International Journal of Molecular Sciences

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Open AccessArticle

Reductive Activity and Mechanism of Hypoxia- Targeted AGT Inhibitors: An Experimental and Theoretical Investigation

by Weinan Xiao, Guohui Sun, Tengjiao Fan, Junjun Liu, Na Zhang, Lijiao Zhao and Rugang Zhong

Int. J. Mol. Sci. 2019, 20(24), 6308; https://doi.org/10.3390/ijms20246308 - 13 Dec 2019

Cited by 4 | Viewed by 2926

Abstract

O⁶-alkylguanine-DNA alkyltransferase (AGT) is the main cause of tumor cell resistance to DNA-alkylating agents, so it is valuable to design tumor-targeted AGT inhibitors with hypoxia activation. Based on the existing benchmark inhibitor O⁶-benzylguanine (O⁶-BG), four derivatives with [...] Read more.

O⁶-alkylguanine-DNA alkyltransferase (AGT) is the main cause of tumor cell resistance to DNA-alkylating agents, so it is valuable to design tumor-targeted AGT inhibitors with hypoxia activation. Based on the existing benchmark inhibitor O⁶-benzylguanine (O⁶-BG), four derivatives with hypoxia-reduced potential and their corresponding reduction products were synthesized. A reductase system consisting of glucose/glucose oxidase, xanthine/xanthine oxidase, and catalase were constructed, and the reduction products of the hypoxia-activated prodrugs under normoxic and hypoxic conditions were determined by high-performance liquid chromatography electrospray ionization tandem mass spectrometry (HPLC-ESI-MS/MS). The results showed that the reduction products produced under hypoxic conditions were significantly higher than that under normoxic condition. The amount of the reduction product yielded from ANBP (2-nitro-6-(3-amino) benzyloxypurine) under hypoxic conditions was the highest, followed by AMNBP (2-nitro-6-(3-aminomethyl)benzyloxypurine), 2-NBP (2-nitro-6-benzyloxypurine), and 3-NBG (O6-(3-nitro)benzylguanine). It should be noted that although the levels of the reduction products of 2-NBP and 3-NBG were lower than those of ANBP and AMNBP, their maximal hypoxic/normoxic ratios were higher than those of the other two prodrugs. Meanwhile, we also investigated the single electron reduction mechanism of the hypoxia-activated prodrugs using density functional theory (DFT) calculations. As a result, the reduction of the nitro group to the nitroso was proven to be a rate-limiting step. Moreover, the 2-nitro group of purine ring was more ready to be reduced than the 3-nitro group of benzyl. The energy barriers of the rate-limiting steps were 34–37 kcal/mol. The interactions between these prodrugs and nitroreductase were explored via molecular docking study, and ANBP was observed to have the highest affinity to nitroreductase, followed by AMNBP, 2-NBP, and 3-NBG. Interestingly, the theoretical results were generally in a good agreement with the experimental results. Finally, molecular docking and molecular dynamics simulations were performed to predict the AGT-inhibitory activity of the four prodrugs and their reduction products. In summary, simultaneous consideration of reduction potential and hypoxic selectivity is necessary to ensure that such prodrugs have good hypoxic tumor targeting. This study provides insights into the hypoxia-activated mechanism of nitro-substituted prodrugs as AGT inhibitors, which may contribute to reasonable design and development of novel tumor-targeted AGT inhibitors. Full article

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

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13 pages, 3999 KiB

Open AccessCommunication

In Silico Insights towards the Identification of NLRP3 Druggable Hot Spots

by Nedra Mekni, Maria De Rosa, Chiara Cipollina, Maria Rita Gulotta, Giada De Simone, Jessica Lombino, Alessandro Padova and Ugo Perricone

Int. J. Mol. Sci. 2019, 20(20), 4974; https://doi.org/10.3390/ijms20204974 - 9 Oct 2019

Cited by 21 | Viewed by 6312

Abstract

NLRP3 (NOD-like receptor family, pyrin domain-containing protein 3) activation has been linked to several chronic pathologies, including atherosclerosis, type-II diabetes, fibrosis, rheumatoid arthritis, and Alzheimer’s disease. Therefore, NLRP3 represents an appealing target for the development of innovative therapeutic approaches. A few companies are [...] Read more.

