1
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Urra G, Valdés-Muñoz E, Suardiaz R, Hernández-Rodríguez EW, Palma JM, Ríos-Rozas SE, Flores-Morales CA, Alegría-Arcos M, Yáñez O, Morales-Quintana L, D’Afonseca V, Bustos D. From Proteome to Potential Drugs: Integration of Subtractive Proteomics and Ensemble Docking for Drug Repurposing against Pseudomonas aeruginosa RND Superfamily Proteins. Int J Mol Sci 2024; 25:8027. [PMID: 39125594 PMCID: PMC11312079 DOI: 10.3390/ijms25158027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2024] [Revised: 07/08/2024] [Accepted: 07/17/2024] [Indexed: 08/12/2024] Open
Abstract
Pseudomonas aeruginosa (P. aeruginosa) poses a significant threat as a nosocomial pathogen due to its robust resistance mechanisms and virulence factors. This study integrates subtractive proteomics and ensemble docking to identify and characterize essential proteins in P. aeruginosa, aiming to discover therapeutic targets and repurpose commercial existing drugs. Using subtractive proteomics, we refined the dataset to discard redundant proteins and minimize potential cross-interactions with human proteins and the microbiome proteins. We identified 12 key proteins, including a histidine kinase and members of the RND efflux pump family, known for their roles in antibiotic resistance, virulence, and antigenicity. Predictive modeling of the three-dimensional structures of these RND proteins and subsequent molecular ensemble-docking simulations led to the identification of MK-3207, R-428, and Suramin as promising inhibitor candidates. These compounds demonstrated high binding affinities and effective inhibition across multiple metrics. Further refinement using non-covalent interaction index methods provided deeper insights into the electronic effects in protein-ligand interactions, with Suramin exhibiting superior binding energies, suggesting its broad-spectrum inhibitory potential. Our findings confirm the critical role of RND efflux pumps in antibiotic resistance and suggest that MK-3207, R-428, and Suramin could be effectively repurposed to target these proteins. This approach highlights the potential of drug repurposing as a viable strategy to combat P. aeruginosa infections.
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Affiliation(s)
- Gabriela Urra
- Laboratorio de Bioinformática y Química Computacional, Departamento de Medicina Traslacional, Facultad de Medicina, Universidad Católica del Maule, Talca 3480094, Chile; (G.U.); (E.W.H.-R.); (S.E.R.-R.)
| | - Elizabeth Valdés-Muñoz
- Doctorado en Biotecnología Traslacional, Facultad de Ciencias Agrarias y Forestales, Universidad Católica del Maule, Talca 3480094, Chile;
| | - Reynier Suardiaz
- Departamento de Química Física, Facultad de Ciencias Químicas, Universidad Complutense de Madrid, 28040 Madrid, Spain;
| | - Erix W. Hernández-Rodríguez
- Laboratorio de Bioinformática y Química Computacional, Departamento de Medicina Traslacional, Facultad de Medicina, Universidad Católica del Maule, Talca 3480094, Chile; (G.U.); (E.W.H.-R.); (S.E.R.-R.)
- Unidad de Bioinformática Clínica, Centro Oncológico, Facultad de Medicina, Universidad Católica del Maule, Talca 3480094, Chile
| | - Jonathan M. Palma
- Facultad de Ingeniería, Universidad de Talca, Curicó 3344158, Chile;
| | - Sofía E. Ríos-Rozas
- Laboratorio de Bioinformática y Química Computacional, Departamento de Medicina Traslacional, Facultad de Medicina, Universidad Católica del Maule, Talca 3480094, Chile; (G.U.); (E.W.H.-R.); (S.E.R.-R.)
| | | | - Melissa Alegría-Arcos
- Núcleo de Investigación en Data Science, Facultad de Ingeniería y Negocios, Universidad de las Américas, Santiago 7500000, Chile; (M.A.-A.); (O.Y.)
| | - Osvaldo Yáñez
- Núcleo de Investigación en Data Science, Facultad de Ingeniería y Negocios, Universidad de las Américas, Santiago 7500000, Chile; (M.A.-A.); (O.Y.)
| | - Luis Morales-Quintana
- Multidisciplinary Agroindustry Research Laboratory, Instituto de Ciencias Biomédicas, Facultad de Ciencias de la Salud, Universidad Autónoma de Chile, Cinco Pte. N° 1670, Talca 3467987, Chile;
| | - Vívian D’Afonseca
- Departamento de Ciencias Preclínicas, Facultad de Medicina, Universidad Católica del Maule, Ave. San Miguel 3605, Talca 3466706, Chile
| | - Daniel Bustos
- Laboratorio de Bioinformática y Química Computacional, Departamento de Medicina Traslacional, Facultad de Medicina, Universidad Católica del Maule, Talca 3480094, Chile; (G.U.); (E.W.H.-R.); (S.E.R.-R.)
