the poses of ligands on protein surfaces. The simulation workflow proposed here was implemented in a widely used code, namely GROMACS, and it could straightforwardly be applied to various drug-design campaigns targeting ligands' binding to protein surfaces.

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.

Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence

Only around 20% of the human proteome is considered to be druggable with small-molecule antagonists. This leaves some of the most compelling therapeutic targets outside the reach of ligand discovery. The concept of targeted protein degradation (TPD) promises to overcome some of these limitations. In brief, TPD is dependent on small molecules that induce the proximity between a protein of interest (POI) and an E3 ubiquitin ligase, causing ubiquitination and degradation of the POI. In this perspective, we want to reflect on current challenges in the field, and discuss how advances in multiomics profiling, artificial intelligence, and machine learning (AI/ML) will be vital in overcoming them. The presented roadmap is discussed in the context of small-molecule degraders but is equally applicable for other emerging proximity-inducing modalities.

Structure-Based Design of the Indole-Substituted Triazolopyrimidines as New EED-H3K27me3 Inhibitors for the Treatment of Lymphoma

Interrupting the embryonic ectoderm development (EED)-H3K27me3 interaction represents a promising strategy to allosterically inhibit polycomb repressive complex 2 (PRC2) for cancer therapy. In this work, we report the structure-based design of new triazolopyrimidine-based EED inhibitors, which structurally feature the electron-rich indole ring at the C8 position. Particularly, ZJH-16 directly binds to EED (HTRF IC₅₀ = 2.72 nM, BLI K_D = 4.4 nM) and potently inhibits the growth of KARPAS422 and Pfeiffer cells. In both cells, ZJH-16 is selectively engaged with EED and reduces H3K27 trimethylation levels. ZJH-16 inhibits the gene silencing function of PRC2 in KARPAS422 cells. ZJH-16 possesses favorable pharmacokinetic (PK) profiles with an excellent oral bioavailability (F = 94.7%). More importantly, ZJH-16 shows robust tumor regression in the KARPAS422 xenograft model after oral administration with the tumor growth inhibition reaching nearly 100%. The robust antitumor efficacy and favorable PK profiles of ZJH-16 warrant further advanced preclinical development for lymphoma treatment.

Reliable prediction of cannabinoid receptor 2 ligand by machine learning based on combined fingerprints

Cannabinoid receptors, as part of the family of the G protein-coupled receptors (GPCRs), are involved in various physiological functions. Its subtype cannabinoid receptor subtype 2 (CB2), mainly distributed in the periphery, is a crucial therapeutic target for anti-epileptic, anti-inflammation, anti-fibrosis, and bone metabolism regulation, and it regulates these physiological functions without psychiatric side effects. Recently machine learning methods for predicting biophysics properties have attracted much attention. Successful application of machine learning usually highly depends on the appropriate representation of the compounds. In this study, we comprehensively evaluate the performance of the descriptor-based models (including XGBoost, Random Forest, and KNN) and two graph-based models (D-MPNN, MolMap) for the prediction of the CB2 regulators, and found that XGBoost offers outstanding performance for both regression tasks and classification tasks. 13 different molecular fingerprints and 12 descriptors, as well as their combination were further screened; AvalonFP + AtomPairFP + RDkitFP + MorganFP and AtomPairFP + MorganFP + AvalonFP were the optimum combinations for regression task (R² increase to 0.667) and classification task (AUC-ROC increase to 0.933), respectively. Specifically, the best XGBoost regression model with optimum features achieves better performance than Mizera's QSAR model on the same dataset developed by Mizera (R² 0.664 versus 0.62). It also achieves optimal performance with an AUC-ROC of 0.917 on the external validation set. By comparison, MolMap and D-MPNN only provide 0.912 and 0.898. The Shapley additive explanation method was used to interpret the models, and features importance were shown for both regression and classification task. The XGBoost model equipped with essential molecular fingerprints combination in this paper may provide valuable clues to designing novel CB2 ligands and developing models for other properties prediction.

