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Structure-based virtual screening methods are, nowadays, one of the key pillars of computational drug discovery. In recent years, a series of studies have reported docking-based virtual screening campaigns of large databases ranging from hundreds to thousands of millions compounds, further identifying novel hits after experimental validation. As these larg-scale efforts are not generally accessible, machine learning-based protocols have emerged to accelerate the identification of virtual hits within an ultralarge chemical space, reaching impressive reductions in computational time. Herein, we illustrate the motivation and the problem behind the screening of large databases, providing an overview of key concepts and essential applications of machine learning-accelerated protocols, specifically concerning supervised learning methods. We also discuss where the field stands with these novel developments, highlighting possible insights for future studies.

A crucial component in structure-based drug discovery is the availability of high-quality three-dimensional structures of the protein target. Whenever experimental structures were not available, homology modeling has been, so far, the method of choice. Recently, AlphaFold (AF), an artificial-intelligence-based protein structure prediction method, has shown impressive results in terms of model accuracy. This outstanding success prompted us to evaluate how accurate AF models are from the perspective of docking-based drug discovery. We compared the high-throughput docking (HTD) performance of AF models with their corresponding experimental PDB structures using a benchmark set of 22 targets. The AF models showed consistently worse performance using four docking programs and two consensus techniques. Although AlphaFold shows a remarkable ability to predict protein architecture, this might not be enough to guarantee that AF models can be reliably used for HTD, and post-modeling refinement strategies might be key to increase the chances of success., Competing Interests: The authors declare no competing interests., (© 2022 The Authors.)

Machine learning (ML) models to predict the toxicity of small molecules have garnered great attention and have become widely used in recent years. Computational toxicity prediction is particularly advantageous in the early stages of drug discovery in order to filter out molecules with high probability of failing in clinical trials. This has been helped by the increase in the number of large toxicology databases available. However, being an area of recent application, a greater understanding of the scope and applicability of ML methods is still necessary. There are various kinds of toxic end points that have been predicted in silico . Acute oral toxicity, hepatotoxicity, cardiotoxicity, mutagenicity, and the 12 Tox21 data end points are among the most commonly investigated. Machine learning methods exhibit different performances on different data sets due to dissimilar complexity, class distributions, or chemical space covered, which makes it hard to compare the performance of algorithms over different toxic end points. The general pipeline to predict toxicity using ML has already been analyzed in various reviews. In this contribution, we focus on the recent progress in the area and the outstanding challenges, making a detailed description of the state-of-the-art models implemented for each toxic end point. The type of molecular representation, the algorithm, and the evaluation metric used in each research work are explained and analyzed. A detailed description of end points that are usually predicted, their clinical relevance, the available databases, and the challenges they bring to the field are also highlighted., Competing Interests: The authors declare no competing financial interest., (© 2022 The Authors. Published by American Chemical Society.)

Stacking effects are among the most important effects in DNA. We have recently studied their influence in fragments of DNA through the analysis of NMR magnetic shieldings, firstly in vacuo . As a continuation of this line of research we show here the influence of solvent effects on the shieldings through the application of both explicit and implicit models. We found that the explicit solvent model is more appropriate for consideration due to the results matching better in general with experiments, as well as providing clear knowledge of the electronic origin of the value of the shieldings. Our study is grounded on a recently developed theoretical model of our own, by which we are able to learn about the magnetic effects of given fragments of DNA molecules on selected base pairs. We use the shieldings of the atoms of a central base pair (guanine-cytosine) of a selected fragment of DNA molecules as descriptors of physical effects, like π-stacking and solvent effects. They can be taken separately and altogether. The effect of π-stacking is introduced through the addition of some pairs above and below of the central base pair, and now, the solvent effect is considered including a network of water molecules that consist of two solvation layers, which were fixed in the calculations performed in all fragments. We show that the solvent effects enhance the stacking effects on the magnetic shieldings of atoms that belong to the external N-H bonds. The net effect is of deshielding on both atoms. There is also a deshielding effect on the carbon atoms that belong to CO bonds, for which the oxygen atom has an explicit hydrogen bond (HB) with a solvent water molecule. Solvent effects are found to be no higher than a few percent of the total value of the shieldings (between 1% and 5%) for most atoms, although there are few for which such an effect can be higher. There is one nitrogen atom, the acceptor of the HB between guanine and cytosine, that is more highly shielded (around 15 ppm or 10%) when the explicit solvent is considered. In a similar manner, the most external nitrogen atom of cytosine and the hydrogen atom that is bonded to it are highly deshielded (around 10 ppm for nitrogen and around 3 ppm for hydrogen).

The non-structural protein 3 helicase (NS3h) is a multifunctional protein that is critical in RNA replication and other stages in the flavivirus life cycle. NS3h uses energy from ATP hydrolysis to translocate along single stranded nucleic acid and to unwind double stranded RNA. Here we present a detailed mechanistic analysis of the product release stage in the catalytic cycle of the dengue virus (DENV) NS3h. This study is based on a combined experimental and computational approach of product-inhibition studies and free energy calculations. Our results support a model in which the catalytic cycle of ATP hydrolysis proceeds through an ordered sequential mechanism that includes a ternary complex intermediate (NS3h-Pi-ADP), which evolves releasing the first product, phosphate (Pi), and subsequently ADP. Our results indicate that in the product release stage of the DENV NS3h a novel open-loop conformation plays an important role that may be conserved in NS3 proteins of other flaviviruses as well., (© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Introduction: The implementation of Artificial Intelligence (AI) methodologies to drug discovery (DD) are on the rise. Several applications have been developed for structure-based DD, where AI methods provide an alternative framework for the identification of ligands for validated therapeutic targets, as well as the de novo design of ligands through generative models., Areas Covered: Herein, the authors review the contributions between the 2019 to present period regarding the application of AI methods to structure-based virtual screening (SBVS) which encompasses mainly molecular docking applications - binding pose prediction and binary classification for ligand or hit identification-, as well as de novo drug design driven by machine learning (ML) generative models, and the validation of AI models in structure-based screening. Studies are reviewed in terms of their main objective, used databases, implemented methodology, input and output, and key results ., Expert Opinion: More profound analyses regarding the validity and applicability of AI methods in DD have begun to appear. In the near future, we expect to see more structure-based generative models- which are scarce in comparison to ligand-based generative models-, the implementation of standard guidelines for validating the generated structures, and more analyses regarding the validation of AI methods in structure-based DD.