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2
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Yáñez O, Alegría-Arcos M, Suardiaz R, Morales-Quintana L, Castro RI, Palma-Olate J, Galarza C, Catagua-González Á, Rojas-Pérez V, Urra G, Hernández-Rodríguez EW, Bustos D. Calcium-Alginate-Chitosan Nanoparticle as a Potential Solution for Pesticide Removal, a Computational Approach. Polymers (Basel) 2023; 15:3020. [PMID: 37514411 PMCID: PMC10383139 DOI: 10.3390/polym15143020] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 07/06/2023] [Accepted: 07/10/2023] [Indexed: 07/30/2023] Open
Abstract
Pesticides have a significant negative impact on the environment, non-target organisms, and human health. To address these issues, sustainable pest management practices and government regulations are necessary. However, biotechnology can provide additional solutions, such as the use of polyelectrolyte complexes to encapsulate and remove pesticides from water sources. We introduce a computational methodology to evaluate the capture capabilities of Calcium-Alginate-Chitosan (CAC) nanoparticles for a broad range of pesticides. By employing ensemble-docking and molecular dynamics simulations, we investigate the intermolecular interactions and absorption/adsorption characteristics between the CAC nanoparticles and selected pesticides. Our findings reveal that charged pesticide molecules exhibit more than double capture rates compared to neutral counterparts, owing to their stronger affinity for the CAC nanoparticles. Non-covalent interactions, such as van der Waals forces, π-π stacking, and hydrogen bonds, are identified as key factors which stabilized the capture and physisorption of pesticides. Density profile analysis confirms the localization of pesticides adsorbed onto the surface or absorbed into the polymer matrix, depending on their chemical nature. The mobility and diffusion behavior of captured compounds within the nanoparticle matrix is assessed using mean square displacement and diffusion coefficients. Compounds with high capture levels exhibit limited mobility, indicative of effective absorption and adsorption. Intermolecular interaction analysis highlights the significance of hydrogen bonds and electrostatic interactions in the pesticide-polymer association. Notably, two promising candidates, an antibiotic derived from tetracycline and a rodenticide, demonstrate a strong affinity for CAC nanoparticles. This computational methodology offers a reliable and efficient screening approach for identifying effective pesticide capture agents, contributing to the development of eco-friendly strategies for pesticide removal.
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Affiliation(s)
- Osvaldo Yáñez
- Núcleo de Investigación en Data Science, Facultad de Ingeniería y Negocios, Universidad de las Américas, Santiago 7500000, Chile
| | - Melissa Alegría-Arcos
- Núcleo de Investigación en Data Science, Facultad de Ingeniería y Negocios, Universidad de las Américas, Santiago 7500000, Chile
| | - Reynier Suardiaz
- Departamento de Química Física, Facultad de Ciencias Químicas, Universidad Complutense de Madrid, 28040 Madrid, Spain
| | - Luis Morales-Quintana
- Multidisciplinary Agroindustry Research Laboratory, Facultad de Ciencias de la Salud, Universidad Autónoma de Chile, Talca 3400000, Chile
| | - Ricardo I Castro
- Multidisciplinary Agroindustry Research Laboratory, Carrera de Ingeniería en Construcción, Instituto de Ciencias Químicas Aplicadas, Universidad Autónoma de Chile, Talca 3400000, Chile
| | | | - Christian Galarza
- Escuela Superior Politécnica del Litoral, Guayaquil EC090903, Ecuador
| | | | - Víctor Rojas-Pérez
- Doctorado en Biotecnología Traslacional, Facultad de Ciencias Agrarias y Forestales, Universidad Católica del Maule, Talca 3480094, Chile
| | - Gabriela Urra
- Laboratorio de Bioinformática y Química Computacional, Departamento de Medicina Traslacional, Facultad de Medicina, Universidad Católica del Maule, Talca 3480094, Chile
| | - Erix W Hernández-Rodríguez
- Laboratorio de Bioinformática y Química Computacional, Departamento de Medicina Traslacional, Facultad de Medicina, Universidad Católica del Maule, Talca 3480094, Chile
- Unidad de Bioinformática Clínica, Centro Oncológico, Facultad de Medicina, Universidad Católica del Maule, Talca 3480094, Chile
| | - Daniel Bustos
- Laboratorio de Bioinformática y Química Computacional, Departamento de Medicina Traslacional, Facultad de Medicina, Universidad Católica del Maule, Talca 3480094, Chile
- Centro de Investigación de Estudios Avanzados del Maule (CIEAM), Vicerrectoría de Investigación y Postgrado Universidad Católica del Maule, Talca 3460000, Chile
