Dynamic Docking: A Paradigm Shift in Computational Drug Discovery

Binding Mechanism between Platelet Glycoprotein and Cyclic Peptide Elucidated by McMD-Based Dynamic Docking

The cyclic peptide OS1 (amino acid sequence: CTERMALHNLC), which has a disulfide bond between both termini cysteine residues, inhibits complex formation between the platelet glycoprotein Ibα (GPIbα) and the von Willebrand factor (vWF) by forming a complex with GPIbα. To study the binding mechanism between GPIbα and OS1 and, therefore, the inhibition mechanism of the protein-protein GPIbα-vWF complex, we have applied our multicanonical molecular dynamics (McMD)-based dynamic docking protocol starting from the unbound state of the peptide. Our simulations have reproduced the experimental complex structure, although the top-ranking structure was an intermediary one, where the peptide was bound in the same location as in the experimental structure; however, the β-switch of GPIbα attained a different conformation. Our analysis showed that subsequent refolding of the β-switch results in a more stable binding configuration, although the transition to the native configuration appears to take some time, during which OS1 could dissociate. Our results show that conformational changes in the β-switch are crucial for successful binding of OS1. Furthermore, we identified several allosteric binding sites of GPIbα that might also interfere with vWF binding, and optimization of the peptide to target these allosteric sites might lead to a more effective inhibitor, as these are not dependent on the β-switch conformation.

In Silico Assisted Identification, Synthesis, and In Vitro Pharmacological Characterization of Potent and Selective Blockers of the Epilepsy-Associated KCNT1 Channel

Gain-of-function (GoF) variants in KCNT1 channels cause severe, drug-resistant forms of epilepsy. Quinidine is a known KCNT1 blocker, but its clinical use is limited due to severe drawbacks. To identify novel KCNT1 blockers, a homology model of human KCNT1 was built and used to screen an in-house library of compounds. Among the 20 molecules selected, five (CPK4, 13, 16, 18, and 20) showed strong KCNT1-blocking ability in an in vitro fluorescence-based assay. Patch-clamp experiments confirmed a higher KCNT1-blocking potency of these compounds when compared to quinidine, and their selectivity for KCNT1 over hERG and Kv7.2 channels. Among identified molecules, CPK20 displayed the highest metabolic stability; this compound also blocked KCNT2 currents, although with a lower potency, and counteracted GoF effects prompted by 2 recurrent epilepsy-causing KCNT1 variants (G288S and A934T). The present results provide solid rational basis for future design of novel compounds to counteract KCNT1-related neurological disorders.

Nature-inspired substituted 3-(imidazol-2-yl) morpholines targeting human topoisomerase IIα: Dynophore-derived discovery

The molecular nanomachine, human DNA topoisomerase IIα, plays a crucial role in replication, transcription, and recombination by catalyzing topological changes in the DNA, rendering it an optimal target for cancer chemotherapy. Current clinical topoisomerase II poisons often cause secondary tumors as side effects due to the accumulation of double-strand breaks in the DNA, spurring the development of catalytic inhibitors. Here, we used a dynamic pharmacophore approach to develop catalytic inhibitors targeting the ATP binding site of human DNA topoisomerase IIα. Our screening of a library of nature-inspired compounds led to the discovery of a class of 3-(imidazol-2-yl) morpholines as potent catalytic inhibitors that bind to the ATPase domain. Further experimental and computational studies identified hit compound 17, which exhibited selectivity against the human DNA topoisomerase IIα versus human protein kinases, cytotoxicity against several human cancer cells, and did not induce DNA double-strand breaks, making it distinct from clinical topoisomerase II poisons. This study integrates an innovative natural product-inspired chemistry and successful implementation of a molecular design strategy that incorporates a dynamic component of ligand-target molecular recognition, with comprehensive experimental characterization leading to hit compounds with potential impact on the development of more efficient chemotherapies.

Rational Design of a Potential New Nematicide Targeting Chitin Deacetylase

In a previously published study, the authors devised a molecular topology QSAR (quantitative structure-activity relationship) approach to detect novel fungicides acting as inhibitors of chitin deacetylase (CDA). Several of the chosen compounds exhibited noteworthy activity. Due to the close relationship between chitin-related proteins present in fungi and other chitin-containing plant-parasitic species, the authors decided to test these molecules against nematodes, based on their negative impact on agriculture. From an overall of 20 fungal CDA inhibitors, six showed to be active against Caenorhabditis elegans. These experimental results made it possible to develop two new molecular topology-based QSAR algorithms for the rational design of potential nematicides with CDA inhibitor activity for crop protection. Linear discriminant analysis was employed to create the two algorithms, one for identifying the chemo-mathematical pattern of commercial nematicides and the other for identifying nematicides with activity on CDA. After creating and validating the QSAR models, the authors screened several natural and synthetic compound databases, searching for alternatives to current nematicides. Finally one compound, the N2-(dimethylsulfamoyl)-N-{2-[(2-methyl-2-propanyl)sulfanyl]ethyl}-N2-phenylglycinamide or nematode chitin deacetylase inhibitor, was selected as the best candidate and was further investigated both in silico, through molecular docking and molecular dynamic simulations, and in vitro, through specific experimental assays. The molecule shows favorable binding behavior on the catalytic pocket of C. elegans CDA and the experimental assays confirm potential nematicide activity.

Phenolic Compounds of Therapeutic Interest in Neuroprotection

The number of elderly people is projected to double in the next 50 years worldwide, resulting in an increased prevalence of neurodegenerative diseases. Aging causes changes in brain tissue homeostasis, thus contributing to the development of neurodegenerative disorders. Current treatments are not entirely effective, so alternative treatments or adjuvant agents are being actively sought. Antioxidant properties of phenolic compounds are of particular interest for neurodegenerative diseases whose psychopathological mechanisms strongly rely on oxidative stress at the brain level. Moreover, phenolic compounds display other advantages such as the permeability of the blood-brain barrier (BBB) and the interesting molecular mechanisms that we reviewed in this work. We began by briefly outlining the physiopathology of neurodegenerative diseases to understand the mechanisms that result in irreversible brain damage, then we provided an overall classification of the phenolic compounds that would be addressed later. We reviewed in vitro and in vivo studies, as well as some clinical trials in which neuroprotective mechanisms were demonstrated in models of different neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS), Alzheimer's disease (AD), Parkinson's disease (PD), ischemia, and traumatic brain injury (TBI).

Binding Mechanism of Riboswitch to Natural Ligand Elucidated by McMD-Based Dynamic Docking Simulations

Flavin mononucleotide riboswitches are common among many pathogenic bacteria and are therefore considered to be an attractive target for antibiotics development. The riboswitch binds riboflavin (RBF, also known as vitamin B2), and although an experimental structure of their complex has been solved with the ligand bound deep inside the RNA molecule in a seemingly unreachable state, the binding mechanism between these molecules is not yet known. We have therefore used our Multicanonical Molecular Dynamics (McMD)-based dynamic docking protocol to analyze their binding mechanism by simulating the binding process between the riboswitch aptamer domain and the RBF, starting from the apo state of the riboswitch. Here, the refinement stage was crucial to identify the native binding configuration, as several other binding configurations were also found by McMD-based docking simulations. RBF initially binds the interface between P4 and P6 including U61 and G62, which forms a gateway where the ligand lingers until this gateway opens sufficiently to allow the ligand to pass through and slip into the hidden binding site including A48, A49, and A85.

