Recent advances in dynamic docking for drug discovery

Design and evaluation of α-helix-based peptide inhibitors for blocking PD-1/PD-L1 interaction

The current research in tumor immunotherapy indicates that blocking the protein-protein interaction (PPI) between PD-1 and its ligand, PD-L1, may be one of the most effective treatments for cancer patients. The α-helix is a common elements of protein secondary structure and is often involved in protein interaction. Thus, α-helix-based peptides could mimic proteins involved in such interactions and are also capable of modulating PPI in vivo. In this study, starting from a potential α-helix-rich protein, we designed a series of α-helix-based peptide candidates to block PD-1/PD-L1 interaction. These candidates were first screened using molecular docking and molecular dynamics simulations, and then their capacities to inhibit PD-1/PD-L1 interactions and to restore antitumor immune activities were investigated using the HTRF assay, SPR assay, cellular co-culture experiments and animal model experiments. Two peptides exhibited the best anti-tumor effects and the strong ability to restore the immunity of tumor-infiltrating T-cells. Further D-amino acid substitution was employed to improve the serum stability of peptide candidate, making the intravenous administration easier while maintaining the therapeutic efficacy. The resultant peptides showed promise as checkpoint inhibitors for application in tumor immunotherapy. These findings suggested that our strategy for developing peptides starting from an α-helical structure could be used in the design of bioactive inhibitors to potential block protein-protein interactions.

Unbinding Kinetics of Muscarinic M3 Receptor Antagonists Explained by Metadynamics Simulations

The residence time (RT), the time for which a drug remains bound to its biological target, is a critical parameter for drug design. The prediction of this key kinetic property has been proven to be challenging and computationally demanding in the framework of atomistic simulations. In the present work, we setup and applied two distinct metadynamics protocols to estimate the RTs of muscarinic M3 receptor antagonists. In the first method, derived from the conformational flooding approach, the kinetics of unbinding is retrieved from a physics-based parameter known as the acceleration factor α (i.e., the running average over time of the potential deposited in the bound state). Such an approach is expected to recover the absolute RT value for a compound of interest. In the second method, known as the t_META-D approach, a qualitative estimation of the RT is given by the time of simulation required to drive the ligand from the binding site to the solvent bulk. This approach has been developed to reproduce the change of experimental RTs for compounds targeting the same target. Our analysis shows that both computational protocols are able to rank compounds in agreement with their experimental RTs. Quantitative structure-kinetics relationship (SKR) models can be identified and employed to predict the impact of a chemical modification on the experimental RT once a calibration study has been performed.

Alchemical Free Energy Calculations to Investigate Protein-Protein Interactions: the Case of the CDC42/PAK1 Complex

Here, we show that alchemical free energy calculations can quantitatively compute the effect of mutations at the protein–protein interface. As a test case, we have used the protein complex formed by the small Rho-GTPase CDC42 and its downstream effector PAK1, a serine/threonine kinase. Notably, the CDC42/PAK1 complex offers a wealth of structural, mutagenesis, and binding affinity data because of its central role in cellular signaling and cancer progression. In this context, we have considered 16 mutations in the CDC42/PAK1 complex and obtained excellent agreement between computed and experimental data on binding affinity. Importantly, we also show that a careful analysis of the side-chain conformations in the mutated amino acids can considerably improve the computed estimates, solving issues related to sampling limitations. Overall, this study demonstrates that alchemical free energy calculations can conveniently be integrated into the design of experimental mutagenesis studies.

Quercetin and luteolin are single-digit micromolar inhibitors of the SARS-CoV-2 RNA-dependent RNA polymerase

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly become a global health pandemic. Among the viral proteins, RNA-dependent RNA polymerase (RdRp) is responsible for viral genome replication and has emerged as one of the most promising targets for pharmacological intervention against SARS-CoV-2. To this end, we experimentally tested luteolin and quercetin for their ability to inhibit the RdRp enzyme. These two compounds are ancestors of flavonoid natural compounds known for a variety of basal pharmacological activities. Luteolin and quercetin returned a single-digit IC₅₀ of 4.6 µM and 6.9 µM, respectively. Then, through dynamic docking simulations, we identified possible binding modes of these compounds to a recently published cryo-EM structure of RdRp. Collectively, these data indicate that these two compounds are a valid starting point for further optimization and development of a new class of RdRp inhibitors to treat SARS-CoV-2 and potentially other viral infections.