The use of high-throughput docking (HTD) in the drug discovery pipeline is today widely established. In spite of methodological improvements in docking accuracy (pose prediction), scoring power, ranking power, and screening power in HTD remain challenging. In fact, pose prediction is of critical importance in view of the pose-dependent scoring process, since incorrect poses will necessarily decrease the ranking power of scoring functions. The combination of results from different docking programs (consensus scoring) has been shown to improve the performance of HTD. Moreover, it has been also shown that a pose consensus approach might also result in database enrichment. We present a new methodology named Pose/Ranking Consensus (PRC) that combines both pose and ranking consensus approaches, to overcome the limitations of each stand-alone strategy. This approach has been developed using four docking programs (ICM, rDock, Auto Dock 4, and PLANTS; the first one is commercial, the other three are free). We undertook a thorough analysis for the best way of combining pose and rank strategies, and applied the PRC to a wide range of 34 targets sampling different protein families and binding site properties. Our approach exhibits an improved systematic performance in terms of enrichment factor and hit rate with respect to either pose consensus or consensus ranking alone strategies at a lower computational cost, while always ensuring the recovery of a suitable number of ligands. An analysis using four free docking programs (replacing ICM by Auto Dock Vina) displayed comparable results., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

CO 2 thickeners have the potential to be a game changer for enhanced oil recovery, carbon capture utilization and storage, and hydraulic fracturing. Thickener design is challenging due to polymers' low solubility in supercritical CO 2 (scCO 2 ) and the difficulty of substantially increasing the viscosity of CO 2 . In this contribution, we present a framework to design CO 2 soluble thickeners, combining calculations using a quantum mechanical solvation model with direct laboratory viscosity testing. The conductor-like polarizable continuum model for solvation free-energy calculations was used to determine functional silicone and silsesquioxane solubilities in scCO 2 . This method allowed for a fast and efficient identification of CO 2 -soluble compounds, revealing silsesquioxanes as more CO 2 -philic than linear polydimethylsiloxane (PDMS), the most efficient non-fluorinated thickener know to date. The rolling ball apparatus was used to measure the viscosity of scCO 2 with both PDMS and silicone resins with added silica nanoparticles. Methyl silicone resins were found to be stable and fast to disperse in scCO 2 while having a significant thickening effect. They have a larger effect on the solution viscosity than higher-molecular-weight PDMS and are able to thicken CO 2 even at high temperatures. Silicone resins are thus shown to be promising scCO 2 thickeners, exhibiting enhanced solubility and good rheological properties, while also having a moderate cost and being easily commercially attainable., Competing Interests: The authors declare no competing financial interest., (© 2021 The Authors. Published by American Chemical Society.)

The development of computational models for assessing the transfer of chemicals across the placental membrane would be of the utmost importance in drug discovery campaigns, in order to develop safe therapeutic options. We have developed a low-dimensional machine learning model capable of classifying compounds according to whether they can cross or not the placental barrier. To this aim, we compiled a database of 248 compounds with experimental information about their placental transfer, characterizing each compound with a set of ∼5.4 thousand descriptors, including physicochemical properties and structural features. We evaluated different machine learning classifiers and implemented a genetic algorithm, in a five cross validation scheme, to perform feature selection. The optimization was guided towards models displaying a low number of false positives (molecules that actually cross the placental barrier, but are predicted as not crossing it). A Linear Discriminant Analysis model trained with only four structural features resulted to be robust for this task, exhibiting only one false positive case across all testing folds. This model is expected to be useful in predicting placental drug transfer during pregnancy, and thus could be used as a filter for chemical libraries in virtual screening campaigns., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Di Filippo, Bollini and Cavasotto.)

Although the use of computational methods within the pharmaceutical industry is well established, there is an urgent need for new approaches that can improve and optimize the pipeline of drug discovery and development. In spite of the fact that there is no unique solution for this need for innovation, there has recently been a strong interest in the use of Artificial Intelligence for this purpose. As a matter of fact, not only there have been major contributions from the scientific community in this respect, but there has also been a growing partnership between the pharmaceutical industry and Artificial Intelligence companies. Beyond these contributions and efforts there is an underlying question, which we intend to discuss in this review: can the intrinsic difficulties within the drug discovery process be overcome with the implementation of Artificial Intelligence? While this is an open question, in this work we will focus on the advantages that these algorithms provide over the traditional methods in the context of early drug discovery., (Copyright © 2020 Elsevier Inc. All rights reserved.)

The infectious coronavirus disease (COVID-19) pandemic, caused by the coronavirus SARS-CoV-2, appeared in December 2019 in Wuhan, China, and has spread worldwide. As of today, more than 46 million people have been infected and over 1.2 million fatalities. With the purpose of contributing to the development of effective therapeutics, we performed an in silico determination of binding hot-spots and an assessment of their druggability within the complete SARS-CoV-2 proteome. All structural, non-structural, and accessory proteins have been studied, and whenever experimental structural data of SARS-CoV-2 proteins were not available, homology models were built based on solved SARS-CoV structures. Several potential allosteric or protein-protein interaction druggable sites on different viral targets were identified, knowledge that could be used to expand current drug discovery endeavors beyond the currently explored cysteine proteases and the polymerase complex. It is our hope that this study will support the efforts of the scientific community both in understanding the molecular determinants of this disease and in widening the repertoire of viral targets in the quest for repurposed or novel drugs against COVID-19., (Copyright © 2020 Elsevier B.V. All rights reserved.)

In December 2019, an infectious disease caused by the coronavirus SARS-CoV-2 appeared in Wuhan, China. This disease (COVID-19) spread rapidly worldwide, and on March 2020 was declared a pandemic by the World Health Organization (WHO). Today, over 21 million people have been infected, with more than 750.000 casualties. Today, no vaccine or antiviral drug is available. While the development of a vaccine might take at least a year, and for a novel drug, even longer; finding a new use to an old drug (drug repurposing) could be the most effective strategy. We present a docking-based screening using a quantum mechanical scoring of a library built from approved drugs and compounds undergoing clinical trials, against three SARS-CoV-2 target proteins: the spike or S-protein, and two proteases, the main protease and the papain-like protease. The S-protein binds directly to the Angiotensin Converting Enzyme 2 receptor of the human host cell surface, while the two proteases process viral polyproteins. Following the analysis of our structure-based compound screening, we propose several structurally diverse compounds (either FDA-approved or in clinical trials) that could display antiviral activity against SARS-CoV-2. Clearly, these compounds should be further evaluated in experimental assays and clinical trials to confirm their actual activity against the disease. We hope that these findings may contribute to the rational drug design against COVID-19., (© 2020 Wiley-VCH GmbH.)

