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New Avenues in Molecular Docking for Drug Design 2022

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A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Informatics".

Deadline for manuscript submissions: closed (25 January 2023) | Viewed by 39608

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Prof. Dr. Giulio Vistoli

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Department of Pharmaceutical Sciences, Università degli Studi di Milano, via Mangiagalli 25, 20133 Milano, Italy
Interests: computational chemistry; drug design; cheminformatics; molecular docking; ADME predictions; lipophilicity
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Dear Colleagues,

Molecular docking is gaining increased interest in drug design approaches, especially considering its noteworthy potentialities in performing successful virtual screening campaigns. Currently available computing resources allow for simulations involving huge molecular libraries on extended panels of targets in a reasonable time, and these extremely extended simulations appear to be particularly fruitful in the field of multi-target ligand design as well as in the repurposing studies. Clearly, these powerful simulations require new algorithms and new methodological approaches to optimize their performances and to match the advancements in the hardware architectures. Molecular docking requires continuous improvements especially focused on the algorithms for scoring function and pose evaluation. Molecular docking is often combined with other computational approaches to further improve the reliability of the obtained results in terms of both computed complexes and predictive power, and, in this context, machine learning techniques can offer new avenues with which to improve docking simulations and virtual screening campaigns.

On these grounds, this Special Issue seeks manuscripts dealing with novel approaches of molecular docking in drug design by considering both methodological and applicative studies with a view to offering a picture of the areas in which docking simulations can have an ever-increasing impact in the drug discovery pipeline, as well as with the new trends that will impact on such a field in the next future.

Prof. Dr. Giulio Vistoli
Guest Editor

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Keywords

structure-based drug design
molecular recognition
ligand binding
virtual screening
drug repositioning
multi-target ligands
pose generation and evaluation
big data

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New Avenues in Molecular Docking for Drug Design 2023 in International Journal of Molecular Sciences (2 articles)

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23 pages, 7458 KiB

Open AccessArticle

Virtual Screening Combined with Enzymatic Assays to Guide the Discovery of Novel SIRT2 Inhibitors

by Naomi Scarano, Elena Abbotto, Francesca Musumeci, Annalisa Salis, Chiara Brullo, Paola Fossa, Silvia Schenone, Santina Bruzzone and Elena Cichero

Int. J. Mol. Sci. 2023, 24(11), 9363; https://doi.org/10.3390/ijms24119363 - 27 May 2023

Cited by 5 | Viewed by 2019

Abstract

Sirtuin isoform 2 (SIRT2) is one of the seven sirtuin isoforms present in humans, being classified as class III histone deacetylases (HDACs). Based on the high sequence similarity among SIRTs, the identification of isoform selective modulators represents a challenging task, especially for the high conservation observed in the catalytic site. Efforts in rationalizing selectivity based on key residues belonging to the SIRT2 enzyme were accompanied in 2015 by the publication of the first X-ray crystallographic structure of the potent and selective SIRT2 inhibitor SirReal2. The subsequent studies led to different experimental data regarding this protein in complex with further different chemo-types as SIRT2 inhibitors. Herein, we reported preliminary Structure-Based Virtual Screening (SBVS) studies using a commercially available library of compounds to identify novel scaffolds for the design of new SIRT2 inhibitors. Biochemical assays involving five selected compounds allowed us to highlight the most effective chemical features supporting the observed SIRT2 inhibitory ability. This information guided the following in silico evaluation and in vitro testing of further compounds from in-house libraries of pyrazolo-pyrimidine derivatives towards novel SIRT2 inhibitors (1–5). The final results indicated the effectiveness of this scaffold for the design of promising and selective SIRT2 inhibitors, featuring the highest inhibition among the tested compounds, and validating the applied strategy. Full article

(This article belongs to the Special Issue New Avenues in Molecular Docking for Drug Design 2022)

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18 pages, 13696 KiB

Open AccessArticle

Chemical Space Virtual Screening against Hard-to-Drug RNA Methyltransferases DNMT2 and NSUN6

by Robert A. Zimmermann, Tim R. Fischer, Marvin Schwickert, Zarina Nidoieva, Tanja Schirmeister and Christian Kersten

