Can molecular dynamics simulations improve the structural accuracy and virtual screening performance of GPCR models?

Molecular insights on Eltrombopag: potential mitogen stimulants, angiogenesis, and therapeutic radioprotectant through TPO-R activation

The purpose of this study is to investigate the molecular interactions and potential therapeutic uses of Eltrombopag (EPAG), a small molecule that activates the cMPL receptor. EPAG has been found to be effective in increasing platelet levels and alleviating thrombocytopenia. We utilized computational techniques to predict and confirm the complex formed by the ligand (EPAG) and the Thrombopoietin receptor (TPO-R) cMPL, elucidating the role of RAS, JAK-2, STAT-3, and other essential elements for downstream signaling. Molecular dynamics (MD) simulations were employed to evaluate the stability of the ligand across specific proteins, showing favorable characteristics. For the first time, we examined the presence of TPO-R in human umbilical cord mesenchymal stem cells (hUCMSC) and human gingival mesenchymal stem cells (hGMSC) proliferation. Furthermore, treatment with EPAG demonstrated angiogenesis and vasculature formation of endothelial lineage derived from both MSCs. It also indicated the activation of critical factors such as RUNX-1, GFI-1b, VEGF-A, MYB, GOF-1, and FLI-1. Additional experiments confirmed that EPAG could be an ideal molecule for protecting against UVB radiation damage, as gene expression (JAK-2, ERK-2, MCL-1, NFkB, and STAT-3) and protein CD90/cMPL analysis showed TPO-R activation in both hUCMSC and hGMSC. Overall, EPAG exhibits significant potential in treating radiation damage and mitigating the side effects of radiotherapy, warranting further clinical exploration.

Ergosterol and its metabolites as agonists of Liver X receptor and their anticancer potential in colorectal cancer

Aberrant cholesterol homeostasis is a well-recognized hallmark of cancer and is implicated in metastasis as well as chemotherapeutic resistance, the two major causes of cancer associated mortality. Liver X receptors (LXRs) are the key transcription factors that induce cholesterol efflux via enhancing the expression of ABCA1 and ABCG1. Therefore, a comprehensive analysis of several novel sterols namely ergosta-7,22,24(28)-trien-3β-ol (Erg1), ergosta-5,22,25-trien-3-ol (Erg2), ergosta-5,7,22,24(28)-tetraen-3β-ol (Erg3), and ergosta-7,22-dien-3β-ol (Erg4) as LXR agonists has been performed. Molecular docking studies have shown that these sterols possess higher binding affinities for LXRs as compared to the reference ligands (GW3965 and TO901317) and also formed critical activating interactions. Molecular dynamic (MD) simulations further confirmed that docking complexes made of these sterols possess significant stability. To assess the extent of LXR activation, ABCA1 promoter was cloned into luciferase reporter plasmid and transfected into HCT116 cells. It was observed that treatment with Erg, Erg2 and Erg4 led to a significant LXR activation with an EC50 of 5.64 µM, 4.83 and 3.03 µM respectively. Furthermore, a significant increase in mRNA expression of NR1H2 and LXR target genes i.e. ABCA1, ABCG1 and ApoE was observed upon Erg treatment. Flow cytometric analysis have revealed a significant increase in the accumulation of ABCA1 upon Erg treatment. Cytotoxicity studies conducted on colorectal cancer cell and normal epithelial cell line showed that these sterols are selectively toxic towards cancer cells. Taken together, our findings suggests that ergosterol activates LXRs, have significant anticancer activity and could be a likely candidate to manage aberrant cholesterol homeostasis.

Can Current Molecular Docking Methods Accurately Predict RNA Inhibitors?