Mechanistic insights into the role of calcium in the allosteric regulation of the calmodulin-regulated death-associated protein kinase

Calcium (Ca²⁺) signaling plays an important role in the regulation of many cellular functions. Ca²⁺-binding protein calmodulin (CaM) serves as a primary effector of calcium function. Ca²⁺/CaM binds to the death-associated protein kinase 1 (DAPK1) to regulate intracellular signaling pathways. However, the mechanism underlying the influence of Ca²⁺ on the conformational dynamics of the DAPK1-CaM interactions is still unclear. Here, we performed large-scale molecular dynamics (MD) simulations of the DAPK1-CaM complex in the Ca²⁺-bound and-unbound states to reveal the importance of Ca²⁺. MD simulations revealed that removal of Ca²⁺ increased the anti-correlated inter-domain motions between DAPK1 and CaM, which weakened the DAPK1-CaM interactions. Binding free energy calculations validated the decreased DAPK1-CaM interactions in the Ca²⁺-unbound state. Structural analysis further revealed that Ca²⁺ removal caused the significant conformational changes at the DAPK1-CaM interface, especially the helices α1, α2, α4, α6, and α7 from the CaM and the basic loop and the phosphate-binding loop from the DAPK1. These results may be useful to understand the biological role of Ca²⁺ in physiological processes.

Molecular Insights into the Heterotropic Allosteric Mechanism in Cytochrome P450 3A4-Mediated Midazolam Metabolism

Cytochrome P450 3A4 (CYP3A4) is the main P450 enzyme for drug metabolism and drug-drug interactions (DDIs), as it is involved in the metabolic process of approximately 50% of drugs. A detailed mechanistic elucidation of DDIs mediated by CYP3A4 is commonly believed to be critical for drug optimization and rational use. Here, two typical probes, midazolam (MDZ, substrate) and testosterone (TST, allosteric effector), are used to investigate the molecular mechanism of CYP3A4-mediated heterotropic allosteric interactions, through conventional molecular dynamics (cMD) and well-tempered metadynamics (WT-MTD) simulations. Distance monitoring shows that TST can stably bind in two potential peripheral sites (Site 1 and Site 2) of CYP3A4. The binding of TST at these two sites can induce conformational changes in CYP3A4 flexible loops on the basis of conformational analysis, thereby promoting the transition of the MDZ binding mode and affecting the ratio of MDZ metabolites. According to the results of the residue interaction network, multiple allosteric communication pathways are identified that can provide vivid and applicable insights into the heterotropic allostery of TST on MDZ metabolism. Comparing the regulatory effects and the communication pathways, the allosteric effect caused by TST binding in Site 2 seems to be more pronounced than in Site 1. Our findings could provide a deeper understanding of CYP3A4-mediated heterotropic allostery at the atomic level and would be helpful for rational drug use as well as the design of new allosteric modulators.

Extended Applications of Small-Molecule Covalent Inhibitors toward Novel Therapeutic Targets

Recently, small-molecule covalent inhibitors have been accepted as a practical tool for targeting previously "undruggable" proteins. The high target selectivity of modern covalent inhibitors is now alleviating toxicity concerns regarding the covalent modifications of proteins. However, despite the tremendous clinical success of current covalent inhibitors, there are still unmet medical needs that covalent inhibitors have not yet addressed. This review categorized representative covalent inhibitors based on their mechanism of covalent inhibition: conventional covalent inhibitors, targeted covalent inhibitors (TCIs), and expanded TCIs. By reviewing both Food and Drug Administration (FDA)-approved drugs and drug candidates from recent literature, we provide insight into the future direction of covalent inhibitor development.

Large-Scale Crystallographic Fragment Screening Expedites Compound Optimization and Identifies Putative Protein-Protein Interaction Sites

The identification of starting points for compound development is one of the key steps in early-stage drug discovery. Information-rich techniques such as crystallographic fragment screening can potentially increase the efficiency of this step by providing the structural information of the binding mode of the ligands in addition to the mere binding information. Here, we present the crystallographic screening of our 1000-plus-compound F2X-Universal Library against the complex of the yeast spliceosomal Prp8 RNaseH-like domain and the snRNP assembly factor Aar2. The observed 269 hits are distributed over 10 distinct binding sites on the surface of the protein-protein complex. Our work shows that hit clusters from large-scale crystallographic fragment screening campaigns identify known interaction sites with other proteins and suggest putative additional interaction sites. Furthermore, the inherent binding pose validation within the hit clusters may accelerate downstream compound optimization.

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.

