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Molecular Modeling in Drug Design

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A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Medicinal Chemistry".

Deadline for manuscript submissions: closed (31 May 2018) | Viewed by 109138

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Prof. Dr. Rebecca Wade

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Guest Editor

Heidelberg Institute for Theoretical Studies (HITS) and Universität Heidelberg, Heidelberg, Germany
Interests: molecular modeling and simulation; protein-ligand interactions; molecular recognition; structure-based drug design

Dr. Outi Salo-Ahen

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Guest Editor

Pharmaceutical Sciences and Structural Bioinformatics Laboratory, Faculty of Science and Engineering, Abo Akademi University, Biocity, Tykistökatu 6A, FI 20520 Turku, Finland
Interests: computer-aided drug design; molecular dynamics simulations; anti-virulence agents; antibacterials; natural compounds
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Special Issue Information

Dear Colleagues,

Since the first attempts at structure-based drug design about four decades ago, molecular modelling techniques for drug design have developed enormously, along with the increasing computational power and structural and biological information on active compounds and potential target molecules. Currently, molecular modeling can be considered an integral component of the contemporary drug discovery and development process. Rational target-based drug development projects benefit significantly from understanding the essential ligand-receptor interactions for designing a potent and efficacious drug to the desired target.

Although current modeling techniques can give important insights and speed up the drug discovery and design stages significantly, there are still many challenges in, for example, predicting accurate ligand binding energies, considering protein flexibility upon ligand binding, or mapping off-target effects of designed compounds. Nowadays, there is also a need for modelling bigger entities, such as antibodies and nanoparticles, as well as targeting macromolecular interfaces.

The aim of this Special Issue is to present a contemporary overview of recent developments in the field of molecular modeling in the context of modern drug design. Reviews, full papers, and short communications, covering the methodological and theoretical aspects of the current trends in molecular modeling are all welcome. The submission of papers addressing the topics listed below is particularly encouraged.

Prof. Dr. Rebecca Wade
Prof. Dr. Outi Salo-Ahen
Guest Editors

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Keywords

Advanced molecular simulation techniques
Big data analysis
Molecular dynamics simulations
Quantum mechanics
Chemoinformatics
Water in binding sites
Cryptic binding pockets
Drug binding kinetics
Covalent inhibitors
Off-target prediction
Protein-protein interaction inhibitors
Antibody design
Nanoparticles

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Published Papers (14 papers)

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Editorial

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3 pages, 158 KiB

Open AccessEditorial

Molecular Modeling in Drug Design

by Rebecca C. Wade and Outi M. H. Salo-Ahen

Molecules 2019, 24(2), 321; https://doi.org/10.3390/molecules24020321 - 17 Jan 2019

Cited by 12 | Viewed by 6125

Abstract

This Special Issue contains thirteen articles that provide a vivid snapshot of the state-of-the-art of molecular modeling in drug design, illustrating recent advances and critically discussing important challenges [...] Full article

(This article belongs to the Special Issue Molecular Modeling in Drug Design)

Research

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17 pages, 3719 KiB

Open AccessArticle

Role of Extracellular Loops and Membrane Lipids for Ligand Recognition in the Neuronal Adenosine Receptor Type 2A: An Enhanced Sampling Simulation Study

by Ruyin Cao, Alejandro Giorgetti, Andreas Bauer, Bernd Neumaier, Giulia Rossetti and Paolo Carloni

Molecules 2018, 23(10), 2616; https://doi.org/10.3390/molecules23102616 - 12 Oct 2018

Cited by 15 | Viewed by 4466

Abstract

Human G-protein coupled receptors (GPCRs) are important targets for pharmaceutical intervention against neurological diseases. Here, we use molecular simulation to investigate the key step in ligand recognition governed by the extracellular domains in the neuronal adenosine receptor type 2A (hA_2AR), a target for neuroprotective compounds. The ligand is the high-affinity antagonist (4-(2-(7-amino-2-(furan-2-yl)-[1,2,4]triazolo[1,5-a][1,3,5]triazin-5-ylamino)ethyl)phenol), embedded in a neuronal membrane mimic environment. Free energy calculations, based on well-tempered metadynamics, reproduce the experimentally measured binding affinity. The results are consistent with the available mutagenesis studies. The calculations identify a vestibular binding site, where lipids molecules can actively participate to stabilize ligand binding. Bioinformatic analyses suggest that such vestibular binding site and, in particular, the second extracellular loop, might drive the ligand toward the orthosteric binding pocket, possibly by allosteric modulation. Taken together, these findings point to a fundamental role of the interaction between extracellular loops and membrane lipids for ligands’ molecular recognition and ligand design in hA_2AR. Full article