Molecular Dynamics as a Tool for Virtual Ligand Screening

Rational drug design is essential for new drugs to emerge, especially when the structure of a target protein or nucleic acid is known. To that purpose, high-throughput virtual ligand screening campaigns aim at discovering computationally new binding molecules or fragments to modulate particular biomolecular interactions or biological activities, related to a disease process. The structure-based virtual ligand screening process primarily relies on docking methods which allow predicting the binding of a molecule to a biological target structure with a correct conformation and the best possible affinity. The docking method itself is not sufficient as it suffers from several and crucial limitations (lack of full protein flexibility information, no solvation and ion effects, poor scoring functions, and unreliable molecular affinity estimation).At the interface of computer techniques and drug discovery, molecular dynamics (MD) allows introducing protein flexibility before or after a docking protocol, refining the structure of protein-drug complexes in the presence of water, ions, and even in membrane-like environments, describing more precisely the temporal evolution of the biological complex and ranking these complexes with more accurate binding energy calculations. In this chapter, we describe the up-to-date MD, which plays the role of supporting tools in the virtual ligand screening (VS) process.Without a doubt, using docking in combination with MD is an attractive approach in structure-based drug discovery protocols nowadays. It has proved its efficiency through many examples in the literature and is a powerful method to significantly reduce the amount of required wet experimentations (Tarcsay et al, J Chem Inf Model 53:2990-2999, 2013; Barakat et al, PLoS One 7:e51329, 2012; De Vivo et al, J Med Chem 59:4035-4061, 2016; Durrant, McCammon, BMC Biol 9:71-79, 2011; Galeazzi, Curr Comput Aided Drug Des 5:225-240, 2009; Hospital et al, Adv Appl Bioinforma Chem 8:37-47, 2015; Jiang et al, Molecules 20:12769-12786, 2015; Kundu et al, J Mol Graph Model 61:160-174, 2015; Mirza et al, J Mol Graph Model 66:99-107, 2016; Moroy et al, Future Med Chem 7:2317-2331, 2015; Naresh et al, J Mol Graph Model 61:272-280, 2015; Nichols et al, J Chem Inf Model 51:1439-1446, 2011; Nichols et al, Methods Mol Biol 819:93-103, 2012; Okimoto et al, PLoS Comput Biol 5:e1000528, 2009; Rodriguez-Bussey et al, Biopolymers 105:35-42, 2016; Sliwoski et al, Pharmacol Rev 66:334-395, 2014).

A combined in silico approaches of 2D-QSAR, molecular docking, molecular dynamics and ADMET prediction of anti-cancer inhibitor activity for actinonin derivatives

Inhibition of human mitochondrial peptide deformylase (HsPDF) plays a major role in reducing growth, proliferation, and cellular cancer survival. In this work, a series of 32 actinonin derivatives for HsPDF (PDB: 3G5K) inhibitor's anticancer activity was computationally analyzed for the first time, using an in silico study considering 2D-QSAR modeling, and molecular docking studies, and validated by molecular dynamics and ADMET properties. The results of multilinear regression (MLR) and artificial neural networks (ANN) statistical analysis reveal a good correlation between pIC50 activity and the seven (7) descriptors. The developed models were highly significant with cross-validation, the Y-randomization test and their applicability range. In addition, all considered data sets show that the AC30 compound, exhibits the best binding affinity (docking score = -212.074 kcal/mol and H-bonding energy = -15.879 kcal/mol). Furthermore, molecular dynamics simulations were performed at 500 ns, confirming the stability of the studied complexes under physiological conditions and validating the molecular docking results. Five selected actinonin derivatives (AC1, AC8, AC15, AC18 and AC30), exhibiting best docking score, were rationalized as potential leads for HsPDF inhibition, in well agreement with experimental outcomes. Furthermore, based on the in silico study, new six molecules (AC32, AC33, AC34, AC35, AC36 and AC37) were suggested as HsPDF inhibition candidates, which would be combined with in-vitro and in-vivo studies to perspective validation of their anticancer activity. Indeed, the ADMET predictions indicate that these six new ligands have demonstrated a fairly good drug-likeness profile.

Computational workflow for discovering small molecular binders for shallow binding sites by integrating molecular dynamics simulation, pharmacophore modeling, and machine learning: STAT3 as case study

STAT3 belongs to a family of seven transcription factors. It plays an important role in activating the transcription of various genes involved in a variety of cellular processes. High levels of STAT3 are detected in several types of cancer. Hence, STAT3 inhibition is considered a promising therapeutic anti-cancer strategy. However, since STAT3 inhibitors bind to the shallow SH2 domain of the protein, it is expected that hydration water molecules play significant role in ligand-binding complicating the discovery of potent binders. To remedy this issue, we herein propose to extract pharmacophores from molecular dynamics (MD) frames of a potent co-crystallized ligand complexed within STAT3 SH2 domain. Subsequently, we employ genetic function algorithm coupled with machine learning (GFA-ML) to explore the optimal combination of MD-derived pharmacophores that can account for the variations in bioactivity among a list of inhibitors. To enhance the dataset, the training and testing lists were augmented nearly a 100-fold by considering multiple conformers of the ligands. A single significant pharmacophore emerged after 188 ns of MD simulation to represent STAT3-ligand binding. Screening the National Cancer Institute (NCI) database with this model identified one low micromolar inhibitor most likely binds to the SH2 domain of STAT3 and inhibits this pathway.

Screening of potential inhibitors of Leishmania major N-myristoyltransferase from Azadirachta indica phytochemicals for leishmaniasis drug discovery by molecular docking, molecular dynamics simulation and density functional theory methods

Leishmaniasis is one of the most neglected parasitic diseases worldwide. The toxicity of current drugs used for its treatment is a major obstacle to their effectiveness, necessitating the discovery and development of new therapeutic agents for better disease control. In Leishmania parasites, N-Myristoyltransferase (NMT) has been identified as a promising target for drug development. Thus, exploring well-known medicinal plants such as Azadirachta indica and their phytochemicals can offer a diverse range of treatment options, potentially leading to disease prevention and control. To assess the therapeutic potential of these compounds, their ADMET prediction and drug-likeness properties were analyzed. The top 4 compounds were selected which had better and significantly low binding energy than the reference molecule QMI. Based on the binding energy score of the top compounds, the results show that Isonimocinolide has the highest binding affinity (-9.8 kcal/mol). In addition, a 100 ns MD simulation of the four best compounds showed that Isonimocinolide and Nimbolide have good stability with LmNMT. These compounds were then subjected to MMPBSA (last 30 ns) calculation to analyze protein-ligand stability and dynamic behavior. Nimbolide and Meldenin showed lowest binding free energy i.e. -84.301 kJ/mol and -91.937 kJ/mol respectively. DFT was employed to calculate the HOMO-LUMO energy gap, global reactivity parameters, and molecular electrostatic potential of all hit molecules. The promising results obtained from MD simulations and MMPBSA analyses provide compelling evidence for the potential use of these compounds in future drug development efforts for the treatment of leishmaniasis.Communicated by Ramaswamy H. Sarma.

