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Special Issue "New Avenues in Molecular Docking for Drug Design 2020"

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 (31 August 2020).

Special Issue Editor

Prof. Dr. Giulio Vistoli
E-Mail Website
Guest Editor
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
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

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

Manuscript Submission Information

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Keywords

  • Structure-based drug design
  • Molecular targets
  • Molecular recognition
  • Ligand binding
  • Virtual screening
  • Drug repositioning
  • Multi-target ligands
  • Scoring function
  • Pose generation and evaluation
  • Big data

Published Papers (13 papers)

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Research

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Article
Discovery of Potential Inhibitors for RNA-Dependent RNA Polymerase of Norovirus: Virtual Screening, and Molecular Dynamics
Int. J. Mol. Sci. 2021, 22(1), 171; https://doi.org/10.3390/ijms22010171 - 26 Dec 2020
Cited by 1 | Viewed by 893
Abstract
Noroviruses are non-enveloped viruses with a positive-sense single-stranded RNA (ssRNA) genome belonging to the genus Norovirus, from the family Caliciviridae, which are accountable for acute gastroenteritis in humans. The Norovirus genus is subdivided into seven genogroups, i.e., (GI-GVII); among these, the genogroup [...] Read more.
Noroviruses are non-enveloped viruses with a positive-sense single-stranded RNA (ssRNA) genome belonging to the genus Norovirus, from the family Caliciviridae, which are accountable for acute gastroenteritis in humans. The Norovirus genus is subdivided into seven genogroups, i.e., (GI-GVII); among these, the genogroup II and genotype 4 (GII.4) strains caused global outbreaks of human norovirus (HuNov) disease. The viral genome comprises three open reading frames (ORFs). ORF1 encodes the nonstructural polyprotein that is cleaved into six nonstructural proteins, which include 3C-like cysteine protease (3CLpro) and a viral RNA-dependent RNA polymerase. ORF2 and ORF3 encode the proteins VP1 and VP2. The RNA-dependent RNA polymerase (RdRp) from noroviruses is one of the multipurpose enzymes of RNA viruses vital for replicating and transcribing the viral genome, making the virally encoded enzyme one of the critical targets for the development of novel anti-norovirus agents. In the quest for a new antiviral agent that could combat HuNov, high throughput virtual screening (HTVS), combined with e-pharmacophore screening, was applied to screen compounds from the PubChem database. CMX521 molecule was selected as a prototype for a similarity search in the PubChem online database. Molecular dynamics simulations were employed to identify different compounds that may inhibit HuNov. The results predicted that compound CID-57930781 and CID-44396095 formed stable complexes with MNV-RdRp within 50 ns; hence, they may signify as promising human norovirus inhibitors. Full article
(This article belongs to the Special Issue New Avenues in Molecular Docking for Drug Design 2020)
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Article
Binding Mode Exploration of B1 Receptor Antagonists’ by the Use of Molecular Dynamics and Docking Simulation—How Different Target Engagement Can Determine Different Biological Effects
Int. J. Mol. Sci. 2020, 21(20), 7677; https://doi.org/10.3390/ijms21207677 - 16 Oct 2020
Cited by 1 | Viewed by 729
Abstract
The kinin B1 receptor plays a critical role in the chronic phase of pain and inflammation. The development of B1 antagonists peaked in recent years but almost all promising molecules failed in clinical trials. Little is known about these molecules’ mechanisms of action [...] Read more.
The kinin B1 receptor plays a critical role in the chronic phase of pain and inflammation. The development of B1 antagonists peaked in recent years but almost all promising molecules failed in clinical trials. Little is known about these molecules’ mechanisms of action and additional information will be necessary to exploit the potential of the B1 receptor. With the aim of contributing to the available knowledge of the pharmacology of B1 receptors, we designed and characterized a novel class of allosteric non-peptidic inhibitors with peculiar binding characteristics. Here, we report the binding mode analysis and pharmacological characterization of a new allosteric B1 antagonist, DFL20656. We analyzed the binding of DFL20656 by single point mutagenesis and radioligand binding assays and we further characterized its pharmacology in terms of IC50, B1 receptor internalization and in vivo activity in comparison with different known B1 antagonists. We highlighted how different binding modes of DFL20656 and a Merck compound (compound 14) within the same molecular pocket can affect the biological and pharmacological properties of B1 inhibitors. DFL20656, by its peculiar binding mode, involving tight interactions with N114, efficiently induced B1 receptor internalization and evoked a long-lasting effect in an in vivo model of neuropathic pain. The pharmacological characterization of different B1 antagonists highlighted the effects of their binding modes on activity, receptor occupancy and internalization. Our results suggest that part of the failure of most B1 inhibitors could be ascribed to a lack of knowledge about target function and engagement. Full article