Computer-aided strategies are useful for reducing the costs and increasing the success-rate in drug discovery. Among these strategies, methods based on pharmacophores (an ensemble of electronic and steric features representing the target active site) are efficient to implement over large compound libraries. However, traditional pharmacophore-based methods require knowledge of active compounds or ligand-receptor structures, and only few ones account for target flexibility. Here, we developed a pharmacophore-based virtual screening protocol, Flexi-pharma, that overcomes these limitations. The protocol uses molecular dynamics (MD) simulations to explore receptor flexibility, and performs a pharmacophore-based virtual screening over a set of MD conformations without requiring prior knowledge about known ligands or ligand-receptor structures for building the pharmacophores. The results from the different receptor conformations are combined using a "voting" approach, where a vote is given to each molecule that matches at least one pharmacophore from each MD conformation. Contrarily to other approaches that reduce the pharmacophore ensemble to some representative models and score according to the matching models or molecule conformers, the Flexi-pharma approach takes directly into account the receptor flexibility by scoring in regards to the receptor conformations. We tested the method over twenty systems, finding an enrichment of the dataset for 19 of them. Flexi-pharma is computationally efficient allowing for the screening of thousands of compounds in minutes on a single CPU core. Moreover, the ranking of molecules by vote is a general strategy that can be applied with any pharmacophore-filtering program.

Today high-throughput docking is one of the most commonly used computational tools in drug lead discovery. While there has been an impressive methodological improvement in docking accuracy, docking scoring still remains an open challenge. Most docking programs are rooted in classical molecular mechanics. However, to better characterize protein-ligand interactions, the use of a more accurate quantum mechanical (QM) description would be necessary. In this work, we introduce a QM-based docking scoring function for high-throughput docking and evaluate it on 10 protein systems belonging to diverse protein families, and with different binding site characteristics. Outstanding results were obtained, with our QM scoring function displaying much higher enrichment (screening power) than a traditional docking method. It is acknowledged that developments in quantum mechanics theory, algorithms and computer hardware throughout the upcoming years will allow semi-empirical (or low-cost) quantum mechanical methods to slowly replace force-field calculations. It is thus urgently needed to develop and validate novel quantum mechanical-based scoring functions for high-throughput docking toward more accurate methods for the identification and optimization of modulators of pharmaceutically relevant targets., (Copyright © 2020 Cavasotto and Aucar.)

The routine use of in silico tools is already established in drug lead design. Besides the use of molecular docking methods to screen large chemical libraries and thus prioritize compounds for purchase or synthesis, more accurate calculations of protein-ligand binding free energy has shown the potential to guide lead optimization, thus saving time and resources. Theoretical developments and advances in computing power have allowed quantum mechanical-based methods applied to calculations on biomacromolecules to be increasingly explored and used, with the purpose of providing a more accurate description of protein-ligand interactions and an enhanced level of accuracy in the calculation of binding affinities. It should be noted that the quantum mechanical formulation includes, in principle, all contributions to the energy, considering terms usually neglected in molecular mechanics force fields, such as electronic polarization, metal coordination, and covalent binding; moreover, quantum mechanical approaches are systematically improvable. By treating all elements and interactions on equal footing, and avoiding the need of system-dependent parameterizations, they provide a greater degree of transferability. In this review, we illustrate the increasing relevance of quantum mechanical methods for binding free energy calculation in the context of structure-based drug lead optimization, showing representative applications of the different approaches available.

Computational methods are a powerful and consolidated tool in the early stage of the drug lead discovery process. Among these techniques, high-throughput molecular docking has proved to be extremely useful in identifying novel bioactive compounds within large chemical libraries. In the docking procedure, the predominant binding mode of each small molecule within a target binding site is assessed, and a docking score reflective of the likelihood of binding is assigned to them. These methods also shed light on how a given hit could be modified in order to improve protein-ligand interactions and are thus able to guide lead optimization. The possibility of reducing time and cost compared to experimental approaches made this technology highly appealing. Due to methodological developments and the increase of computational power, the application of quantum mechanical methods to study macromolecular systems has gained substantial attention in the last decade. A quantum mechanical description of the interactions involved in molecular association of biomolecules may lead to better accuracy compared to molecular mechanics, since there are many physical phenomena that cannot be correctly described within a classical framework, such as covalent bond formation, polarization effects, charge transfer, bond rearrangements, halogen bonding, and others, that require electrons to be explicitly accounted for. Considering the fact that quantum mechanics-based approaches in biomolecular simulation constitute an active and important field of research, we highlight in this work the recent developments of quantum mechanical-based molecular docking and high-throughput docking.

Dengue fever is a mosquito-borne viral disease that has become a major public health concern worldwide. This disease presents with a wide range of clinical manifestations, from a mild cold-like illness to the more serious hemorrhagic dengue fever and dengue shock syndrome. Currently, neither an approved drug nor an effective vaccine for the treatment are available to fight the disease. The envelope protein (E) is a major component of the virion surface. This protein plays a key role during the viral entry process, constituting an attractive target for the development of antiviral drugs. The crystal structure of the E protein reveals the existence of a hydrophobic pocket occupied by the detergent n-octyl-β-d-glucoside (β-OG). This pocket lies at the hinge region between domains I and II and is important for the low pH-triggered conformational rearrangement required for the fusion of the virion with the host's cell. Aiming at the design of novel molecules which bind to E and act as virus entry inhibitors, we undertook a de novo design approach by "growing" molecules inside the hydrophobic site (β-OG). From more than 240000 small-molecules generated, the 2,4 pyrimidine scaffold was selected as the best candidate, from which one synthesized compound displayed micromolar activity. Molecular dynamics-based optimization was performed on this hit, and thirty derivatives were designed in silico, synthesized and evaluated on their capacity to inhibit dengue virus entry into the host cell. Four compounds were found to be potent antiviral compounds in the low-micromolar range. The assessment of drug-like physicochemical and in vitro pharmacokinetic properties revealed that compounds 3e and 3h presented acceptable solubility values and were stable in mouse plasma, simulated gastric fluid, simulated intestinal fluid, and phosphate buffered saline solution., (Copyright © 2019 Elsevier Masson SAS. All rights reserved.)