Int. J. Mol. Sci. 2023, 24(7), 6109; https://doi.org/10.3390/ijms24076109 - 24 Mar 2023

Cited by 8 | Viewed by 2319

Abstract

Targeting RNA methyltransferases with small molecules as inhibitors or tool compounds is an emerging field of interest in epitranscriptomics and medicinal chemistry. For two challenging RNA methyltransferases that introduce the 5-methylcytosine (m⁵C) modification in different tRNAs, namely DNMT2 and NSUN6, an ultra-large commercially available chemical space was virtually screened by physicochemical property filtering, molecular docking, and clustering to identify new ligands for those enzymes. Novel chemotypes binding to DNMT2 and NSUN6 with affinities down to K_D,app = 37 µM and K_D,app = 12 µM, respectively, were identified using a microscale thermophoresis (MST) binding assay. These compounds represent the first molecules with a distinct structure from the cofactor SAM and have the potential to be developed into activity-based probes for these enzymes. Additionally, the challenges and strategies of chemical space docking screens with special emphasis on library focusing and diversification are discussed. Full article

(This article belongs to the Special Issue New Avenues in Molecular Docking for Drug Design 2022)

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22 pages, 10433 KiB

Open AccessArticle

Thermal Titration Molecular Dynamics (TTMD): Not Your Usual Post-Docking Refinement

by Silvia Menin, Matteo Pavan, Veronica Salmaso, Mattia Sturlese and Stefano Moro

Int. J. Mol. Sci. 2023, 24(4), 3596; https://doi.org/10.3390/ijms24043596 - 10 Feb 2023

Cited by 12 | Viewed by 3195

Abstract

Molecular docking is one of the most widely used computational approaches in the field of rational drug design, thanks to its favorable balance between the rapidity of execution and the accuracy of provided results. Although very efficient in exploring the conformational degrees of freedom available to the ligand, docking programs can sometimes suffer from inaccurate scoring and ranking of generated poses. To address this issue, several post-docking filters and refinement protocols have been proposed throughout the years, including pharmacophore models and molecular dynamics simulations. In this work, we present the first application of Thermal Titration Molecular Dynamics (TTMD), a recently developed method for the qualitative estimation of protein-ligand unbinding kinetics, to the refinement of docking results. TTMD evaluates the conservation of the native binding mode throughout a series of molecular dynamics simulations performed at progressively increasing temperatures with a scoring function based on protein-ligand interaction fingerprints. The protocol was successfully applied to retrieve the native-like binding pose among a set of decoy poses of drug-like ligands generated on four different pharmaceutically relevant biological targets, including casein kinase 1δ, casein kinase 2, pyruvate dehydrogenase kinase 2, and SARS-CoV-2 main protease. Full article

(This article belongs to the Special Issue New Avenues in Molecular Docking for Drug Design 2022)

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12 pages, 3257 KiB

Open AccessArticle

Structural Investigation of Beta-Cyclodextrin Complexes with Cannabidiol and Delta-9-Tetrahydrocannabinol in 1:1 and 2:1 Host-Guest Stoichiometry: Molecular Docking and Density Functional Calculations

by Nat Triamchaisri, Pisanu Toochinda and Luckhana Lawtrakul

Int. J. Mol. Sci. 2023, 24(2), 1525; https://doi.org/10.3390/ijms24021525 - 12 Jan 2023