Mechanisms of Nelumbinis folium targeting PPARγ for weight management: A molecular docking and molecular dynamics simulations study

The lotus leaf, Nelumbinis folium (NF), has frequently appeared in obesity clinical trials as an intervention to promote weight loss and improve metabolic profiles. However, the molecular mechanisms by which it interacts with important obesity targets and pathways, such as the peroxisome proliferator-activated receptor gamma (PPARγ) within the PPAR signalling pathway, were not well understood. This study aims to screen for candidate compounds from NF with desirable pharmacokinetic properties and examine their binding feasibility at the PPARγ ligand-binding domain (LBD). Ligand- and structure-based screening of NF compounds were performed, and a consensus approach has been applied to identify druggable candidates. By examining the pharmacokinetic profiles, a large proportion of NF compounds exhibited favourable drug-likeness and oral bioavailability properties. Furthermore, the binding affinity scores and poses provided new insights on the distinctive binding behaviours of NF compounds at the LBD of PPARγ in its inactive form. Several NF compounds could bind strongly to PPARγ at sub-pockets where partial agonists and antagonists were found to bind and may induce conformational changes that influence co-repressor binding, trans-repression, and gene expression inhibition. Subsequent molecular dynamics simulations of a candidate compound (NF129 narcissin) bound to PPARγ revealed conformational stability, residue fluctuation, and binding behaviours comparable to that of the known inhibitor, SR1664. Therefore, it can be proposed that narcissin exhibits characteristics of a PPARγ antagonist. Further experimental validation to support the development of NF129 as a future anti-obesity agent is warranted.

Metadynamics simulations for the investigation of drug loading on functionalized inorganic nanoparticles

Inorganic nanoparticles show promising properties that allow them to be efficiently used as drug carriers. The main limitation in this type of application is currently the drug loading capacity, which can be overcome with a proper functionalization of the nanoparticle surface. In this study, we present, for the first time, a computational approach based on metadynamics to estimate the binding free energy of the doxorubicin drug (DOX) to a functionalized TiO₂ nanoparticle under different pH conditions. On a thermodynamic basis, we demonstrate the robustness of our approach to capture the overall mechanism behind the pH-triggered release of DOX due to environmental pH changes. Notably, binding free energy estimations align well with what is expected for a pH-sensitive drug delivery system. Based on our results, we envision the use of metadynamics as a promising computational tool for the rational design and in silico optimization of organic ligands with improved drug carrier properties.

Mutual induced-fit mechanism drives binding between intrinsically disordered Bim and cryptic binding site of Bcl-xL

The intrinsically disordered region (IDR) of Bim binds to the flexible cryptic site of Bcl-xL, a pro-survival protein involved in cancer progression that plays an important role in initiating apoptosis. However, their binding mechanism has not yet been elucidated. We have applied our dynamic docking protocol, which correctly reproduced both the IDR properties of Bim and the native bound configuration, as well as suggesting other stable/meta-stable binding configurations and revealed the binding pathway. Although the cryptic site of Bcl-xL is predominantly in a closed conformation, initial binding of Bim in an encounter configuration leads to mutual induced-fit binding, where both molecules adapt to each other; Bcl-xL transitions to an open state as Bim folds from a disordered to an α-helical conformation while the two molecules bind each other. Finally, our data provides new avenues to develop novel drugs by targeting newly discovered stable conformations of Bcl-xL.

Benzimidazole Derivatives Suppress Fusarium Wilt Disease via Interaction with ERG6 of Fusarium equiseti and Activation of the Antioxidant Defense System of Pepper Plants

Sweet pepper (Capsicum annuum L.), also known as bell pepper, is one of the most widely grown vegetable crops worldwide. It is attacked by numerous phytopathogenic fungi, such as Fusarium equiseti, the causal agent of Fusarium wilt disease. In the current study, we proposed two benzimidazole derivatives, including 2-(2-hydroxyphenyl)-1-H benzimidazole (HPBI) and its aluminum complex (Al-HPBI complex), as potential control alternatives to F. equiseti. Our findings showed that both compounds demonstrated dose-dependent antifungal activity against F. equiseti in vitro and significantly suppressed disease development in pepper plants under greenhouse conditions. According to in silico analysis, the F. equiseti genome possesses a predicted Sterol 24-C-methyltransferase (FeEGR6) protein that shares a high degree of homology with EGR6 from F. oxysporum (FoEGR6). It is worth mentioning that molecular docking analysis confirmed that both compounds can interact with FeEGR6 from F. equiseti as well as FoEGR6 from F. oxysporum. Moreover, root application of HPBI and its aluminum complex significantly enhanced the enzymatic activities of guaiacol-dependent peroxidases (POX), polyphenol oxidase (PPO), and upregulated four antioxidant-related enzymes, including superoxide dismutase [Cu-Zn] (CaSOD-Cu), L-ascorbate peroxidase 1, cytosolic (CaAPX), glutathione reductase, chloroplastic (CaGR), and monodehydroascorbate reductase (CaMDHAR). Additionally, both benzimidazole derivatives induced the accumulation of total soluble phenolics and total soluble flavonoids. Collectively, these findings suggest that the application of HPBI and Al-HPBI complex induce both enzymatic and nonenzymatic antioxidant defense machinery.

Structural Basis for Agonistic Activity and Selectivity toward Melatonin Receptors hMT1 and hMT2

Glaucoma, a major ocular neuropathy originating from a progressive degeneration of retinal ganglion cells, is often associated with increased intraocular pressure (IOP). Daily IOP fluctuations are physiologically influenced by the antioxidant and signaling activities of melatonin. This endogenous modulator has limited employment in treating altered IOP disorders due to its low stability and bioavailability. The search for low-toxic compounds as potential melatonin agonists with higher stability and bioavailability than melatonin itself could start only from knowing the molecular basis of melatonergic activity. Thus, using a computational approach, we studied the melatonin binding toward its natural macromolecular targets, namely melatonin receptors 1 (MT1) and 2 (MT2), both involved in IOP signaling regulation. Besides, agomelatine, a melatonin-derivative agonist and, at the same time, an atypical antidepressant, was also included in the study due to its powerful IOP-lowering effects. For both ligands, we evaluated both stability and ligand positioning inside the orthosteric site of MTs, mapping the main molecular interactions responsible for receptor activation. Affinity values in terms of free binding energy (ΔG_bind) were calculated for the selected poses of the chosen compounds after stabilization through a dynamic molecular docking protocol. The results were compared with experimental in vivo effects, showing a higher potency and more durable effect for agomelatine with respect to melatonin, which could be ascribed both to its higher affinity for hMT2 and to its additional activity as an antagonist for the serotonin receptor 5-HT2c, in agreement with the in silico results.

Comprehensive Survey of Consensus Docking for High-Throughput Virtual Screening

The rapid advances of 3D techniques for the structural determination of proteins and the development of numerous computational methods and strategies have led to identifying highly active compounds in computer drug design. Molecular docking is a method widely used in high-throughput virtual screening campaigns to filter potential ligands targeted to proteins. A great variety of docking programs are currently available, which differ in the algorithms and approaches used to predict the binding mode and the affinity of the ligand. All programs heavily rely on scoring functions to accurately predict ligand binding affinity, and despite differences in performance, none of these docking programs is preferable to the others. To overcome this problem, consensus scoring methods improve the outcome of virtual screening by averaging the rank or score of individual molecules obtained from different docking programs. The successful application of consensus docking in high-throughput virtual screening highlights the need to optimize the predictive power of molecular docking methods.