Consensus-scoring methods are commonly used with molecular docking in virtual screening campaigns to filter potential ligands for a protein target. Traditional consensus methods combine results from different docking programs by averaging the score or rank of each molecule obtained from individual programs. Unfortunately, these methods fail if one of the docking programs has poor performance, which is likely to occur due to training-set dependencies and scoring-function parameterization. In this work, we introduce a novel consensus method that overcomes these limitations. We combine the results from individual docking programs using a sum of exponential distributions as a function of the molecule rank for each program. We test the method over several benchmark systems using individual and ensembles of target structures from diverse protein families with challenging decoy/ligand datasets. The results demonstrate that the novel method outperforms the best traditional consensus strategies over a wide range of systems. Moreover, because the novel method is based on the rank rather than the score, it is independent of the score units, scales and offsets, which can hinder the combination of results from different structures or programs. Our method is simple and robust, providing a theoretical basis not only for molecular docking but also for any consensus strategy in general.

In response to nutrient deprivation, the cell mobilizes an extensive amount of membrane to form and grow the autophagosome, allowing the progression of autophagy. By providing membranes and stimulating LC3 lipidation, COPII (Coat Protein Complex II) promotes autophagosome biogenesis. Here, we show that the F-box protein FBXW5 targets SEC23B, a component of COPII, for proteasomal degradation and that this event limits the autophagic flux in the presence of nutrients. In response to starvation, ULK1 phosphorylates SEC23B on Serine 186, preventing the interaction of SEC23B with FBXW5 and, therefore, inhibiting SEC23B degradation. Phosphorylated and stabilized SEC23B associates with SEC24A and SEC24B, but not SEC24C and SEC24D, and they re-localize to the ER-Golgi intermediate compartment, promoting autophagic flux. We propose that, in the presence of nutrients, FBXW5 limits COPII-mediated autophagosome biogenesis. Inhibition of this event by ULK1 ensures efficient execution of the autophagic cascade in response to nutrient starvation., Competing Interests: YJ, DS, SK, DM, NA, AS, LF, MW, CC, DF, AC, MR, MP No competing interests declared, (© 2018, Jeong et al.)

Class B G protein-coupled receptors (GPCRs) are involved in a variety of human pathophysiological states. These groups of membrane receptors are less studied than class A GPCRs due to the lack of structural information, delayed small molecule drug discovery, and scarce fluorescence detection tools available. The class B corticotropin-releasing hormone type 1 receptor (CRHR1) is a key player in the stress response whose dysregulation is critically involved in stress-related disorders: psychiatric conditions (i.e. depression, anxiety, and addictions), neuroendocrinological alterations, and neurodegenerative diseases. Here, we present a strategy to label GPCRs with a small fluorescent antagonist that permits the observation of the receptor in live cells through stochastic optical reconstruction microscopy (STORM) with 23 nm resolution. The marker, an aza-BODIPY derivative, was designed based on computational docking studies, then synthesized, and finally tested in biological cells. Experiments on hippocampal neurons demonstrate antagonist effects in similar concentrations as the well-established antagonist CP-376395. A quantitative analysis of two color STORM images enabled the determination of the binding affinity of the new marker in the cellular environment.

Today computational chemistry is a consolidated tool in drug lead discovery endeavors. Due to methodological developments and to the enormous advance in computer hardware, methods based on quantum mechanics (QM) have gained great attention in the last 10 years, and calculations on biomacromolecules are becoming increasingly explored, aiming to provide better accuracy in the description of protein-ligand interactions and the prediction of binding affinities. In principle, the QM formulation includes all contributions to the energy, accounting for terms usually missing in molecular mechanics force-fields, such as electronic polarization effects, metal coordination, and covalent binding; moreover, QM methods are systematically improvable, and provide a greater degree of transferability. In this mini-review we present recent applications of explicit QM-based methods in small-molecule docking and scoring, and in the calculation of binding free-energy in protein-ligand systems. Although the routine use of QM-based approaches in an industrial drug lead discovery setting remains a formidable challenging task, it is likely they will increasingly become active players within the drug discovery pipeline.

Bovine viral diarrhea virus (BVDV) is a member of the genus Pestivirus within the family Flaviviridae. BVDV causes both acute and persistent infections in cattle, leading to substantial financial losses to the livestock industry each year. The global prevalence of persistent BVDV infection and the lack of a highly effective antiviral therapy have spurred intensive efforts to discover and develop novel anti-BVDV therapies in the pharmaceutical industry. Antiviral targeting of virus envelope proteins is an effective strategy for therapeutic intervention of viral infections. We performed prospective small-molecule high-throughput docking to identify molecules that likely bind to the region delimited by domains I and II of the envelope protein E2 of BVDV. Several structurally different compounds were purchased or synthesized, and assayed for antiviral activity against BVDV. Five of the selected compounds were active displaying IC 50 values in the low- to mid-micromolar range. For these compounds, their possible binding determinants were characterized by molecular dynamics simulations. A common pattern of interactions between active molecules and aminoacid residues in the binding site in E2 was observed. These findings could offer a better understanding of the interaction of BVDV E2 with these inhibitors, as well as benefit the discovery of novel and more potent BVDV antivirals.

Antiviral targeting of virus envelope proteins is an effective strategy for therapeutic intervention of viral infections. Here, we took a computer-guided approach with the aim of identifying new antivirals against the envelope protein E2 of bovine viral diarrhea virus (BVDV). BVDV is an enveloped virus with an RNA genome responsible for major economic losses of the cattle industry worldwide. Based on the crystal structure of the envelope protein E2, we defined a binding site at the interface of the two most distal domains from the virus membrane and pursued a hierarchical docking-based virtual screening search to identify small-molecule ligands of E2. Phenyl thiophene carboxamide derivative 12 (PTC12) emerged as a specific inhibitor of BVDV replication from in vitro antiviral activity screening of candidate molecules, displaying an IC 50 of 0.30 μM against the reference NADL strain of the virus. Using reverse genetics we constructed a recombinant BVDV expressing GFP that served as a sensitive reporter for the study of the mechanism of action of antiviral compounds. Time of drug addition assays showed that PTC12 inhibited an early step of infection. The mechanism of action was further dissected to find that the compound specifically acted at the internalization step of virus entry. Interestingly, we demonstrated that similar to PTC12, the benzimidazole derivative 03 (BI03) selected in the virtual screen also inhibited internalization of BVDV. Furthermore, docking analysis of PTC12 and BI03 into the binding site revealed common interactions with amino acid residues in E2 suggesting that both compounds could share the same molecular target. In conclusion, starting from a targeted design strategy of antivirals against E2 we identified PTC12 as a potent inhibitor of BVDV entry. The compound can be valuable in the design of antiviral strategies in combination with already well-characterized polymerase inhibitors of BVDV., (Copyright © 2017 Elsevier B.V. All rights reserved.)