Cited by 7 | Viewed by 2627

Abstract

The complexation of β-cyclodextrin (β-CD) with cannabidiol (CBD) and Δ⁹-tetrahydrocannabinol (THC) was investigated using molecular docking and M062X/6-31G(d,p) calculations. The calculations suggested two possible complex formations of 1:1 and 2:1 host-guest molecular ratio of β-CD with CBD and THC. The preferred orientation of all complexes in this study exhibited the hydrogen bonding between hydroxy-substituted benzene ring of CBD and THC with the β-CD’s secondary hydroxy groups at the wide rim. The calculated complexation energies indicate that formation of the 2:1 complexes (−83.53 to −135.36 kcal/mol) was more energetically favorable and chemically stable than the 1:1 complexes (−30.00 to −34.92 kcal/mol). However, the deformation energies of the host and the guest components in the 2:1 complexes (37.47–96.91 kcal/mol) are much higher than those in the 1:1 complexes (3.49–8.69 kcal/mol), which means the formation processes of the 2:1 complexes are more difficult due to the rigidity of the dimeric β-CDs. Therefore, the inclusion complexes of β-CD with CBD and THC are more likely to be in 1:1 host-guest ratio than in 2:1 molecular ratio. The results of this study supported the experimental results that the complexation constant of 1:1 β-CD/CBD (Ks = 300 M⁻¹) is greater than that of 2:1 β-CDs/CBD (Kss = 0.833 M⁻¹). Altogether, this study introduced the fitting parameters that could indicate the stability of the molecular fits in complex formation of each stoichiometry host-guest ratio, which are important for the assessment of the inclusion mechanisms as well as the relationships of reactants and products in chemical reactions. Full article

(This article belongs to the Special Issue New Avenues in Molecular Docking for Drug Design 2022)

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17 pages, 3684 KiB

Open AccessArticle

Ligand-Based Drug Design of Novel Antimicrobials against Staphylococcus aureus by Targeting Bacterial Transcription

by Jiqing Ye, Xiao Yang and Cong Ma

Int. J. Mol. Sci. 2023, 24(1), 339; https://doi.org/10.3390/ijms24010339 - 25 Dec 2022

Cited by 10 | Viewed by 3209

Abstract

Staphylococcus aureus is a common human commensal pathogen that causes a wide range of infectious diseases. Due to the generation of antimicrobial resistance, the pathogen becomes resistant to more and more antibiotics, resulting in methicillin-resistant S. aureus (MRSA) and even multidrug-resistant S. aureus (MDRSA), namely ‘superbugs’. This situation highlights the urgent need for novel antimicrobials. Bacterial transcription, which is responsible for bacterial RNA synthesis, is a valid but underutilized target for developing antimicrobials. Previously, we reported a novel class of antimicrobials, coined nusbiarylins, that inhibited bacterial transcription by interrupting the protein–protein interaction (PPI) between two transcription factors NusB and NusE. In this work, we developed a ligand-based workflow based on the chemical structures of nusbiarylins and their activity against S. aureus. The ligand-based models—including the pharmacophore model, 3D QSAR, AutoQSAR, and ADME/T calculation—were integrated and used in the following virtual screening of the ChemDiv PPI database. As a result, four compounds, including J098-0498, 1067-0401, M013-0558, and F186-026, were identified as potential antimicrobials against S. aureus, with predicted pMIC values ranging from 3.8 to 4.2. The docking study showed that these molecules bound to NusB tightly with the binding free energy ranging from −58 to −66 kcal/mol. Full article

(This article belongs to the Special Issue New Avenues in Molecular Docking for Drug Design 2022)

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21 pages, 1044 KiB

Open AccessArticle

A Hybrid Docking and Machine Learning Approach to Enhance the Performance of Virtual Screening Carried out on Protein–Protein Interfaces

by Natesh Singh and Bruno O. Villoutreix

Int. J. Mol. Sci. 2022, 23(22), 14364; https://doi.org/10.3390/ijms232214364 - 18 Nov 2022

