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Benková Z, Cordeiro MNDS. Structural behavior of monomer of SARS-CoV-2 spike protein during initial stage of adsorption on graphene. Mater Today Chem 2021; 22:100572. [PMID: 34485782 PMCID: PMC8405511 DOI: 10.1016/j.mtchem.2021.100572] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Revised: 08/22/2021] [Accepted: 08/26/2021] [Indexed: 05/14/2023]
Abstract
Spike glycoprotein of the SARS-CoV-2 virus and its structure play a crucial role in the infections of cells containing angiotensin-converting enzyme 2 (ACE2) as well as in the interactions of this virus with surfaces. Protection against viruses and often even their deactivation is one of the great varieties of graphene applications. The structural changes of the non-glycosylated monomer of the spike glycoprotein trimer (denoted as S-protein in this work) triggered by its adsorption onto graphene at the initial stage are investigated by means of atomistic molecular dynamics simulations. The adsorption of the S-protein happens readily during the first 10 ns. The shape of the S-protein becomes more prolate during the adsorption, but this trend, albeit less pronounced, is observed also for the freely relaxing S-protein in water. The receptor-binding domain (RBD) of the free and adsorbed S-protein manifests itself as the most rigid fragment of the whole S-protein. The adsorption even enhances the rigidity of the whole S-protein as well as its subunits. Only one residue of the RBD involved in the specific interactions with ACE2 during the cell infection is involved in the direct contact of the adsorbed S-protein with the graphene. The new intramolecular hydrogen bonds formed during the S-protein adsorption replace the S-protein-water hydrogen bonds; this trend, although less apparent, is observed also during the relaxation of the free S-protein in water. In the initial phase, the secondary structure of the RBD fragment specifically interacting with ACE2 receptor is not affected during the S-protein adsorption onto the graphene.
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Affiliation(s)
- Z Benková
- Polymer Institute, Slovak Academy of Sciences, Dúbravská Cesta 9, 845 41 Bratislava, Slovakia
| | - M N D S Cordeiro
- LAQV@REQUIMTE, Department of Chemistry and Biochemistry, University of Porto, Rua Do Campo Alegre 687, 4168-007 Porto, Portugal
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2
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Abstract
Quantitative structure activity relationships (QSAR) modelling is a well-known computational tool, often used in a wide variety of applications. Yet one of the major drawbacks of conventional QSAR modelling is that models are set up based on a limited number of experimental and/or theoretical conditions. To overcome this, the so-called multitasking or multitarget QSAR (mt-QSAR) approaches have emerged as new computational tools able to integrate diverse chemical and biological data into a single model equation, thus extending and improving the reliability of this type of modelling. We have developed QSAR-Co-X, an open source python–based toolkit (available to download at https://github.com/ncordeirfcup/QSAR-Co-X) for supporting mt-QSAR modelling following the Box-Jenkins moving average approach. The new toolkit embodies several functionalities for dataset selection and curation plus computation of descriptors, for setting up linear and non-linear models, as well as for a comprehensive results analysis. The workflow within this toolkit is guided by a cohort of multiple statistical parameters and graphical outputs onwards assessing both the predictivity and the robustness of the derived mt-QSAR models. To monitor and demonstrate the functionalities of the designed toolkit, four case-studies pertaining to previously reported datasets are examined here. We believe that this new toolkit, along with our previously launched QSAR-Co code, will significantly contribute to make mt-QSAR modelling widely and routinely applicable. ![]()
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Affiliation(s)
- Amit Kumar Halder
- LAQV@REQUIMTE/Faculty of Sciences, University of Porto, 4169-007, Porto, Portugal.