(This article belongs to the Special Issue Molecular Modeling in Drug Design)

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10 pages, 950 KiB

Open AccessArticle

Role of Resultant Dipole Moment in Mechanical Dissociation of Biological Complexes

by Maksim Kouza, Anirban Banerji, Andrzej Kolinski, Irina Buhimschi and Andrzej Kloczkowski

Molecules 2018, 23(8), 1995; https://doi.org/10.3390/molecules23081995 - 10 Aug 2018

Cited by 22 | Viewed by 4440

Abstract

Protein-peptide interactions play essential roles in many cellular processes and their structural characterization is the major focus of current experimental and theoretical research. Two decades ago, it was proposed to employ the steered molecular dynamics (SMD) to assess the strength of protein-peptide interactions. The idea behind using SMD simulations is that the mechanical stability can be used as a promising and an efficient alternative to computationally highly demanding estimation of binding affinity. However, mechanical stability defined as a peak in force-extension profile depends on the choice of the pulling direction. Here we propose an uncommon choice of the pulling direction along resultant dipole moment (RDM) vector, which has not been explored in SMD simulations so far. Using explicit solvent all-atom MD simulations, we apply SMD technique to probe mechanical resistance of ligand-receptor system pulled along two different vectors. A novel pulling direction—when ligand unbinds along the RDM vector—results in stronger forces compared to commonly used ligand unbinding along center of masses vector. Our observation that RDM is one of the factors influencing the mechanical stability of protein-peptide complex can be used to improve the ranking of binding affinities by using mechanical stability as an effective scoring function. Full article

(This article belongs to the Special Issue Molecular Modeling in Drug Design)

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19 pages, 2369 KiB

Open AccessArticle

Truly Target-Focused Pharmacophore Modeling: A Novel Tool for Mapping Intermolecular Surfaces

by Jérémie Mortier, Pratik Dhakal and Andrea Volkamer

Molecules 2018, 23(8), 1959; https://doi.org/10.3390/molecules23081959 - 6 Aug 2018

Cited by 28 | Viewed by 7212

Abstract

Pharmacophore models are an accurate and minimal tridimensional abstraction of intermolecular interactions between chemical structures, usually derived from a group of molecules or from a ligand-target complex. Only a limited amount of solutions exists to model comprehensive pharmacophores using the information of a particular target structure without knowledge of any binding ligand. In this work, an automated and customable tool for truly target-focused (T²F) pharmacophore modeling is introduced. Key molecular interaction fields of a macromolecular structure are calculated using the AutoGRID energy functions. The most relevant points are selected by a newly developed filtering cascade and clustered to pharmacophore features with a density-based algorithm. Using five different protein classes, the ability of this method to identify essential pharmacophore features was compared to structure-based pharmacophores derived from ligand-target interactions. This method represents an extremely valuable instrument for drug design in a situation of scarce ligand information available, but also in the case of underexplored therapeutic targets, as well as to investigate protein allosteric pockets and protein-protein interactions. Full article

(This article belongs to the Special Issue Molecular Modeling in Drug Design)

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15 pages, 5794 KiB

Open AccessArticle

Be Aware of Aggregators in the Search for Potential Human ecto-5′-Nucleotidase Inhibitors

by Lucas G. Viviani, Erika Piccirillo, Arquimedes Cheffer, Leandro De Rezende, Henning Ulrich, Ana Maria Carmona-Ribeiro and Antonia T.-do Amaral

Molecules 2018, 23(8), 1876; https://doi.org/10.3390/molecules23081876 - 27 Jul 2018

Cited by 10 | Viewed by 4341

Abstract

Promiscuous inhibition due to aggregate formation has been recognized as a major concern in drug discovery campaigns. Here, we report some aggregators identified in a virtual screening (VS) protocol to search for inhibitors of human ecto-5′-nucleotidase (ecto-5′-NT/CD73), a promising target for several diseases and pathophysiological events, including cancer, inflammation and autoimmune diseases. Four compounds (A, B, C and D), selected from the ZINC-11 database, showed IC₅₀ values in the micromolar range, being at the same time computationally predicted as potential aggregators. To confirm if they inhibit human ecto-5′-NT via promiscuous mechanism, forming aggregates, enzymatic assays were done in the presence of 0.01% (v/v) Triton X-100 and an increase in the enzyme concentration by 10-fold. Under both experimental conditions, these four compounds showed a significant decrease in their inhibitory activities. To corroborate these findings, turbidimetric assays were performed, confirming that they form aggregate species. Additionally, aggregation kinetic studies were done by dynamic light scattering (DLS) for compound C. None of the identified aggregators has been previously reported in the literature. For the first time, aggregation and promiscuous inhibition issues were systematically studied and evaluated for compounds selected by VS as potential inhibitors for human ecto-5′-NT. Together, our results reinforce the importance of accounting for potential false-positive hits acting by aggregation in drug discovery campaigns to avoid misleading assay results. Full article