NLRP3 (NOD-like receptor family, pyrin domain-containing protein 3) activation has been linked to several chronic pathologies, including atherosclerosis, type-II diabetes, fibrosis, rheumatoid arthritis, and Alzheimer’s disease. Therefore, NLRP3 represents an appealing target for the development of innovative therapeutic approaches. A few companies are currently working on the discovery of selective modulators of NLRP3 inflammasome. Unfortunately, limited structural data are available for this target. To date, MCC950 represents one of the most promising noncovalent NLRP3 inhibitors. Recently, a possible region for the binding of MCC950 to the NLRP3 protein was described but no details were disclosed regarding the key interactions. In this communication, we present an in silico multiple approach as an insight useful for the design of novel NLRP3 inhibitors. In detail, combining different computational techniques, we propose consensus-retrieved protein residues that seem to be essential for the binding process and for the stabilization of the protein–ligand complex. Full article

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

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13 pages, 4127 KiB

Open AccessArticle

Can We Still Trust Docking Results? An Extension of the Applicability of DockBench on PDBbind Database

by Giovanni Bolcato, Alberto Cuzzolin, Maicol Bissaro, Stefano Moro and Mattia Sturlese

Int. J. Mol. Sci. 2019, 20(14), 3558; https://doi.org/10.3390/ijms20143558 - 20 Jul 2019

Cited by 14 | Viewed by 5411

Abstract

The number of entries in the Protein Data Bank (PDB) has doubled in the last decade, and it has increased tenfold in the last twenty years. The availability of an ever-growing number of structures is having a huge impact on the Structure-Based Drug [...] Read more.

The number of entries in the Protein Data Bank (PDB) has doubled in the last decade, and it has increased tenfold in the last twenty years. The availability of an ever-growing number of structures is having a huge impact on the Structure-Based Drug Discovery (SBDD), allowing investigation of new targets and giving the possibility to have multiple structures of the same macromolecule in a complex with different ligands. Such a large resource often implies the choice of the most suitable complex for molecular docking calculation, and this task is complicated by the plethora of possible posing and scoring function algorithms available, which may influence the quality of the outcomes. Here, we report a large benchmark performed on the PDBbind database containing more than four thousand entries and seventeen popular docking protocols. We found that, even in protein families wherein docking protocols generally showed acceptable results, certain ligand-protein complexes are poorly reproduced in the self-docking procedure. Such a trend in certain protein families is more pronounced, and this underlines the importance in identification of a suitable protein–ligand conformation coupled to a well-performing docking protocol. Full article

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

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11 pages, 2536 KiB

Open AccessArticle

Pathway-Centric Structure-Based Multi-Target Compound Screening for Anti-Virulence Drug Repurposing

by Li Xie and Lei Xie

Int. J. Mol. Sci. 2019, 20(14), 3504; https://doi.org/10.3390/ijms20143504 - 17 Jul 2019

Cited by 8 | Viewed by 3167

Abstract

The emergence of superbugs that are resistant to last-resort antibiotics poses a serious threat to human health, and we are in a “race against time to develop new antibiotics.” New approaches are urgently needed to control drug-resistant pathogens, and to reduce the emergence [...] Read more.

The emergence of superbugs that are resistant to last-resort antibiotics poses a serious threat to human health, and we are in a “race against time to develop new antibiotics.” New approaches are urgently needed to control drug-resistant pathogens, and to reduce the emergence of new drug-resistant microbes. Targeting bacterial virulence has emerged as an important strategy for combating drug-resistant pathogens. It has been shown that pyocyanin, which is produced by the phenazine biosynthesis pathway, plays a key role in the virulence of Pseudomonas aeruginosa infection, making it an attractive target for anti-infective drug discovery. In order to discover efficient therapeutics that inhibit the phenazine biosynthesis in a timely fashion, we screen 2004 clinical and pre-clinical drugs to target multiple enzymes in the phenazine biosynthesis pathway, using a novel procedure of protein–ligand docking. Our detailed analysis suggests that kinase inhibitors, notably Lifirafenib, are promising lead compounds for inhibiting aroQ, phzG, and phzS enzymes that are involved in the phenazine biosynthesis, and merit further experimental validations. In principle, inhibiting multiple targets in a pathway will be more effective and have less chance of the emergence of drug resistance than targeting a single protein. Our multi-target structure-based drug design strategy can be applied to other pathways, as well as provide a systematic approach to polypharmacological drug repositioning. Full article