(This article belongs to the Special Issue New Avenues in Molecular Docking for Drug Design 2020)
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Article
Structure-Based Discovery of Novel Chemical Classes of Autotaxin Inhibitors
Int. J. Mol. Sci. 2020, 21(19), 7002; https://doi.org/10.3390/ijms21197002 - 23 Sep 2020
Cited by 2 | Viewed by 965
Abstract
Autotaxin (ATX) is a secreted glycoprotein, widely present in biological fluids, largely responsible for extracellular lysophosphatidic acid (LPA) production. LPA is a bioactive growth-factor-like lysophospholipid that exerts pleiotropic effects in almost all cell types, exerted through at least six G-protein-coupled receptors (LPAR1-6). Increased [...] Read more.
Autotaxin (ATX) is a secreted glycoprotein, widely present in biological fluids, largely responsible for extracellular lysophosphatidic acid (LPA) production. LPA is a bioactive growth-factor-like lysophospholipid that exerts pleiotropic effects in almost all cell types, exerted through at least six G-protein-coupled receptors (LPAR1-6). Increased ATX expression has been detected in different chronic inflammatory diseases, while genetic or pharmacological studies have established ATX as a promising therapeutic target, exemplified by the ongoing phase III clinical trial for idiopathic pulmonary fibrosis. In this report, we employed an in silico drug discovery workflow, aiming at the identification of structurally novel series of ATX inhibitors that would be amenable to further optimization. Towards this end, a virtual screening protocol was applied involving the search into molecular databases for new small molecules potentially binding to ATX. The crystal structure of ATX in complex with a known inhibitor (HA-155) was used as a molecular model docking reference, yielding a priority list of 30 small molecule ATX inhibitors, validated by a well-established enzymatic assay of ATX activity. The two most potent, novel and structurally different compounds were further structurally optimized by deploying further in silico tools, resulting to the overall identification of six new ATX inhibitors that belong to distinct chemical classes than existing inhibitors, expanding the arsenal of chemical scaffolds and allowing further rational design. Full article
(This article belongs to the Special Issue New Avenues in Molecular Docking for Drug Design 2020)
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Article
Repositioning Dequalinium as Potent Muscarinic Allosteric Ligand by Combining Virtual Screening Campaigns and Experimental Binding Assays
Int. J. Mol. Sci. 2020, 21(17), 5961; https://doi.org/10.3390/ijms21175961 - 19 Aug 2020
Cited by 2 | Viewed by 689
Abstract
Structure-based virtual screening is a truly productive repurposing approach provided that reliable target structures are available. Recent progresses in the structural resolution of the G-Protein Coupled Receptors (GPCRs) render these targets amenable for structure-based repurposing studies. Hence, the present study describes structure-based virtual [...] Read more.
Structure-based virtual screening is a truly productive repurposing approach provided that reliable target structures are available. Recent progresses in the structural resolution of the G-Protein Coupled Receptors (GPCRs) render these targets amenable for structure-based repurposing studies. Hence, the present study describes structure-based virtual screening campaigns with a view to repurposing known drugs as potential allosteric (and/or orthosteric) ligands for the hM2 muscarinic subtype which was indeed resolved in complex with an allosteric modulator thus allowing a precise identification of this binding cavity. First, a docking protocol was developed and optimized based on binding space concept and enrichment factor optimization algorithm (EFO) consensus approach by using a purposely collected database including known allosteric modulators. The so-developed consensus models were then utilized to virtually screen the DrugBank database. Based on the computational results, six promising molecules were selected and experimentally tested and four of them revealed interesting affinity data; in particular, dequalinium showed a very impressive allosteric modulation for hM2. Based on these results, a second campaign was focused on bis-cationic derivatives and allowed the identification of other two relevant hM2 ligands. Overall, the study enhances the understanding of the factors governing the hM2 allosteric modulation emphasizing the key role of ligand flexibility as well as of arrangement and delocalization of the positively charged moieties. Full article
(This article belongs to the Special Issue New Avenues in Molecular Docking for Drug Design 2020)
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Article
Assessing Molecular Docking Tools to Guide Targeted Drug Discovery of CD38 Inhibitors