[This corrects the article DOI: 10.1371/journal.pone.0024563.].

Dengue is a mosquito-borne virus that has become a major public health concern worldwide in recent years. However, the current treatment for dengue disease is only supportive therapy, and no specific antivirals are available to control the infections. Therefore, the need for safe and effective antiviral drugs against this virus is of utmost importance. Entry of the dengue virus (DENV) into a host cell is mediated by its major envelope protein, E. The crystal structure of the E protein reveals a hydrophobic pocket occupied by the detergent n-octyl-β-d-glucoside (β-OG) lying at a hinge region between domains I and II, which is important for the low-pH-triggered conformational rearrangement required for fusion. Thus, the E protein is an attractive target for the development of antiviral agents. In this work, we performed prospective docking-based virtual screening to identify small molecules that likely bind to the β-OG binding site. Twenty-three structurally different compounds were identified and two of them had an EC 50 value in the low micromolar range. In particular, compound 2 (EC 50 =3.1μM) showed marked antiviral activity with a good therapeutic index. Molecular dynamics simulations were used in an attempt to characterize the interaction of 2 with protein E, thus paving the way for future ligand optimization endeavors. These studies highlight the possibility of using a new class of DENV inhibitors against dengue., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

The epidermal growth factor receptor (EGFR) is part of an extended family of proteins that together control aspects of cell growth and development, and thus a validated target for drug discovery. We explore in this work the suitability of a molecular dynamics-based end-point binding free energy protocol to estimate the relative affinities of a virtual combinatorial library designed around the EGFR model inhibitor 6{1} as a tool to guide chemical synthesis toward the most promising compounds. To investigate the validity of this approach, selected analogs including some with better and worse predicted affinities relative to 6{1} were synthesized, and their biological activity determined. To understand the binding determinants of the different analogs, hydrogen bonding and van der Waals contributions, and water molecule bridging in the EGFR-analog complexes were analyzed. The experimental validation was in good qualitative agreement with our theoretical calculations, while also a 6-dibromophenyl-substituted compound with enhanced inhibitory effect on EGFR compared to the reference ligand was obtained., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

Structure-based virtual screening is currently an established tool in drug lead discovery projects. Although in the last years the field saw an impressive progress in terms of algorithm development, computational performance, and retrospective and prospective applications in ligand identification, there are still long-standing challenges where further improvement is needed. In this review, we consider the conceptual frame, state-of-the-art and recent developments of three critical "structural" issues in structure-based drug lead discovery: the use of homology modeling to accurately model the binding site when no experimental structures are available, the necessity of accounting for the dynamics of intrinsically flexible systems as proteins, and the importance of considering active site water molecules in lead identification and optimization campaigns., (Copyright © 2015 Elsevier Inc. All rights reserved.)

With >800 members in humans, the G protein-coupled receptor (GPCR) super-family is the target for more than 30% of the marketed drugs. The recent boom in GPCR crystallography has enabled the solution of ∼30 different GPCR structures, which boosted the identification and optimization of novel modulators and new chemical entities through structure-based strategies. However, the number of available structures represents a small part of the human GPCR druggable target space, and its complete coverage in the near future seems unlikely. Homology modelling represents a reliable tool to fill this gap, and hence to vastly expand the horizons of structure-based drug discovery and design. In this Feature Article, we show from a wealth of retrospective and prospective studies that in spite of the pitfalls of and standing challenges in homology modelling, structural models have been critical for the blossoming and success of GPCR structure-based lead discovery and optimization endeavours; in addition, they have also been instrumental in characterizing receptor-ligand interaction, guiding the design of site-directed mutagenesis and SAR studies, and assessing off-target effects. Considering though their current limitations, we also discuss the most pressing issues to develop more accurate homology modelling strategies, with a special focus on the integration of computational tools with biochemical, biophysical and QSAR data, highlighting methodological aspects and recent progress. The teachings of the three GPCR Dock community-wide assessments and the fresh developments in GPCR classes B, C and F are commented. This is a fast growing and highly promising field of research, and in the coming years, the use of high-quality models should enable the discovery of a growing number of potent, selective and efficient GPCR drug leads with high therapeutic potential through receptor structure-based strategies.

Inhibition of distinct ubiquitin E3 ligases might represent a powerful therapeutic tool. ITCH is a HECT domain-containing E3 ligase that promotes the ubiquitylation and degradation of several proteins, including p73, p63, c-Jun, JunB, Notch and c-FLIP, thus affecting cell fate. Accordingly, ITCH depletion potentiates the effect of chemotherapeutic drugs, revealing ITCH as a potential pharmacological target in cancer therapy. Using high throughput screening of ITCH auto-ubiquitylation, we identified several putative ITCH inhibitors, one of which is clomipramine--a clinically useful antidepressant drug. Previously, we have shown that clomipramine inhibits autophagy by blocking autophagolysosomal fluxes and thus could potentiate chemotherapy in vitro. Here, we found that clomipramine specifically blocks ITCH auto-ubiquitylation, as well as p73 ubiquitylation. By screening structural homologs of clomipramine, we identified several ITCH inhibitors and putative molecular moieties that are essential for ITCH inhibition. Treating a panel of breast, prostate and bladder cancer cell lines with clomipramine, or its homologs, we found that they reduce cancer cell growth, and synergize with gemcitabine or mitomycin in killing cancer cells by blocking autophagy. We also discuss a potential mechanism of inhibition. Together, our study (i) demonstrates the feasibility of using high throughput screening to identify E3 ligase inhibitors and (ii) provides insight into how clomipramine and its structural homologs might interfere with ITCH and other HECT E3 ligase catalytic activity in (iii) potentiating chemotherapy by regulating autophagic fluxes. These results may have direct clinical applications.