Advancing the field of computational drug design using multicanonical molecular dynamics-based dynamic docking

Multicanonical molecular dynamics (McMD)-based dynamic docking is a powerful tool to not only predict the native binding configuration between two flexible molecules, but it can also be used to accurately simulate the binding/unbinding pathway. Furthermore, it can also predict alternative binding sites, including allosteric ones, by employing an exhaustive sampling approach. Since McMD-based dynamic docking accurately samples binding/unbinding events, it can thus be used to determine the molecular mechanism of binding between two molecules. We developed the McMD-based dynamic docking methodology based on the powerful, but woefully underutilized McMD algorithm, combined with a toolset to perform the docking and to analyze the results. Here, we showcase three of our recent works, where we have applied McMD-based dynamic docking to advance the field of computational drug design. In the first case, we applied our method to perform an exhaustive search between Hsp90 and one of its inhibitors to successfully predict the native binding configuration in its binding site, as we refined our analysis methods. For our second case, we performed an exhaustive search of two medium-sized ligands and Bcl-xL, which has a cryptic binding site that differs greatly between the apo and holo structures. Finally, we performed a dynamic docking simulation between a membrane-embedded GPCR molecule and a high affinity ligand that binds deep within its receptor's pocket. These advanced simulations showcase the power that the McMD-based dynamic docking method has, and provide a glimpse of the potential our methodology has to unravel and solve the medical and biophysical issues in the modern world.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12551-022-01010-z.

Navigating Transcriptomic Connectivity Mapping Workflows to Link Chemicals with Bioactivities

Screening new compounds for potential bioactivities against cellular targets is vital for drug discovery and chemical safety. Transcriptomics offers an efficient approach for assessing global gene expression changes, but interpreting chemical mechanisms from these data is often challenging. Connectivity mapping is a potential data-driven avenue for linking chemicals to mechanisms based on the observation that many biological processes are associated with unique gene expression signatures (gene signatures). However, mining the effects of a chemical on gene signatures for biological mechanisms is challenging because transcriptomic data contain thousands of noisy genes. New connectivity mapping approaches seeking to distinguish signal from noise continue to be developed, spurred by the promise of discovering chemical mechanisms, new drugs, and disease targets from burgeoning transcriptomic data. Here, we analyze these approaches in terms of diverse transcriptomic technologies, public databases, gene signatures, pattern-matching algorithms, and statistical evaluation criteria. To navigate the complexity of connectivity mapping, we propose a harmonized scheme to coherently organize and compare published workflows. We first standardize concepts underlying transcriptomic profiles and gene signatures based on various transcriptomic technologies such as microarrays, RNA-Seq, and L1000 and discuss the widely used data sources such as Gene Expression Omnibus, ArrayExpress, and MSigDB. Next, we generalize connectivity mapping as a pattern-matching task for finding similarity between a query (e.g., transcriptomic profile for new chemical) and a reference (e.g., gene signature of known target). Published pattern-matching approaches fall into two main categories: vector-based use metrics like correlation, Jaccard index, etc., and aggregation-based use parametric and nonparametric statistics (e.g., gene set enrichment analysis). The statistical methods for evaluating the performance of different approaches are described, along with comparisons reported in the literature on benchmark transcriptomic data sets. Lastly, we review connectivity mapping applications in toxicology and offer guidance on evaluating chemical-induced toxicity with concentration-response transcriptomic data. In addition to serving as a high-level guide and tutorial for understanding and implementing connectivity mapping workflows, we hope this review will stimulate new algorithms for evaluating chemical safety and drug discovery using transcriptomic data.

Going Retro, Going Viral: Experiences and Lessons in Drug Discovery from COVID-19

The severity of the COVID-19 pandemic and the pace of its global spread have motivated researchers to opt for repurposing existing drugs against SARS-CoV-2 rather than discover or develop novel ones. For reasons of speed, throughput, and cost-effectiveness, virtual screening campaigns, relying heavily on in silico docking, have dominated published reports. A particular focus as a drug target has been the principal active site (i.e., RNA synthesis) of RNA-dependent RNA polymerase (RdRp), despite the existence of a second, and also indispensable, active site in the same enzyme. Here we report the results of our experimental interrogation of several small-molecule inhibitors, including natural products proposed to be effective by in silico studies. Notably, we find that two antibiotics in clinical use, fidaxomicin and rifabutin, inhibit RNA synthesis by SARS-CoV-2 RdRp in vitro and inhibit viral replication in cell culture. However, our mutagenesis studies contradict the binding sites predicted computationally. We discuss the implications of these and other findings for computational studies predicting the binding of ligands to large and flexible protein complexes and therefore for drug discovery or repurposing efforts utilizing such studies. Finally, we suggest several improvements on such efforts ongoing against SARS-CoV-2 and future pathogens as they arise.

Heat Shock Protein 90 (HSP90) Inhibitors as Anticancer Medicines: A Review on the Computer-Aided Drug Discovery Approaches over the Past Five Years

Cancer is a disease caused by the uncontrolled, abnormal growth of cells in different anatomic sites. In 2018, it was predicted that the worldwide cancer burden would rise to 18.1 million new cases and 9.6 million deaths. Anticancer compounds, often known as chemotherapeutic medicines, have gained much interest in recent cancer research. These medicines work through various biological processes in targeting cells at various stages of the cell's life cycle. One of the most significant roadblocks to developing anticancer drugs is that traditional chemotherapy affects normal cells and cancer cells, resulting in substantial side effects. Recently, advancements in new drug development methodologies and the prediction of the targeted interatomic and intermolecular ligand interaction sites have been beneficial. This has prompted further research into developing and discovering novel chemical species as preferred therapeutic compounds against specific cancer types. Identifying new drug molecules with high selectivity and specificity for cancer is a prerequisite in the treatment and management of the disease. The overexpression of HSP90 occurs in patients with cancer, and the HSP90 triggers unstable harmful kinase functions, which enhance carcinogenesis. Therefore, the development of potent HSP90 inhibitors with high selectivity and specificity becomes very imperative. The activities of HSP90 as chaperones and cochaperones are complex due to the conformational dynamism, and this could be one of the reasons why no HSP90 drugs have made it beyond the clinical trials. Nevertheless, HSP90 modulations appear to be preferred due to the competitive inhibition of the targeted N-terminal adenosine triphosphate pocket. This study, therefore, presents an overview of the various computational models implored in the development of HSP90 inhibitors as anticancer medicines. We hereby suggest an extensive investigation of advanced computational modelling of the three different domains of HSP90 for potent, effective inhibitor design with minimal off-target effects.

Recent Developments in Methicillin-Resistant Staphylococcus aureus (MRSA) Treatment: A Review

Staphylococcus aureus (S. aureus) is a Gram-positive bacterium that may cause life-threatening diseases and some minor infections in living organisms. However, it shows notorious effects when it becomes resistant to antibiotics. Strain variants of bacteria, viruses, fungi, and parasites that have become resistant to existing multiple antimicrobials are termed as superbugs. Methicillin is a semisynthetic antibiotic drug that was used to inhibit staphylococci pathogens. The S. aureus resistant to methicillin is known as methicillin-resistant Staphylococcus aureus (MRSA), which became a superbug due to its defiant activity against the antibiotics and medications most commonly used to treat major and minor infections. Successful MRSA infection management involves rapid identification of the infected site, culture and susceptibility tests, evidence-based treatment, and appropriate preventive protocols. This review describes the clinical management of MRSA pathogenesis, recent developments in rapid diagnosis, and antimicrobial treatment choices for MRSA.

Dopamine D3 receptor ligands: a patent review (2014-2020)

INTRODUCTION

Compelling evidence identified D3 dopamine receptor (D3R) as a suitable target for therapeutic intervention on CNS-associated disorders, cancer and other conditions. Several efforts have been made toward developing potent and selective ligands for modulating signalling pathways operated by these GPCRs. The rational design of D3R ligands endowed with a pharmacologically relevant profile has traditionally not encountered much support from computational methods due to a very limited knowledge of the receptor structure and of its conformational dynamics. We believe that recent progress in structural biology will change this state of affairs in the next decade.