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3
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Miñarro-Lleonar M, Ruiz-Carmona S, Alvarez-Garcia D, Schmidtke P, Barril X. Development of an Automatic Pipeline for Participation in the CELPP Challenge. Int J Mol Sci 2022; 23:ijms23094756. [PMID: 35563148 PMCID: PMC9105952 DOI: 10.3390/ijms23094756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 04/20/2022] [Accepted: 04/21/2022] [Indexed: 12/01/2022] Open
Abstract
The prediction of how a ligand binds to its target is an essential step for Structure-Based Drug Design (SBDD) methods. Molecular docking is a standard tool to predict the binding mode of a ligand to its macromolecular receptor and to quantify their mutual complementarity, with multiple applications in drug design. However, docking programs do not always find correct solutions, either because they are not sampled or due to inaccuracies in the scoring functions. Quantifying the docking performance in real scenarios is essential to understanding their limitations, managing expectations and guiding future developments. Here, we present a fully automated pipeline for pose prediction validated by participating in the Continuous Evaluation of Ligand Pose Prediction (CELPP) Challenge. Acknowledging the intrinsic limitations of the docking method, we devised a strategy to automatically mine and exploit pre-existing data, defining—whenever possible—empirical restraints to guide the docking process. We prove that the pipeline is able to generate predictions for most of the proposed targets as well as obtain poses with low RMSD values when compared to the crystal structure. All things considered, our pipeline highlights some major challenges in the automatic prediction of protein–ligand complexes, which will be addressed in future versions of the pipeline.
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Affiliation(s)
- Marina Miñarro-Lleonar
- Pharmacy Faculty, University of Barcelona, Av. de Joan XXIII 27-31, 08028 Barcelona, Spain;
| | | | - Daniel Alvarez-Garcia
- GAIN Therapeutics, Parc Cientific de Barcelona, Baldiri i Reixac 10, 08029 Barcelona, Spain;
| | - Peter Schmidtke
- Discngine S.A.S., 79 Avenue Ledru Rollin, 75012 Paris, France;
| | - Xavier Barril
- Pharmacy Faculty, University of Barcelona, Av. de Joan XXIII 27-31, 08028 Barcelona, Spain;
- GAIN Therapeutics, Parc Cientific de Barcelona, Baldiri i Reixac 10, 08029 Barcelona, Spain;
- Catalan Institute for Research and Advanced Studies (ICREA), Passeig de Lluis Companys 23, 08010 Barcelona, Spain
- Correspondence:
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4
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Ricci-Lopez J, Aguila SA, Gilson MK, Brizuela CA. Improving Structure-Based Virtual Screening with Ensemble Docking and Machine Learning. J Chem Inf Model 2021; 61:5362-5376. [PMID: 34652141 DOI: 10.1021/acs.jcim.1c00511] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
One of the main challenges of structure-based virtual screening (SBVS) is the incorporation of the receptor's flexibility, as its explicit representation in every docking run implies a high computational cost. Therefore, a common alternative to include the receptor's flexibility is the approach known as ensemble docking. Ensemble docking consists of using a set of receptor conformations and performing the docking assays over each of them. However, there is still no agreement on how to combine the ensemble docking results to obtain the final ligand ranking. A common choice is to use consensus strategies to aggregate the ensemble docking scores, but these strategies exhibit slight improvement regarding the single-structure approach. Here, we claim that using machine learning (ML) methodologies over the ensemble docking results could improve the predictive power of SBVS. To test this hypothesis, four proteins were selected as study cases: CDK2, FXa, EGFR, and HSP90. Protein conformational ensembles were built from crystallographic structures, whereas the evaluated compound library comprised up to three benchmarking data sets (DUD, DEKOIS 2.0, and CSAR-2012) and cocrystallized molecules. Ensemble docking results were processed through 30 repetitions of 4-fold cross-validation to train and validate two ML classifiers: logistic regression and gradient boosting trees. Our results indicate that the ML classifiers significantly outperform traditional consensus strategies and even the best performance case achieved with single-structure docking. We provide statistical evidence that supports the effectiveness of ML to improve the ensemble docking performance.