Ribonucleic acids (RNAs), particularly the noncoding RNAs, play key roles in cancer, making them attractive drug targets. While conventional methods such as high throughput screening are resource-intensive, computational methods such as RNA-ligand docking can be used as an alternative. However, currently available docking methods are fine-tuned to perform protein-ligand and protein-protein docking. In this work, we evaluated three commonly used docking methods─AutoDock Vina, HADDOCK, and HDOCK─alongside RLDOCK, which is specifically designed for RNA-ligand docking. Our evaluation was based on several criteria including cognate docking, blind docking, scoring potential, and ranking potential. In cognate docking, only RLDOCK showed a success rate of 70% for the top-scoring docked pose. Despite this, all four docking methods did not achieve an overall success rate exceeding 50% amidst our attempt to refine the top-scoring docked poses using molecular dynamics simulations. Meanwhile, all four docking methods showed poor performance in scoring potential evaluation. Although AutoDock Vina achieved an area under the receiver operating characteristic curve of 0.70, it showed poor performance in terms of Matthews' correlation coefficient, precision, enrichment factors, and normalized enrichment factors at 1, 2, and 5%. These results highlight the growing need for further optimization of docking methods to assess RNA-ligand interactions.

Characterization of thiamine pyrophosphokinase of vitamin B1 biosynthetic pathway as a drug target of Leishmania donovani

Vitamin B1 is an essential cofactor for enzymes involved in the metabolism of carbohydrates, particularly Transketolases. These enzymes are amenable to therapeutic interventions because of their specificity. In the final step of the Vitamin B1 biosynthesis pathway, Thiamine Pyrophosphokinase (TPK) converts thiamin into its active form, Thiamin Pyrophosphate (TPP), allowing researchers to investigate the functional importance of this enzyme and the pathway's dispensability in Leishmania donovani, a protozoan parasite that causes visceral leishmaniasis. In this study, various in silico, biochemical, biophysical, and cellular assays-based experiments have been conducted to identify and characterize LdTPK, and to provide a sound platform for the discovery of potential LdTPK inhibitors. LdTPK structural modelling ensured high protein quality. Oxythiamine and pyrithiamine were found to bind well with LdTPK with considerable binding energies, and MD simulation-based experiments indicated the stability of the complexation. Additionally, LdTPK1 was found to activate ROS defense in amastigotes, and its inhibition using oxythiamine and pyrithiamine led to the growth inhibition of L. donovani promastigotes and intracellular amastigotes. These findings highlight LdTPK as a promising target for the development of new anti-leishmanial agents. An in-depth analysis of the enzymes involved in TPP biosynthesis in L. donovani has the potential to yield novel therapeutic strategies for Leishmaniasis.Communicated by Ramaswamy H. Sarma.

Structural and functional effects of the L84S mutant in the SARS-COV-2 ORF8 dimer based on microsecond molecular dynamics study

The L84S mutation has been observed frequently in the ORF8 protein of SARS-CoV-2, which is an accessory protein involved in various important functions such as virus propagation, pathogenesis, and evading the immune response. However, the specific effects of this mutation on the dimeric structure of ORF8 and its impacts on interactions with host components and immune responses are not well understood. In this study, we performed one microsecond molecular dynamics (MD) simulation and analyzed the dimeric behavior of the L84S and L84A mutants in comparison to the native protein. The MD simulations revealed that both mutations caused changes in the conformation of the ORF8 dimer, influenced protein folding mechanisms, and affected the overall structural stability. In particular, the 73YIDI76 motif has found to be significantly affected by the L84S mutation, leading to structural flexibility in the region connecting the C-terminal β4 and β5 strands. This flexibility might be responsible for virus immune modulation.  The free energy landscape (FEL) and principle component analysis (PCA) have also supported our investigation. Overall, the L84S and L84A mutations affect the ORF8 dimeric interfaces by reducing the frequency of protein-protein interacting residues (Arg52, Lys53, Arg98, Ile104, Arg115, Val117, Asp119, Phe120, and Ile121) in the ORF8 dimer.  Our findings provide detail insights for further research in designing structure-based therapeutics against the SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Efficient Refinement of Complex Structures of Flexible Histone Peptides Using Post-Docking Molecular Dynamics Protocols