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

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28 pages, 7172 KiB

Open AccessArticle

Molecular Docking Guided Grid-Independent Descriptor Analysis to Probe the Impact of Water Molecules on Conformational Changes of hERG Inhibitors in Drug Trapping Phenomenon

by Saba Munawar, Jamie I. Vandenberg and Ishrat Jabeen

Int. J. Mol. Sci. 2019, 20(14), 3385; https://doi.org/10.3390/ijms20143385 - 10 Jul 2019

Cited by 15 | Viewed by 4969

Abstract

Human ether a-go-go related gene (hERG) or KV11.1 potassium channels mediate the rapid delayed rectifier current (I_Kr) in cardiac myocytes. Drug-induced inhibition of hERG channels has been implicated in the development of acquired long QT syndrome type (aLQTS) and fatal [...] Read more.

Human ether a-go-go related gene (hERG) or KV11.1 potassium channels mediate the rapid delayed rectifier current (I_Kr) in cardiac myocytes. Drug-induced inhibition of hERG channels has been implicated in the development of acquired long QT syndrome type (aLQTS) and fatal arrhythmias. Several marketed drugs have been withdrawn for this reason. Therefore, there is considerable interest in developing better tests for predicting drugs which can block the hERG channel. The drug-binding pocket in hERG channels, which lies below the selectivity filter, normally contains K⁺ ions and water molecules. In this study, we test the hypothesis that these water molecules impact drug binding to hERG. We developed 3D QSAR models based on alignment independent descriptors (GRIND) using docked ligands in open and closed conformations of hERG in the presence (solvated) and absence (non-solvated) of water molecules. The ligand–protein interaction fingerprints (PLIF) scheme was used to summarize and compare the interactions. All models delineated similar 3D hERG binding features, however, small deviations of about ~0.4 Å were observed between important hotspots of molecular interaction fields (MIFs) between solvated and non-solvated hERG models. These small changes in conformations do not affect the performance and predictive power of the model to any significant extent. The model that exhibits the best statistical values was attained with a cryo_EM structure of the hERG channel in open state without water. This model also showed the best R² of 0.58 and 0.51 for the internal and external validation test sets respectively. Our results suggest that the inclusion of water molecules during the docking process has little effect on conformations and this conformational change does not impact the predictive ability of the 3D QSAR models. Full article

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

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18 pages, 3954 KiB

Open AccessArticle

GPathFinder: Identification of Ligand-Binding Pathways by a Multi-Objective Genetic Algorithm

by José-Emilio Sánchez-Aparicio, Giuseppe Sciortino, Daniel Viladrich Herrmannsdoerfer, Pablo Orenes Chueca, Jaime Rodríguez-Guerra Pedregal and Jean-Didier Maréchal

Int. J. Mol. Sci. 2019, 20(13), 3155; https://doi.org/10.3390/ijms20133155 - 28 Jun 2019

Cited by 14 | Viewed by 6144

Abstract

Protein–ligand docking is a widely used method to generate solutions for the binding of a small molecule with its target in a short amount of time. However, these methods provide identification of physically sound protein–ligand complexes without a complete view of the binding [...] Read more.