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Affiliation(s)
- Joel Ricci-Lopez
- Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE), Ensenada, Baja California C.P. 22860, Mexico.,Centro de Nanociencias y Nanotecnología, Universidad Nacional Autónoma de México (UNAM), Ensenada, Baja California C.P. 22860, Mexico
| | - Sergio A Aguila
- Centro de Nanociencias y Nanotecnología, Universidad Nacional Autónoma de México (UNAM), Ensenada, Baja California C.P. 22860, Mexico
| | - Michael K Gilson
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, La Jolla, San Diego, California 92093, United States
| | - Carlos A Brizuela
- Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE), Ensenada, Baja California C.P. 22860, Mexico
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5
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Rácz A, Palkó R, Csányi D, Riedl Z, Bajusz D, Keserű GM. Consensus Virtual Screening Identified [1,2,4]Triazolo[1,5-b]isoquinolines As MELK Inhibitor Chemotypes. ChemMedChem 2021; 17:e202100569. [PMID: 34632716 PMCID: PMC9298037 DOI: 10.1002/cmdc.202100569] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 10/06/2021] [Indexed: 11/09/2022]
Abstract
Maternal Embryonic Leucine‐zipper Kinase (MELK) is a current oncotarget involved in a diverse range of human cancers, with the usage of MELK inhibitors being explored clinically. Here, we aimed to discover new MELK inhibitor chemotypes from our in‐house compound library with a consensus‐based virtual screening workflow, employing three screening concepts. After careful retrospective validation, prospective screening and in vitro enzyme inhibition testing revealed a series of [1,2,4]triazolo[1,5‐b]isoquinolines as a new structural class of MELK inhibitors, with the lead compound of the series exhibiting a sub‐micromolar inhibitory activity. The structure‐activity relationship of the series was explored by testing further analogs based on a structure‐guided selection process. Importantly, the present work marks the first disclosure of the synthesis and bioactivity of this class of compounds.
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Affiliation(s)
- Anita Rácz
- Plasma Chemistry Research Group, Research Centre for Natural Sciences, Magyar tudósok krt. 2, 1117, Budapest, Hungary.,Medicinal Chemistry Research Group, Research Centre for Natural Sciences, Magyar tudósok krt. 2, 1117, Budapest, Hungary
| | - Roberta Palkó
- Medicinal Chemistry Research Group, Research Centre for Natural Sciences, Magyar tudósok krt. 2, 1117, Budapest, Hungary.,Present affiliation: Organocatalysis Research Group, Research Centre for Natural Sciences, Magyar tudósok krt. 2, 1117, Budapest, Hungary
| | - Dorottya Csányi
- Medicinal Chemistry Research Group, Research Centre for Natural Sciences, Magyar tudósok krt. 2, 1117, Budapest, Hungary
| | - Zsuzsanna Riedl
- Medicinal Chemistry Research Group, Research Centre for Natural Sciences, Magyar tudósok krt. 2, 1117, Budapest, Hungary
| | - Dávid Bajusz
- Medicinal Chemistry Research Group, Research Centre for Natural Sciences, Magyar tudósok krt. 2, 1117, Budapest, Hungary
| | - György M Keserű
- Medicinal Chemistry Research Group, Research Centre for Natural Sciences, Magyar tudósok krt. 2, 1117, Budapest, Hungary
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6
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Bajusz D, Miranda-Quintana RA, Rácz A, Héberger K. Extended many-item similarity indices for sets of nucleotide and protein sequences. Comput Struct Biotechnol J 2021; 19:3628-3639. [PMID: 34257841 PMCID: PMC8253954 DOI: 10.1016/j.csbj.2021.06.021] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 06/07/2021] [Accepted: 06/14/2021] [Indexed: 12/16/2022] Open
Abstract
Quantification of similarities between protein sequences or DNA/RNA strands is a (sub-)task that is ubiquitously present in bioinformatics workflows, and is usually accomplished by pairwise comparisons of sequences, utilizing simple (e.g. percent identity) or more intricate concepts (e.g. substitution scoring matrices). Complex tasks (such as clustering) rely on a large number of pairwise comparisons under the hood, instead of a direct quantification of set similarities. Based on our recently introduced framework that enables multiple comparisons of binary molecular fingerprints (i.e., direct calculation of the similarity of fingerprint sets), here we introduce novel symmetric similarity indices for analogous calculations on sets of character sequences with more than two (t) possible items (e.g. DNA/RNA sequences with t = 4, or protein sequences with t = 20). The features of these new indices are studied in detail with analysis of variance (ANOVA), and demonstrated with three case studies of protein/DNA sequences with varying degrees of similarity (or evolutionary proximity). The Python code for the extended many-item similarity indices is publicly available at: https://github.com/ramirandaq/tn_Comparisons.