RNA-ligand molecular docking: advances and challenges

With rapid advances in computer algorithms and hardware, fast and accurate virtual screening has led to a drastic acceleration in selecting potent small molecules as drug candidates. Computational modeling of RNA-small molecule interactions has become an indispensable tool for RNA-targeted drug discovery. The current models for RNA-ligand binding have mainly focused on the docking-and-scoring method. Accurate docking and scoring should tackle four crucial problems: (1) conformational flexibility of ligand, (2) conformational flexibility of RNA, (3) efficient sampling of binding sites and binding poses, and (4) accurate scoring of different binding modes. Moreover, compared with the problem of protein-ligand docking, predicting ligand binding to RNA, a negatively charged polymer, is further complicated by additional effects such as metal ion effects. Thermodynamic models based on physics-based and knowledge-based scoring functions have shown highly encouraging success in predicting ligand binding poses and binding affinities. Recently, kinetic models for ligand binding have further suggested that including dissociation kinetics (residence time) in ligand docking would result in improved performance in estimating in vivo drug efficacy. More recently, the rise of deep-learning approaches has led to new tools for predicting RNA-small molecule binding. In this review, we present an overview of the recently developed computational methods for RNA-ligand docking and their advantages and disadvantages.

Structure-based design of CDC42 effector interaction inhibitors for the treatment of cancer

CDC42 family GTPases (RHOJ, RHOQ, CDC42) are upregulated but rarely mutated in cancer and control both the ability of tumor cells to invade surrounding tissues and the ability of endothelial cells to vascularize tumors. Here, we use computer-aided drug design to discover a chemical entity (ARN22089) that has broad activity against a panel of cancer cell lines, inhibits S6 phosphorylation and MAPK activation, activates pro-inflammatory and apoptotic signaling, and blocks tumor growth and angiogenesis in 3D vascularized microtumor models (VMT) in vitro. Additionally, ARN22089 has a favorable pharmacokinetic profile and can inhibit the growth of BRAF mutant mouse melanomas and patient-derived xenografts in vivo. ARN22089 selectively blocks CDC42 effector interactions without affecting the binding between closely related GTPases and their downstream effectors. Taken together, we identify a class of therapeutic agents that influence tumor growth by modulating CDC42 signaling in both the tumor cell and its microenvironment.

Computer-Aided Drug Design (CADD) to De-Orphanize Marine Molecules: Finding Potential Therapeutic Agents for Neurodegenerative and Cardiovascular Diseases

Computer-aided drug design (CADD) techniques allow the identification of compounds capable of modulating protein functions in pathogenesis-related pathways, which is a promising line on drug discovery. Marine natural products (MNPs) are considered a rich source of bioactive compounds, as the oceans are home to much of the planet's biodiversity. Biodiversity is directly related to chemodiversity, which can inspire new drug discoveries. Therefore, natural products (NPs) in general, and MNPs in particular, have been used for decades as a source of inspiration for the design of new drugs. However, NPs present both opportunities and challenges. These difficulties can be technical, such as the need to dive or trawl to collect the organisms possessing the compounds, or biological, due to their particular marine habitats and the fact that they can be uncultivable in the laboratory. For all these difficulties, the contributions of CADD can play a very relevant role in simplifying their study, since, for example, no biological sample is needed to carry out an in-silico analysis. Therefore, the amount of natural product that needs to be used in the entire preclinical and clinical study is significantly reduced. Here, we exemplify how this combination between CADD and MNPs can help unlock their therapeutic potential. In this study, using a set of marine invertebrate molecules, we elucidate their possible molecular targets and associated therapeutic potential, establishing a pipeline that can be replicated in future studies.

Support Vector Machine as a Supervised Learning for the Prioritization of Novel Potential SARS-CoV-2 Main Protease Inhibitors

In the last year, the COVID-19 pandemic has highly affected the lifestyle of the world population, encouraging the scientific community towards a great effort on studying the infection molecular mechanisms. Several vaccine formulations are nowadays available and helping to reach immunity. Nevertheless, there is a growing interest towards the development of novel anti-covid drugs. In this scenario, the main protease (Mpro) represents an appealing target, being the enzyme responsible for the cleavage of polypeptides during the viral genome transcription. With the aim of sharing new insights for the design of novel Mpro inhibitors, our research group developed a machine learning approach using the support vector machine (SVM) classification. Starting from a dataset of two million commercially available compounds, the model was able to classify two hundred novel chemo-types as potentially active against the viral protease. The compounds labelled as actives by SVM were next evaluated through consensus docking studies on two PDB structures and their binding mode was compared to well-known protease inhibitors. The best five compounds selected by consensus docking were then submitted to molecular dynamics to deepen binding interactions stability. Of note, the compounds selected via SVM retrieved all the most important interactions known in the literature.