Protein–ligand docking is a widely used method to generate solutions for the binding of a small molecule with its target in a short amount of time. However, these methods provide identification of physically sound protein–ligand complexes without a complete view of the binding process dynamics, which has been recognized to be a major discriminant in binding affinity and ligand selectivity. In this paper, a novel piece of open-source software to approach this problem is presented, called GPathFinder. It is built as an extension of the modular GaudiMM platform and is able to simulate ligand diffusion pathways at atomistic level. The method has been benchmarked on a set of 20 systems whose ligand-binding routes were studied by other computational tools or suggested from experimental “snapshots”. In all of this set, GPathFinder identifies those channels that were already reported in the literature. Interestingly, the low-energy pathways in some cases indicate novel possible binding routes. To show the usefulness of GPathFinder, the analysis of three case systems is reported. We believe that GPathFinder is a software solution with a good balance between accuracy and computational cost, and represents a step forward in extending protein–ligand docking capacities, with implications in several fields such as drug or enzyme design. Full article

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

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14 pages, 2075 KiB

Open AccessArticle

Molecular Docking and Molecular Dynamics (MD) Simulation of Human Anti-Complement Factor H (CFH) Antibody Ab42 and CFH Polypeptide

by Bing Yang, Shu-Jian Lin, Jia-Yi Ren, Tong Liu, Yue-Ming Wang, Cheng-Ming Li, Wen-Wen Xu, You-Wen He, Wei-Hong Zheng, Jian Zhao, Xiao-Hui Yuan and Hua-Xin Liao

Int. J. Mol. Sci. 2019, 20(10), 2568; https://doi.org/10.3390/ijms20102568 - 25 May 2019

Cited by 31 | Viewed by 5152

Abstract

An understanding of the interaction between the antibody and its targeted antigen and knowing of the epitopes are critical for the development of monoclonal antibody drugs. Complement factor H (CFH) is implied to play a role in tumor growth and metastasis. An autoantibody [...] Read more.

An understanding of the interaction between the antibody and its targeted antigen and knowing of the epitopes are critical for the development of monoclonal antibody drugs. Complement factor H (CFH) is implied to play a role in tumor growth and metastasis. An autoantibody to CHF is associated with anti-tumor cell activity. The interaction of a human monoclonal antibody Ab42 that was isolated from a cancer patient with CFH polypeptide (pCFH) antigen was analyzed by molecular docking, molecular dynamics (MD) simulation, free energy calculation, and computational alanine scanning (CAS). Experimental alanine scanning (EAS) was then carried out to verify the results of the theoretical calculation. Our results demonstrated that the Ab42 antibody interacts with pCFH by hydrogen bonds through the Tyr315, Ser100, Gly33, and Tyr53 residues on the complementarity-determining regions (CDRs), respectively, with the amino acid residues of Pro441, Ile442, Asp443, Asn444, Ile447, and Thr448 on the pCFH antigen. In conclusion, this study has explored the mechanism of interaction between Ab42 antibody and its targeted antigen by both theoretical and experimental analysis. Our results have important theoretical significance for the design and development of relevant antibody drugs. Full article

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

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18 pages, 2091 KiB

Open AccessArticle

Fragment-Based Ligand-Protein Contact Statistics: Application to Docking Simulations

by Gabriele Macari, Daniele Toti, Carlo Del Moro and Fabio Polticelli

Int. J. Mol. Sci. 2019, 20(10), 2499; https://doi.org/10.3390/ijms20102499 - 21 May 2019

Cited by 7 | Viewed by 3539

Abstract

In this work, the information contained in the contacts between fragments of small-molecule ligands and protein residues has been collected and its exploitability has been verified by using the scoring of docking simulations as a test case for bringing about a proof of [...] Read more.

In this work, the information contained in the contacts between fragments of small-molecule ligands and protein residues has been collected and its exploitability has been verified by using the scoring of docking simulations as a test case for bringing about a proof of concept. Contact statistics between small-molecule fragments and binding site residues were collected and analyzed using a dataset composed of 200,000+ binding sites and associated ligands, derived from the database of the LIBRA ligand binding site recognition software, as a starting point. The fragments were generated by applying the decomposition algorithm implemented in BRICS. A simple “potential” based on the contact frequencies was tested against the CASF-2013 benchmark; its performance was then evaluated through the rescoring of docking poses generated for the DUD-E dataset. The results obtained indicate that this approach, its simplicity notwithstanding, yields promising results that are comparable, and in some cases, superior, to those obtained with other, more complex scoring functions. Full article