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Affiliation(s)
- Dávid Bajusz
- Medicinal Chemistry Research Group, Research Centre for Natural Sciences, Magyar tudósok krt. 2, 1117 Budapest, Hungary
| | | | - Anita Rácz
- Plasma Chemistry Research Group, Research Centre for Natural Sciences, Magyar tudósok krt. 2, 1117 Budapest, Hungary
| | - Károly Héberger
- Plasma Chemistry Research Group, Research Centre for Natural Sciences, Magyar tudósok krt. 2, 1117 Budapest, Hungary
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7
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Combination of consensus and ensemble docking strategies for the discovery of human dihydroorotate dehydrogenase inhibitors. Sci Rep 2021; 11:11417. [PMID: 34075175 PMCID: PMC8169699 DOI: 10.1038/s41598-021-91069-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Accepted: 05/21/2021] [Indexed: 02/06/2023] Open
Abstract
The inconsistencies in the performance of the virtual screening (VS) process, depending on the used software and structural conformation of the protein, is a challenging issue in the drug design and discovery field. Varying performance, especially in terms of early recognition of the potential hit compounds, negatively affects the whole process and leads to unnecessary waste of the time and resources. Appropriate application of the ensemble docking and consensus-scoring approaches can significantly increase reliability of the VS results. Dihydroorotate dehydrogenase (DHODH) is a key enzyme in the pyrimidine biosynthesis pathway. It is considered as a valuable therapeutic target in cancer, autoimmune and viral diseases. Based on the conducted benchmark study and analysis of the effect of different combinations of the applied methods and approaches, here we suggested a structure-based virtual screening (SBVS) workflow that can be used to increase the reliability of VS.
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8
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Domínguez-Mendoza EA, Galván-Ciprés Y, Martínez-Miranda J, Miranda-González C, Colín-Lozano B, Hernández-Núñez E, Hernández-Bolio GI, Palomino-Hernández O, Navarrete-Vazquez G. Design, Synthesis, and In Silico Multitarget Pharmacological Simulations of Acid Bioisosteres with a Validated In Vivo Antihyperglycemic Effect. Molecules 2021; 26:799. [PMID: 33557136 PMCID: PMC7913794 DOI: 10.3390/molecules26040799] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 01/31/2021] [Accepted: 02/02/2021] [Indexed: 01/16/2023] Open
Abstract
Substituted phenylacetic (1-3), phenylpropanoic (4-6), and benzylidenethiazolidine-2,4-dione (7-9) derivatives were designed according to a multitarget unified pharmacophore pattern that has shown robust antidiabetic activity. This bioactivity is due to the simultaneous polypharmacological stimulation of receptors PPARα, PPARγ, and GPR40 and the enzyme inhibition of aldose reductase (AR) and protein tyrosine phosphatase 1B (PTP-1B). The nine compounds share the same four pharmacophore elements: an acid moiety, an aromatic ring, a bulky hydrophobic group, and a flexible linker between the latter two elements. Addition and substitution reactions were performed to obtain molecules at moderated yields. In silico pharmacological consensus analysis (PHACA) was conducted to determine their possible modes of action, protein affinities, toxicological activities, and drug-like properties. The results were combined with in vivo assays to evaluate the ability of these compounds to decrease glucose levels in diabetic mice at a 100 mg/kg single dose. Compounds 6 (a phenylpropanoic acid derivative) and 9 (a benzylidenethiazolidine-2,4-dione derivative) ameliorated the hyperglycemic peak in a statically significant manner in a mouse model of type 2 diabetes. Finally, molecular dynamics simulations were executed on the top performing compounds to shed light on their mechanism of action. The simulations showed the flexible nature of the binding pocket of AR, and showed that both compounds remained bound during the simulation time, although not sharing the same binding mode. In conclusion, we designed nine acid bioisosteres with robust in vivo antihyperglycemic activity that were predicted to have favorable pharmacokinetic and toxicological profiles. Together, these findings provide evidence that supports the molecular design we employed, where the unified pharmacophores possess a strong antidiabetic action due to their multitarget activation.