(This article belongs to the Special Issue Molecular Modeling in Drug Design)

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21 pages, 3979 KiB

Open AccessArticle

Comparative Study of Carborane- and Phenyl-Modified Adenosine Derivatives as Ligands for the A2A and A3 Adenosine Receptors Based on a Rigid in Silico Docking and Radioligand Replacement Assay

by Marian Vincenzi, Katarzyna Bednarska and Zbigniew J. Leśnikowski

Molecules 2018, 23(8), 1846; https://doi.org/10.3390/molecules23081846 - 25 Jul 2018

Cited by 12 | Viewed by 4286

Abstract

Adenosine receptors are involved in many physiological processes and pathological conditions and are therefore attractive therapeutic targets. To identify new types of effective ligands for these receptors, a library of adenosine derivatives bearing a boron cluster or phenyl group in the same position was designed. The ligands were screened in silico to determine their calculated affinities for the A2A and A3 adenosine receptors. An virtual screening protocol based on the PatchDock web server was developed. In the first screening phase, the effects of the functional group (organic or inorganic modulator) on the adenosine ligand affinity for the receptors were determined. Then, the lead compounds were identified for each receptor in the second virtual screening phase. Two pairs of the most promising ligands, compounds 3 and 4, and two ligands with lower affinity scores (compounds 11 and 12, one with a boron cluster and one with a phenyl group) were synthesized and tested in a radioligand replacement assay for affinity to the A2A and A3 receptors. A reasonable correlation of in silico and biological assay results was observed. In addition, the effects of a phenyl group and boron cluster, which is new adenosine modifiers, on the adenosine ligand binding were compared. Full article

(This article belongs to the Special Issue Molecular Modeling in Drug Design)

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19 pages, 769 KiB

Open AccessArticle

Theoretical Model of EphA2-Ephrin A1 Inhibition

by Wiktoria Jedwabny, Alessio Lodola and Edyta Dyguda-Kazimierowicz

Molecules 2018, 23(7), 1688; https://doi.org/10.3390/molecules23071688 - 11 Jul 2018

Cited by 4 | Viewed by 3708

Abstract

This work aims at the theoretical description of EphA2-ephrin A1 inhibition by small molecules. Recently proposed ab initio-based scoring models, comprising long-range components of interaction energy, is tested on lithocholic acid class inhibitors of this protein–protein interaction (PPI) against common empirical descriptors. We show that, although limited to compounds with similar solvation energy, the ab initio model is able to rank the set of selected inhibitors more effectively than empirical scoring functions, aiding the design of novel compounds. Full article

(This article belongs to the Special Issue Molecular Modeling in Drug Design)

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15 pages, 4685 KiB

Open AccessFeature PaperArticle

Computational Insight into the Effect of Natural Compounds on the Destabilization of Preformed Amyloid-β(1–40) Fibrils

by Francesco Tavanti, Alfonso Pedone and Maria Cristina Menziani

Molecules 2018, 23(6), 1320; https://doi.org/10.3390/molecules23061320 - 31 May 2018

Cited by 26 | Viewed by 4697

Abstract

One of the principal hallmarks of Alzheimer’s disease (AD) is related to the aggregation of amyloid-β fibrils in an insoluble form in the brain, also known as amyloidosis. Therefore, a prominent therapeutic strategy against AD consists of either blocking the amyloid aggregation and/or destroying the already formed aggregates. Natural products have shown significant therapeutic potential as amyloid inhibitors from in vitro studies as well as in vivo animal tests. In this study, the interaction of five natural biophenols (curcumin, dopamine, (-)-epigallocatechin-3-gallate, quercetin, and rosmarinic acid) with amyloid-β(1–40) fibrils has been studied through computational simulations. The results allowed the identification and characterization of the different binding modalities of each compounds and their consequences on fibril dynamics and aggregation. It emerges that the lateral aggregation of the fibrils is strongly influenced by the intercalation of the ligands, which modulates the double-layered structure stability. Full article