AREAS COVERED

This review provides an overview of the recent (2014-2020) patent literature on novel classes of D3R ligands developed within the framework of CNS-related diseases, cancer and additional conditions. When possible, an in-depth description of both in vitro and in vivo generated data is presented. New therapeutic applications of known molecules with activity at D3R are discussed.

EXPERT OPINION

Building on current knowledge, future D3R-focused drug discovery campaigns will be propelled by a combination of unprecedented availability of structural information with advanced computational and analytical methods. The design of D3R ligands with the sought activity, efficacy and selectivity profile will become increasingly more streamlined.

PathDetect-SOM: A Neural Network Approach for the Identification of Pathways in Ligand Binding Simulations

Understanding the process of ligand–protein recognition is important to unveil biological mechanisms and to guide drug discovery and design. Enhanced-sampling molecular dynamics is now routinely used to simulate the ligand binding process, resulting in the need for suitable tools for the analysis of large data sets of binding events. Here, we designed, implemented, and tested PathDetect-SOM, a tool based on self-organizing maps to build concise visual models of the ligand binding pathways sampled along single simulations or replicas. The tool performs a geometric clustering of the trajectories and traces the pathways over an easily interpretable 2D map and, using an approximate transition matrix, it can build a graph model of concurrent pathways. The tool was tested on three study cases representing different types of problems and simulation techniques. A clear reconstruction of the sampled pathways was derived in all cases, and useful information on the energetic features of the processes was recovered. The tool is available at https://github.com/MottaStefano/PathDetect-SOM.

Molecular Dynamic Simulations and Molecular Docking as a Potential Way for Designed New Inhibitor Drug without Resistance

Mycobacterium tuberculosis is the cause of tuberculosis in humans and is responsible for more than 2 million deaths per year. Despite the development of anti-tuberculosis drugs (Isoniazid, Rifampicin, Ethambutol, pyrazinamide, streptomycin, etc.) and the TB vaccine, this disease has claimed the lives of many people around the world. Drug resistance in this disease is increasing day by day. Conventional methods for discovering and developing drugs are usually time-consuming and expensive. Therefore, a better method is needed to identify, design, and manufacture TB drugs without drug resistance. Bioinformatics applications in obtaining new drugs at the structural level include studies of the mechanism of drug resistance, detection of drug interactions, and prediction of mutant protein structure. In the present study, computer-based approaches including molecular dynamics simulation and molecular docking as a novel and efficient method for the identification and investigation of new cases as well as the investigation of mutated proteins and compounds will be examined .

N-Terminal-Driven Binding Mechanism of an Antigen Peptide to Human Leukocyte Antigen-A*2402 Elucidated by Multicanonical Molecular Dynamic-Based Dynamic Docking and Path Sampling Simulations

We have applied our advanced multicanonical molecular dynamics (McMD)-based dynamic docking methodology to investigate the binding mechanism of an HIV-1 Nef protein epitope to the Asian-dominant allele human leukocyte antigen (HLA)-A*2402. Even though pMHC complex formation [between a Major histocompatibility complex (MHC) class I molecule, which is encoded by an HLA allele, and an antigen peptide] is one of the fundamental processes of the adaptive human immune response, its binding mechanism has not yet been well studied, partially due to the high allelic variation of HLAs in the population. We have used our developed McMD-based dynamic docking method and have successfully reproduced the native complex structure, which is located near the free energy global minimum. Subsequent path sampling MD simulations elucidated the atomic details of the binding process and indicated that the peptide binding is initially driven by the highly positively charged N-terminus of the peptide that is attracted to the various negatively charged residues on the MHC molecule's surface. Upon nearing the pocket, the second tyrosine residue of the peptide anchors the peptide by strongly binding to the B-site of the MHC molecule via hydrophobic driven interactions, resulting in a very strong bound complex structure. Our methodology can be effectively used to predict the bound complex structures between MHC molecules and their antigens to study their binding mechanism in close detail, which would help with the development of new vaccines against cancers, as well as viral infections such as HIV and COVID-19.

Worldwide Protein Data Bank (wwPDB): A virtual treasure for research in biotechnology

The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RSCB PDB) provides a wide range of digital data regarding biology and biomedicine. This huge internet resource involves a wide range of important biological data, obtained from experiments around the globe by different scientists. The Worldwide Protein Data Bank (wwPDB) represents a brilliant collection of 3D structure data associated with important and vital biomolecules including nucleic acids (RNAs and DNAs) and proteins. Moreover, this database accumulates knowledge regarding function and evolution of biomacromolecules which supports different disciplines such as biotechnology. 3D structure, functional characteristics and phylogenetic properties of biomacromolecules give a deep understanding of the biomolecules' characteristics. An important advantage of the wwPDB database is the data updating time, which is done every week. This updating process helps users to have the newest data and information for their projects. The data and information in wwPDB can be a great support to have an accurate imagination and illustrations of the biomacromolecules in biotechnology. As demonstrated by the SARS-CoV-2 pandemic, rapidly reliable and accessible biological data for microbiology, immunology, vaccinology, and drug development are critical to address many healthcare-related challenges that are facing humanity. The aim of this paper is to introduce the readers to wwPDB, and to highlight the importance of this database in biotechnology, with the expectation that the number of scientists interested in the utilization of Protein Data Bank's resources will increase substantially in the coming years.

Photopharmacology of Ion Channels through the Light of the Computational Microscope

The optical control and investigation of neuronal activity can be achieved and carried out with photoswitchable ligands. Such compounds are designed in a modular fashion, combining a known ligand of the target protein and a photochromic group, as well as an additional electrophilic group for tethered ligands. Such a design strategy can be optimized by including structural data. In addition to experimental structures, computational methods (such as homology modeling, molecular docking, molecular dynamics and enhanced sampling techniques) can provide structural insights to guide photoswitch design and to understand the observed light-regulated effects. This review discusses the application of such structure-based computational methods to photoswitchable ligands targeting voltage- and ligand-gated ion channels. Structural mapping may help identify residues near the ligand binding pocket amenable for mutagenesis and covalent attachment. Modeling of the target protein in a complex with the photoswitchable ligand can shed light on the different activities of the two photoswitch isomers and the effect of site-directed mutations on photoswitch binding, as well as ion channel subtype selectivity. The examples presented here show how the integration of computational modeling with experimental data can greatly facilitate photoswitchable ligand design and optimization. Recent advances in structural biology, both experimental and computational, are expected to further strengthen this rational photopharmacology approach.

Accurate Binding Configuration Prediction of a G-Protein-Coupled Receptor to Its Antagonist Using Multicanonical Molecular Dynamics-Based Dynamic Docking

We have performed dynamic docking between a prototypic G-protein-coupled receptor (GPCR) system, the β₂-adrenergic receptor, and its antagonist, alprenolol, using one of the enhanced conformation sampling methods, multicanonical molecular dynamics (McMD), which does not rely on any prior knowledge for the definition of the reaction coordinate. Although we have previously applied our McMD-based dynamic docking protocol to various globular protein systems, its application to GPCR systems would be difficult because of their complicated design, which include a lipid bilayer, and because of the difficulty in sampling the configurational space of a binding site that exists deep inside the GPCR. Our simulations sampled a wide array of ligand-bound and ligand-unbound structures, and we measured 427 binding events during our 48 μs production run. Analysis of the ensemble revealed several stable and meta-stable structures, where the most stable structure at the global free energy minimum matches the experimental one. Additional canonical MD simulations were used for refinement and validation of the structures, revealing that most of the intermediates are sufficiently stable to trap the ligand in these intermediary states and furthermore validated our prediction results. Given the difficulty in reaching the orthosteric binding site, chemical optimization of the compound for the second ranking configuration, which binds near the pocket's entrance, might lead to a high-affinity allosteric inhibitor. Accordingly, we show that the application of our methodology can be used to provide crucial insights for the rational design of drugs that target GPCRs.