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Affiliation(s)
- Elix Alberto Domínguez-Mendoza
- Facultad de Farmacia, Universidad Autónoma del Estado de Morelos, Cuernavaca, Morelos 62209, Mexico; (E.A.D.-M.); (Y.G.-C.); (J.M.-M.); (C.M.-G.); (B.C.-L.)
| | - Yelzyn Galván-Ciprés
- Facultad de Farmacia, Universidad Autónoma del Estado de Morelos, Cuernavaca, Morelos 62209, Mexico; (E.A.D.-M.); (Y.G.-C.); (J.M.-M.); (C.M.-G.); (B.C.-L.)
| | - Josué Martínez-Miranda
- Facultad de Farmacia, Universidad Autónoma del Estado de Morelos, Cuernavaca, Morelos 62209, Mexico; (E.A.D.-M.); (Y.G.-C.); (J.M.-M.); (C.M.-G.); (B.C.-L.)
| | - Cristian Miranda-González
- Facultad de Farmacia, Universidad Autónoma del Estado de Morelos, Cuernavaca, Morelos 62209, Mexico; (E.A.D.-M.); (Y.G.-C.); (J.M.-M.); (C.M.-G.); (B.C.-L.)
| | - Blanca Colín-Lozano
- Facultad de Farmacia, Universidad Autónoma del Estado de Morelos, Cuernavaca, Morelos 62209, Mexico; (E.A.D.-M.); (Y.G.-C.); (J.M.-M.); (C.M.-G.); (B.C.-L.)
| | - Emanuel Hernández-Núñez
- Cátedra CONACyT, Departamento de Recursos del Mar, Centro de Investigación y de Estudios Avanzados, IPN, Unidad Mérida, Yucatan 97310, Mexico; (E.H.-N.); (G.I.H.-B.)
| | - Gloria I. Hernández-Bolio
- Cátedra CONACyT, Departamento de Recursos del Mar, Centro de Investigación y de Estudios Avanzados, IPN, Unidad Mérida, Yucatan 97310, Mexico; (E.H.-N.); (G.I.H.-B.)
| | - Oscar Palomino-Hernández
- Computational Biomedicine (IAS-5/INM-9), Forschungszentrum Juelich, 52425 Julich, Germany;
- Department of Chemistry, Rheinisch-Westfälische Technische Hochschule Aachen, 52425 Aachen, Germany
| | - Gabriel Navarrete-Vazquez
- Facultad de Farmacia, Universidad Autónoma del Estado de Morelos, Cuernavaca, Morelos 62209, Mexico; (E.A.D.-M.); (Y.G.-C.); (J.M.-M.); (C.M.-G.); (B.C.-L.)
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9
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Zhang X, Shen C, Guo X, Wang Z, Weng G, Ye Q, Wang G, He Q, Yang B, Cao D, Hou T. ASFP (Artificial Intelligence based Scoring Function Platform): a web server for the development of customized scoring functions. J Cheminform 2021; 13:6. [PMID: 33541407 PMCID: PMC7860246 DOI: 10.1186/s13321-021-00486-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Accepted: 01/17/2021] [Indexed: 12/18/2022] Open
Abstract
Virtual screening (VS) based on molecular docking has emerged as one of the mainstream technologies of drug discovery due to its low cost and high efficiency. However, the scoring functions (SFs) implemented in most docking programs are not always accurate enough and how to improve their prediction accuracy is still a big challenge. Here, we propose an integrated platform called ASFP, a web server for the development of customized SFs for structure-based VS. There are three main modules in ASFP: (1) the descriptor generation module that can generate up to 3437 descriptors for the modelling of protein–ligand interactions; (2) the AI-based SF construction module that can establish target-specific SFs based on the pre-generated descriptors through three machine learning (ML) techniques; (3) the online prediction module that provides some well-constructed target-specific SFs for VS and an additional generic SF for binding affinity prediction. Our methodology has been validated on several benchmark datasets. The target-specific SFs can achieve an average ROC AUC of 0.973 towards 32 targets and the generic SF can achieve the Pearson correlation coefficient of 0.81 on the PDBbind version 2016 core set. To sum up, the ASFP server is a powerful tool for structure-based VS.
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Affiliation(s)
- Xujun Zhang
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, 310058, China
| | - Chao Shen
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, 310058, China
| | - Xueying Guo
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, 310058, China
| | - Zhe Wang
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, 310058, China
| | - Gaoqi Weng
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, 310058, China
| | - Qing Ye
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, 310058, China
| | - Gaoang Wang
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, 310058, China
| | - Qiaojun He
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, 310058, China
| | - Bo Yang
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, 310058, China.
| | - Dongsheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, Hunan, 10013, China.
| | - Tingjun Hou
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, 310058, China. .,State Key Lab of CAD&CG, Zhejiang University, Hangzhou, Zhejiang, 310058, China.