Molecular Human Targets of Bioactive Alkaloid-Type Compounds from Tabernaemontana cymose Jacq

Alkaloids are a group of secondary metabolites that have been widely studied for the discovery of new drugs due to their properties on the central nervous system and their anti-inflammatory, antioxidant and anti-cancer activities. Molecular docking was performed for 10 indole alkaloids identified in the ethanol extract of Tabernaemontana cymosa Jacq. with 951 human targets involved in different diseases. The results were analyzed through the KEGG and STRING databases, finding the most relevant physiological associations for alkaloids. The molecule 5-oxocoronaridine proved to be the most active molecule against human proteins (binding energy affinity average = −9.2 kcal/mol) and the analysis of the interactions between the affected proteins pointed to the PI3K/ Akt/mTOR signaling pathway as the main target. The above indicates that indole alkaloids from T. cymosa constitute a promising source for the search and development of new treatments against different types of cancer.

Identification of Potential SARS-CoV-2 Main Protease and Spike Protein Inhibitors from the Genus Aloe: An In Silico Study for Drug Development

Severe acute respiratory syndrome coronavirus (SARS-CoV-2) disease is a global rapidly spreading virus showing very high rates of complications and mortality. Till now, there is no effective specific treatment for the disease. Aloe is a rich source of isolated phytoconstituents that have an enormous range of biological activities. Since there are no available experimental techniques to examine these compounds for antiviral activity against SARS-CoV-2, we employed an in silico approach involving molecular docking, dynamics simulation, and binding free energy calculation using SARS-CoV-2 essential proteins as main protease and spike protein to identify lead compounds from Aloe that may help in novel drug discovery. Results retrieved from docking and molecular dynamics simulation suggested a number of promising inhibitors from Aloe. Root mean square deviation (RMSD) and root mean square fluctuation (RMSF) calculations indicated that compounds 132, 134, and 159 were the best scoring compounds against main protease, while compounds 115, 120, and 131 were the best scoring ones against spike glycoprotein. Compounds 120 and 131 were able to achieve significant stability and binding free energies during molecular dynamics simulation. In addition, the highest scoring compounds were investigated for their pharmacokinetic properties and drug-likeness. The Aloe compounds are promising active phytoconstituents for drug development for SARS-CoV-2.

In silico investigation on alkaloids of Rauwolfia serpentina as potential inhibitors of 3-hydroxy-3-methyl-glutaryl-CoA reductase

Present work aimed to investigate the in silico activity of the alkaloids of roots of Rauwolfia serpentina as inhibitors of 3-hydroxy-3-methyl-glutaryl-CoA reductase (HMGCR). For this purpose, the three-dimensional (3D) structure of the protein HMGCR (PDB ID: 1HW9) was downloaded from Protein Data Bank (PDB) database, as a target enzyme. The structures of twelve alkaloids from the roots of R. serpentina were selected as ligands and docked with the selected HMGCR enzyme using Molegro Virtual Docker (MVD) software. The software ‘MVD’ computes the binding (atom) energies of selected protein (enzyme) and each ligand at minimum energetic conformation state by using the PLP (Piecewise Linear Potential) scoring mechanism. Docking results of twelve tested alkaloids showed that five alkaloids including compound 1 (ajmalicine), 2 (reserpine), 3 (indobinine), 4 (yohimbine), and 5 (indobine) have displayed the highest MolDock scores and best fit within the prominent active site residues (positioned between 684 and 692 of cis-loop) of HMGCR. According to the lowest MolDock energies obtained through non-covalent interactions of alkaloids with HMGCR, these are characterized to be the potential inhibitors of HMGCR. Therefore, the alkaloids from R. serpentina can effectively suppress the cholesterol biosynthesis pathway through inhibition of HMGCR and can serve as potential lead compounds for the development of new drugs for the treatment of hyperlipidaemia.