There is growing interest in using cannabinoid receptor 2 (CB2) agonists for the treatment of neuropathic pain and other indications. In continuation of our ongoing program aiming for the development of new small molecule cannabinoid ligands, we have synthesized a novel series of carbazole and γ-carboline derivatives. The affinities of the newly synthesized compounds were determined by a competitive radioligand displacement assay for human CB2 cannabinoid receptor and rat CB1 cannabinoid receptor. Functional activity and selectivity at human CB1 and CB2 receptors were characterized using receptor internalization and [(35)S]GTP-γ-S assays. The structure-activity relationship and optimization studies of the carbazole series have led to the discovery of a non-selective CB1 and CB2 agonist, compound 4. Our subsequent research efforts to increase CB2 selectivity of this lead compound have led to the discovery of CB2 selective compound 64, which robustly internalized CB2 receptors. Compound 64 had potent inhibitory effects on pain hypersensitivity in a rat model of neuropathic pain. Other potent and CB2 receptor-selective compounds, including compounds 63 and 68, and a selective CB1 agonist, compound 74 were also discovered. In addition, we identified the CB2 ligand 35 which failed to promote CB2 receptor internalization and inhibited compound CP55,940-induced CB2 internalization despite a high CB2 receptor affinity. The present study provides novel tricyclic series as a starting point for further investigations of CB2 pharmacology and pain treatment., (Copyright © 2013 Elsevier Masson SAS. All rights reserved.)

Nine membrane-bound adenylyl cyclase (AC) isoforms catalyze the production of the second messenger cyclic AMP (cAMP) in response to various stimuli. Reduction of AC activity has well documented benefits, including benefits for heart disease and pain. These roles have inspired development of isoform-selective AC inhibitors, a lack of which currently limits exploration of functions and/or treatment of dysfunctions involving AC/cAMP signaling. However, inhibitors described as AC5- or AC1-selective have not been screened against the full panel of AC isoforms. We have measured pharmacological inhibitor profiles for all transmembrane AC isoforms. We found that 9-(tetrahydro-2-furanyl)-9H-purin-6-amine (SQ22,536), 2-amino-7-(furanyl)-7,8-dihydro-5(6H)-quinazolinone (NKY80), and adenine 9-β-d-arabinofuranoside (Ara-A), described as supposedly AC5-selective, do not discriminate between AC5 and AC6, whereas the putative AC1-selective inhibitor 5-[[2-(6-amino-9H-purin-9-yl)ethyl]amino]-1-pentanol (NB001) does not directly target AC1 to reduce cAMP levels. A structure-based virtual screen targeting the ATP binding site of AC was used to identify novel chemical structures that show some preference for AC1 or AC2. Mutation of the AC2 forskolin binding pocket does not interfere with inhibition by SQ22,536 or the novel AC2 inhibitor, suggesting binding to the catalytic site. Thus, we show that compounds lacking the adenine chemical signature and targeting the ATP binding site can potentially be used to develop AC isoform-specific inhibitors, and discuss the need to reinterpret literature using AC5/6-selective molecules SQ22,536, NKY80, and Ara-A.

The phosphotransferase system (PTS) is involved in the use of carbon sources in bacteria. Bacillus sphaericus, a bacterium with the ability to produce insecticidal proteins, is unable to use hexoses and pentoses as the sole carbon source, but it has ptsHI genes encoding the two general proteins of the PTS: enzyme I (EI) and the histidine phosphocarrier (HPr). In this work, we describe the biophysical and structural properties of HPr from B. sphaericus, HPr(bs), and its affinity towards EI of other species to find out whether there is inter-species binding. Conversely to what happens to other members of the HPr family, HPr(bs) forms several self-associated species. The conformational stability of the protein is low, and it unfolds irreversibly during heating. The protein binds to the N-terminal domain of EI from Streptomyces coelicolor, EIN(sc), with a higher affinity than that of the natural partner of EIN(sc), HPr(sc). Modelling of the complex between EIN(sc) and HPr(bs) suggests that binding occurs similarly to that observed in other HPr species. We discuss the functional implications of the oligomeric states of HPr(bs) for the glycolytic activity of B. sphaericus, as well as a strategy to inhibit binding between HPr(sc) and EIN(sc).

By combining pseudorandom bead-based aptamer libraries with conjugation chemistry, we have created next-generation aptamers, X-aptamers (XAs). Several X-ligands can be added in a directed or random fashion to the aptamers to further enhance their binding affinities for the target proteins. Here we describe the addition of a drug (N-acetyl-2,3-dehydro-2-deoxyneuraminic acid), demonstrated to bind to CD44-HABD, to a complete monothioate backbone-substituted aptamer to increase its binding affinity for the target protein by up to 23-fold, while increasing the drug's level of binding 1-million fold.

Predicting the conformational preferences of flexible compounds is still a challenging problem with important implications in areas such as molecular recognition and drug design. In this work, we describe a multilevel strategy to explore the conformational preferences of molecules. The method relies on the predominant-state approximation, which partitions the conformational space into distinct conformational wells. Moreover, it combines low-level (LL) methods for sampling the conformational minima and high-level (HL) techniques for calibrating their relative stability. In the implementation used in this study, the LL sampling is performed with the semiempirical RM1 Hamiltonian, and solvent effects are included using the RM1 version of the MST continuum solvation model. The HL refinement of the conformational wells is performed by combining geometry optimizations of the minima at the B3LYP (gas phase) or MST-B3LYP (solution) level, followed by single point MP2 computations using Dunning's augmented basis sets. Then, the effective free energy of a conformational well is estimated by combining the MP2 energy, supplemented with the MST-B3LYP solvation free energy for a conformational search in solution, with the local curvature of the well sampled at the semiempirical level. Applications of this strategy involve the exploration of the conformational preferences of 1,2-dichloroethane and neutral histamine in both the gas phase and water solution. Finally, the multilevel strategy is used to estimate the reorganization cost required for selecting the bioactive conformation of HIV reverse transcriptase inhibitors, which is estimated to be at most 1.3 kcal/mol.