Int. J. Mol. Sci. 2020, 21(15), 5183; https://doi.org/10.3390/ijms21155183 - 22 Jul 2020
Cited by 10 | Viewed by 1548
Abstract
A promising protein target for computational drug development, the human cluster of differentiation 38 (CD38), plays a crucial role in many physiological and pathological processes, primarily through the upstream regulation of factors that control cytoplasmic Ca2+ concentrations. Recently, a small-molecule inhibitor of [...] Read more.
A promising protein target for computational drug development, the human cluster of differentiation 38 (CD38), plays a crucial role in many physiological and pathological processes, primarily through the upstream regulation of factors that control cytoplasmic Ca2+ concentrations. Recently, a small-molecule inhibitor of CD38 was shown to slow down pathways relating to aging and DNA damage. We examined the performance of seven docking programs for their ability to model protein-ligand interactions with CD38. A test set of twelve CD38 crystal structures, containing crystallized biologically relevant substrates, were used to assess pose prediction. The rankings for each program based on the median RMSD between the native and predicted were Vina, AD4 > PLANTS, Gold, Glide, Molegro > rDock. Forty-two compounds with known affinities were docked to assess the accuracy of the programs at affinity/ranking predictions. The rankings based on scoring power were: Vina, PLANTS > Glide, Gold > Molegro >> AutoDock 4 >> rDock. Out of the top four performing programs, Glide had the only scoring function that did not appear to show bias towards overpredicting the affinity of the ligand-based on its size. Factors that affect the reliability of pose prediction and scoring are discussed. General limitations and known biases of scoring functions are examined, aided in part by using molecular fingerprints and Random Forest classifiers. This machine learning approach may be used to systematically diagnose molecular features that are correlated with poor scoring accuracy. Full article
(This article belongs to the Special Issue New Avenues in Molecular Docking for Drug Design 2020)
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Article
Computational Drug Repositioning for Chagas Disease Using Protein-Ligand Interaction Profiling
Int. J. Mol. Sci. 2020, 21(12), 4270; https://doi.org/10.3390/ijms21124270 - 16 Jun 2020
Cited by 5 | Viewed by 1324
Abstract
Chagas disease, caused by Trypanosoma cruzi (T. cruzi), affects nearly eight million people worldwide. There are currently only limited treatment options, which cause several side effects and have drug resistance. Thus, there is a great need for a novel, improved Chagas [...] Read more.
Chagas disease, caused by Trypanosoma cruzi (T. cruzi), affects nearly eight million people worldwide. There are currently only limited treatment options, which cause several side effects and have drug resistance. Thus, there is a great need for a novel, improved Chagas treatment. Bifunctional enzyme dihydrofolate reductase-thymidylate synthase (DHFR-TS) has emerged as a promising pharmacological target. Moreover, some human dihydrofolate reductase (HsDHFR) inhibitors such as trimetrexate also inhibit T. cruzi DHFR-TS (TcDHFR-TS). These compounds serve as a starting point and a reference in a screening campaign to search for new TcDHFR-TS inhibitors. In this paper, a novel virtual screening approach was developed that combines classical docking with protein-ligand interaction profiling to identify drug repositioning opportunities against T. cruzi infection. In this approach, some food and drug administration (FDA)-approved drugs that were predicted to bind with high affinity to TcDHFR-TS and whose predicted molecular interactions are conserved among known inhibitors were selected. Overall, ten putative TcDHFR-TS inhibitors were identified. These exhibited a similar interaction profile and a higher computed binding affinity, compared to trimetrexate. Nilotinib, glipizide, glyburide and gliquidone were tested on T. cruzi epimastigotes and showed growth inhibitory activity in the micromolar range. Therefore, these compounds could lead to the development of new treatment options for Chagas disease. Full article
(This article belongs to the Special Issue New Avenues in Molecular Docking for Drug Design 2020)
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Article
Investigation of Some Antiviral N-Heterocycles as COVID 19 Drug: Molecular Docking and DFT Calculations
Int. J. Mol. Sci. 2020, 21(11), 3922; https://doi.org/10.3390/ijms21113922 - 30 May 2020
Cited by 57 | Viewed by 3487
Abstract
The novel coronavirus, COVID-19, caused by SARS-CoV-2, is a global health pandemic that started in December 2019. The effective drug target among coronaviruses is the main protease Mpro, because of its essential role in processing the polyproteins that are translated from [...] Read more.