Assessing hERG1 Blockade from Bayesian Machine-Learning-Optimized Site Identification by Ligand Competitive Saturation Simulations

Drug-induced cardiotoxicity is a potentially lethal and yet one of the most common side effects with the drugs in clinical use. Most of the drug-induced cardiotoxicity is associated with an off-target pharmacological blockade of K+ currents carried out by the cardiac Human-Ether-a-go-go-Related (hERG1) potassium channel. There is a compulsory preclinical stage safety assessment for the hERG1 blockade for all classes of drugs, which adds substantially to the cost of drug development. The availability of a high-resolution cryogenic electron microscopy (cryo-EM) structure for the channel in its open/depolarized state solved in 2017 enabled the application of molecular modeling for rapid assessment of drug blockade by molecular docking and simulation techniques. More importantly, if successful, in silico methods may allow a path to lead-compound salvaging by mapping out key block determinants. Here, we report the blind application of the site identification by the ligand competitive saturation (SILCS) protocol to map out druggable/regulatory hotspots in the hERG1 channel available for blockers and activators. The SILCS simulations use small solutes representative of common functional groups to sample the chemical space for the entire protein and its environment using all-atom simulations. The resulting chemical maps, FragMaps, explicitly account for receptor flexibility, protein-fragment interactions, and fragment desolvation penalty allowing for rapid ranking of potential ligands as blockers or nonblockers of hERG1. To illustrate the power of the approach, SILCS was applied to a test set of 55 blockers with diverse chemical scaffolds and pIC50 values measured under uniform conditions. The original SILCS model was based on the all-atom modeling of the hERG1 channel in an explicit lipid bilayer and was further augmented with a Bayesian-optimization/machine-learning (BML) stage employing an independent literature-derived training set of 163 molecules. BML approach was used to determine weighting factors for the FragMaps contributions to the scoring function. pIC50 predictions from the combined SILCS/BML approach to the 55 blockers showed a Pearson correlation (PC) coefficient of >0.535 relative to the experimental data. SILCS/BML model was shown to yield substantially improved performance as compared to commonly used rigid and flexible molecular docking methods for a well-established cohort of hERG1 blockers, where no correlation with experimental data was recorded. SILCS/BML results also suggest that a proper weighting of protonation states of common blockers present at physiological pH is essential for accurate predictions of blocker potency. The precalculated and optimized SILCS FragMaps can now be used for the rapid screening of small molecules for their cardiotoxic potential as well as for exploring alternative binding pockets in the hERG1 channel with applications to the rational design of activators.

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.

Metadynamics as a Postprocessing Method for Virtual Screening with Application to the Pseudokinase Domain of JAK2

With standard scoring methods, top-ranked compounds from virtual screening by docking often turn out to be inactive. For this reason, metadynamics, a method used to sample rare events, was studied to further evaluate docking poses with the aim of reducing false positives. Specifically, virtual screening was performed with Glide SP to seek potential molecules to bind to the ATP site in the pseudokinase domain of JAK2 kinase, and promising compounds were selected from the top-ranked 1000 based on visualization. Rescoring with Glide XP, GOLD, and MM/GBSA was unable to differentiate well between active and inactive compounds. Metadynamics was then used to gauge the relative binding affinity from the required time or the potential of mean force needed to dissociate the ligand from the bound complex. With consideration of previously known binders of varying affinities, metadynamics was able to differentiate between the most active compounds and inactive or weakly active ones, and it could identify correctly most of the selected virtual screening compounds as false positives. Thus, metadynamics has the potential to be a viable postprocessing method for virtual screening, minimizing the expense of buying or synthesizing inactive compounds.

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.

Design, Synthesis, Dynamic Docking, Biochemical Characterization, and in Vivo Pharmacokinetics Studies of Novel Topoisomerase II Poisons with Promising Antiproliferative Activity

We previously reported a first set of hybrid topoisomerase II (topoII) poisons whose chemical core merges key pharmacophoric elements of etoposide and merbarone, which are two well-known topoII blockers. Here, we report on the expansion of this hybrid molecular scaffold and present 16 more hybrid derivatives that have been designed, synthesized, and characterized for their ability to block topoII and for their overall drug-like profile. Some of these compounds act as topoII poison and exhibit good solubility, metabolic (microsomal) stability, and promising cytotoxicity in three cancer cell lines (DU145, HeLa, A549). Compound 3f (ARN24139) is the most promising drug-like candidate, with a good pharmacokinetics profile in vivo. Our results indicate that this hybrid new chemical class of topoII poisons deserves further exploration and that 3f is a favorable lead candidate as a topoII poison, meriting future studies to test its efficacy in in vivo tumor models.