Histones are keys to many epigenetic events and their complexes have therapeutic and diagnostic importance. The determination of the structures of histone complexes is fundamental in the design of new drugs. Computational molecular docking is widely used for the prediction of target-ligand complexes. Large, linear peptides like the tail regions of histones are challenging ligands for docking due to their large conformational flexibility, extensive hydration, and weak interactions with the shallow binding pockets of their reader proteins. Thus, fast docking methods often fail to produce complex structures of such peptide ligands at a level appropriate for drug design. To address this challenge, and improve the structural quality of the docked complexes, post-docking refinement has been applied using various molecular dynamics (MD) approaches. However, a final consensus has not been reached on the desired MD refinement protocol. In this present study, MD refinement strategies were systematically explored on a set of problematic complexes of histone peptide ligands with relatively large errors in their docked geometries. Six protocols were compared that differ in their MD simulation parameters. In all cases, pre-MD hydration of the complex interface regions was applied to avoid the unwanted presence of empty cavities. The best-performing protocol achieved a median of 32% improvement over the docked structures in terms of the change in root mean squared deviations from the experimental references. The influence of structural factors and explicit hydration on the performance of post-docking MD refinements are also discussed to help with their implementation in future methods and applications.

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.

Computational Mutagenesis of Antibody Fragments: Disentangling Side Chains from ΔΔG Predictions

The development of highly potent antibodies and antibody fragments as binding agents holds significant implications in fields such as biosensing and biotherapeutics. Their binding strength is intricately linked to the arrangement and composition of residues at the binding interface. Computational techniques offer a robust means to predict the three-dimensional structure of these complexes and to assess the affinity changes resulting from mutations. Given the interdependence of structure and affinity prediction, our objective here is to disentangle their roles. We aim to evaluate independently six side-chain reconstruction methods and ten binding affinity estimation techniques. This evaluation was pivotal in predicting affinity alterations due to single mutations, a key step in computational affinity maturation protocols. Our analysis focuses on a data set comprising 27 distinct antibody/hen egg white lysozyme complexes, each with crystal structures and experimentally determined binding affinities. Using six different side-chain reconstruction methods, we transformed each structure into its corresponding mutant via in silico single-point mutations. Subsequently, these structures undergo minimization and molecular dynamics simulation. We therefore estimate ΔΔG values based on the original crystal structure, its energy-minimized form, and the ensuing molecular dynamics trajectories. Our research underscores the critical importance of selecting reliable side-chain reconstruction methods and conducting thorough molecular dynamics simulations to accurately predict the impact of mutations. In summary, our study demonstrates that the integration of conformational sampling and scoring is a potent approach to precisely characterizing mutation processes in single-point mutagenesis protocols and crucial for computational antibody design.

In the quest for histone deacetylase inhibitors: current trends in the application of multilayered computational methods

Histone deacetylase (HDAC) inhibitors have gained attention over the past three decades because of their potential in the treatment of different diseases including various forms of cancers, neurodegenerative disorders, autoimmune, inflammatory diseases, and other metabolic disorders. To date, 5 HDAC inhibitor drugs are marketed for the treatment of hematological malignancies and several drug-candidate HDAC inhibitors are at different stages of clinical trials. However, due to the toxic side effects of these drugs resulting from the lack of target selectivity, active studies are ongoing to design and develop either class-selective or isoform-selective inhibitors. Computational methods have aided the discovery of HDAC inhibitors with the desired potency and/or selectivity. These methods include ligand-based approaches such as scaffold hopping, pharmacophore modeling, three-dimensional quantitative structure-activity relationships (3D-QSAR); and structure-based virtual screening (molecular docking). The current trends involve the application of the combination of these methods and incorporating molecular dynamics simulations coupled with Poisson-Boltzmann/molecular mechanics generalized Born surface area (MM-PBSA/MM-GBSA) to improve the prediction of ligand binding affinity. This review aimed at understanding the current trends in applying these multilayered strategies and their contribution to the design/identification of HDAC inhibitors.