Cited by 5 | Viewed by 2450

Abstract

The modulation of protein–protein interactions (PPIs) by small chemical compounds is challenging. PPIs play a critical role in most cellular processes and are involved in numerous disease pathways. As such, novel strategies that assist the design of PPI inhibitors are of major importance. We previously reported that the knowledge-based DLIGAND2 scoring tool was the best-rescoring function for improving receptor-based virtual screening (VS) performed with the Surflex docking engine applied to several PPI targets with experimentally known active and inactive compounds. Here, we extend our investigation by assessing the vs. potential of other types of scoring functions with an emphasis on docking-pose derived solvent accessible surface area (SASA) descriptors, with or without the use of machine learning (ML) classifiers. First, we explored rescoring strategies of Surflex-generated docking poses with five GOLD scoring functions (GoldScore, ChemScore, ASP, ChemPLP, ChemScore with Receptor Depth Scaling) and with consensus scoring. The top-ranked poses were post-processed to derive a set of protein and ligand SASA descriptors in the bound and unbound states, which were combined to derive descriptors of the docked protein-ligand complexes. Further, eight ML models (tree, bagged forest, random forest, Bayesian, support vector machine, logistic regression, neural network, and neural network with bagging) were trained using the derivatized SASA descriptors and validated on test sets. The results show that many SASA descriptors are better than Surflex and GOLD scoring functions in terms of overall performance and early recovery success on the used dataset. The ML models were superior to all scoring functions and rescoring approaches for most targets yielding up to a seven-fold increase in enrichment factors at 1% of the screened collections. In particular, the neural networks and random forest-based ML emerged as the best techniques for this PPI dataset, making them robust and attractive vs. tools for hit-finding efforts. The presented results suggest that exploring further docking-pose derived SASA descriptors could be valuable for structure-based virtual screening projects, and in the present case, to assist the rational design of small-molecule PPI inhibitors. Full article

(This article belongs to the Special Issue New Avenues in Molecular Docking for Drug Design 2022)

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18 pages, 5955 KiB

Open AccessArticle

In Vitro and In Silico Analysis of New n-Butyl and Isobutyl Quinoxaline-7-carboxylate 1,4-di-N-oxide Derivatives against Trypanosoma cruzi as Trypanothione Reductase Inhibitors

by Alonzo González-González, Oscar Sánchez-Sánchez, R. Luise Krauth-Siegel, Maria Laura Bolognesi, Rogelio Gớmez-Escobedo, Benjamín Nogueda-Torres, Lenci K. Vázquez-Jiménez, Emma Saavedra, Rusely Encalada, José Carlos Espinoza-Hicks, Alma D. Paz-González and Gildardo Rivera

Int. J. Mol. Sci. 2022, 23(21), 13315; https://doi.org/10.3390/ijms232113315 - 1 Nov 2022

Cited by 10 | Viewed by 2035

Abstract

American trypanosomiasis is a worldwide health problem that requires attention due to ineffective treatment options. We evaluated n-butyl and isobutyl quinoxaline-7-carboxylate 1,4-di-N-oxide derivatives against trypomastigotes of the Trypanosoma cruzi strains NINOA and INC-5. An in silico analysis of the interactions of 1,4-di-N-oxide on the active site of trypanothione reductase (TR) and an enzyme inhibition study was carried out. The n-butyl series compound identified as T-150 had the best trypanocidal activity against T. cruzi trypomastigotes, with a 13% TR inhibition at 44 μM. The derivative T-147 behaved as a mixed inhibitor with Ki and Ki’ inhibition constants of 11.4 and 60.8 µM, respectively. This finding is comparable to the TR inhibitor mepacrine (Ki = 19 µM). Full article

(This article belongs to the Special Issue New Avenues in Molecular Docking for Drug Design 2022)

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15 pages, 2709 KiB

Open AccessArticle

Docking and Molecular Dynamics-Based Identification of Interaction between Various Beta-Amyloid Isoforms and RAGE Receptor

by Anna P. Tolstova, Alexei A. Adzhubei, Vladimir A. Mitkevich, Irina Yu. Petrushanko and Alexander A. Makarov

Int. J. Mol. Sci. 2022, 23(19), 11816; https://doi.org/10.3390/ijms231911816 - 5 Oct 2022

Cited by 13 | Viewed by 2656

Abstract

Beta-amyloid peptide (Aβ) is a ligand associated with RAGE (Advanced glycosylation end product-specific receptor). Aβ is translocated in complexes with RAGE from the blood to brain across the blood–brain barrier (BBB) by transcytosis. Aβ and its isoforms are important factors in the Alzheimer’s disease (AD) pathogenesis. However, interaction with RAGE was previously studied for Aβ but not for its isoforms. The present study has been directed at identifying the key interaction interfaces between RAGE and Aβ isoforms (Aβ₄₀, Aβ₄₂, phosphorylated and isomerized isoforms pS8-Aβ₄₂, isoD7-Aβ₄₂). Two interfaces have been identified by docking: they are represented by an extended area at the junction of RAGE domains V and C1 and a smaller area linking C1 and C2 domains. Molecular dynamics (MD) simulations have shown that all Aβ isoforms form stable and tightly bound complexes. This indicates that all Aβ isoforms potentially can be transported through the cell as part of a complex with RAGE. Modeling of RAGE interaction interfaces with Aβ indicates which chemical compounds can potentially be capable of blocking this interaction, and impair the associated pathogenic cascades. The ability of three RAGE inhibitors (RAP, FPS-ZM1 and RP-1) to disrupt the RAGE:Aβ interaction has been probed by docking and subsequently the complexes’ stability verified by MD. The RP-1 and Aβ interaction areas coincide and therefore this inhibitor is very promising for the RAGE:Aβ interaction inhibition. Full article