Combined strategies in structure-based virtual screening

The identification and optimization of lead compounds are inalienable components in drug design and discovery pipelines. As a powerful computational approach for the identification of hits with novel structural scaffolds, structure-based virtual screening (SBVS) has exhibited a remarkably increasing influence in the early stages of drug discovery. During the past decade, a variety of techniques and algorithms have been proposed and tested with different purposes in the scope of SBVS. Although SBVS has been a common and proven technology, it still shows some challenges and problems that are needed to be addressed, where the negative influence regardless of protein flexibility and the inaccurate prediction of binding affinity are the two major challenges. Here, focusing on these difficulties, we summarize a series of combined strategies or workflows developed by our group and others. Furthermore, several representative successful applications from recent publications are also discussed to demonstrate the effectiveness of the combined SBVS strategies in drug discovery campaigns.

Binding site matching in rational drug design: algorithms and applications

Interactions between proteins and small molecules are critical for biological functions. These interactions often occur in small cavities within protein structures, known as ligand-binding pockets. Understanding the physicochemical qualities of binding pockets is essential to improve not only our basic knowledge of biological systems, but also drug development procedures. In order to quantify similarities among pockets in terms of their geometries and chemical properties, either bound ligands can be compared to one another or binding sites can be matched directly. Both perspectives routinely take advantage of computational methods including various techniques to represent and compare small molecules as well as local protein structures. In this review, we survey 12 tools widely used to match pockets. These methods are divided into five categories based on the algorithm implemented to construct binding-site alignments. In addition to the comprehensive analysis of their algorithms, test sets and the performance of each method are described. We also discuss general pharmacological applications of computational pocket matching in drug repurposing, polypharmacology and side effects. Reflecting on the importance of these techniques in drug discovery, in the end, we elaborate on the development of more accurate meta-predictors, the incorporation of protein flexibility and the integration of powerful artificial intelligence technologies such as deep learning.

The In Silico Drug Discovery Toolbox: Applications in Lead Discovery and Optimization

BACKGROUND

Discovery and development of a new drug is a long lasting and expensive journey that takes around 20 years from starting idea to approval and marketing of new medication. Despite R&D expenditures have been constantly increasing in the last few years, the number of new drugs introduced into market has been steadily declining. This is mainly due to preclinical and clinical safety issues, which still represent about 40% of drug discontinuation. To cope with this issue, a number of in silico techniques are currently being used for an early stage evaluation/prediction of potential safety issues, allowing to increase the drug-discovery success rate and reduce costs associated with the development of a new drug.

METHODS

In the present review, we will analyse the early steps of the drug-discovery pipeline, describing the sequence of steps from disease selection to lead optimization and focusing on the most common in silico tools used to assess attrition risks and build a mitigation plan.

RESULTS

A comprehensive list of widely used in silico tools, databases, and public initiatives that can be effectively implemented and used in the drug discovery pipeline has been provided. A few examples of how these tools can be problem-solving and how they may increase the success rate of a drug discovery and development program have been also provided. Finally, selected examples where the application of in silico tools had effectively contributed to the development of marketed drugs or clinical candidates will be given.

CONCLUSION

The in silico toolbox finds great application in every step of early drug discovery: (i) target identification and validation; (ii) hit identification; (iii) hit-to-lead; and (iv) lead optimization. Each of these steps has been described in details, providing a useful overview on the role played by in silico tools in the decision-making process to speed-up the discovery of new drugs.