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3
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Halder AK, Dias Soeiro Cordeiro MN. Advanced in Silico Methods for the Development of Anti- Leishmaniasis and Anti-Trypanosomiasis Agents. Curr Med Chem 2020; 27:697-718. [DOI: 10.2174/0929867325666181031093702] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Revised: 07/24/2018] [Accepted: 09/19/2018] [Indexed: 11/22/2022]
Abstract
Leishmaniasis and trypanosomiasis occur primarily in undeveloped countries and account
for millions of deaths and disability-adjusted life years. Limited therapeutic options, high toxicity of
chemotherapeutic drugs and the emergence of drug resistance associated with these diseases demand
urgent development of novel therapeutic agents for the treatment of these dreadful diseases. In the last
decades, different in silico methods have been successfully implemented for supporting the lengthy and
expensive drug discovery process. In the current review, we discuss recent advances pertaining to in
silico analyses towards lead identification, lead modification and target identification of antileishmaniasis
and anti-trypanosomiasis agents. We describe recent applications of some important in
silico approaches, such as 2D-QSAR, 3D-QSAR, pharmacophore mapping, molecular docking, and so
forth, with the aim of understanding the utility of these techniques for the design of novel therapeutic
anti-parasitic agents. This review focuses on: (a) advanced computational drug design options; (b) diverse
methodologies - e.g.: use of machine learning tools, software solutions, and web-platforms; (c)
recent applications and advances in the last five years; (d) experimental validations of in silico predictions;
(e) virtual screening tools; and (f) rationale or justification for the selection of these in silico
methods.
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Affiliation(s)
- Amit Kumar Halder
- LAQV@ REQUIMTE/Department of Chemistry and Biochemistry, University of Porto, Porto 4169-007, Portugal
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4
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Ambure P, Cordeiro MNDS. Importance of Data Curation in QSAR Studies Especially While Modeling Large-Size Datasets. Methods in Pharmacology and Toxicology 2020. [DOI: 10.1007/978-1-0716-0150-1_5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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Halder AK, Cordeiro MNDS. Development of Multi-Target Chemometric Models for the Inhibition of Class I PI3K Enzyme Isoforms: A Case Study Using QSAR-Co Tool. Int J Mol Sci 2019; 20:ijms20174191. [PMID: 31461863 PMCID: PMC6747073 DOI: 10.3390/ijms20174191] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2019] [Revised: 08/23/2019] [Accepted: 08/24/2019] [Indexed: 12/12/2022] Open
Abstract
The present work aims at establishing multi-target chemometric models using the recently launched quantitative structure–activity relationship (QSAR)-Co tool for predicting the activity of inhibitor compounds against different isoforms of phosphoinositide 3-kinase (PI3K) under various experimental conditions. The inhibitors of class I phosphoinositide 3-kinase (PI3K) isoforms have emerged as potential therapeutic agents for the treatment of various disorders, especially cancer. The cell-based enzyme inhibition assay results of PI3K inhibitors were curated from the CHEMBL database. Factors such as the nature and mutation of cell lines that may significantly alter the assay outcomes were considered as important experimental elements for mt-QSAR model development. The models, in turn, were developed using two machine learning techniques as implemented in QSAR-Co: linear discriminant analysis (LDA) and random forest (RF). Both techniques led to models with high accuracy (ca. 90%). Several molecular fragments were extracted from the current dataset, and their quantitative contributions to the inhibitory activity against all the proteins and experimental conditions under study were calculated. This case study also demonstrates the utility of QSAR-Co tool in solving multi-factorial and complex chemometric problems. Additionally, the combination of different in silico methods employed in this work can serve as a valuable guideline to speed up early discovery of PI3K inhibitors.
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Affiliation(s)
- Amit Kumar Halder
- Department of Chemistry and Biochemistry, University of Porto, 4169-007 Porto, Portugal
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Luan F, Wang T, Tang L, Zhang S, Cordeiro MNDS. Estimation of the Toxicity of Different Substituted Aromatic Compounds to the Aquatic Ciliate Tetrahymena pyriformis by QSAR Approach. Molecules 2018; 23:molecules23051002. [PMID: 29695132 PMCID: PMC6099972 DOI: 10.3390/molecules23051002] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2018] [Revised: 04/20/2018] [Accepted: 04/21/2018] [Indexed: 11/16/2022] Open
Abstract
Nowadays, quantitative structure–activity relationship (QSAR) methods have been widely performed to predict the toxicity of compounds to organisms due to their simplicity, ease of implementation, and low hazards. In this study, to estimate the toxicities of substituted aromatic compounds to Tetrahymena pyriformis, the QSAR models were established by the multiple linear regression (MLR) and radial basis function neural network (RBFNN). Unlike other QSAR studies, according to the difference of functional groups (−NO2, −X), the whole dataset was divided into three groups and further modeled separately. The statistical characteristics for the models are obtained as the following: MLR: n = 36, R2 = 0.829, RMS (root mean square) = 0.192, RBFNN: n = 36, R2 = 0.843, RMS = 0.167 for Group 1; MLR: n = 60, R2 = 0.803, RMS = 0.222, RBFNN: n = 60, R2 = 0.821, RMS = 0.193 for Group 2; MLR: n = 31 R2 = 0.852, RMS = 0.192; RBFNN: n = 31, R2 = 0.885, RMS = 0.163 for Group 3, respectively. The results were within the acceptable range, and the models were found to be statistically robust with high external predictivity. Moreover, the models also gave some insight on those characteristics of the structures that most affect the toxicity.