Protein-protein interaction and non-interaction predictions using gene sequence natural vector

Predicting protein–protein interaction and non-interaction are two important different aspects of multi-body structure predictions, which provide vital information about protein function. Some computational methods have recently been developed to complement experimental methods, but still cannot effectively detect real non-interacting protein pairs. We proposed a gene sequence-based method, named NVDT (Natural Vector combine with Dinucleotide and Triplet nucleotide), for the prediction of interaction and non-interaction. For protein–protein non-interactions (PPNIs), the proposed method obtained accuracies of 86.23% for Homo sapiens and 85.34% for Mus musculus, and it performed well on three types of non-interaction networks. For protein-protein interactions (PPIs), we obtained accuracies of 99.20, 94.94, 98.56, 95.41, and 94.83% for Saccharomyces cerevisiae, Drosophila melanogaster, Helicobacter pylori, Homo sapiens, and Mus musculus, respectively. Furthermore, NVDT outperformed established sequence-based methods and demonstrated high prediction results for cross-species interactions. NVDT is expected to be an effective approach for predicting PPIs and PPNIs.

Protein-protein non-interactions and interactions are distinguished and predicted by gene sequence using single nucleotide and contiguous nucleotides combined with machine learning models.

Biophysical Insight into the SARS-CoV2 Spike-ACE2 Interaction and Its Modulation by Hepcidin through a Multifaceted Computational Approach

At the center of the SARS-CoV2 infection, the spike protein and its interaction with the human receptor ACE2 play a central role in the molecular machinery of SARS-CoV2 infection of human cells. Vaccine therapies are a valuable barrier to the worst effects of the virus and to its diffusion, but the need of purposed drugs is emerging as a core target of the fight against COVID19. In this respect, the repurposing of drugs has already led to discovery of drugs thought to reduce the effects of the cytokine storm, but still a drug targeting the spike protein, in the infection stage, is missing. In this work, we present a multifaceted computational approach strongly grounded on a biophysical modeling of biological systems, so to disclose the interaction of the SARS-CoV2 spike protein with ACE2 with a special focus to an allosteric regulation of the spike-ACE2 interaction. Our approach includes the following methodologies: Protein Contact Networks and Network Clustering, Targeted Molecular Dynamics, Elastic Network Modeling, Perturbation Response Scanning, and a computational analysis of energy flow and SEPAS as a protein-softness and monomer-based affinity predictor. We applied this approach to free (closed and open) states of spike protein and spike-ACE2 complexes. Eventually, we analyzed the interactions of free and bound forms of spike with hepcidin (HPC), the major hormone in iron regulation, recently addressed as a central player in the COVID19 pathogenesis, with a special emphasis to the most severe outcomes. Our results demonstrate that, compared with closed and open states, the spike protein in the ACE2-bound state shows higher allosteric potential. The correspondence between hinge sites and the Allosteric Modulation Region (AMR) in the S-ACE complex suggests a molecular basis for hepcidin involvement in COVID19 pathogenesis. We verify the importance of AMR in different states of spike and then study its interactions with HPC and the consequence of the HPC-AMR interaction on spike dynamics and its affinity for ACE2. We propose two complementary mechanisms for HPC effects on spike of SARS-CoV-2; (a) HPC acts as a competitive inhibitor when spike is in a preinfection state (open and with no ACE2), (b) the HPC-AMR interaction pushes the spike structure into the safer closed state. These findings need clear molecular in vivo verification beside clinical observations.

Medicinal Chemistry Strategies for the Development of Bruton's Tyrosine Kinase Inhibitors against Resistance

Despite significant efficacy, one of the major limitations of small-molecule Bruton's tyrosine kinase (BTK) agents is the presence of clinically acquired resistance, which remains a major clinical challenge. This Perspective focuses on medicinal chemistry strategies for the development of BTK small-molecule inhibitors against resistance, including the structure-based design of BTK inhibitors targeting point mutations, e.g., (i) developing noncovalent inhibitors from covalent inhibitors, (ii) avoiding steric hindrance from mutated residues, (iii) making interactions with the mutated residue, (iv) modifying the solvent-accessible region, and (v) developing new scaffolds. Additionally, a comparative analysis of multi-inhibitions of BTK is presented based on cross-comparisons between 2916 unique BTK ligands and 283 other kinases that cover 7108 dual/multiple inhibitions. Finally, targeting the BTK allosteric site and uding proteolysis-targeting chimera (PROTAC) as two potential strategies are addressed briefly, while also illustrating the possibilities and challenges to find novel ligands of BTK.