The novel coronavirus, COVID-19, caused by SARS-CoV-2, is a global health pandemic that started in December 2019. The effective drug target among coronaviruses is the main protease Mpro, because of its essential role in processing the polyproteins that are translated from the viral RNA. In this study, the bioactivity of some selected heterocyclic drugs named Favipiravir (1), Amodiaquine (2), 2′-Fluoro-2′-deoxycytidine (3), and Ribavirin (4) was evaluated as inhibitors and nucleotide analogues for COVID-19 using computational modeling strategies. The density functional theory (DFT) calculations were performed to estimate the thermal parameters, dipole moment, polarizability, and molecular electrostatic potential of the present drugs; additionally, Mulliken atomic charges of the drugs as well as the chemical reactivity descriptors were investigated. The nominated drugs were docked on SARS-CoV-2 main protease (PDB: 6LU7) to evaluate the binding affinity of these drugs. Besides, the computations data of DFT the docking simulation studies was predicted that the Amodiaquine (2) has the least binding energy (−7.77 Kcal/mol) and might serve as a good inhibitor to SARS-CoV-2 comparable with the approved medicines, hydroxychloroquine, and remdesivir which have binding affinity −6.06 and −4.96 Kcal/mol, respectively. The high binding affinity of 2 was attributed to the presence of three hydrogen bonds along with different hydrophobic interactions between the drug and the critical amino acids residues of the receptor. Finally, the estimated molecular electrostatic potential results by DFT were used to illustrate the molecular docking findings. The DFT calculations showed that drug 2 has the highest of lying HOMO, electrophilicity index, basicity, and dipole moment. All these parameters could share with different extent to significantly affect the binding affinity of these drugs with the active protein sites. Full article
(This article belongs to the Special Issue New Avenues in Molecular Docking for Drug Design 2020)
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Article
Prediction of Novel Inhibitors of the Main Protease (M-pro) of SARS-CoV-2 through Consensus Docking and Drug Reposition
Int. J. Mol. Sci. 2020, 21(11), 3793; https://doi.org/10.3390/ijms21113793 - 27 May 2020
Cited by 56 | Viewed by 14175
Abstract
Since the outbreak of the COVID-19 pandemic in December 2019 and its rapid spread worldwide, the scientific community has been under pressure to react and make progress in the development of an effective treatment against the virus responsible for the disease. Here, we [...] Read more.
Since the outbreak of the COVID-19 pandemic in December 2019 and its rapid spread worldwide, the scientific community has been under pressure to react and make progress in the development of an effective treatment against the virus responsible for the disease. Here, we implement an original virtual screening (VS) protocol for repositioning approved drugs in order to predict which of them could inhibit the main protease of the virus (M-pro), a key target for antiviral drugs given its essential role in the virus’ replication. Two different libraries of approved drugs were docked against the structure of M-pro using Glide, FRED and AutoDock Vina, and only the equivalent high affinity binding modes predicted simultaneously by the three docking programs were considered to correspond to bioactive poses. In this way, we took advantage of the three sampling algorithms to generate hypothetic binding modes without relying on a single scoring function to rank the results. Seven possible SARS-CoV-2 M-pro inhibitors were predicted using this approach: Perampanel, Carprofen, Celecoxib, Alprazolam, Trovafloxacin, Sarafloxacin and ethyl biscoumacetate. Carprofen and Celecoxib have been selected by the COVID Moonshot initiative for in vitro testing; they show 3.97 and 11.90% M-pro inhibition at 50 µM, respectively. Full article
(This article belongs to the Special Issue New Avenues in Molecular Docking for Drug Design 2020)
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Article
A Computational Probe into the Structure and Dynamics of the Full-Length Toll-Like Receptor 3 in a Phospholipid Bilayer
Int. J. Mol. Sci. 2020, 21(8), 2857; https://doi.org/10.3390/ijms21082857 - 19 Apr 2020
Cited by 4 | Viewed by 1165
Abstract
Toll-like receptor 3 (TLR3) provides the host with antiviral defense by initiating an immune signaling cascade for the production of type I interferons. The X-ray structures of isolated TLR3 ectodomain (ECD) and transmembrane (TM) domains have been reported; however, the structure of a [...] Read more.
Toll-like receptor 3 (TLR3) provides the host with antiviral defense by initiating an immune signaling cascade for the production of type I interferons. The X-ray structures of isolated TLR3 ectodomain (ECD) and transmembrane (TM) domains have been reported; however, the structure of a membrane-solvated, full-length receptor remains elusive. We investigated an all-residue TLR3 model embedded inside a phospholipid bilayer using molecular dynamics simulations. The TLR3-ECD exhibited a ~30°–35° tilt on the membrane due to the electrostatic interaction between the N-terminal subdomain and phospholipid headgroups. Although the movement of dsRNA did not affect the dimer integrity of TLR3, its sugar-phosphate backbone was slightly distorted with the orientation of the ECD. TM helices exhibited a noticeable tilt and curvature but maintained a consistent crossing angle, avoiding the hydrophobic mismatch with the bilayer. Residues from the αD helix and the CD and DE loops of the Toll/interleukin-1 receptor (TIR) domains were partially absorbed into the lower leaflet of the bilayer. We found that the previously unknown TLR3-TIR dimerization interface could be stabilized by the reciprocal contact between αC and αD helices of one subunit and the αC helix and the BB loop of the other. Overall, the present study can be helpful to understand the signaling-competent form of TLR3 in physiological environments. Full article
