Are the Apo Proteins Suitable for the Rational Discovery of Allosteric Drugs?

Insights into the chirality-dependent recognition of Danshensu Bingpian Zhi stereoisomers with PPARγ

Peroxisome proliferator-activated receptor γ (PPARγ), a nuclear receptor involved in metabolic processes, inflammation, and energy balance, represents a promising therapeutic target for cardiovascular diseases. Danshensu Bingpian Zhi (DBZ), a chiral compound derived from traditional Chinese medicine, exhibits potential as a PPARγ agonist. Using an ensemble-based docking approach, molecular dynamics (MD) simulations, and the molecular mechanics generalized born surface area (MM/GBSA) methods, we explored the binding modes and energetics of DBZ stereoisomers with the PPARγ ligand-binding domain (LBD). The results indicated that the right-handed stereoisomer (DBZR) binds like a full agonist, while the left-handed stereoisomer (DBZS) binds as a partial agonist with stronger binding energies (ΔGbind), indicating a robust interaction with PPARγ. Both the stereoisomers stabilize the β-sheet region of PPARγ-LBD, potentially protecting Ser245 from phosphorylation by Cdk5, a process implicated in atherosclerosis. Principal component analysis (PCA) and dynamic cross-correlation matrices (DCCM) revealed the complex structural dynamics within the Ω loop, β-sheet, and AF-2 region of PPARγ-LBD upon ligand binding, which may contribute to the unique binding mode and efficacy of DBZS. These findings provide insights into the molecular recognition of PPARγ-LBD by DBZ stereoisomers and their impact on the conformational dynamics of PPARγ, highlighting the therapeutic potential of DBZ and the significance of chirality in drug design.

In silico discovery of potential PPI inhibitors for anti-lung cancer activity by targeting the CCND1-CDK4 complex via the P21 inhibition mechanism

Non-Small Cell Lung Cancer (NSCLC) is a prevalent and deadly form of lung cancer worldwide with a low 5-year survival rate. Current treatments have limitations, particularly for advanced-stage patients. P21, a protein that inhibits the CCND1-CDK4 complex, plays a crucial role in cell proliferation. Computer-Aided Drug Design (CADD) based on pharmacophores can screen and design PPI inhibitors targeting the CCND1-CDK4 complex. By analyzing known inhibitors, key pharmacophores are identified, and computational methods are used to screen potential PPI inhibitors. Molecular docking, pharmacophore matching, and structure-activity relationship studies optimize the inhibitors. This approach accelerates the discovery of CCND1-CDK4 PPI inhibitors for NSCLC treatment. Molecular dynamics simulations of CCND1-CDK4-P21 and CCND1-CDK4 complexes showed stable behavior, comprehensive sampling, and P21's impact on complex stability and hydrogen bond formation. A pharmacophore model facilitated virtual screening, identifying compounds with favorable binding affinities. Further simulations confirmed the stability and interactions of selected compounds, including 513457. This study demonstrates the potential of CADD in optimizing PPI inhibitors targeting the CCND1-CDK4 complex for NSCLC treatment. Extended simulations and experimental validations are necessary to assess their efficacy and safety.

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.

Coevolution-based prediction of key allosteric residues for protein function regulation

Allostery is fundamental to many biological processes. Due to the distant regulation nature, how allosteric mutations, modifications, and effector binding impact protein function is difficult to forecast. In protein engineering, remote mutations cannot be rationally designed without large-scale experimental screening. Allosteric drugs have raised much attention due to their high specificity and possibility of overcoming existing drug-resistant mutations. However, optimization of allosteric compounds remains challenging. Here, we developed a novel computational method KeyAlloSite to predict allosteric site and to identify key allosteric residues (allo-residues) based on the evolutionary coupling model. We found that protein allosteric sites are strongly coupled to orthosteric site compared to non-functional sites. We further inferred key allo-residues by pairwise comparing the difference of evolutionary coupling scores of each residue in the allosteric pocket with the functional site. Our predicted key allo-residues are in accordance with previous experimental studies for typical allosteric proteins like BCR-ABL1, Tar, and PDZ3, as well as key cancer mutations. We also showed that KeyAlloSite can be used to predict key allosteric residues distant from the catalytic site that are important for enzyme catalysis. Our study demonstrates that weak coevolutionary couplings contain important information of protein allosteric regulation function. KeyAlloSite can be applied in studying the evolution of protein allosteric regulation, designing and optimizing allosteric drugs, and performing functional protein design and enzyme engineering.

How good are AlphaFold models for docking-based virtual screening?

A crucial component in structure-based drug discovery is the availability of high-quality three-dimensional structures of the protein target. Whenever experimental structures were not available, homology modeling has been, so far, the method of choice. Recently, AlphaFold (AF), an artificial-intelligence-based protein structure prediction method, has shown impressive results in terms of model accuracy. This outstanding success prompted us to evaluate how accurate AF models are from the perspective of docking-based drug discovery. We compared the high-throughput docking (HTD) performance of AF models with their corresponding experimental PDB structures using a benchmark set of 22 targets. The AF models showed consistently worse performance using four docking programs and two consensus techniques. Although AlphaFold shows a remarkable ability to predict protein architecture, this might not be enough to guarantee that AF models can be reliably used for HTD, and post-modeling refinement strategies might be key to increase the chances of success.