Smoothed Potential MD Simulations for Dissociation Kinetics of Etoposide To Unravel Isoform Specificity in Targeting Human Topoisomerase II

Human type II topoisomerases (TopoII) are essential for controlling DNA topology within the cell. For this reason, there are a number of TopoII-targeted anticancer drugs that act by inducing DNA cleavage mediated by both TopoII isoforms (TopoIIα and TopoIIβ) in cells. However, recent studies suggest that specific poisoning of TopoIIα may be a safer strategy for treating cancer. This is because poisoning of TopoIIβ appears to be linked to the generation of secondary leukemia in patients. We recently reported that enzyme-mediated DNA cleavage complexes (in which TopoII is covalently linked to the cleaved DNA during catalysis) formed in the presence of the anticancer drug etoposide persisted approximately 3-fold longer with TopoIIα than TopoIIβ. Notably, enhanced drug-target residence time may reduce the adverse effects of specific TopoIIα poisons. However, it is still not clear how to design drugs that are specific for the α isoform. In this study, we report the results of classical molecular dynamics (MD) simulations to comparatively analyze the molecular interactions formed within the TopoII/DNA/etoposide complex with both isoforms. We also used smoothed potential MD to estimate etoposide dissociation kinetics from the two isoform complexes. These extensive classical and enhanced sampling simulations revealed stabilizing interactions of etoposide with two serine residues (Ser763 and Ser800) in TopoIIα. These interactions are missing in TopoIIβ, where both amino acids are alanine residues. This may explain the greater persistence of etoposide-stabilized cleavage complexes formed with Topo TopoIIα. These findings could be useful for the rational design of specific TopoIIα poisons.

Molecular Docking: Shifting Paradigms in Drug Discovery

Molecular docking is an established in silico structure-based method widely used in drug discovery. Docking enables the identification of novel compounds of therapeutic interest, predicting ligand-target interactions at a molecular level, or delineating structure-activity relationships (SAR), without knowing a priori the chemical structure of other target modulators. Although it was originally developed to help understanding the mechanisms of molecular recognition between small and large molecules, uses and applications of docking in drug discovery have heavily changed over the last years. In this review, we describe how molecular docking was firstly applied to assist in drug discovery tasks. Then, we illustrate newer and emergent uses and applications of docking, including prediction of adverse effects, polypharmacology, drug repurposing, and target fishing and profiling, discussing also future applications and further potential of this technique when combined with emergent techniques, such as artificial intelligence.

Kinetics of Drug Binding and Residence Time

The kinetics of drug binding and unbinding is assuming an increasingly crucial role in the long, costly process of bringing a new medicine to patients. For example, the time a drug spends in contact with its biological target is known as residence time (the inverse of the kinetic constant of the drug-target unbinding, 1/ koff). Recent reports suggest that residence time could predict drug efficacy in vivo, perhaps even more effectively than conventional thermodynamic parameters (free energy, enthalpy, entropy). There are many experimental and computational methods for predicting drug-target residence time at an early stage of drug discovery programs. Here, we review and discuss the methodological approaches to estimating drug binding kinetics and residence time. We first introduce the theoretical background of drug binding kinetics from a physicochemical standpoint. We then analyze the recent literature in the field, starting from the experimental methodologies and applications thereof and moving to theoretical and computational approaches to the kinetics of drug binding and unbinding. We acknowledge the central role of molecular dynamics and related methods, which comprise a great number of the computational methods and applications reviewed here. However, we also consider kinetic Monte Carlo. We conclude with the outlook that drug (un)binding kinetics may soon become a go/no go step in the discovery and development of new medicines.

Energy Transfer as A Driving Force in Nucleic Acid⁻Protein Interactions

Many nucleic acid–protein structures have been resolved, though quantitative structure-activity relationship remains unclear in many cases. Thrombin complexes with G-quadruplex aptamers are striking examples of a lack of any correlation between affinity, interface organization, and other common parameters. Here, we tested the hypothesis that affinity of the aptamer–protein complex is determined with the capacity of the interface to dissipate energy of binding. Description and detailed analysis of 63 nucleic acid–protein structures discriminated peculiarities of high-affinity nucleic acid–protein complexes. The size of the amino acid sidechain in the interface was demonstrated to be the most significant parameter that correlates with affinity of aptamers. This observation could be explained in terms of need of efficient energy transfer from interacting residues. Application of energy dissipation theory provided an illustrative tool for estimation of efficiency of aptamer–protein complexes. These results are of great importance for a design of efficient aptamers.