(This article belongs to the Special Issue New Avenues in Molecular Docking for Drug Design 2020)
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Article
Combining Molecular Dynamics and Docking Simulations to Develop Targeted Protocols for Performing Optimized Virtual Screening Campaigns on the hTRPM8 Channel
Int. J. Mol. Sci. 2020, 21(7), 2265; https://doi.org/10.3390/ijms21072265 - 25 Mar 2020
Cited by 8 | Viewed by 1269
Abstract
Background: There is an increasing interest in TRPM8 ligands of medicinal interest, the rational design of which can be nowadays supported by structure-based in silico studies based on the recently resolved TRPM8 structures. Methods: The study involves the generation of a [...] Read more.
Background: There is an increasing interest in TRPM8 ligands of medicinal interest, the rational design of which can be nowadays supported by structure-based in silico studies based on the recently resolved TRPM8 structures. Methods: The study involves the generation of a reliable hTRPM8 homology model, the reliability of which was assessed by a 1.0 μs MD simulation which was also used to generate multiple receptor conformations for the following structure-based virtual screening (VS) campaigns; docking simulations utilized different programs and involved all monomers of the selected frames; the so computed docking scores were combined by consensus approaches based on the EFO algorithm. Results: The obtained models revealed very satisfactory performances; LiGen™ provided the best results among the tested docking programs; the combination of docking results from the four monomers elicited a markedly beneficial effect on the computed consensus models. Conclusions: The generated hTRPM8 model appears to be amenable for successful structure-based VS studies; cross-talk modulating effects between interacting monomers on the binding sites can be accounted for by combining docking simulations as performed on all the monomers; this strategy can have general applicability for docking simulations involving quaternary protein structures with multiple identical binding pockets. Full article
(This article belongs to the Special Issue New Avenues in Molecular Docking for Drug Design 2020)
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Article
Structural Requirements of N-alpha-Mercaptoacetyl Dipeptide (NAMdP) Inhibitors of Pseudomonas Aeruginosa Virulence Factor LasB: 3D-QSAR, Molecular Docking, and Interaction Fingerprint Studies
Int. J. Mol. Sci. 2019, 20(24), 6133; https://doi.org/10.3390/ijms20246133 - 05 Dec 2019
Cited by 8 | Viewed by 1218
Abstract
The zinc metallopeptidase Pseudomonas elastase (LasB) is a virulence factor of Pseudomonas aeruginosa (P. aeruginosa), a pathogenic bacterium that can cause nosocomial infections. The present study relates the structural analysis of 118 N-alpha-mercaptoacetyl dipeptides (NAMdPs) as LasB inhibitors. Field-based 3D-QSAR [...] Read more.
The zinc metallopeptidase Pseudomonas elastase (LasB) is a virulence factor of Pseudomonas aeruginosa (P. aeruginosa), a pathogenic bacterium that can cause nosocomial infections. The present study relates the structural analysis of 118 N-alpha-mercaptoacetyl dipeptides (NAMdPs) as LasB inhibitors. Field-based 3D-QSAR and molecular docking methods were employed to describe the essential interactions between NAMdPs and LasB binding sites, and the chemical features that determine their differential activities. We report a predictive 3D-QSAR model that was developed according to the internal and external validation tests. The best model, including steric, electrostatic, hydrogen bond donor, hydrogen bond acceptor, and hydrophobic fields, was found to depict a three-dimensional map with the local positive and negative effects of these chemotypes on the LasB inhibitory activities. Furthermore, molecular docking experiments yielded bioactive conformations of NAMdPs inside the LasB binding site. The series of NAMdPs adopted a similar orientation with respect to phosphoramidon within the LasB binding site (crystallographic reference), where the backbone atoms of NAMdPs are hydrogen-bonded to the LasB residues N112, A113, and R198, similarly to phosphoramidon. Our study also included a deep description of the residues involved in the protein–ligand interaction patterns for the whole set of NAMdPs, through the use of interaction fingerprints (IFPs). Full article
(This article belongs to the Special Issue New Avenues in Molecular Docking for Drug Design 2020)
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Review