Holo Protein Conformation Generation from Apo Structures by Ligand Binding Site Refinement

An important part in structure-based drug design is the selection of an appropriate protein structure. It has been revealed that a holo protein structure that contains a well-defined binding site is a much better choice than an apo structure in structure-based drug discovery. Therefore, it is valuable to obtain a holo-like protein conformation from apo structures in the case where no holo structure is available. Meeting the need, we present a robust approach to generate reliable holo-like structures from apo structures by ligand binding site refinement with restraints derived from holo templates with low homology. Our method was tested on a test set of 32 proteins from the DUD-E data set and compared with other approaches. It was shown that our method successfully refined the apo structures toward the corresponding holo conformations for 23 of 32 proteins, reducing the average all-heavy-atom RMSD of binding site residues by 0.48 Å. In addition, when evaluated against all the holo structures in the protein data bank, our method can improve the binding site RMSD for 14 of 19 cases that experience significant conformational changes. Furthermore, our refined structures also demonstrate their advantages over the apo structures in ligand binding mode predictions by both rigid docking and flexible docking and in virtual screening on the database of active and decoy ligands from the DUD-E. These results indicate that our method is effective in recovering holo-like conformations and will be valuable in structure-based drug discovery.

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.

Uncovering the Dominant Motion Modes of Allosteric Regulation Improves Allosteric Site Prediction

Allostery is an important mechanism that biological systems use to regulate function at a distant site. Allosteric drugs have attracted much attention in recent years due to their high specificity and the possibility of overcoming existing drug-resistant mutations. However, the discovery of allosteric drugs remains challenging as allosteric regulation mechanisms are not clearly understood and allosteric sites cannot be accurately predicted. In this study, we analyzed the dominant modes that determine motion correlations between allosteric and orthosteric sites using the Gaussian network model and found that motion correlations between allosteric and orthosteric sites are dominated by either fast or slow vibrational modes. This dependence of modes results from the relative locations of the two sites and local secondary structures. Based on these analyses, we developed CorrSite2.0 to predict allosteric sites by taking the maximum of the Z-scores calculated from using either slow or fast modes. The prediction accuracy of CorrSite2.0 outperformed other commonly used allosteric site prediction methods with prediction accuracy over 90.0%. Our study uncovers the relationship of protein structure, dynamics, and allosteric regulation and demonstrates that using the dominant motion modes greatly improves allosteric site prediction accuracy. CorrSite2.0 has been integrated into the CavityPlus web server, which can be accessed at http://www.pkumdl.cn/cavityplus. CorrSite2.0 provides a powerful and user-friendly tool for allosteric drug and protein design.

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.

Molecular Dynamics Study of Conformational Changes of Tankyrase 2 Binding Subsites upon Ligand Binding

The interactions between proteins and ligands are involved in various biological functions. While experimental structures provide key static structural information of ligand-unbound and ligand-bound proteins, dynamic information is often insufficient for understanding the detailed mechanism of protein-ligand binding. Here, we studied the conformational changes of the tankyrase 2 binding pocket upon ligand binding using molecular dynamics simulations of the ligand-unbound and ligand-bound proteins. The ligand-binding pocket has two subsites: the nicotinamide and adenosine subsite. Comparative analysis of these molecular dynamics trajectories revealed that the conformational change of the ligand-binding pocket was characterized by four distinct conformations of the ligand-binding pocket. Two of the four conformations were observed only in molecular dynamics simulations. We found that the pocket conformational change on ligand binding was based on the connection between the nicotinamide and adenosine subsites that are located adjacently in the pocket. From the analysis, we proposed the protein-ligand binding mechanism of tankyrase 2. Finally, we discussed the computational prediction of the ligand binding pose using the tankyrase 2 structures obtained from the molecular dynamics simulations.

Deciphering the selective binding mechanisms of anaplastic lymphoma kinase-derived neuroblastoma tumor neoepitopes to human leukocyte antigen

Neuroblastoma (NB), as a metastatic form of solid tumor, has a high fatality rate found in early childhood. The two anaplastic lymphoma kinase (ALK) neoepitopes nonamer and decamer used in cancer immunotherapy against NB cancer can selectively bind to the human leukocyte antigen (HLA-B*15:01) groove with high affinities, whereas the native self-peptide is unable to interact with the HLA-B*15:01. Here, we performed molecular dynamics (MD) simulations and subsequent molecular mechanics-generalized born surface area (MM-GBSA) binding free energy calculations to explore the selective binding mechanisms of nonamer and decamer to the HLA-B*15:01 against the self-peptide. MD simulations revealed the significant conformational dynamics of the self-peptide in the HLA-B*15:01 groove against the nonamer and decamer. Binding free energy calculations showed that the binding affinities of HLA-B*15:01-neoepitope complexes were followed in the order decamer > nonamer > self-peptide. Detailed analysis of HLA-B*15:01-neoepitope structural complexes showed that compared to the nonamer, the self-peptide tended to move outward to the solvent, whereas the decamer moved deep to the HLA-B*15:01 groove. These different dynamic observations of the ALK neoepitopes can explain the distinct binding affinities of self-peptide, nonamer, and decamer to the HLA-B*15:01. The results may be useful for the design of more selective ALK neoepitopes.