NanoModeler: A Webserver for Molecular Simulations and Engineering of Nanoparticles

Functionalized nanoparticles (NPs) are at the frontier of nanoscience. They hold the promise of innovative applications for human health and technology. In this context, molecular dynamics (MD) simulations of NPs are increasingly employed to understand the fundamental structural and dynamical features of NPs. While informative, such simulations demand a laborious two-step process for their setup. In-house scripts are required to (i) construct complex 3D models of the inner metal core and outer layer of organic ligands, and (ii) correctly assign force-field parameters to these composite systems. Here, we present NanoModeler ( www.nanomodeler.it ), the first Webserver designed to automatically generate and parametrize model systems of monolayer-protected gold NPs and gold nanoclusters. The only required input is a structure file of one or two ligand(s) to be grafted onto the gold core, with the option of specifying homogeneous or heterogeneous NP morphologies. NanoModeler then generates 3D models of the nanosystem and the associated topology files. These files are ready for use with the Gromacs MD engine, and they are compatible with the AMBER family of force fields. We illustrate NanoModeler's capabilities with MD simulations of selected representative NP model systems. NanoModeler is the first platform to automate and standardize the construction and parametrization of realistic models for atomistic simulations of gold NPs and gold nanoclusters.

Abietane-Type Diterpenoids Inhibit Protein Tyrosine Phosphatases by Stabilizing an Inactive Enzyme Conformation

Protein tyrosine phosphatases (PTPs) contribute to a striking variety of human diseases, yet they remain vexingly difficult to inhibit with uncharged, cell-permeable molecules; no inhibitors of PTPs have been approved for clinical use. This study uses a broad set of biophysical analyses to evaluate the use of abietane-type diterpenoids, a biologically active class of phytometabolites with largely nonpolar structures, for the development of pharmaceutically relevant PTP inhibitors. Results of nuclear magnetic resonance analyses, mutational studies, and molecular dynamics simulations indicate that abietic acid can inhibit protein tyrosine phosphatase 1B, a negative regulator of insulin signaling and an elusive drug target, by binding to its active site in a non-substrate-like manner that stabilizes the catalytically essential WPD loop in an inactive conformation; detailed kinetic studies, in turn, show that minor changes in the structures of abietane-type diterpenoids (e.g., the addition of hydrogens) can improve potency (i.e., lower IC₅₀) by 7-fold. These findings elucidate a previously uncharacterized mechanism of diterpenoid-mediated inhibition and suggest, more broadly, that abietane-type diterpenoids are a promising source of structurally diverse-and, intriguingly, microbially synthesizable-molecules on which to base the design of new PTP-inhibiting therapeutics.

Bridging Molecular Docking to Molecular Dynamics in Exploring Ligand-Protein Recognition Process: An Overview

Computational techniques have been applied in the drug discovery pipeline since the 1980s. Given the low computational resources of the time, the first molecular modeling strategies relied on a rigid view of the ligand-target binding process. During the years, the evolution of hardware technologies has gradually allowed simulating the dynamic nature of the binding event. In this work, we present an overview of the evolution of structure-based drug discovery techniques in the study of ligand-target recognition phenomenon, going from the static molecular docking toward enhanced molecular dynamics strategies.

Fast Dynamic Docking Guided by Adaptive Electrostatic Bias: The MD-Binding Approach

Engineering chemical entities to modify how pharmaceutical targets function, as it is done in drug design, requires a good understanding of molecular recognition and binding. In this context, the limitations of statically describing bimolecular recognition, as done in docking/scoring, call for insightful and efficient dynamical investigations. On the experimental side, the characterization of dynamical binding processes is still in its infancy. Thus, computer simulations, particularly molecular dynamics (MD), are compelled to play a prominent role, allowing a deeper comprehension of the binding process and its causes and thus a more informed compound selection, making more significant the computational contribution to drug discovery (Carlson, H. A. Curr. Opin. Chem. Biol. 2002, 6, 447-452). Unfortunately, MD-based approaches cannot yet describe complex events without incurring prohibitive time and computational costs. Here, we present a new method for fully and dynamically simulating drug-target-complex formations, tested against a real world and pharmaceutically relevant benchmark set. The method, based on an adaptive, electrostatics-inspired bias, envisions a campaign of trivially parallel short MD simulations and a strategy to identify a near native binding pose from the sampled configurations. At an affordable computational cost, this method provided predictions of good accuracy also when the starting protein conformation was different from that of the crystal complex, a known hurdle for traditional molecular docking (Lexa, K. W.; Carlson, H. A. Q. Rev. Biophys. 2012, 45, 301-343). Moreover, along the observed binding routes, it identified some key features also found by much more computationally expensive plain-MD simulations. Overall, this methodology represents significant progress in the description of binding phenomena.