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Review
Computational Simulations to Guide Enzyme-Mediated Prodrug Activation
Int. J. Mol. Sci. 2020, 21(10), 3621; https://doi.org/10.3390/ijms21103621 - 20 May 2020
Cited by 7 | Viewed by 1289
Abstract
Prodrugs are designed to improve pharmaceutical/biopharmaceutical characteristics, pharmacokinetic/pharmacodynamic properties, site-specificity, and more. A crucial step in successful prodrug is its activation, which releases the active parent drug, exerting a therapeutic effect. Prodrug activation can be based on oxidation/reduction processes, or through enzyme-mediated hydrolysis, [...] Read more.
Prodrugs are designed to improve pharmaceutical/biopharmaceutical characteristics, pharmacokinetic/pharmacodynamic properties, site-specificity, and more. A crucial step in successful prodrug is its activation, which releases the active parent drug, exerting a therapeutic effect. Prodrug activation can be based on oxidation/reduction processes, or through enzyme-mediated hydrolysis, from oxidoreductases (i.e., Cytochrome P450) to hydrolytic enzymes (i.e., carboxylesterase). This study provides an overview of the novel in silico methods for the optimization of enzyme-mediated prodrug activation. Computational methods simulating enzyme-substrate binding can be simpler like molecular docking, or more complex, such as quantum mechanics (QM), molecular mechanics (MM), and free energy perturbation (FEP) methods such as molecular dynamics (MD). Examples for MD simulations used for elucidating the mechanism of prodrug (losartan, paclitaxel derivatives) metabolism via CYP450 enzyme are presented, as well as an MD simulation for optimizing linker length in phospholipid-based prodrugs. Molecular docking investigating quinazolinone prodrugs as substrates for alkaline phosphatase is also presented, as well as QM and MD simulations used for optimal fit of different prodrugs within the human carboxylesterase 1 catalytical site. Overall, high quality computational simulations may show good agreement with experimental results, and should be used early in the prodrug development process. Full article
(This article belongs to the Special Issue New Avenues in Molecular Docking for Drug Design 2020)
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Review
Computer-Aided Drug Design of β-Secretase, γ-Secretase and Anti-Tau Inhibitors for the Discovery of Novel Alzheimer’s Therapeutics
Int. J. Mol. Sci. 2020, 21(3), 703; https://doi.org/10.3390/ijms21030703 - 21 Jan 2020
Cited by 12 | Viewed by 2211
Abstract
Aging-associated neurodegenerative diseases, which are characterized by progressive neuronal death and synapses loss in human brain, are rapidly growing affecting millions of people globally. Alzheimer’s is the most common neurodegenerative disease and it can be caused by genetic and environmental risk factors. This [...] Read more.
Aging-associated neurodegenerative diseases, which are characterized by progressive neuronal death and synapses loss in human brain, are rapidly growing affecting millions of people globally. Alzheimer’s is the most common neurodegenerative disease and it can be caused by genetic and environmental risk factors. This review describes the amyloid-β and Tau hypotheses leading to amyloid plaques and neurofibrillary tangles, respectively which are the predominant pathways for the development of anti-Alzheimer’s small molecule inhibitors. The function and structure of the druggable targets of these two pathways including β-secretase, γ-secretase, and Tau are discussed in this review article. Computer-Aided Drug Design including computational structure-based design and ligand-based design have been employed successfully to develop inhibitors for biomolecular targets involved in Alzheimer’s. The application of computational molecular modeling for the discovery of small molecule inhibitors and modulators for β-secretase and γ-secretase is summarized. Examples of computational approaches employed for the development of anti-amyloid aggregation and anti-Tau phosphorylation, proteolysis and aggregation inhibitors are also reported. Full article
(This article belongs to the Special Issue New Avenues in Molecular Docking for Drug Design 2020)
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