Targeting the N-Terminus Domain of the Coronavirus Nucleocapsid Protein Induces Abnormal Oligomerization via Allosteric Modulation

Epidemics caused by coronaviruses (CoVs), namely the severe acute respiratory syndrome (SARS) (2003), Middle East respiratory syndrome (MERS) (2012), and coronavirus disease 2019 (COVID-19) (2019), have triggered a global public health emergency. Drug development against CoVs is inherently arduous. The nucleocapsid (N) protein forms an oligomer and facilitates binding with the viral RNA genome, which is critical in the life cycle of the virus. In the current study, we found a potential allosteric site (Site 1) using PARS, an online allosteric site predictor, in the CoV N-N-terminal RNA-binding domain (NTD) to modulate the N protein conformation. We identified 5-hydroxyindole as the lead via molecular docking to target Site 1. We designed and synthesized four 5-hydroxyindole derivatives, named P4-1 to P4-4, based on the pose of 5-hydroxyindole in the docking model complex. Small-angle X-ray scattering (SAXS) data indicate that two 5-hydroxyindole compounds with higher hydrophobic R-groups mediate the binding between N-NTD and N-C-terminal dimerization domain (CTD) and elicit high-order oligomerization of the whole N protein. Furthermore, the crystal structures suggested that these two compounds act on this novel cavity and create a flat surface with higher hydrophobicity, which may mediate the interaction between N-NTD and N-CTD. Taken together, we discovered an allosteric binding pocket targeting small molecules that induces abnormal aggregation of the CoV N protein. These novel concepts will facilitate protein-protein interaction (PPI)-based drug design against various CoVs.

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.

Probing GPCR Dimerization Using Peptides

G protein-coupled receptors (GPCRs) are the largest class of membrane proteins and the most common and extensively studied pharmacological target. Numerous studies over the last decade have confirmed that GPCRs do not only exist and function in their monomeric form but in fact, have the ability to form dimers or higher order oligomers with other GPCRs, as well as other classes of receptors. GPCR oligomers have become increasingly attractive to investigate as they have the ability to modulate the pharmacological responses of the receptors which in turn, could have important functional roles in diseases, such as cancer and several neurological & neuropsychiatric disorders. Despite the growing evidence in the field of GPCR oligomerisation, the lack of structural information, as well as targeting the 'undruggable' protein-protein interactions (PPIs) involved in these complexes, has presented difficulties. Outside the field of GPCRs, targeting PPIs has been widely studied, with a variety of techniques being investigated; from small-molecule inhibitors to disrupting peptides. In this review, we will demonstrate several physiologically relevant GPCR dimers and discuss an array of strategies and techniques that can be employed when targeting these complexes, as well as provide ideas for future development.

Harnessing Reversed Allosteric Communication: A Novel Strategy for Allosteric Drug Discovery

Allostery is a fundamental and extensive mechanism of intramolecular signal transmission. Allosteric drugs possess several unique pharmacological advantages over traditional orthosteric drugs, including greater selectivity, better physicochemical properties, and lower off-target toxicity. However, owing to the complexity of allosteric regulation, experimental approaches for the development of allosteric modulators are traditionally serendipitous. Recently, the reversed allosteric communication theory has been proposed, providing a feasible tool for the unbiased detection of allosteric sites. Herein, we review the latest research on the reversed allosteric communication effect using the examples of sirtuin 6, epidermal growth factor receptor, 3-phosphoinositide-dependent protein kinase 1, and Related to A and C kinases (RAC) serine/threonine protein kinase B and recapitulate the methodologies of reversed allosteric communication strategy. The novel reversed allosteric communication strategy greatly expands the horizon of allosteric site identification and allosteric mechanism exploration and is expected to accelerate an end-to-end framework for drug discovery.