(This article belongs to the Special Issue Molecular Modeling in Drug Design)

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18 pages, 4702 KiB

Open AccessArticle

Discovery of Potential Inhibitors of Squalene Synthase from Traditional Chinese Medicine Based on Virtual Screening and In Vitro Evaluation of Lipid-Lowering Effect

by Yankun Chen, Xi Chen, Ganggang Luo, Xu Zhang, Fang Lu, Liansheng Qiao, Wenjing He, Gongyu Li and Yanling Zhang

Molecules 2018, 23(5), 1040; https://doi.org/10.3390/molecules23051040 - 28 Apr 2018

Cited by 23 | Viewed by 5088

Abstract

Squalene synthase (SQS), a key downstream enzyme involved in the cholesterol biosynthetic pathway, plays an important role in treating hyperlipidemia. Compared to statins, SQS inhibitors have shown a very significant lipid-lowering effect and do not cause myotoxicity. Thus, the paper aims to discover potential SQS inhibitors from Traditional Chinese Medicine (TCM) by the combination of molecular modeling methods and biological assays. In this study, cynarin was selected as a potential SQS inhibitor candidate compound based on its pharmacophoric properties, molecular docking studies and molecular dynamics (MD) simulations. Cynarin could form hydrophobic interactions with PHE54, LEU211, LEU183 and PRO292, which are regarded as important interactions for the SQS inhibitors. In addition, the lipid-lowering effect of cynarin was tested in sodium oleate-induced HepG2 cells by decreasing the lipidemic parameter triglyceride (TG) level by 22.50%. Finally. cynarin was reversely screened against other anti-hyperlipidemia targets which existed in HepG2 cells and cynarin was unable to map with the pharmacophore of these targets, which indicated that the lipid-lowering effects of cynarin might be due to the inhibition of SQS. This study discovered cynarin is a potential SQS inhibitor from TCM, which could be further clinically explored for the treatment of hyperlipidemia. Full article

(This article belongs to the Special Issue Molecular Modeling in Drug Design)

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Review

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14 pages, 885 KiB

Open AccessReview

Solvents to Fragments to Drugs: MD Applications in Drug Design

by Lucas A. Defelipe, Juan Pablo Arcon, Carlos P. Modenutti, Marcelo A. Marti, Adrián G. Turjanski and Xavier Barril

Molecules 2018, 23(12), 3269; https://doi.org/10.3390/molecules23123269 - 11 Dec 2018

Cited by 27 | Viewed by 5753

Abstract

Simulations of molecular dynamics (MD) are playing an increasingly important role in structure-based drug discovery (SBDD). Here we review the use of MD for proteins in aqueous solvation, organic/aqueous mixed solvents (MDmix) and with small ligands, to the classic SBDD problems: Binding mode and binding free energy predictions. The simulation of proteins in their condensed state reveals solvent structures and preferential interaction sites (hot spots) on the protein surface. The information provided by water and its cosolvents can be used very effectively to understand protein ligand recognition and to improve the predictive capability of well-established methods such as molecular docking. The application of MD simulations to the study of the association of proteins with drug-like compounds is currently only possible for specific cases, as it remains computationally very expensive and labor intensive. MDmix simulations on the other hand, can be used systematically to address some of the common tasks in SBDD. With the advent of new tools and faster computers we expect to see an increase in the application of mixed solvent MD simulations to a plethora of protein targets to identify new drug candidates. Full article

(This article belongs to the Special Issue Molecular Modeling in Drug Design)

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13 pages, 2374 KiB

Open AccessReview

Artificial Intelligence in Drug Design

by Gerhard Hessler and Karl-Heinz Baringhaus

Molecules 2018, 23(10), 2520; https://doi.org/10.3390/molecules23102520 - 2 Oct 2018

Cited by 230 | Viewed by 26449

Abstract

Artificial Intelligence (AI) plays a pivotal role in drug discovery. In particular artificial neural networks such as deep neural networks or recurrent networks drive this area. Numerous applications in property or activity predictions like physicochemical and ADMET properties have recently appeared and underpin the strength of this technology in quantitative structure-property relationships (QSPR) or quantitative structure-activity relationships (QSAR). Artificial intelligence in de novo design drives the generation of meaningful new biologically active molecules towards desired properties. Several examples establish the strength of artificial intelligence in this field. Combination with synthesis planning and ease of synthesis is feasible and more and more automated drug discovery by computers is expected in the near future. Full article