In silico characterization of ligand-receptor interactions for U-47700, N,N-didesmethyl-U-47700, U-50488 at mu- and kappa-opioid receptors

The increasing misuse of novel synthetic opioids (NSOs) represents a serious public health concern. In this regard, U-47700 (trans-3,4-dichloro-N-[2-(dimethylamino)cyclohexyl]-N-methylbenzamide) and related "U-compounds" emerged on recreational drug markets as synthetic substitutes for illicit heroin and constituents of counterfeit pain medications. While the pharmacology of U-compounds has been investigated using in vitro and in vivo methods, there is still a lack of understanding about the details of ligand-receptor interactions at the molecular level. To this end, we have developed a molecular modeling protocol based on docking and molecular dynamics simulations to assess the nature of ligand-receptor interactions for U-47700, N,N-didesmethyl U-47700, and U-50488 at the mu-opioid receptor (MOR) and kappa-opioid receptor (KOR). The evaluation of ligand-receptor and ligand-receptor-membrane interaction energies enabled the identification of subtle conformational shifts in the receptors induced by ligand binding. Interestingly, the removal of two key methyl groups from U-47700, to form N,N-didesmethyl U-47700, caused a loss of hydrogen bond contact with tryptophan (Trp)229, which may underlie the lower interaction energy and reduced MOR affinity for the compound. Taken together, our results are consistent with the reported biological findings for U-compounds and provide a molecular basis for the MOR selectivity of U-47700 and KOR selectivity of U-50488.

Unraveling the Role of Hydrogen Bonds in Thrombin via Two Machine Learning Methods

Hydrogen bonds play a critical role in the folding and stability of proteins, such as proteins and nucleic acids, by providing strong and directional interactions. They help to maintain the secondary and 3D structure of proteins, and structural changes in these molecules often result from the formation or breaking of hydrogen bonds. To gain insights into these hydrogen bonding networks, we applied two machine learning models - a logistic regression model and a decision tree model - to study four variants of thrombin: wild-type, ΔK9, E8K, and R4A. Our results showed that both models have their unique advantages. The logistic regression model highlighted potential key residues (GLU295) in thrombin's allosteric pathways, while the decision tree model identified important hydrogen bonding motifs. This information can aid in understanding the mechanisms of folding in proteins and has potential applications in drug design and other therapies. The use of these two models highlights their usefulness in studying hydrogen bonding networks in proteins.

New paradigms in purinergic receptor ligand discovery

The discovery and clinical implementation of modulators of adenosine, P2Y and P2X receptors (comprising nineteen subtypes) have progressed dramatically in ∼50 years since Burnstock's definition of purinergic signaling. Although most clinical trials of selective ligands (agonists and antagonists) of certain purinergic receptors failed, there is a renewed impetus to redirect efforts to new disease conditions and the discovery of more selective or targeted compounds with potentially reduced side effects, such as biased GPCR agonists. The elucidation of new receptor and enzyme structures is steering rational design of potent and selective agonists, antagonists, allosteric modulators and inhibitors. A2A adenosine receptor (AR) antagonists are being applied to neurodegenerative conditions and cancer immunotherapy. A3AR agonists have potential for treating chronic inflammation (e.g. psoriasis), stroke and pain, as well as cancer. P2YR modulators are being considered for treating inflammation, metabolic disorders, acute kidney injury, cancer, pain and other conditions, often with an immune mechanism. ADP-activated P2Y12R antagonists are widely used as antithrombotic drugs, while their repurposing toward neuroinflammation is considered. P2X3 antagonists have been in clinical trials for chronic cough. P2X7 antagonists have been in clinical trials for inflammatory diseases and depression (compounds that penetrate the blood-brain barrier). Thus, purinergic signaling is now recognized as an immense regulatory system in the body for rebalancing tissues and organs under stress, which can be adjusted by drug intervention for therapeutic purposes. The lack of success of many previous clinical trials can be overcome given more advanced pharmacokinetic and pharmacodynamic approaches, including structure-based drug design, prodrugs and biased signaling. This article is part of the Special Issue on "Purinergic Signaling: 50 years".

Can molecular dynamics simulations improve predictions of protein-ligand binding affinity with machine learning?