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Affiliation(s)
- Feng Luan
- College of Chemistry and Chemical Engineering, Yantai University, Yantai 264005, China.
| | - Ting Wang
- College of Chemistry and Chemical Engineering, Yantai University, Yantai 264005, China.
| | - Lili Tang
- College of Chemistry and Chemical Engineering, Yantai University, Yantai 264005, China.
| | - Shuang Zhang
- College of Chemistry and Chemical Engineering, Yantai University, Yantai 264005, China.
| | - M Natália Dias Soeiro Cordeiro
- LAQV/REQUIMTE, Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal.
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Speck-Planche A, Dias Soeiro Cordeiro MN. Speeding up Early Drug Discovery in Antiviral Research: A Fragment-Based in Silico Approach for the Design of Virtual Anti-Hepatitis C Leads. ACS Comb Sci 2017; 19:501-512. [PMID: 28437091 DOI: 10.1021/acscombsci.7b00039] [Citation(s) in RCA: 49] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Hepatitis C constitutes an unresolved global health problem. This infectious disease is caused by the hepatotropic hepatitis C virus (HCV), and it can lead to the occurrence of life-threatening medical conditions, such as cirrhosis and liver cancer. Nowadays, major clinical concerns have arisen because of the appearance of multidrug resistance (MDR) and the side effects especially associated with long-term treatments. In this work, we report the first multitasking model for quantitative structure-biological effect relationships (mtk-QSBER), focused on the simultaneous exploration of anti-HCV activity and in vitro safety profiles related to the absorption, distribution, metabolism, elimination, and toxicity (ADMET). The mtk-QSBER model was created from a data set formed by 40 158 cases, displaying accuracy higher than 95% in both training and prediction (test) sets. Several molecular fragments were selected, and their quantitative contributions to anti-HCV activity and ADMET profiles were calculated. By combining the analysis of the fragments with positive contributions and the physicochemical meanings of the different molecular descriptors in the mtk-QSBER, six new molecules were designed. These new molecules were predicted to exhibit potent anti-HCV activity and desirable in vitro ADMET properties. In addition, the designed molecules have good druglikeness according to the Lipinski's rule of five and its variants.
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Affiliation(s)
- Alejandro Speck-Planche
- LAQV@REQUIMTE/Department
of Chemistry and Biochemistry, University of Porto, 4169-007 Porto, Portugal
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8
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Kleandrova VV, Ruso JM, Speck-Planche A, Dias Soeiro Cordeiro MN. Enabling the Discovery and Virtual Screening of Potent and Safe Antimicrobial Peptides. Simultaneous Prediction of Antibacterial Activity and Cytotoxicity. ACS Comb Sci 2016; 18:490-8. [PMID: 27280735 DOI: 10.1021/acscombsci.6b00063] [Citation(s) in RCA: 66] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Antimicrobial peptides (AMPs) represent promising alternatives to fight against bacterial pathogens. However, cellular toxicity remains one of the main concerns in the early development of peptide-based drugs. This work introduces the first multitasking (mtk) computational model focused on performing simultaneous predictions of antibacterial activities, and cytotoxicities of peptides. The model was created from a data set containing 3592 cases, and it displayed accuracy higher than 96% for classifying/predicting peptides in both training and prediction (test) sets. The technique known as alanine scanning was computationally applied to illustrate the calculation of the quantitative contributions of the amino acids (in their respective positions of the sequence) to the biological effects of a defined peptide. A small library formed by 10 peptides was generated, where peptides were designed by considering the interpretations of the different descriptors in the mtk-computational model. All the peptides were predicted to exhibit high antibacterial activities against multiple bacterial strains, and low cytotoxicity against various cell types. The present mtk-computational model can be considered a very useful tool to support high throughput research for the discovery of potent and safe AMPs.