(This article belongs to the Special Issue New Avenues in Molecular Docking for Drug Design 2022)

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34 pages, 9155 KiB

Open AccessArticle

A Computational QSAR, Molecular Docking and In Vitro Cytotoxicity Study of Novel Thiouracil-Based Drugs with Anticancer Activity against Human-DNA Topoisomerase II

by Doaa M. Khaled, Mohamed E. Elshakre, Mahmoud A. Noamaan, Haider Butt, Marwa M. Abdel Fattah and Dalia A. Gaber

Int. J. Mol. Sci. 2022, 23(19), 11799; https://doi.org/10.3390/ijms231911799 - 5 Oct 2022

Cited by 18 | Viewed by 3130

Abstract

Computational chemistry, molecular docking, and drug design approaches, combined with the biochemical evaluation of the antitumor activity of selected derivatives of the thiouracil-based dihydroindeno pyrido pyrimidines against topoisomerase I and II. The IC50 of other cell lines including the normal human lung cell line W138, lung cancer cell line, A549, breast cancer cell line, MCF-7, cervical cancer, HeLa, and liver cancer cell line HepG2 was evaluated using biochemical methods. The global reactivity descriptors and physicochemical parameters were computed, showing good agreement with the Lipinski and Veber’s rules of the drug criteria. The molecular docking study of the ligands with the topoisomerase protein provides the binding sites, binding energies, and deactivation constant for the inhibition pocket. Various biochemical methods were used to evaluate the IC50 of the cell lines. The QSAR model was developed for colorectal cell line HCT as a case study. Four QSAR statistical models were predicted between the IC50 of the colorectal cell line HCT to correlate the anticancer activity and the computed physicochemical and quantum chemical global reactivity descriptors. The predictive power of the models indicates a good correlation between the observed and the predicted activity. Full article

(This article belongs to the Special Issue New Avenues in Molecular Docking for Drug Design 2022)

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23 pages, 7281 KiB

Open AccessArticle

Molecular Docking and In-Silico Analysis of Natural Biomolecules against Dengue, Ebola, Zika, SARS-CoV-2 Variants of Concern and Monkeypox Virus

by Mackingsley Kushan Dassanayake, Teng-Jin Khoo, Chien Hwa Chong and Patrick Di Martino

Int. J. Mol. Sci. 2022, 23(19), 11131; https://doi.org/10.3390/ijms231911131 - 22 Sep 2022

Cited by 20 | Viewed by 4677

Abstract

The emergence and rapid evolution of human pathogenic viruses, combined with the difficulties in developing effective vaccines, underline the need to develop innovative broad-spectrum antiviral therapeutic agents. The present study aims to determine the in silico antiviral potential of six bacterial antimicrobial peptides (AMPs), two phytochemicals (silvestrol, andrographolide), and two bacterial secondary metabolites (lyngbyabellin A, hapalindole H) against dengue virus, Zika virus, Ebola virus, the major variants of SARS-CoV-2 and monkeypox virus. The comparison of docking scores obtained with natural biomolecules was performed with specific neutralizing antibodies (positive controls for ClusPro) and antiviral drugs (negative controls for Autodock Vina). Glycocin F was the only natural biomolecule tested to show high binding energies to all viral surface proteins and the corresponding viral cell receptors. Lactococcin G and plantaricin ASM1 also achieved high docking scores with all viral surface proteins and most corresponding cell surface receptors. Silvestrol, andrographolide, hapalindole H, and lyngbyabellin A showed variable docking scores depending on the viral surface proteins and cell receptors tested. Three glycocin F mutants with amino acid modifications showed an increase in their docking energy to the spike proteins of SARS-CoV-2 B.1.617.2 Indian variant, and of the SARS-CoV-2 P.1 Japan/Brazil variant, and the dengue DENV envelope protein. All mutant AMPs indicated a frequent occurrence of valine and proline amino acid rotamers. AMPs and glycocin F in particular are the most promising biomolecules for the development of broad-spectrum antiviral treatments targeting the attachment and entry of viruses into their target cell. Full article