Novel Bacterial Topoisomerase Inhibitors Exploit Asp83 and the Intrinsic Flexibility of the DNA Gyrase Binding Site

DNA gyrases are enzymes that control the topology of DNA in bacteria cells. This is a vital function for bacteria. For this reason, DNA gyrases are targeted by widely used antibiotics such as quinolones. Recently, structural and biochemical investigations identified a new class of DNA gyrase inhibitors called NBTIs (i.e., novel bacterial topoisomerase inhibitors). NBTIs are particularly promising because they are active against multi-drug resistant bacteria, an alarming clinical issue. Structural data recently demonstrated that these NBTIs bind tightly to a newly identified pocket at the dimer interface of the DNA–protein complex. In the present study, we used molecular dynamics (MD) simulations and docking calculations to shed new light on the binding of NBTIs to this site. Interestingly, our MD simulations demonstrate the intrinsic flexibility of this binding site, which allows the pocket to adapt its conformation and form optimal interactions with the ligand. In particular, we examined two ligands, AM8085 and AM8191, which induced a repositioning of a key aspartate (Asp83B), whose side chain can rotate within the binding site. The conformational rearrangement of Asp83B allows the formation of a newly identified H-bond interaction with an NH on the bound NBTI, which seems important for the binding of NBTIs having such functionality. We validated these findings through docking calculations using an extended set of cognate oxabicyclooctane-linked NBTIs derivatives (~150, in total), screened against multiple target conformations. The newly identified H-bond interaction significantly improves the docking enrichment. These insights could be helpful for future virtual screening campaigns against DNA gyrase.

Computer-Aided Drug Design Applied to Marine Drug Discovery: Meridianins as Alzheimer's Disease Therapeutic Agents

Computer-aided drug discovery/design (CADD) techniques allow the identification of natural products that are capable of modulating protein functions in pathogenesis-related pathways, constituting one of the most promising lines followed in drug discovery. In this paper, we computationally evaluated and reported the inhibitory activity found in meridianins A-G, a group of marine indole alkaloids isolated from the marine tunicate Aplidium, against various protein kinases involved in Alzheimer's disease (AD), a neurodegenerative pathology characterized by the presence of neurofibrillary tangles (NFT). Balance splitting between tau kinase and phosphate activities caused tau hyperphosphorylation and, thereby, its aggregation and NTF formation. Inhibition of specific kinases involved in its phosphorylation pathway could be one of the key strategies to reverse tau hyperphosphorylation and would represent an approach to develop drugs to palliate AD symptoms. Meridianins bind to the adenosine triphosphate (ATP) binding site of certain protein kinases, acting as ATP competitive inhibitors. These compounds show very promising scaffolds to design new drugs against AD, which could act over tau protein kinases Glycogen synthetase kinase-3 Beta (GSK3β) and Casein kinase 1 delta (CK1δ, CK1D or KC1D), and dual specificity kinases as dual specificity tyrosine phosphorylation regulated kinase 1 (DYRK1A) and cdc2-like kinases (CLK1). This work is aimed to highlight the role of CADD techniques in marine drug discovery and to provide precise information regarding the binding mode and strength of meridianins against several protein kinases that could help in the future development of anti-AD drugs.

Dynamic Docking: A Paradigm Shift in Computational Drug Discovery

Molecular docking is the methodology of choice for studying in silico protein-ligand binding and for prioritizing compounds to discover new lead candidates. Traditional docking simulations suffer from major limitations, mostly related to the static or semi-flexible treatment of ligands and targets. They also neglect solvation and entropic effects, which strongly limits their predictive power. During the last decade, methods based on full atomistic molecular dynamics (MD) have emerged as a valid alternative for simulating macromolecular complexes. In principle, compared to traditional docking, MD allows the full exploration of drug-target recognition and binding from both the mechanistic and energetic points of view (dynamic docking). Binding and unbinding kinetic constants can also be determined. While dynamic docking is still too computationally expensive to be routinely used in fast-paced drug discovery programs, the advent of faster computing architectures and advanced simulation methodologies are changing this scenario. It is feasible that dynamic docking will replace static docking approaches in the near future, leading to a major paradigm shift in in silico drug discovery. Against this background, we review the key achievements that have paved the way for this progress.