We compiled a G protein-coupled receptor (GPCR) ligand library (GLL) for 147 targets, selecting for each ligand 39 decoy molecules, collected in the GPCR Decoy Database (GDD). Decoys were chosen ensuring a ligand-decoy similarity of six physical properties, while enforcing ligand-decoy chemical dissimilarity. The performance in docking of the GDD was evaluated on 19 GPCRs, showing a marked decrease in enrichment compared to bias-uncorrected decoy sets. Both the GLL and GDD are freely available for the scientific community.

Explicitly accounting for target flexibility in docking still constitutes a difficult challenge due to the high dimensionality of the conformational space to be sampled. This especially applies to the high-throughput scenario, where the screening of hundreds of thousands compounds takes place. The use of multiple receptor conformations (MRCs) to perform ensemble docking in a sequential fashion is a simple but powerful approach that allows to incorporate binding site structural diversity in the docking process. Whenever enough experimental structures to build a diverse ensemble are not available, normal mode analysis provides an appealing and efficient approach to in silico generate MRCs by distortion along few low-frequency modes that represent collective mid- and large-scale displacements. In this way, the dimension of the conformational space to be sampled is heavily reduced. This methodology is especially suited to incorporate target flexibility at the backbone level. In this chapter, the main components of normal mode-based approaches in the context of ensemble docking are presented and explained, including the theoretical and practical considerations needed for the successful development and implementation of this methodology.

Boronic acids, known to bind diols, were screened to identify non-inflammatory cross-linkers for the preparation of glucose sensitive and insulin releasing agglomerates of liposomes (Agglomerated Vesicle Technology-AVT). This was done in order to select a suitable replacement for the previously used cross-linker, ConcanavalinA (ConA), a lectin known to have both toxic and inflammatory effects in vivo. Lead-compounds were selected from screens that involved testing for inflammatory potential, cytotoxicity and glucose-binding. These were then conjugated to insulin-encapsulating nanoparticles and agglomerated via sugar-boronate ester linkages to form AVTs. In vitro, the particles demonstrated triggered release of insulin upon exposure to physiologically relevant concentrations of glucose (10 mmoles/L-40 mmoles/L). The agglomerates were also shown to be responsive to multiple spikes in glucose levels over several hours, releasing insulin at a rate defined by the concentration of the glucose trigger.

The C-terminal domain of BRCA1(BRCT) is involved in the DNA repair pathway by recognizing the pSXXF motif in interacting proteins. It has been reported that short peptides containing this motif bind to BRCA1(BRCT) in the micromolar range with high specificity. In this work, the binding of pSXXF peptides has been studied computationally and experimentally in order to characterize their interaction with BRCA1(BRCT). Elucidation of the contacts that drive the protein-ligand interaction is critical for the development of high affinity small-molecule BRCA1 inhibitors. Molecular dynamics (MD) simulations revealed the key role of threonine at the peptide P+2 position in providing structural rigidity to the ligand in the bound state. The mutation at P+1 had minor effects. Peptide extension at the N-terminal position with the naphthyl amino acid exhibited a modest increase in binding affinity, what could be explained by the dispersion interaction of the naphthyl side-chain with a hydrophobic patch. Three in silico end-point methods were considered for the calculation of binding free energy. The Molecular Mechanics Poisson-Boltzmann Surface Area and the Solvated Interaction Energy methods gave reasonable agreement with experimental data, exhibiting a Pearlman predictive index of 0.71 and 0.78, respectively. The MM-quantum mechanics-surface area method yielded improved results, which was characterized by a Pearlman index of 0.78. The correlation coefficients were 0.59, 0.61 and 0.69, respectively. The ability to apply a QM level of theory within an end-point binding free energy protocol may provide a way for a consistent improvement of accuracy in computer-aided drug design.

The accurate and efficient calculation of binding free energies is essential in computational biophysics. We present a linear-scaling quantum mechanical (QM)-based end-point method termed MM/QM-COSMO to calculate binding free energies in biomolecular systems, with an improved description of entropic changes. Molecular dynamics trajectories are re-evaluated using a semiempirical Hamiltonian and a continuum solvent model; translational and rotational entropies are calculated using configurational integrals, and internal entropy is calculated using the harmonic oscillator approximation. As an application, we studied the binding of a series of phosphotyrosine tetrapeptides to the human Lck SH2 domain, a key component in intracellular signal transduction, modulation of which can have therapeutic relevance in the treatment of cancer, osteoporosis, and autoimmune diseases. Calculations with molecular mechanics Poisson-Boltzmann, and generalized Born surface area methods showed large discrepancies with experimental data stemming from the enthalpic component, in agreement with an earlier report. The empirical force field-based solvent interaction energy scoring function yielded improved results, with average unsigned error of 3.6 kcal/mol, and a better ligand ranking. The MM/QM-COSMO method exhibited the best agreement both for absolute (average unsigned error = 0.7 kcal/mol) and relative binding free energy calculations. These results show the feasibility and promise of a full QM-based end-point method with an adequate balance of accuracy and computational efficiency., (Copyright © 2011 Wiley Periodicals, Inc.)

To build the foundation for accurate quantum mechanical (QM) simulation of biomacromolecules in an aqueous environment, we undertook the optimization of the COnductor-like Screening MOdel (COSMO) atomic radii and atomic surface tension coefficients for different semiempirical Hamiltonians adhering to the same computational conditions recently followed in the simulation of biomolecular systems. This optimization was achieved by reproducing experimental hydration free energies of a set consisting of 507 neutral and 99 ionic molecules. The calculated hydration free energies were significantly improved by introducing a multiple atomic-type scheme that reflects different chemical environments. The nonpolar contribution was treated according to the scaled particle Claverie-Pierotti formalism. Separate radii and surface tension coefficient sets have been developed for AM1, PM3, PM5, and RM1 semiempirical Hamiltonians, with an average unsigned error for neutral molecules of 0.64, 0.66, 0.73, and 0.71 kcal/mol, respectively. Free energy calculation of each molecule took on average 0.5 s on a single processor. The new sets of parameters will enhance the quality of semiempirical QM calculations using COSMO in biomolecular systems. Overall, these results further extend the utility of QM methods to chemical and biological systems in the condensed phase.