(This article belongs to the Special Issue New Avenues in Molecular Docking for Drug Design 2022)

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26 pages, 9838 KiB

Open AccessArticle

Ligand-Based Virtual Screening and Molecular Docking of Benzimidazoles as Potential Inhibitors of Triosephosphate Isomerase Identified New Trypanocidal Agents

by Lenci K. Vázquez-Jiménez, Alfredo Juárez-Saldivar, Rogelio Gómez-Escobedo, Timoteo Delgado-Maldonado, Domingo Méndez-Álvarez, Isidro Palos, Debasish Bandyopadhyay, Carlos Gaona-Lopez, Eyra Ortiz-Pérez, Benjamín Nogueda-Torres, Esther Ramírez-Moreno and Gildardo Rivera

Int. J. Mol. Sci. 2022, 23(17), 10047; https://doi.org/10.3390/ijms231710047 - 2 Sep 2022

Cited by 6 | Viewed by 3975

Abstract

Trypanosoma cruzi (T. cruzi) is a parasite that affects humans and other mammals. T. cruzi depends on glycolysis as a source of adenosine triphosphate (ATP) supply, and triosephosphate isomerase (TIM) plays a key role in this metabolic pathway. This enzyme is an attractive target for the design of new trypanocidal drugs. In this study, a ligand-based virtual screening (LBVS) from the ZINC15 database using benzimidazole as a scaffold was accomplished. Later, a molecular docking on the interface of T. cruzi TIM (TcTIM) was performed and the compounds were grouped by interaction profiles. Subsequently, a selection of compounds was made based on cost and availability for in vitro evaluation against blood trypomastigotes. Finally, the compounds were analyzed by molecular dynamics simulation, and physicochemical and pharmacokinetic properties were determined using SwissADME software. A total of 1604 molecules were obtained as potential TcTIM inhibitors. BP2 and BP5 showed trypanocidal activity with half-maximal lytic concentration (LC₅₀) values of 155.86 and 226.30 µM, respectively. Molecular docking and molecular dynamics simulation analyzes showed a favorable docking score of BP5 compound on TcTIM. Additionally, BP5 showed a low docking score (−5.9 Kcal/mol) on human TIM compared to the control ligand (−7.2 Kcal/mol). Both compounds BP2 and BP5 showed good physicochemical and pharmacokinetic properties as new anti-T. cruzi agents. Full article

(This article belongs to the Special Issue New Avenues in Molecular Docking for Drug Design 2022)

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24 pages, 2797 KiB

Open AccessReview

Estimating the Similarity between Protein Pockets

by Merveille Eguida and Didier Rognan

Int. J. Mol. Sci. 2022, 23(20), 12462; https://doi.org/10.3390/ijms232012462 - 18 Oct 2022

Cited by 10 | Viewed by 5185

Abstract

With the exponential increase in publicly available protein structures, the comparison of protein binding sites naturally emerged as a scientific topic to explain observations or generate hypotheses for ligand design, notably to predict ligand selectivity for on- and off-targets, explain polypharmacology, and design target-focused libraries. The current review summarizes the state-of-the-art computational methods applied to pocket detection and comparison as well as structural druggability estimates. The major strengths and weaknesses of current pocket descriptors, alignment methods, and similarity search algorithms are presented. Lastly, an exhaustive survey of both retrospective and prospective applications in diverse medicinal chemistry scenarios illustrates the capability of the existing methods and the hurdle that still needs to be overcome for more accurate predictions. Full article

(This article belongs to the Special Issue New Avenues in Molecular Docking for Drug Design 2022)

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