Establishment of high-throughput screening HTRF assay for identification small molecule inhibitors of Skp2-Cks1

S-phase kinase associated protein 2 (Skp2), a member of the F-box family that constitute the largest known class of ubiquitin E3 specificity components, is responsible for recognizing and recruiting cyclin-dependent kinase inhibitor p27 for its ubiquitination in the presence of the small accessory protein cyclin-dependent kinase regulatory subunit 1(Cks1). Skp2 is overexpressed in esophageal carcinoma tissues and closely related with tumor poor prognosis, and perturbation of the Skp2-Cks1 interaction by inhibitors or RNAi could inhibit the proliferation and metastasis of tumor cells. Therefore, inhibition of Skp2 function by small-molecule compounds targeting Skp2-Cks1 interaction is emerging as a promising and novel anti-cancer strategy. In this study, we establish an improved high-throughput screening platform to screen Skp2 inhibitors targeting Skp2-Cks1interaction, which may provide a new therapeutic approach for the clinic.

A Bridge to Alkaloid Synthesis

The construction of a structurally rigid architecture with chiral complexity, necessary to enhance the interaction with binding sites of drug targets, has been adapted as an intriguing approach in drug development. In the past few years, we have been interested in the synthesis of biologically significant and bridged alkaloids via novel synthetic methods and strategies based on recognition of the privileged pattern. Therefore, nitroso-ene and aza-Wacker cyclizations were elevated for the first time to construct bridged alkaloids, such as hosieine A, kopsone, melinonine-E and strychnoxanthine. Mechanistic investigations, including computational calculations for nitroso-ene reaction and deuterated experiments for aza-Wacker reaction, enable us to gain more insights into the chemical reactivity and selectivity of specific functional groups in developing viable synthetic methods.

Strategies for targeting undruggable targets

INTRODUCTION

Undruggable targets refer to clinically meaningful therapeutic targets that are 'difficult to drug' or 'yet to be drugged' via traditional approaches. Featuring characteristics of lacking defined ligand-binding pockets, non-catalytic protein-protein interaction functional modes and less-investigated 3D structures, these undruggable targets have been targeted with novel therapeutic entities developed with the progress of unconventional drug discovery approaches, such as targeted degradation molecules and display technologies.

AREA COVERED

This review first presents the concept of 'undruggable' exemplified by RAS and other targets. Next, detailed strategies are illustrated in two aspects: innovation of therapeutic entities and development of unconventional drug discovery technologies. Finally, case studies covering typical undruggable targets (Bcl-2, p53, and RAS) are depicted to further demonstrate the feasibility of the strategies and entities above.

EXPERT OPINION

Targeting the undruggable expands the scope of therapeutically reachable targets. Consequently, it represents the drug discovery frontier. Biomedical studies are capable of dissecting disease mechanisms, thus broadening the list of undruggable targets. Encouraged by the recent approval of the KRAS inhibitor Sotorasib, we believe that merging multiple discovery approaches and exploiting various novel therapeutic entities would pave the way for dealing with more 'undruggable' targets in the future.

Effective and transient mapping of protein-protein interactions: Application of novel releasable photoaffinity linkers

Herein, two novel multifunctional releasable photoaffinity linkers were developed for effective and transient tracking interacting proteins with the overall objective of understanding their in vivo biological functions in real-time. These linkers could be used for the chemical modification of protein under moderate experimental conditions to form protein photoaffinity probes. These probes incorporated with both photoaffinity labels and tag-transfer, enable photo-crosslinking of bait proteins along with the release of unrelated groups. These photoaffinity linkers can be utilized to construct probes for disease markers, which could enable rapid diagnosis in a clinical setting at minimal interference with normal physiology.

Assessing the binding properties of CASP14 targets and models

An important question is how well the models submitted to CASP retain the properties of target structures. We investigate several properties related to binding. First we explore the binding of small molecules as probes, and count the number of interactions between each residue and such probes, resulting in a binding fingerprint. The similarity between two fingerprints, one for the X-ray structure and the other for a model, is determined by calculating their correlation coefficient. The fingerprint similarity weakly correlates with global measures of accuracy, and GDT_TS higher than 80 is a necessary but not sufficient condition for the conservation of surface binding properties. The advantage of this approach is that it can be carried out without information on potential ligands and their binding sites. The latter information was available for a few targets, and we explored whether the CASP14 models can be used to predict binding sites and to dock small ligands. Finally, we tested the ability of models to reproduce protein-protein interactions by docking both the X-ray structures and the models to their interaction partners in complexes. The analysis showed that in CASP14 the quality of individual domain models is approaching that offered by X-ray crystallography, and hence such models can be successfully used for the identification of binding and regulatory sites, as well as for assembling obligatory protein-protein complexes. Success of ligand docking, however, often depends on fine details of the binding interface, and thus may require accounting for conformational changes by simulation methods.