Into the Fray! A Beginner's Guide to Medicinal Chemistry

Modern medicinal chemistry is a complex, multidimensional discipline that operates at the interface of the chemical and biological sciences. The medicinal chemistry contribution to drug discovery is typically described in the context of the well-recited linear progression of the drug discovery pipeline. However, compound optimization is idiosyncratic to each project, and clear definitions of hit and lead molecules and the subsequent progress along the pipeline becomes easily blurred. In addition, this description lacks insight into the entangled relationship between chemical and pharmacological properties, and thus provides limited guidance on how innovative medicinal chemistry strategies can be applied to solve optimization problems, regardless of the stage in the pipeline. Through discussion and illustrative examples, this article seeks to provide insights into the finesse of medicinal chemistry and the subtlety of balancing chemical properties pharmacology. In so doing, it aims to serve as an accessible and simple-to-digest guide for anyone who wishes to learn about the underlying principles of medicinal chemistry, in a context that has been decoupled from the pipeline description.

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.

Residue-Level Allostery Propagates through the Effective Coarse-Grained Hessian

The long-ranged coupling between residues that gives rise to allostery in a protein is built up from short-ranged physical interactions. Computational tools used to predict this coupling and its functional relevance have relied on the application of graph theoretical metrics to residue-level correlations measured from all-atom molecular dynamics simulations. The short-ranged interactions that yield these long-ranged residue-level correlations are quantified by the effective coarse-grained Hessian. Here we compute an effective harmonic coarse-grained Hessian from simulations of a benchmark allosteric protein, IGPS, and demonstrate the improved locality of this graph Laplacian over two other connectivity matrices. Additionally, two centrality metrics are developed that indicate the direct and indirect importance of each residue at producing the covariance between the effector binding pocket and the active site. The residue importance indicated by these two metrics is corroborated by previous mutagenesis experiments and leads to unique functional insights; in contrast to previous computational analyses, our results suggest that fP76-hK181 is the most important contact for conveying direct allosteric paths across the HisF-HisH interface. The connectivity around fD98 is found to be important at affecting allostery through indirect means.

Evaluation of binding and inhibition mechanism of dietary phytochemicals with sphingosine kinase 1: Towards targeted anticancer therapy

Sphingosine kinase 1 (SphK1) has recently gained attention as a potential drug target for its association with cancer and other inflammatory diseases. Here, we have investigated the binding affinity of dietary phytochemicals viz., ursolic acid, capsaicin, DL-α tocopherol acetate, quercetin, vanillin, citral, limonin and simvastatin with the SphK1. Docking studies revealed that all these compounds bind to the SphK1 with varying affinities. Fluorescence binding and isothermal titration calorimetric measurements suggested that quercetin and capsaicin bind to SphK1 with an excellent affinity, and significantly inhibits its activity with an admirable IC50 values. The binding mechanism of quercetin was assessed by docking and molecular dynamics simulation studies for 100 ns in detail. We found that quercetin acts as a lipid substrate competitive inhibitor, and it interacts with important residues of active-site pocket through hydrogen bonds and other non-covalent interactions. Quercetin forms a stable complex with SphK1 without inducing any significant conformational changes in the protein structure. In conclusion, we infer that quercetin and capsaicin provide a chemical scaffold to develop potent and selective inhibitors of SphK1 after required modifications for the clinical management of cancer.

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

Protein-protein interactions (PPIs) are closely implicated in various types of cellular activities and are thus pivotal to health and disease states. Given their fundamental roles in a wide range of biological processes, the modulation of PPIs has enormous potential in drug discovery. However, owing to the general properties of large, flat, and featureless interfaces of PPIs, previous attempts have demonstrated that the generation of therapeutic agents targeting PPI interfaces is challenging, rendering them almost "undruggable" for decades. To date, rapid progress in chemical and structural biology techniques has promoted the exploitation of allostery as a novel approach in drug discovery. By attaching to allosteric sites that are topologically and spatially distinct from PPI interfaces, allosteric modulators can achieve improved physiochemical properties. Thus, allosteric modulators may represent an alternative strategy to target intractable PPIs and have attracted intense pharmaceutical interest. In this review, we first briefly introduce the characteristics of PPIs and then present different approaches for investigating PPIs, as well as the latest methods for modulating PPIs. Importantly, we comprehensively review the recent progress in the development of allosteric modulators to inhibit or stabilize PPIs. Finally, we conclude with future perspectives on the discovery of allosteric PPI modulators, especially the application of computational methods to aid in allosteric PPI drug discovery.