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

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14 pages, 2370 KiB

Open AccessArticle

Molecular Modeling-Guided Design of Phospholipid-Based Prodrugs

by Milica Markovic, Shimon Ben-Shabat, Shahar Keinan, Aaron Aponick, Ellen M. Zimmermann and Arik Dahan

Int. J. Mol. Sci. 2019, 20(9), 2210; https://doi.org/10.3390/ijms20092210 - 5 May 2019

Cited by 17 | Viewed by 3801

Abstract

The lipidic prodrug approach is an emerging field for improving a number of biopharmaceutical and drug delivery aspects. Owing to their structure and nature, phospholipid (PL)-based prodrugs may join endogenous lipid processing pathways, and hence significantly improve the pharmacokinetics and/or bioavailability of the [...] Read more.

The lipidic prodrug approach is an emerging field for improving a number of biopharmaceutical and drug delivery aspects. Owing to their structure and nature, phospholipid (PL)-based prodrugs may join endogenous lipid processing pathways, and hence significantly improve the pharmacokinetics and/or bioavailability of the drug. Additional advantages of this approach include drug targeting by enzyme-triggered drug release, blood–brain barrier permeability, lymphatic targeting, overcoming drug resistance, or enabling appropriate formulation. The PL-prodrug design includes various structural modalities-different conjugation strategies and/or the use of linkers between the PL and the drug moiety, which considerably influence the prodrug characteristics and the consequent effects. In this article, we describe how molecular modeling can guide the structural design of PL-based prodrugs. Computational simulations can predict the extent of phospholipase A₂ (PLA₂)-mediated activation, and facilitate prodrug development. Several computational methods have been used to facilitate the design of the pro-drugs, which will be reviewed here, including molecular docking, the free energy perturbation method, molecular dynamics simulations, and free density functional theory. Altogether, the studies described in this article indicate that computational simulation-guided PL-based prodrug molecular design correlates well with the experimental results, allowing for more mechanistic and less empirical development. In the future, the use of molecular modeling techniques to predict the activity of PL-prodrugs should be used earlier in the development process. Full article

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

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14 pages, 583 KiB

Open AccessArticle

Rescoring and Linearly Combining: A Highly Effective Consensus Strategy for Virtual Screening Campaigns

by Alessandro Pedretti, Angelica Mazzolari, Silvia Gervasoni and Giulio Vistoli

Int. J. Mol. Sci. 2019, 20(9), 2060; https://doi.org/10.3390/ijms20092060 - 26 Apr 2019

Cited by 20 | Viewed by 3396

Abstract

The study proposes a novel consensus strategy based on linear combinations of different docking scores to be used in the evaluation of virtual screening campaigns. The consensus models are generated by applying the recently proposed Enrichment Factor Optimization (EFO) method, which develops the [...] Read more.

The study proposes a novel consensus strategy based on linear combinations of different docking scores to be used in the evaluation of virtual screening campaigns. The consensus models are generated by applying the recently proposed Enrichment Factor Optimization (EFO) method, which develops the linear equations by exhaustively combining the available docking scores and by optimizing the resulting enrichment factors. The performances of such a consensus strategy were evaluated by simulating the entire Directory of Useful Decoys (DUD datasets). In detail, the poses were initially generated by the PLANTS docking program and then rescored by ReScore+ with and without the minimization of the complexes. The so calculated scores were then used to generate the mentioned consensus models including two or three different scoring functions. The reliability of the generated models was assessed by a per target validation as performed by default by the EFO approach. The encouraging performances of the here proposed consensus strategy are emphasized by the average increase of the 17% in the Top 1% enrichment factor (EF) values when comparing the single best score with the linear combination of three scores. Specifically, kinases offer a truly convincing demonstration of the efficacy of the here proposed consensus strategy since their Top 1% EF average ranges from 6.4 when using the single best performing primary score to 23.5 when linearly combining scoring functions. The beneficial effects of this consensus approach are clearly noticeable even when considering the entire DUD datasets as evidenced by the area under the curve (AUC) averages revealing a 14% increase when combining three scores. The reached AUC values compare very well with those reported in literature by an extended set of recent benchmarking studies and the three-variable models afford the highest AUC average. Full article