Phenolic Compounds from Mori Cortex Ameliorate Sodium Oleate-Induced Epithelial-Mesenchymal Transition and Fibrosis in NRK-52e Cells through CD36

Lipid deposition in the kidney can cause serious damage to the kidney, and there is an obvious epithelial–mesenchymal transition (EMT) and fibrosis in the late stage. To investigate the interventional effects and mechanisms of phenolic compounds from Mori Cortex on the EMT and fibrosis induced by sodium oleate-induced lipid deposition in renal tubular epithelial cells (NRK-52e cells), and the role played by CD36 in the adjustment process, NRK-52e cells induced by 200 μmol/L sodium oleate were given 10 μmoL/L moracin-P-2″-O-β-d-glucopyranoside (Y-1), moracin-P-3′-O-β-d-glucopyranoside (Y-2), moracin-P-3′-O-α-l-arabinopyranoside (Y-3), and moracin-P-3′-O-[β-glucopyranoside-(1→2)arabinopyranoside] (Y-4), and Oil Red O staining was used to detect lipid deposition. A Western blot was used to detect lipid deposition-related protein CD36, inflammation-related protein (p-NF-κB-P65, NF-κB-P65, IL-1β), oxidative stress-related protein (NOX1, Nrf2, Keap1), EMT-related proteins (CD31, α-SMA), and fibrosis-related proteins (TGF-β, ZEB1, Snail1). A qRT-PCR test detected inflammation, EMT, and fibrosis-related gene mRNA levels. The TNF-α levels were detected by ELISA, and the colorimetric method was used to detects SOD and MDA levels. The ROS was measured by flow cytometry. A high-content imaging analysis system was applied to observe EMT and fibrosis-related proteins. At the same time, the experiment silenced CD36 and compared the difference between before and after drug treatment, then used molecular docking technology to predict the potential binding site of the active compounds with CD36. The research results show that sodium oleate can induce lipid deposition, inflammation, oxidative stress, and fibrosis in NRK-52e cells. Y-1 and Y-2 could significantly ameliorate the damage caused by sodium oleate, and Y-2 had a better ameliorating effect, while there was no significant change in Y-3 or Y-4. The amelioration effect of Y-1 and Y-2 disappeared after silencing CD36. Molecular docking technology showed that the Y-1 and Y-2 had hydrogen bonds to CD36 and that, compared with Y-1, Y-2 requires less binding energy. In summary, moracin-P-2″-O-β-d-glucopyranoside and moracin-P-3′-O-β-d-glucopyranoside from Mori Cortex ameliorated lipid deposition, EMT, and fibrosis induced by sodium oleate in NRK-52e cells through CD36.

Differences in Gluco and Galacto Substrate-Binding Interactions in a Dual 6Pβ-Glucosidase/6Pβ-Galactosidase Glycoside Hydrolase 1 Enzyme from Bacillus licheniformis

Bacterial glycoside hydrolase 1 (GH1) enzymes with 6-phospho-β-galactosidase and 6-phospho-β-glucosidase activities have the important task of releasing phosphorylated and nonphosphorylated monosaccharides into the cytoplasm. Curiously, dual 6-phospho-β-galactosidase/6-phospho-β-glucosidase (dual-phospho) enzymes have broad specificity and are able to hydrolyze galacto- and gluco-derived substrates. This study investigates the structure and substrate specificity of a GH family 1 enzyme from Bacillus licheniformis, hereafter known as BlBglC. The enzyme structure has been solved, and sequence analysis, molecular dynamics simulations, and binding free energy calculations offered evidence of dual-phospho activity. Both test ligands p-nitrophenyl-β-d-galactoside-6-phosphate (PNP6Pgal) and p-nitrophenyl-β-d-glucoside-6-phosphate (PNP6Pglc) demonstrated strong binding to BlBglC although the pose and interactions of the PNP6Pglc triplicates were slightly more consistent. Interestingly, known specificity-inducing residues, Gln23 and Trp433, bind strongly to the ligand O3 hydroxyl group in the PNP6Pgal-BlBglC complex and to the ligand O4 hydroxyl group in the PNP6Pglc-BlBglC complex. Additionally, the BlBglC-His124 residue is a major contributor of hydrogen bonds to the PNP6Pgal O3 hydroxyl group but does not form any hydrogen bonds with PNP6Pglc. On the other hand, BlBglC residues Tyr173, Tyr301, Gln302, and Thr321 form hydrogen bonds with PNP6Pglc but not PNP6Pgal. These findings provide important details of the broad specificity of dual-phospho activity GH1 enzymes.

Antiviral peptides against the main protease of SARS-CoV-2: A molecular docking and dynamics study

The recent coronavirus outbreak has changed the world's economy and health sectors due to the high mortality and transmission rates. Because the development of new effective vaccines or treatments against the virus can take time, an urgent need exists for the rapid development and design of new drug candidates to combat this pathogen. Here, we obtained antiviral peptides obtained from the data repository of antimicrobial peptides (DRAMP) and screened their predicted tertiary structures for the ability to inhibit the main protease of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using multiple combinatorial docking programs, including PatchDock, FireDock, and ClusPro. The four best peptides, DRAMP00877, DRAMP02333, DRAMP02669, and DRAMP03804, had binding energies of -1125.3, -1084.5, -1005.2, and -924.2 Kcal/mol, respectively, as determined using ClusPro, and binding energies of -55.37, -50.96, -49.25, -54.81 Kcal/mol, respectively, as determined using FireDock, which were better binding energy values than observed for other peptide molecules. These peptides were found to bind with the active cavity of the SARS-CoV-2 main protease; at Glu166, Cys145, Asn142, Phe140, and Met165, in addition to the substrate-binding sites, Domain 2 and Domain 3, whereas fewer interactions were observed with Domain 1. The docking studies were further confirmed by a molecular dynamics simulation study, in which several descriptors, including the root-mean-square difference (RMSD), root-mean-square fluctuation (RMSF), solvent-accessible surface area (SASA), radius of gyration (Rg), and hydrogen bond formation, confirmed the stable nature of the peptide-main protease complexes. Toxicity and allergenicity studies confirmed the non-allergenic nature of the peptides. This present study suggests that these identified antiviral peptide molecules might inhibit the main protease of SARS-CoV-2, although further wet-lab experiments remain necessary to verify these findings.

Structure-based in silico approaches for drug discovery against Mycobacterium tuberculosis

Mycobacterium tuberculosis is the causative agent of TB and was estimated to cause 1.4 million death in 2019, alongside 10 million new infections. Drug resistance is a growing issue, with multi-drug resistant infections representing 3.3% of all new infections, hence novel antimycobacterial drugs are urgently required to combat this growing health emergency. Alongside this, increased knowledge of gene essentiality in the pathogenic organism and larger compound databases can aid in the discovery of new drug compounds. The number of protein structures, X-ray based and modelled, is increasing and now accounts for greater than > 80% of all predicted M. tuberculosis proteins; allowing novel targets to be investigated. This review will focus on structure-based in silico approaches for drug discovery, covering a range of complexities and computational demands, with associated antimycobacterial examples. This includes molecular docking, molecular dynamic simulations, ensemble docking and free energy calculations. Applications of machine learning onto each of these approaches will be discussed. The need for experimental validation of computational hits is an essential component, which is unfortunately missing from many current studies. The future outlooks of these approaches will also be discussed.