Binding affinity prediction largely determines the discovery efficiency of lead compounds in drug discovery. Recently, machine learning (ML)-based approaches have attracted much attention in hopes of enhancing the predictive performance of traditional physics-based approaches. In this study, we evaluated the impact of structural dynamic information on the binding affinity prediction by comparing the models trained on different dimensional descriptors, using three targets (i.e. JAK1, TAF1-BD2 and DDR1) and their corresponding ligands as the examples. Here, 2D descriptors are traditional ECFP4 fingerprints, 3D descriptors are the energy terms of the Smina and NNscore scoring functions and 4D descriptors contain the structural dynamic information derived from the trajectories based on molecular dynamics (MD) simulations. We systematically investigate the MD-refined binding affinity prediction performance of three classical ML algorithms (i.e. RF, SVR and XGB) as well as two common virtual screening methods, namely Glide docking and MM/PBSA. The outcomes of the ML models built using various dimensional descriptors and their combinations reveal that the MD refinement with the optimized protocol can improve the predictive performance on the TAF1-BD2 target with considerable structural flexibility, but not for the less flexible JAK1 and DDR1 targets, when taking docking poses as the initial structure instead of the crystal structures. The results highlight the importance of the initial structures to the final performance of the model through conformational analysis on the three targets with different flexibility.

Computational insight into structural basis of human ELOVL1 inhibition

Very long-chain fatty acids (VLCFAs) play a direct role in the development of a neurological disorder, X-linked adrenoleukodystrophy (X-ALD). Since ELOVL1 catalyzes the rate-limiting step of the synthesis of VLCFAs, it has emerged as an attractive target for the treatment of X-ALD. Recently two potent inhibitors, compound 22 (C22) and compound 27 (C27) have been reported to specifically inhibit human ELOVL1 but their structural basis of inhibition has not been explored. In the present study, we have used a homology model of human ELOVL1 to deduce the binding site and binding modes of C22 and C27. We have employed computational approaches to characterize the binding of C22 and C27. Initially, binding of hexacosanoyl-CoA (C26:0-CoA) to ELOVL1 was modelled and further validated by molecular dynamics (MD) simulation. We observed that the fatty acid tail of C26: CoA protrudes from a unique opening located at the occluded end of ELOVL1. Structural comparison of ELOVL1 with the crystal structure of ELOVL7 revealed that the unique opening was not present in human ELOVL7. Combined blind and focused molecular docking approaches revealed that C22 and C27 exhibit favourable binding in the same unique opening. Further, MD simulations and free binding energy calculations confirmed that C22 and C27 maintain the favourable binding in the unique opening of ELOVL1. Overall, our findings suggest that selective human ELOVL1 inhibitors block the binding of long tails of VLCFAs near the occluded end of ELOVL1. Present study will be helpful in the discovery and design of novel, selective and potent inhibitors of human ELOVL1.

Discovery of Biologically Optimized Polymyxin Derivatives Facilitated by Peptide Scanning and In Situ Screening Chemistry

Peptides can be converted to highly active compounds by introducing appropriate substituents on the suitable amino acid residue. Although modifiable residues in peptides can be systematically identified by peptide scanning methodologies, there is no practical method for optimization at the "scanned" position. With the purpose of using derivatives not only for scanning but also as a starting point for further chemical functionalization, we herein report the "scanning and direct derivatization" strategy through chemoselective acylation of embedded threonine residues by a serine/threonine ligation (STL) with the help of in situ screening chemistry. We have applied this strategy to the optimization of the polymyxin antibiotics, which were selected as a model system to highlight the power of the rapid derivatization of active scanning derivatives. Using this approach, we explored the structure-activity relationships of the polymyxins and successfully prepared derivatives with activity against polymyxin-resistant bacteria and those with Pseudomonas aeruginosa selective antibacterial activity. This strategy opens up efficient structural exploration and further optimization of peptide sequences.

Designing drugs when there is low data availability: one-shot learning and other approaches to face the issues of a long-term concern

INTRODUCTION

Modern drug discovery generally is accessed by useful information from previous large databases or uncovering novel data. The lack of biological and/or chemical data tends to slow the development of scientific research and innovation. Here, approaches that may help provide solutions to generate or obtain enough relevant data or improve/accelerate existing methods within the last five years were reviewed.