G protein-coupled receptors (GPCRs) regulate a wide range of physiological functions and hold great pharmaceutical interest. Using the β(2)-adrenergic receptor as a case study, this article explores the applicability of docking-based virtual screening to the discovery of GPCR ligands and defines methods intended to improve the screening performance. Our controlled computational experiments were performed on a compound dataset containing known agonists and blockers of the receptor as well as a large number of decoys. The screening based on the structure of the receptor crystallized in complex with its inverse agonist carazolol yielded excellent results, with a clearly delineated prioritization of ligands over decoys. Blockers generally were preferred over agonists; however, agonists were also well distinguished from decoys. A method was devised to increase the screening yields by generating an ensemble of alternative conformations of the receptor that accounts for its flexibility. Moreover, a method was devised to improve the retrieval of agonists, based on the optimization of the receptor around a known agonist. Finally, the applicability of docking-based virtual screening also to homology models endowed with different levels of accuracy was proved. This last point is of uttermost importance, since crystal structures are available only for a limited number of GPCRs, and extends our conclusions to the entire superfamily. The outcome of this analysis definitely supports the application of computer-aided techniques to the discovery of novel GPCR ligands, especially in light of the fact that, in the near future, experimental structures are expected to be solved and become available for an ever increasing number of GPCRs., (Copyright © 2010 Elsevier Inc. All rights reserved.)

In this work, we introduce an algorithm to compute the derivatives of physical observables along the constrained subspace when flexible constraints are imposed on the system (i.e., constraints in which the constrained coordinates are fixed to configuration-dependent values). The presented scheme is exact, it does not contain any tunable parameter, and it only requires the calculation and inversion of a sub-block of the Hessian matrix of second derivatives of the function through which the constraints are defined. We also present a practical application to the case in which the sought observables are the Euclidean coordinates of complex molecular systems, and the function whose minimization defines the flexible constraints is the potential energy. Finally, and in order to validate the method, which, as far as we are aware, is the first of its kind in the literature, we compare it to the natural and straightforward finite-differences approach in a toy system and in three molecules of biological relevance: methanol, N-methyl-acetamide and a tri-glycine peptide.

The use of homology models in docking-based drug discovery is already established, and provides an effective and computationally affordable alternative whenever experimental structures are not available. Recent methodological studies have confirmed and benchmarked the feasibility of using structural models in docking. However, more accurate methods are expected to be developed in the near future, especially for the model refinement stage. In this review, the latest developments in homology modeling in the context of structure-based virtual screening are presented, together with the recent success stories of homology modeling in actual docking-based drug discovery endeavours.

The drug discovery process mainly relies on the experimental high-throughput screening of huge compound libraries in their pursuit of new active compounds. However, spiraling research and development costs and unimpressive success rates have driven the development of more rational, efficient, and cost-effective methods. With the increasing availability of protein structural information, advancement in computational algorithms, and faster computing resources, in silico docking-based methods are increasingly used to design smaller and focused compound libraries in order to reduce screening efforts and costs and at the same time identify active compounds with a better chance of progressing through the optimization stages. This chapter is a primer on the various docking-based methods developed for the purpose of structure-based library design. Our aim is to elucidate some basic terms related to the docking technique and explain the methodology behind several docking-based library design methods. This chapter also aims to guide the novice computational practitioner by laying out the general steps involved for such an exercise. Selected successful case studies conclude this chapter.

Virus capsid assembly constitutes an attractive target for the development of antiviral therapies; a few experimental inhibitors of this process for HIV-1 and other viruses have been identified by screening compounds or by selection from chemical libraries. As a different, novel approach we have undertaken the rational design of peptides that could act as competitive assembly inhibitors by mimicking capsid structural elements involved in intersubunit interfaces. Several discrete interfaces involved in formation of the mature HIV-1 capsid through polymerization of the capsid protein CA were targeted. We had previously designed a peptide, CAC1, that represents CA helix 9 (a major part of the dimerization interface) and binds the CA C-terminal domain in solution. Here we have mapped the binding site of CAC1, and shown that it substantially overlaps with the CA dimerization interface. We have also rationally modified CAC1 to increase its solubility and CA-binding affinity, and designed four additional peptides that represent CA helical segments involved in other CA interfaces. We found that peptides CAC1, its derivative CAC1M, and H8 (representing CA helix 8) were able to efficiently inhibit the in vitro assembly of the mature HIV-1 capsid. Cocktails of several peptides, including CAC1 or CAC1M plus H8 or CAI (a previously discovered inhibitor of CA polymerization), or CAC1M+H8+CAI, also abolished capsid assembly, even when every peptide was used at lower, sub-inhibitory doses. To provide a preliminary proof that these designed capsid assembly inhibitors could eventually serve as lead compounds for development of anti-HIV-1 agents, they were transported into cultured cells using a cell-penetrating peptide, and tested for antiviral activity. Peptide cocktails that drastically inhibited capsid assembly in vitro were also able to efficiently inhibit HIV-1 infection ex vivo. This study validates a novel, entirely rational approach for the design of capsid assembly interfacial inhibitors that show antiviral activity.

Class A G-protein-coupled receptors (GPCRs) are among the most important targets for drug discovery. However, a large set of experimental structures, essential for a structure-based approach, will likely remain unavailable in the near future. Thus, there is an actual need for modeling tools to characterize satisfactorily at least the binding site of these receptors. Using experimentally solved GPCRs, we have enhanced and validated the ligand-steered homology method through cross-modeling and investigated the performance of the thus generated models in docking-based screening. The ligand-steered modeling method uses information about existing ligands to optimize the binding site by accounting for protein flexibility. We found that our method is able to generate quality models of GPCRs by using one structural template. These models perform better than templates, crude homology models, and random selection in small-scale high-throughput docking. Better quality models typically exhibit higher enrichment in docking exercises. Moreover, they were found to be reliable for selectivity prediction. Our results support the fact that the ligand-steered homology modeling method can successfully characterize pharmacologically relevant sites through a full flexible ligand-flexible receptor procedure.

The diffusivity of glucose was studied in confined environment of a 10 nm silica nanochannel to evaluate the influence of the interface on glucose transport at a nano-scale range. Molecular dynamics studies revealed that glucose forms a weakly adsorbed layer at the silica interface. The calculated diffusion coefficients were overestimated by up to 30% compared to the experimental ones, though they follow the same dependence on concentration. Close to the interface, radial diffusivity was observed to be lower than lateral, while diffusivities were affected by the interface up to 3 nm from it. This slowdown of glucose diffusivity is found to be related to the decrease in the probability of longer random walk jumps. It is also observed that the dynamics on the interface is characterized by adsorption-desorption processes. These results clearly suggest that glucose transport is affected by the interface in 10 nm nano-confinement.