(This article belongs to the Special Issue Molecular Modeling in Drug Design)

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19 pages, 5287 KiB

Open AccessReview

Targeting Dynamical Binding Processes in the Design of Non-Antibiotic Anti-Adhesives by Molecular Simulation—The Example of FimH

by Eva-Maria Krammer, Jerome De Ruyck, Goedele Roos, Julie Bouckaert and Marc F. Lensink

Molecules 2018, 23(7), 1641; https://doi.org/10.3390/molecules23071641 - 5 Jul 2018

Cited by 15 | Viewed by 6146

Abstract

Located at the tip of type I fimbria of Escherichia coli, the bacterial adhesin FimH is responsible for the attachment of the bacteria to the (human) host by specifically binding to highly-mannosylated glycoproteins located on the exterior of the host cell wall. Adhesion represents a necessary early step in bacterial infection and specific inhibition of this process represents a valuable alternative pathway to antibiotic treatments, as such anti-adhesive drugs are non-intrusive and are therefore unlikely to induce bacterial resistance. The currently available anti-adhesives with the highest affinities for FimH still feature affinities in the nanomolar range. A prerequisite to develop higher-affinity FimH inhibitors is a molecular understanding of the FimH-inhibitor complex formation. The latest insights in the formation process are achieved by combining several molecular simulation and traditional experimental techniques. This review summarizes how molecular simulation contributed to the current knowledge of the molecular function of FimH and the importance of dynamics in the inhibitor binding process, and highlights the importance of the incorporation of dynamical aspects in (future) drug-design studies. Full article

(This article belongs to the Special Issue Molecular Modeling in Drug Design)

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Other

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14 pages, 3842 KiB

Open AccessPerspective

Targeting Difficult Protein-Protein Interactions with Plain and General Computational Approaches

by Mariarosaria Ferraro and Giorgio Colombo

Molecules 2018, 23(9), 2256; https://doi.org/10.3390/molecules23092256 - 4 Sep 2018

Cited by 6 | Viewed by 5761

Abstract

Investigating protein-protein interactions (PPIs) holds great potential for therapeutic applications, since they mediate intricate cell signaling networks in physiological and disease states. However, their complex and multifaceted nature poses a major challenge for biochemistry and medicinal chemistry, thereby limiting the druggability of biological partners participating in PPIs. Molecular Dynamics (MD) provides a solid framework to study the reciprocal shaping of proteins’ interacting surfaces. Here, we review successful applications of MD-based methods developed in our group to predict interfacial areas involved in PPIs of pharmaceutical interest. We report two interesting examples of how structural, dynamic and energetic information can be combined into efficient strategies which, complemented by experiments, can lead to the design of new small molecules with promising activities against cancer and infections. Our advances in targeting key PPIs in angiogenic pathways and antigen-antibody recognition events will be discussed for their role in drug discovery and chemical biology. Full article

(This article belongs to the Special Issue Molecular Modeling in Drug Design)

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11 pages, 251 KiB

Open AccessOpinion

Binding Affinity via Docking: Fact and Fiction

by Tatu Pantsar and Antti Poso

Molecules 2018, 23(8), 1899; https://doi.org/10.3390/molecules23081899 - 30 Jul 2018

Cited by 370 | Viewed by 18423

Abstract

In 1982, Kuntz et al. published an article with the title “A Geometric Approach to Macromolecule-Ligand Interactions”, where they described a method “to explore geometrically feasible alignment of ligands and receptors of known structure”. Since then, small molecule docking has been employed as a fast way to estimate the binding pose of a given compound within a specific target protein and also to predict binding affinity. Remarkably, the first docking method suggested by Kuntz and colleagues aimed to predict binding poses but very little was specified about binding affinity. This raises the question as to whether docking is the right tool to estimate binding affinity. The short answer is no, and this has been concluded in several comprehensive analyses. However, in this opinion paper we discuss several critical aspects that need to be reconsidered before a reliable binding affinity prediction through docking is realistic. These are not the only issues that need to be considered, but they are perhaps the most critical ones. We also consider that in spite of the huge efforts to enhance scoring functions, the accuracy of binding affinity predictions is perhaps only as good as it was 10–20 years ago. There are several underlying reasons for this poor performance and these are analyzed. In particular, we focus on the role of the solvent (water), the poor description of H-bonding and the lack of the systems’ true dynamics. We hope to provide readers with potential insights and tools to overcome the challenging issues related to binding affinity prediction via docking. Full article

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