Targeting the JAK/STAT Pathway: A Combined Ligand- and Target-Based Approach

Janus kinases (JAKs) are a family of proinflammatory enzymes able to mediate the immune responses and the inflammatory cascade by modulating multiple cytokine expressions as well as various growth factors. In the present study, the inhibition of the JAK-signal transducer and activator of transcription (STAT) signaling pathway is explored as a potential strategy for treating autoimmune and inflammatory disorders. A computationally driven approach aimed at identifying novel JAK inhibitors based on molecular topology, docking, and molecular dynamics simulations was carried out. For the best candidates selected, the inhibitory activity against JAK2 was evaluated in vitro. Two hit compounds with a novel chemical scaffold, 4 (IC₅₀ = 0.81 μM) and 7 (IC₅₀ = 0.64 μM), showed promising results when compared with the reference drug Tofacitinib (IC₅₀ = 0.031 μM).

Computing Metal-Binding Proteins for Therapeutic Benefit

Over one third of biomolecules rely on metal ions to exert their cellular functions. Metal ions can play a structural role by stabilizing the structure of biomolecules, a functional role by promoting a wide variety of biochemical reactions, and a regulatory role by acting as messengers upon binding to proteins regulating cellular metal-homeostasis. These diverse roles in biology ascribe critical implications to metal-binding proteins in the onset of many diseases. Hence, it is of utmost importance to exhaustively unlock the different mechanistic facets of metal-binding proteins and to harness this knowledge to rationally devise novel therapeutic strategies to prevent or cure pathological states associated with metal-dependent cellular dysfunctions. In this compendium, we illustrate how the use of a computational arsenal based on docking, classical, and quantum-classical molecular dynamics simulations can contribute to extricate the minutiae of the catalytic, transport, and inhibition mechanisms of metal-binding proteins at the atomic level. This knowledge represents a fertile ground and an essential prerequisite for selectively targeting metal-binding proteins with small-molecule inhibitors aiming to (i) abrogate deregulated metal-dependent (mis)functions or (ii) leverage metal-dyshomeostasis to selectively trigger harmful cells death.

In silico validation of novel inhibitors of malarial aspartyl protease, plasmepsin V and antimalarial efficacy prediction

Plasmepsin V (Plm V) is an essential aspartic protease required for survival of the malaria parasite, Plasmodium falciparum (Pf). Plm V is required for cleaving the PEXEL motifs of many Pf proteins and its inhibition leads to a knockout effect, indicating its suitability as potential drug target. To decipher new inhibitors of PfPlm V, molecular docking of four HIV-1 protease inhibitors active against PfPlmV was performed on Glide module of Schrödinger suite that supported saquinavir as a lead drug, and therefore, selected as a control. Saquinavir contains an important hydroxyethylamine (HEA) pharmacophore, which was utilized as backbone coupled with piperazine scaffold to build new library of compounds. Newly designed HEA compounds were screened virtually against Plm V. Molecular docking led to a few hits (1 and 3) with higher docking score over the control drug. Notably, compound 1 showed the highest docking score (-11.90 kcal/mol) and XP Gscore (-11.948 kcal/mol). The Prime MMGBSA binding free energy for compound 1 (-60.88 kcal/mol) and 3 (-50.96 kcal/mol) was higher than saquinavir (-37.51 kcal/mol). The binding free energy for the last frame of molecular dynamic simulation supported compound 1 (-92.88 kcal/mol) as potent inhibitor of PfPlm V over saquinavir (-72.77 kcal/mol), and thus, deserves experimental validations in culture and subsequently in animal models.Communicated by Ramaswamy H. Sarma.

Cryptic-site binding mechanism of medium-sized Bcl-xL inhibiting compounds elucidated by McMD-based dynamic docking simulations

We have performed multicanonical molecular dynamics (McMD) based dynamic docking simulations to study and compare the binding mechanism between two medium-sized inhibitors (ABT-737 and WEHI-539) that bind to the cryptic site of Bcl-xL, by exhaustively sampling the conformational and configurational space. Cryptic sites are binding pockets that are transiently formed in the apo state or are induced upon ligand binding. Bcl-xL, a pro-survival protein involved in cancer progression, is known to have a cryptic site, whereby the shape of the pocket depends on which ligand is bound to it. Starting from the apo-structure, we have performed two independent McMD-based dynamic docking simulations for each ligand, and were able to obtain near-native complex structures in both cases. In addition, we have also studied their interactions along their respective binding pathways by using path sampling simulations, which showed that the ligands form stable binding configurations via predominantly hydrophobic interactions. Although the protein started from the apo state, both ligands modulated the pocket in different ways, shifting the conformational preference of the sub-pockets of Bcl-xL. We demonstrate that McMD-based dynamic docking is a powerful tool that can be effectively used to study binding mechanisms involving a cryptic site, where ligand binding requires a large conformational change in the protein to occur.

A novel strategy for engineering of a smart generation of immune ribonucleases against EGFR+ cells

The overexpression of epidermal growth factor receptor (EGFR) could result in the development of solid tumors of prostate, breast, gastric, colorectal, ovarian, and head and neck, leading to carcinoma. Antibody therapies are ideal methods to overcome malignant diseases. However, immunoribonucleases are a new generation of antibodies in which an RNase binds to a specific antibody and shows a stronger ability to terminate cancer cells. In this study, we engineered Rana pipiens RNase to bind to the scFv of human antiepidermal growth factor receptor antibody. The molecular dynamic simulations confirmed protein stability and the ability of scFv-ranpirnase (rantoxin) to bind to epidermal growth factor receptor protein. Then, the rantoxin construct was synthesized in a pCDNA 3.1 Neo vector. CHO-K1 cells were used as expression hosts and the construct was transfected. Cells were selected by antibiotic therapies using neomycin, 120 mg/ml, and the high-yield colony was screened by real-time polymerase chain reaction (PCR) methods. Then, the recombinant protein production was confirmed using the sodium dodecyl sulfate polyacrylamide gel electrophoresis and western blot analyses. The molecular dynamic simulation (MDS) confirmed that the I467, S468, Q408, and H409 amino acids of EGFR bonded well to rantoxin. As revealed by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and western blot analyses, the rantoxin production and PCR analysis showed that the T3 colony can produce rantoxin messenger RNA fourfold higher than the GAPDH gene. The immunotoxin function was assessed in A431 cancer cells and EGFR-negative HEK293 cells, and IC₅₀  values were estimated to be 22.4 ± 3 and >620.4 ± 5 nM, respectively. The results indicated that the immunotoxins produced in this study had the potential for use as anticancer drugs.

Dynamic Docking Using Multicanonical Molecular Dynamics: Simulating Complex Formation at the Atomistic Level

Multicanonical molecular dynamics (McMD)-based dynamic docking has been applied to predict the native binding configurations for several protein receptors and their ligands. Due to the enhanced sampling capabilities of McMD, it can exhaustively sample bound and unbound ligand configurations, as well as receptor conformations, and thus enables efficient sampling of the conformational and configurational space, not possible using canonical MD simulations. As McMD samples a wide configurational space, extensive analysis is required to study the diverse ensemble consisting of bound and unbound structures. By projecting the reweighted ensemble onto the first two principal axes obtained via principal component analysis of the multicanonical ensemble, the free energy landscape (FEL) can be obtained. Further analysis produces representative structures positioned at the local minima of the FEL, where these structures are then ranked by their free energy. In this chapter, we describe our dynamic docking methodology, which has successfully reproduced the native binding configuration for small compounds, medium-sized compounds, and peptide molecules.