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

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13 pages, 3920 KiB

Open AccessArticle

Structure-Based Virtual Screening and In Vitro Evaluation of New Trypanosoma cruzi Cruzain Inhibitors

by Verónica Herrera-Mayorga, Edgar E. Lara-Ramírez, Karla F. Chacón-Vargas, Charmina Aguirre-Alvarado, Lorena Rodríguez-Páez, Verónica Alcántara-Farfán, Joaquín Cordero-Martínez, Benjamín Nogueda-Torres, Francisco Reyes-Espinosa, Virgilio Bocanegra-García and Gildardo Rivera

Int. J. Mol. Sci. 2019, 20(7), 1742; https://doi.org/10.3390/ijms20071742 - 9 Apr 2019

Cited by 24 | Viewed by 4440

Abstract

Chagas disease (CD), or American trypanosomiasis, causes more than 10,000 deaths per year in the Americas. Current medical therapy for CD has low efficacy in the chronic phase of the disease and serious adverse effects; therefore, it is necessary to search for new [...] Read more.

Chagas disease (CD), or American trypanosomiasis, causes more than 10,000 deaths per year in the Americas. Current medical therapy for CD has low efficacy in the chronic phase of the disease and serious adverse effects; therefore, it is necessary to search for new pharmacological treatments. In this work, the ZINC¹⁵ database was filtered using the N-acylhydrazone moiety and a subsequent structure-based virtual screening was performed using the cruzain enzyme of Trypanosoma cruzi to predict new potential cruzain inhibitors. After a rational selection process, four compounds, Z2 (ZINC9873043), Z3 (ZINC9870651), Z5 (ZINC9715287), and Z6 (ZINC9861447), were chosen to evaluate their in vitro trypanocidal activity and enzyme inhibition. Compound Z5 showed the best trypanocidal activity against epimatigote (IC₅₀ = 36.26 ± 9.9 μM) and trypomastigote (IC₅₀ = 166.21 ± 14.5 μM and 185.1 ± 8.5 μM on NINOA and INC-5 strains, respectively) forms of Trypanosoma cruzi. In addition, Z5 showed a better inhibitory effect on Trypanosoma cruzi proteases than S1 (STK552090, 8-chloro-N-(3-morpholinopropyl)-5H-pyrimido[5,4-b]-indol-4-amine), a known cruzain inhibitor. This study encourages the use of computational tools for the rational search for trypanocidal drugs. Full article

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

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20 pages, 6102 KiB

Open AccessArticle

In Silico Analysis of Homologous Heterodimers of Cruzipain-Chagasin from Structural Models Built by Homology

by Francisco Reyes-Espinosa, Alfredo Juárez-Saldivar, Isidro Palos, Verónica Herrera-Mayorga, Carlos García-Pérez and Gildardo Rivera

Int. J. Mol. Sci. 2019, 20(6), 1320; https://doi.org/10.3390/ijms20061320 - 15 Mar 2019

Cited by 2 | Viewed by 3223

Abstract

The present study gives an overview of the binding energetics of the homologous heterodimers of cruzipain−chagasin based on the binding energy (ΔG_b) prediction obtained with FoldX. This analysis involves a total of 70 homologous models of the cruzipain−chagasin complex which [...] Read more.

The present study gives an overview of the binding energetics of the homologous heterodimers of cruzipain−chagasin based on the binding energy (ΔG_b) prediction obtained with FoldX. This analysis involves a total of 70 homologous models of the cruzipain−chagasin complex which were constructed by homology from the combinatory variation of nine papain-like cysteine peptidase structures and seven cysteine protease inhibitor structures (as chagasin-like and cystatin-like inhibitors). Only 32 systems have been evaluated experimentally, ΔG_b^experimental values previously reported. Therefore, the result of the multiple analysis in terms of the thermodynamic parameters, are shown as relative energy |ΔΔG| = |ΔG_b^from ^FoldX − ΔG_b^experimental|. Nine models were identified that recorded |ΔΔG| < 1.3, five models to 2.8 > |ΔΔG| > 1.3 and the other 18 models, values of |ΔΔG| > 2.8. The energetic analysis of the contribution of ΔH and ΔS to ΔG_b to the 14-molecular model presents a ΔG_b mostly ΔH-driven at neutral pH and at an ionic strength (I) of 0.15 M. The dependence of ΔG_b(I,pH) at 298 K to the cruzipain−chagasin complex predicts a linear dependence of ΔG_b(I). The computational protocol allowed the identification and prediction of thermodynamics binding energy parameters for cruzipain−chagasin-like heterodimers. Full article