Networks of Networks: An Essay on Multi-Level Biological Organization

The multi-level organization of nature is self-evident: proteins do interact among them to give rise to an organized metabolism, while in the same time each protein (a single node of such interaction network) is itself a network of interacting amino-acid residues allowing coordinated motion of the macromolecule and systemic effect as allosteric behavior. Similar pictures can be drawn for structure and function of cells, organs, tissues, and ecological systems. The majority of biologists are used to think that causally relevant events originate from the lower level (the molecular one) in the form of perturbations, that “climb up” the hierarchy reaching the ultimate layer of macroscopic behavior (e.g., causing a specific disease). Such causative model, stemming from the usual genotype-phenotype distinction, is not the only one. As a matter of fact, one can observe top-down, bottom-up, as well as middle-out perturbation/control trajectories. The recent complex network studies allow to go further the pure qualitative observation of the existence of both non-linear and non-bottom-up processes and to uncover the deep nature of multi-level organization. Here, taking as paradigm protein structural and interaction networks, we review some of the most relevant results dealing with between networks communication shedding light on the basic principles of complex system control and dynamics and offering a more realistic frame of causation in biology.

Dual-fluorescent bacterial two-hybrid system for quantitative Protein-Protein interaction measurement via flow cytometry

Characterization of protein-protein interactions (PPIs) is essential for understanding cellular signal transduction pathways. However, quantitative measurement of the binding strength remains challenging. Building upon the classical bacterial adenylate cyclase two-hybrid (BACTH) system, we previously demonstrated that the relative reporter protein expression (RRPE), defined as the level of reporter expression normalized to that of the interacting protein, is an intrinsic characteristic associated with the binding strength between the two interacting proteins. In this study, we inserted fluorescent protein tdTomato in the chromosome as the reporter protein by CRISPR/Cas9 technology and employed a 12-amino acid tetracysteine (TC) to tag one of the interacting proteins, which can be further labeled by a membrane-permeable biarsenical dye. The combined use of tdTomato and TC-tag offers rapid and high-throughput analysis of the expression levels of both the reporter protein and one of the interacting proteins at the single-cell level by multicolor flow cytometry, which simplifies the quantitative measurement of PPI. The use of the as-developed RRPE-tdTomato-TC-BACTH approach was demonstrated in three demanding applications. First, binding affinities could be correctly ranked for discriminating interaction strengths with a tenfold difference or of the same order of magnitude. We demonstrate that the method is sensitive enough to discriminate affinities with a small difference of 1.4-fold. Moreover, residues involved in PPI can be easily mapped and ranked. Lastly, protein interaction inhibitors can be rapidly screened.

Modulating Target Protein Biology Through the Re-mapping of Conformational Distributions Using Small Molecules

Over the last 10 years considerable progress has been made in the application of small molecules to modulating protein-protein interactions (PPIs), and the navigation from "undruggable" to a host of candidate molecules in clinical trials has been well-charted in recent, comprehensive reviews. Structure-based design has played an important role in this scientific journey, with three dimensional structures guiding medicinal chemistry efforts. However, the importance of two additional dimensions: movement and time is only now being realised, as increasing computing power, closely aligned with wet lab validation, is applied to the challenge. Protein dynamics are fundamental to biology and disease, and application to PPI drug discovery has massively widened the scope for new chemical entities to influence function from allosteric, and previously unreported, sites. In this forward-looking perspective we highlight exciting, new opportunities for small molecules to modulate disease biology, by adjusting the frequency profile of natural conformational sampling, through the stabilisation of clinically desired conformers of target proteins.

Rational Design of Peptide-Based Inhibitors Disrupting Protein-Protein Interactions

Protein-protein interactions (PPIs) are well-established as a class of promising drug targets for their implications in a wide range of biological processes. However, drug development toward PPIs is inevitably hampered by their flat and wide interfaces, which generally lack suitable pockets for ligand binding, rendering most PPI systems "undruggable." Here, we summarized drug design strategies for developing peptide-based PPI inhibitors. Importantly, several quintessential examples toward well-established PPI targets such as Bcl-2 family members, p53-MDM2, as well as APC-Asef are presented to illustrate the detailed schemes for peptide-based PPI inhibitor development and optimizations. This review supplies a comprehensive overview of recent progresses in drug discovery targeting PPIs through peptides or peptidomimetics, and will shed light on future therapeutic agent development toward the historically "intractable" PPI systems.