AREAS COVERED

One-shot learning (OSL) approaches, structural modeling, molecular docking, scoring function space (SFS), molecular dynamics (MD), and quantum mechanics (QM) may be used to amplify the amount of available data to drug design and discovery campaigns, presenting methods, their perspectives, and discussions to be employed in the near future.

EXPERT OPINION

Recent works have successfully used these techniques to solve a range of issues in the face of data scarcity, including complex problems such as the challenging scenario of drug design aimed at intrinsically disordered proteins and the evaluation of potential adverse effects in a clinical scenario. These examples show that it is possible to improve and kickstart research from scarce available data to design and discover new potential drugs.

Prediction of binding affinity of 1,2-diphenyline ketone analogues at adenosine triphosphate binding site of glycogen synthase kinase-3β: a molecular docking and dynamic simulation study

Glycogen synthase kinase (GSK)-3β is one of the downstream signalling molecules involved in phosphorylation of glycogen synthase, a key enzyme involved in the synthesis of glycogen from glucose. GSK-3β regulate some of the critical processes underlying structural and functional synaptic plasticity of neurons. Down regulation or inhibition of GSK-3β enhances long-term potentiation and cognitive functions in animal models of Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis. A number of compounds are available to inhibit GSK-3β, however none of them are in clinical practice to treat neurodegenerative diseases. The aim of our study was to predict the molecular interaction and dynamic behaviour of naturally occurring 1,2-diphenyline ketone analogues at the adenosine triphosphate binding site of glycogen synthase kinase (GSK)-3β through simulation studies. Out of all 1,2-diphenyline ketone analogues,1, 3, 5, 6-Tetrahydroxyxanthone (Rank = 1), Secalonic acid F (Rank = 2), and Trihydroxy-2-(2,3-dihydroxy-3-methylbutyl)-7-methoxy-8-(3-methyl-2-butenyl) xanthone (Rank = 3) were found to exhibit lowest docking score of -12.07, -11.49, and -11.24 kcal/mol with dissociation constant of 1.37, 3.84, and 5.99 nM, respectively. The molecular dynamic simulation of rank 1 and rank 3 ligands indicated stable interaction throughout the simulation and interaction analyses has shown that the presence of hydroxyl groups at C1, C3, C5, and C6 around 1,2 diphenyline ketone nucleus to influence their binding affinity at the ATP-binding site of GSK-3β. We predicted that 1,3,5,6-Tetrahydroxyxanthone and 1, 3, 6-Trihydroxy-2-(2,3-dihydroxy-3-methylbutyl)-7-methoxy-8-(3-methyl-2-butenyl) xanthone may act as a potential ligand or lead compound to inhibit GSK-3β and also may play an important role in alleviating neurodegenerative diseases.Communicated by Ramaswamy H. Sarma.

Recent Advances in Structure, Function, and Pharmacology of Class A Lipid GPCRs: Opportunities and Challenges for Drug Discovery

Great progress has been made over the past decade in understanding the structural, functional, and pharmacological diversity of lipid GPCRs. From the first determination of the crystal structure of bovine rhodopsin in 2000, much progress has been made in the field of GPCR structural biology. The extraordinary progress in structural biology and pharmacology of GPCRs, coupled with rapid advances in computational approaches to study receptor dynamics and receptor-ligand interactions, has broadened our comprehension of the structural and functional facets of the receptor family members and has helped usher in a modern age of structure-based drug design and development. First, we provide a primer on lipid mediators and lipid GPCRs and their role in physiology and diseases as well as their value as drug targets. Second, we summarize the current advancements in the understanding of structural features of lipid GPCRs, such as the structural variation of their extracellular domains, diversity of their orthosteric and allosteric ligand binding sites, and molecular mechanisms of ligand binding. Third, we close by collating the emerging paradigms and opportunities in targeting lipid GPCRs, including a brief discussion on current strategies, challenges, and the future outlook.