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10
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Vázquez J, López M, Gibert E, Herrero E, Luque FJ. Merging Ligand-Based and Structure-Based Methods in Drug Discovery: An Overview of Combined Virtual Screening Approaches. Molecules 2020; 25:E4723. [PMID: 33076254 PMCID: PMC7587536 DOI: 10.3390/molecules25204723] [Citation(s) in RCA: 77] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 10/06/2020] [Accepted: 10/11/2020] [Indexed: 12/20/2022] Open
Abstract
Virtual screening (VS) is an outstanding cornerstone in the drug discovery pipeline. A variety of computational approaches, which are generally classified as ligand-based (LB) and structure-based (SB) techniques, exploit key structural and physicochemical properties of ligands and targets to enable the screening of virtual libraries in the search of active compounds. Though LB and SB methods have found widespread application in the discovery of novel drug-like candidates, their complementary natures have stimulated continued efforts toward the development of hybrid strategies that combine LB and SB techniques, integrating them in a holistic computational framework that exploits the available information of both ligand and target to enhance the success of drug discovery projects. In this review, we analyze the main strategies and concepts that have emerged in the last years for defining hybrid LB + SB computational schemes in VS studies. Particularly, attention is focused on the combination of molecular similarity and docking, illustrating them with selected applications taken from the literature.
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Affiliation(s)
- Javier Vázquez
- Pharmacelera, Plaça Pau Vila, 1, Sector C 2a, Edificio Palau de Mar, 08039 Barcelona, Spain;
- Department of Nutrition, Food Science and Gastronomy, Faculty of Pharmacy and Food Sciences, Institute of Biomedicine (IBUB), and Institute of Theoretical and Computational Chemistry (IQTC-UB), University of Barcelona, Av. Prat de la Riba 171, E-08921 Santa Coloma de Gramanet, Spain
| | - Manel López
- AB Science, Parc Scientifique de Luminy, Zone Luminy Enterprise, Case 922, 163 Av. de Luminy, 13288 Marseille, France;
| | - Enric Gibert
- Pharmacelera, Plaça Pau Vila, 1, Sector C 2a, Edificio Palau de Mar, 08039 Barcelona, Spain;
| | - Enric Herrero
- Pharmacelera, Plaça Pau Vila, 1, Sector C 2a, Edificio Palau de Mar, 08039 Barcelona, Spain;
| | - F. Javier Luque
- Department of Nutrition, Food Science and Gastronomy, Faculty of Pharmacy and Food Sciences, Institute of Biomedicine (IBUB), and Institute of Theoretical and Computational Chemistry (IQTC-UB), University of Barcelona, Av. Prat de la Riba 171, E-08921 Santa Coloma de Gramanet, Spain
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11
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Mora Lagares L, Minovski N, Caballero Alfonso AY, Benfenati E, Wellens S, Culot M, Gosselet F, Novič M. Homology Modeling of the Human P-glycoprotein (ABCB1) and Insights into Ligand Binding through Molecular Docking Studies. Int J Mol Sci 2020; 21:ijms21114058. [PMID: 32517082 PMCID: PMC7312539 DOI: 10.3390/ijms21114058] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Revised: 05/28/2020] [Accepted: 06/02/2020] [Indexed: 12/12/2022] Open
Abstract
The ABCB1 transporter also known as P-glycoprotein (P-gp) is a transmembrane protein belonging to the ATP binding cassette super-family of transporters; it is a xenobiotic efflux pump that limits intracellular drug accumulation by pumping the compounds out of cells. P-gp contributes to a decrease of toxicity and possesses broad substrate specificity. It is involved in the failure of numerous anticancer and antiviral chemotherapies due to the multidrug resistance (MDR) phenomenon, where it removes the chemotherapeutics out of the targeted cells. Understanding the details of the ligand–P-gp interaction is therefore crucial for the development of drugs that might overcome the MRD phenomenon and for obtaining a more effective prediction of the toxicity of certain compounds. In this work, an in silico modeling was performed using homology modeling and molecular docking methods with the aim of better understanding the ligand–P-gp interactions. Based on different mouse P-gp structural templates from the PDB repository, a 3D model of the human P-gp (hP-gp) was constructed by means of protein homology modeling. The homology model was then used to perform molecular docking calculations on a set of thirteen compounds, including some well-known compounds that interact with P-gp as substrates, inhibitors, or both. The sum of ranking differences (SRD) was employed for the comparison of the different scoring functions used in the docking calculations. A consensus-ranking scheme was employed for the selection of the top-ranked pose for each docked ligand. The docking results showed that a high number of π interactions, mainly π–sigma, π–alkyl, and π–π type of interactions, together with the simultaneous presence of hydrogen bond interactions contribute to the stability of the ligand–protein complex in the binding site. It was also observed that some interacting residues in hP-gp are the same when compared to those observed in a co-crystallized ligand (PBDE-100) with mouse P-gp (PDB ID: 4XWK). Our in silico approach is consistent with available experimental results regarding P-gp efflux transport assay; therefore it could be useful in the prediction of the role of new compounds in systemic toxicity.