Machine Learning of Allosteric Effects: The Analysis of Ligand-Induced Dynamics to Predict Functional Effects in TRAP1

Allosteric molecules provide a powerful means to modulate protein function. However, the effect of such ligands on distal orthosteric sites cannot be easily described by classical docking methods. Here, we applied machine learning (ML) approaches to expose the links between local dynamic patterns and different degrees of allosteric inhibition of the ATPase function in the molecular chaperone TRAP1. We focused on 11 novel allosteric modulators with similar affinities to the target but with inhibitory efficacy between the 26.3 and 76%. Using a set of experimentally related local descriptors, ML enabled us to connect the molecular dynamics (MD) accessible to ligand-bound (perturbed) and unbound (unperturbed) systems to the degree of ATPase allosteric inhibition. The ML analysis of the comparative perturbed ensembles revealed a redistribution of dynamic states in the inhibitor-bound versus inhibitor-free systems following allosteric binding. Linear regression models were built to quantify the percentage of experimental variance explained by the predicted inhibitor-bound TRAP1 states. Our strategy provides a comparative MD–ML framework to infer allosteric ligand functionality. Alleviating the time scale issues which prevent the routine use of MD, a combination of MD and ML represents a promising strategy to support in silico mechanistic studies and drug design.

Thermodynamics and Kinetics of Drug-Target Binding by Molecular Simulation

Computational studies play an increasingly important role in chemistry and biophysics, mainly thanks to improvements in hardware and algorithms. In drug discovery and development, computational studies can reduce the costs and risks of bringing a new medicine to market. Computational simulations are mainly used to optimize promising new compounds by estimating their binding affinity to proteins. This is challenging due to the complexity of the simulated system. To assess the present and future value of simulation for drug discovery, we review key applications of advanced methods for sampling complex free-energy landscapes at near nonergodicity conditions and for estimating the rate coefficients of very slow processes of pharmacological interest. We outline the statistical mechanics and computational background behind this research, including methods such as steered molecular dynamics and metadynamics. We review recent applications to pharmacology and drug discovery and discuss possible guidelines for the practitioner. Recent trends in machine learning are also briefly discussed. Thanks to the rapid development of methods for characterizing and quantifying rare events, simulation's role in drug discovery is likely to expand, making it a valuable complement to experimental and clinical approaches.

Merging Ligand-Based and Structure-Based Methods in Drug Discovery: An Overview of Combined Virtual Screening Approaches

Virtual screening (VS) is an outstanding cornerstone in the drug discovery pipeline. A variety of computational approaches, which are generally classified as ligand-based (LB) and structure-based (SB) techniques, exploit key structural and physicochemical properties of ligands and targets to enable the screening of virtual libraries in the search of active compounds. Though LB and SB methods have found widespread application in the discovery of novel drug-like candidates, their complementary natures have stimulated continued efforts toward the development of hybrid strategies that combine LB and SB techniques, integrating them in a holistic computational framework that exploits the available information of both ligand and target to enhance the success of drug discovery projects. In this review, we analyze the main strategies and concepts that have emerged in the last years for defining hybrid LB + SB computational schemes in VS studies. Particularly, attention is focused on the combination of molecular similarity and docking, illustrating them with selected applications taken from the literature.

Structural analysis, virtual screening and molecular simulation to identify potential inhibitors targeting 2'-O-ribose methyltransferase of SARS-CoV-2 coronavirus

SARS-CoV-2, an emerging coronavirus, has spread rapidly around the world, resulting in over ten million cases and more than half a million deaths as of July 1, 2020. Effective treatments and vaccines for SARS-CoV-2 infection do not currently exist. Previous studies demonstrated that nonstructural protein 16 (nsp16) of coronavirus is an S-adenosyl methionine (SAM)-dependent 2'-O-methyltransferase (2'-O-MTase) that has an important role in viral replication and prevents recognition by the host innate immune system. In the present study, we employed structural analysis, virtual screening, and molecular simulation approaches to identify clinically investigated and approved drugs which can act as promising inhibitors against nsp16 2'-O-MTase of SARS-CoV-2. Comparative analysis of primary amino acid sequences and crystal structures of seven human CoVs defined the key residues for nsp16 2-O'-MTase functions. Virtual screening and docking analysis ranked the potential inhibitors of nsp16 from more than 4,500 clinically investigated and approved drugs. Furthermore, molecular dynamics simulations were carried out on eight top candidates, including Hesperidin, Rimegepant, Gs-9667, and Sonedenoson, to calculate various structural parameters and understand the dynamic behavior of the drug-protein complexes. Our studies provided the foundation to further test and repurpose these candidate drugs experimentally and/or clinically for COVID-19 treatment.Communicated by Ramaswamy H. Sarma.

Data-Driven Molecular Dynamics: A Multifaceted Challenge

The big data concept is currently revolutionizing several fields of science including drug discovery and development. While opening up new perspectives for better drug design and related strategies, big data analysis strongly challenges our current ability to manage and exploit an extraordinarily large and possibly diverse amount of information. The recent renewal of machine learning (ML)-based algorithms is key in providing the proper framework for addressing this issue. In this respect, the impact on the exploitation of molecular dynamics (MD) simulations, which have recently reached mainstream status in computational drug discovery, can be remarkable. Here, we review the recent progress in the use of ML methods coupled to biomolecular simulations with potentially relevant implications for drug design. Specifically, we show how different ML-based strategies can be applied to the outcome of MD simulations for gaining knowledge and enhancing sampling. Finally, we discuss how intrinsic limitations of MD in accurately modeling biomolecular systems can be alleviated by including information coming from experimental data.

The application of the MM/GBSA method in the binding pose prediction of FGFR inhibitors

The success of a structure-based drug is highly dependent on a known binding pose of the protein-ligand system. However, this is not always available. In this study, we set out to explore the applicability of the popular and easy-to-use MD-based MM/GBSA method to determine the binding poses of known FGFR inhibitors. It was found that MM/GBSA combined with 100 ns of MD simulation significantly improved the success rate of docking methods from 30-40% to 70%. This work demonstrates a way that the MM/GBSA method can be more accurate than it is in ligand ranking, filling a gap in structure-based drug discovery when the binding pose is unknown.

SQM/COSMO Scoring Function: Reliable Quantum-Mechanical Tool for Sampling and Ranking in Structure-Based Drug Design

Quantum mechanical (QM) methods have been gaining importance in structure-based drug design where a reliable description of protein-ligand interactions is of utmost significance. However, strategies i. e. QM/MM, fragmentation or semiempirical (SQM) methods had to be pursued to overcome the unfavorable scaling of QM methods. Various SQM-based approaches have significantly contributed to the accuracy of docking and improvement of lead compounds. Parametrizations of SQM and implicit solvent methods in our laboratory have been instrumental to obtain a reliable SQM-based scoring function. The experience gained in its application for activity ranking of ligands binding to tens of protein targets resulted in setting up a faster SQM/COSMO scoring approach, which outperforms standard scoring methods in native pose identification for two dozen protein targets with ten thousand poses. Recently, SQM/COSMO was effectively applied in a proof-of-concept study of enrichment in virtual screening. Due to its superior performance, feasibility and chemical generality, we propose the SQM/COSMO approach as an efficient tool in structure-based drug design.