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Affiliation(s)
- Valeria V. Kleandrova
- Faculty
of Technology and Production Management, Moscow State University of Food Production, Volokolamskoe shosse 11, Moscow, Russia
| | - Juan M. Ruso
- Department
of Applied Physics, University of Santiago de Compostela (USC), 15782 Santiago de Compostela, Spain
| | - Alejandro Speck-Planche
- Department
of Applied Physics, University of Santiago de Compostela (USC), 15782 Santiago de Compostela, Spain
- LAQV@REQUIMTE,
Department of Chemistry and Biochemistry, University of Porto, 4169-007 Porto, Portugal
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9
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Speck-Planche A, Cordeiro MNDS. Multi-Target QSAR Approaches for Modeling Protein Inhibitors. Simultaneous Prediction of Activities Against Biomacromolecules Present in Gram-Negative Bacteria. Curr Top Med Chem 2016; 15:1801-13. [PMID: 25961517 DOI: 10.2174/1568026615666150506144814] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2014] [Revised: 02/25/2015] [Accepted: 03/24/2015] [Indexed: 11/22/2022]
Abstract
Drug discovery is aimed at finding therapeutic agents for the treatment of many diverse diseases and infections. However, this is a very slow an expensive process, and for this reason, in silico approaches are needed to rationalize the search for new molecular entities with desired biological profiles. Models focused on quantitative structure-activity relationships (QSAR) have constituted useful complementary tools in medicinal chemistry, allowing the virtual predictions of dissimilar pharmacological activities of compounds. In the last 10 years, multi-target (mt) QSAR models have been reported, representing great advances with respect to those models generated from classical approaches. Thus, mt- QSAR models can simultaneously predict activities against different biological targets (proteins, microorganisms, cell lines, etc.) by using large and heterogeneous datasets of chemicals. The present review is devoted to discuss the most promising mt-QSAR models, particularly those developed for the prediction of protein inhibitors. We also report the first multi-tasking QSAR (mtk-QSAR) model for simultaneous prediction of inhibitors against biomacromolecules (specifically proteins) present in Gram-negative bacteria. This model allowed us to consider both different proteins and multiple experimental conditions under which the inhibitory activities of the chemicals were determined. The mtk-QSAR model exhibited accuracies higher than 98% in both training and prediction sets, also displaying a very good performance in the classification of active and inactive cases that depended on the specific elements of the experimental conditions. The physicochemical interpretations of the molecular descriptors were also analyzed, providing important insights regarding the molecular patterns associated with the appearance/enhancement of the inhibitory potency.
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Affiliation(s)
- Alejandro Speck-Planche
- REQUIMTE/Department of Chemistry and Biochemistry, University of Porto, 4169-007 Porto, Portugal.
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Speck-Planche A, Kleandrova VV, Ruso JM, Cordeiro MNDS. First Multitarget Chemo-Bioinformatic Model To Enable the Discovery of Antibacterial Peptides against Multiple Gram-Positive Pathogens. J Chem Inf Model 2016; 56:588-98. [PMID: 26960000 DOI: 10.1021/acs.jcim.5b00630] [Citation(s) in RCA: 44] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Antimicrobial peptides (AMPs) have emerged as promising therapeutic alternatives to fight against the diverse infections caused by different pathogenic microorganisms. In this context, theoretical approaches in bioinformatics have paved the way toward the creation of several in silico models capable of predicting antimicrobial activities of peptides. All current models have several significant handicaps, which prevent the efficient search for highly active AMPs. Here, we introduce the first multitarget (mt) chemo-bioinformatic model devoted to performing alignment-free prediction of antibacterial activity of peptides against multiple Gram-positive bacterial strains. The model was constructed from a data set containing 2488 cases of AMPs sequences assayed against at least 1 out of 50 Gram-positive bacterial strains. This mt-chemo-bioinformatic model displayed percentages of correct classification higher than 90.00% in both training and prediction (test) sets. For the first time, two computational approaches derived from basic concepts in genetics and molecular biology were applied, allowing the calculations of the relative contributions of any amino acid (in a defined position) to the antibacterial activity of an AMP and depending on the bacterial strain used in the biological assay. The present mt-chemo-bioinformatic model constitutes a powerful tool to enable the discovery of potent and versatile AMPs.