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

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Review

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29 pages, 1859 KiB

Open AccessReview

Key Topics in Molecular Docking for Drug Design

by Pedro H. M. Torres, Ana C. R. Sodero, Paula Jofily and Floriano P. Silva-Jr

Int. J. Mol. Sci. 2019, 20(18), 4574; https://doi.org/10.3390/ijms20184574 - 15 Sep 2019

Cited by 330 | Viewed by 29574

Abstract

Molecular docking has been widely employed as a fast and inexpensive technique in the past decades, both in academic and industrial settings. Although this discipline has now had enough time to consolidate, many aspects remain challenging and there is still not a straightforward [...] Read more.

Molecular docking has been widely employed as a fast and inexpensive technique in the past decades, both in academic and industrial settings. Although this discipline has now had enough time to consolidate, many aspects remain challenging and there is still not a straightforward and accurate route to readily pinpoint true ligands among a set of molecules, nor to identify with precision the correct ligand conformation within the binding pocket of a given target molecule. Nevertheless, new approaches continue to be developed and the volume of published works grows at a rapid pace. In this review, we present an overview of the method and attempt to summarise recent developments regarding four main aspects of molecular docking approaches: (i) the available benchmarking sets, highlighting their advantages and caveats, (ii) the advances in consensus methods, (iii) recent algorithms and applications using fragment-based approaches, and (iv) the use of machine learning algorithms in molecular docking. These recent developments incrementally contribute to an increase in accuracy and are expected, given time, and together with advances in computing power and hardware capability, to eventually accomplish the full potential of this area. Full article

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

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23 pages, 1994 KiB

Open AccessReview

Molecular Docking: Shifting Paradigms in Drug Discovery

by Luca Pinzi and Giulio Rastelli

Int. J. Mol. Sci. 2019, 20(18), 4331; https://doi.org/10.3390/ijms20184331 - 4 Sep 2019

Cited by 1192 | Viewed by 45616

Abstract

Molecular docking is an established in silico structure-based method widely used in drug discovery. Docking enables the identification of novel compounds of therapeutic interest, predicting ligand-target interactions at a molecular level, or delineating structure-activity relationships (SAR), without knowing a priori the chemical structure [...] Read more.

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

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

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18 pages, 5018 KiB

Open AccessReview

A Structure-Based Drug Discovery Paradigm

by Maria Batool, Bilal Ahmad and Sangdun Choi

Int. J. Mol. Sci. 2019, 20(11), 2783; https://doi.org/10.3390/ijms20112783 - 6 Jun 2019

Cited by 406 | Viewed by 37576

Abstract

Structure-based drug design is becoming an essential tool for faster and more cost-efficient lead discovery relative to the traditional method. Genomic, proteomic, and structural studies have provided hundreds of new targets and opportunities for future drug discovery. This situation poses a major problem: [...] Read more.

Structure-based drug design is becoming an essential tool for faster and more cost-efficient lead discovery relative to the traditional method. Genomic, proteomic, and structural studies have provided hundreds of new targets and opportunities for future drug discovery. This situation poses a major problem: the necessity to handle the “big data” generated by combinatorial chemistry. Artificial intelligence (AI) and deep learning play a pivotal role in the analysis and systemization of larger data sets by statistical machine learning methods. Advanced AI-based sophisticated machine learning tools have a significant impact on the drug discovery process including medicinal chemistry. In this review, we focus on the currently available methods and algorithms for structure-based drug design including virtual screening and de novo drug design, with a special emphasis on AI- and deep-learning-based methods used for drug discovery. Full article

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

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