Mechanism of allosteric activation of SIRT6 revealed by the action of rationally designed activators

The recent discovery of activator compounds binding to an allosteric site on the NAD⁺-dependent protein lysine deacetylase, sirtuin 6 (SIRT6) has attracted interest and presents a pharmaceutical target for aging-related and cancer diseases. However, the mechanism underlying allosteric activation of SIRT6 by the activator MDL-801 remains largely elusive because no major conformational changes are observed upon activator binding. By combining molecular dynamics simulations with biochemical and kinetic analyses of wild-type SIRT6 and its variant M136A, we show that conformational rotation of 2-methyl-4-fluoro-5-bromo substituent on the right phenyl ring (R-ring) of MDL-801, which uncovers previously unseen hydrophobic interactions, contributes to increased activating deacetylation activity of SIRT6. This hypothesis is further supported by the two newly synthesized MDL-801 derivatives through the removal of the 5-Br atom on the R-ring (MDL-801-D1) or the restraint of the rotation of the R-ring (MDL-801-D2). We further propose that the 5-Br atom serves as an allosteric driver that controls the ligand allosteric efficacy. Our study highlights the effect of allosteric enzyme catalytic activity by activator binding and provides a rational approach for enhancing deacetylation activity.

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.

A novel class of selective non-nucleoside inhibitors of human DNA methyltransferase 3A

Screening of a small chemical library (Medicines for Malaria Venture Pathogen Box) identified two structurally related pyrazolone (inhibitor 1) and pyridazine (inhibitor 2) DNMT3A inhibitors with low micromolar inhibition constants. The uncompetitive and mixed type inhibition patterns with DNA and AdoMet suggest these molecules act through an allosteric mechanism, and thus are unlikely to bind to the enzyme's active site. Unlike the clinically used mechanism based DNMT inhibitors such as decitabine or azacitidine that act via the enzyme active site, the inhibitors described here could lead to the development of more selective drugs. Both inhibitors show promising selectivity for DNMT3A in comparison to DNMT1 and bacterial DNA cytosine methyltransferases. With further study, this could form the basis of preferential targeting of de novo DNA methylation over maintenance DNA methylation.

Conformational Selection Mechanism Provides Structural Insights into the Optimization of APC-Asef Inhibitors

Metastasis is the major cause of death in colorectal cancer and it has been proven that inhibiting an interaction between adenomatous polyposis coli (APC) and Rho guanine nucleotide exchange factor 4 (Asef) efficaciously restrain metastasis. However, current inhibitors cannot achieve a satisfying effect in vivo and need to be optimized. In the present study, we applied molecular dynamics (MD) simulations and extensive analyses to apo and holo APC systems in order to reveal the inhibitor mechanism in detail and provide insights into optimization. MD simulations suggested that apo APC takes on a broad array of conformations and inhibitors stabilize conformation selectively. Representative structures in trajectories show specific APC-ligand interactions, explaining the different binding process. The stability and dynamic properties of systems elucidate the inherent factors of the conformation selection mechanism. Binding free energy analysis quantitatively confirms key interface residues and guide optimization. This study elucidates the conformation selection mechanism in APC-Asef inhibition and provides insights into peptide-based drug design.

Applications of Solution NMR in Drug Discovery

During the past decades, solution nuclear magnetic resonance (NMR) spectroscopy has demonstrated itself as a promising tool in drug discovery. Especially, fragment-based drug discovery (FBDD) has benefited a lot from the NMR development. Multiple candidate compounds and FDA-approved drugs derived from FBDD have been developed with the assistance of NMR techniques. NMR has broad applications in different stages of the FBDD process, which includes fragment library construction, hit generation and validation, hit-to-lead optimization and working mechanism elucidation, etc. In this manuscript, we reviewed the current progresses of NMR applications in fragment-based drug discovery, which were illustrated by multiple reported cases. Moreover, the NMR applications in protein-protein interaction (PPI) modulators development and the progress of in-cell NMR for drug discovery were also briefly summarized.