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Affiliation(s)
- Liadys Mora Lagares
- Theory Department, Laboratory for Cheminformatics, National Institute of Chemistry, 1000 Ljubljana, Slovenia;
- Jožef Stefan International Postgraduate School, 1000 Ljubljana, Slovenia;
- Correspondence: (L.M.L.); (M.N.); Tel.: +386-01-476-0253 (L.M.L. & M.N.)
| | - Nikola Minovski
- Theory Department, Laboratory for Cheminformatics, National Institute of Chemistry, 1000 Ljubljana, Slovenia;
| | - Ana Yisel Caballero Alfonso
- Jožef Stefan International Postgraduate School, 1000 Ljubljana, Slovenia;
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche “Mario Negri”—IRCCS, 20156 Milano, Italy;
| | - Emilio Benfenati
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche “Mario Negri”—IRCCS, 20156 Milano, Italy;
| | - Sara Wellens
- Laboratoire de la Barrière Hémato-Encéphalique (LBHE), University Artois, UR 2465, F-62300 Lens, France; (S.W.); (M.C.); (F.G.)
| | - Maxime Culot
- Laboratoire de la Barrière Hémato-Encéphalique (LBHE), University Artois, UR 2465, F-62300 Lens, France; (S.W.); (M.C.); (F.G.)
| | - Fabien Gosselet
- Laboratoire de la Barrière Hémato-Encéphalique (LBHE), University Artois, UR 2465, F-62300 Lens, France; (S.W.); (M.C.); (F.G.)
| | - Marjana Novič
- Theory Department, Laboratory for Cheminformatics, National Institute of Chemistry, 1000 Ljubljana, Slovenia;
- Correspondence: (L.M.L.); (M.N.); Tel.: +386-01-476-0253 (L.M.L. & M.N.)
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12
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Dos Santos Maia M, Soares Rodrigues GC, Silva Cavalcanti AB, Scotti L, Scotti MT. Consensus Analyses in Molecular Docking Studies Applied to Medicinal Chemistry. Mini Rev Med Chem 2020; 20:1322-1340. [PMID: 32013847 DOI: 10.2174/1389557520666200204121129] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Revised: 10/31/2019] [Accepted: 11/04/2019] [Indexed: 02/08/2023]
Abstract
The increasing number of computational studies in medicinal chemistry involving molecular docking has put the technique forward as promising in Computer-Aided Drug Design. Considering the main method in the virtual screening based on the structure, consensus analysis of docking has been applied in several studies to overcome limitations of algorithms of different programs and mainly to increase the reliability of the results and reduce the number of false positives. However, some consensus scoring strategies are difficult to apply and, in some cases, are not reliable due to the small number of datasets tested. Thus, for such a methodology to be successful, it is necessary to understand why, when and how to use consensus docking. Therefore, the present study aims to present different approaches to docking consensus, applications, and several scoring strategies that have been successful and can be applied in future studies.
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Affiliation(s)
- Mayara Dos Santos Maia
- Program of Natural and Synthetic Bioactive Products (PgPNSB), Health Sciences Center, Federal University of Paraiba, Joao Pessoa-PB, Brazil
| | - Gabriela Cristina Soares Rodrigues
- Program of Natural and Synthetic Bioactive Products (PgPNSB), Health Sciences Center, Federal University of Paraiba, Joao Pessoa-PB, Brazil
| | - Andreza Barbosa Silva Cavalcanti
- Program of Natural and Synthetic Bioactive Products (PgPNSB), Health Sciences Center, Federal University of Paraiba, Joao Pessoa-PB, Brazil
| | - Luciana Scotti
- Program of Natural and Synthetic Bioactive Products (PgPNSB), Health Sciences Center, Federal University of Paraiba, Joao Pessoa-PB, Brazil
| | - Marcus Tullius Scotti
- Program of Natural and Synthetic Bioactive Products (PgPNSB), Health Sciences Center, Federal University of Paraiba, Joao Pessoa-PB, Brazil
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