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Affiliation(s)
- Alejandro Speck-Planche
- Department of Applied Physics, University of Santiago de Compostela (USC) , 15782 Santiago de Compostela, Spain.,REQUIMTE/Department of Chemistry and Biochemistry, University of Porto , 4169-007 Porto, Portugal
| | - Valeria V Kleandrova
- Faculty of Technology and Production Management, Moscow State University of Food Production , Volokolamskoe shosse 11, 125080 Moscow, Russia
| | - Juan M Ruso
- Department of Applied Physics, University of Santiago de Compostela (USC) , 15782 Santiago de Compostela, Spain
| | - M N D S Cordeiro
- REQUIMTE/Department of Chemistry and Biochemistry, University of Porto , 4169-007 Porto, Portugal
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Abstract
Bacteria have been one of the world's most dangerous and deadliest pathogens for mankind, nowadays giving rise to significant public health concerns. Given the prevalence of these microbial pathogens and their increasing resistance to existing antibiotics, there is a pressing need for new antibacterial drugs. However, development of a successful drug is a complex, costly, and time-consuming process. Quantitative Structure-Activity Relationships (QSAR)-based approaches are valuable tools for shortening the time of lead compound identification but also for focusing and limiting time-costly synthetic activities and in vitro/vivo evaluations. QSAR-based approaches, supported by powerful statistical techniques such as artificial neural networks (ANNs), have evolved to the point of integrating dissimilar types of chemical and biological data. This chapter reports an overview of the current research and potential applications of QSAR modeling tools toward the rational design of more efficient antibacterial agents. Particular emphasis is given to the setup of multitasking models along with ANNs aimed at jointly predicting different antibacterial activities and safety profiles of drugs/chemicals under diverse experimental conditions.
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Affiliation(s)
- A Speck-Planche
- Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007, Porto, Portugal
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12
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Affiliation(s)
| | | | - Alejandro Speck-Planche
- Federal University of Paraíba, Department of Engineering and the Environment, Campus IV; 58297-000, Rio Tinto, PB, Brazil.
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Scotti MT, Cordeiro MNDS, Speck-Planche A. Current tendencies in antimicrobial research: medicinal chemistry of antibacterial agents and advances in the use of computational methodologies. Curr Top Med Chem 2013; 13:3011-2. [PMID: 24200364 DOI: 10.2174/15680266113136660216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Affiliation(s)
| | | | - Alejandro Speck-Planche
- Federal University of Paraíba, Department of Engineering and the Environment, Campus IV; 58297-000, Rio Tinto, PB, Brazil.
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Speck-Planche A, Luan F, Cordeiro MNDS. Role of ligand-based drug design methodologies toward the discovery of new anti- Alzheimer agents: futures perspectives in Fragment-Based Ligand Design. Curr Med Chem 2012; 19:1635-45. [PMID: 22376033 DOI: 10.2174/092986712799945058] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2011] [Revised: 01/23/2012] [Accepted: 01/23/2012] [Indexed: 11/22/2022]
Abstract
Alzheimer's disease (AD), a degenerative disease affecting the brain, is the single most common source of dementia in adults. The cause and the progression of AD still remains a mystery among medical experts. As a result, a cure has not yet been discovered, even after decade's worth of research that started since 1906, when the disease was first identified. Despite the efforts of the scientific community, several of the biological receptors associated with AD have not been sufficiently studied to date, limiting in turn the design of new and more potent anti-AD agents. Thus, the search for new drug candidates as inhibitors of different targets associated with AD constitutes an essential part towards the discovery of new and more efficient anti-AD therapies. The present work is focused on the role of the Ligand-Based Drug Design (LBDD) methodologies which have been applied for the elucidation of new molecular entities with high inhibitory activity against targets related with AD. Particular emphasis is given also to the current state of fragment-based ligand approaches as alternatives of the Fragment-Based Drug Discovery (FBDD) methodologies. Finally, several guidelines are offered to show how the use of fragment-based descriptors can be determinant for the design of multi-target inhibitors of proteins associated with AD.
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Affiliation(s)
- A Speck-Planche
- REQUIMTE/Department of Chemistry and Biochemistry, University of Porto, Porto, Portugal.
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Helguera AM, Pérez-Machado G, Cordeiro MNDS, Combes RD. Quantitative structure-activity relationship modelling of the carcinogenic risk of nitroso compounds using regression analysis and the TOPS-MODE approach. SAR QSAR Environ Res 2010; 21:277-304. [PMID: 20544552 DOI: 10.1080/10629361003773930] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
Abstract
Worldwide, legislative and governmental efforts are focusing on establishing simple screening tools for identifying those chemicals most likely to cause adverse effects without experimentally testing all chemicals of regulatory concern. This is because even the most basic biological testing of compounds of concern, apart from requiring a huge number of test animals, would be neither resource nor time effective. Thus, alternative approaches such as the one proposed here, quantitative structure-activity relationship (QSAR) modelling, are increasingly being used for identifying the potential health hazards and subsequent regulation of new industrial chemicals. This paper follows up on our earlier work that demonstrated the use of the TOPological Substructural MOlecular DEsign (TOPS-MODE) approach to QSAR modelling for predictions of the carcinogenic potency of nitroso compounds. The data set comprises 56 nitroso compounds which have been bio-assayed in female rats and administered by the oral water route. The QSAR model was able to account for about 81% of the variance in the experimental activity and exhibited good cross-validation statistics. A reasonable interpretation of the TOPS-MODE descriptors was achieved by means of bond contributions, which in turn afforded the recognition of structural alerts (SAs) regarding carcinogenicity. A comparison of the SAs obtained from different data sets showed that experimental factors, such as the sex and the oral administration route, exert a major influence on the carcinogenicity of nitroso compounds. The present and previous QSAR models combined together provide a reliable tool for estimating the carcinogenic potency of yet untested nitroso compounds and they should allow the identification of SAs, which can be used as the basis of prediction systems for the rodent carcinogenicity of these compounds.
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Affiliation(s)
- A M Helguera
- Department of Chemistry, Central University of Las Villas, Santa Clara, Villa Clara, Cuba.
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16
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Cruz-Monteagudo M, Borges F, Perez González M, Cordeiro MNDS. Computational modeling tools for the design of potent antimalarial bisbenzamidines: Overcoming the antimalarial potential of pentamidine. Bioorg Med Chem 2007; 15:5322-39. [PMID: 17533134 DOI: 10.1016/j.bmc.2007.05.034] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2006] [Revised: 04/24/2007] [Accepted: 05/02/2007] [Indexed: 10/23/2022]
Abstract
Malaria is nowadays a worldwide and serious problem with a significant social, economic, and human cost, mainly in developing countries. In addition, the emergence and spread of resistance to existing antimalarial therapies deteriorate the global malaria situation, and lead thus to an urgent need toward the design and discovery of new antimalarial drugs. In this work, a QSAR predictive model based on GETAWAY descriptors was developed which is able to explain with, only three variables, more than 77% of the variance in antimalarial potency and displays a good internal predictive ability (of 73.3% and 72.9% from leave-one-out cross-validation and bootstrapping analyses, respectively). The performance of the proposed model was judged against other five methodologies providing evidence of the superiority of GETAWAY descriptors in predicting the antimalarial potency of the bisbenzamidine family. Moreover, a desirability analysis based on the final QSAR model showed that to be a useful way of selecting the predictive variable level necessary to obtain potent bisbenzamidines. From the proposed model it is also possible to infer that elevated high atomic masses/polarizabilities/van der Waals volumes could play a negative/positive/positive role in the molecular interactions responsible for the desired drug conformation, which is required for the optimal binding to the macromolecular target. The results obtained point out that our final QSAR model is statistically significant and robust as well as possessing a high predictive effectiveness. Thus, the model provides a feasible and practical tool for looking for new and potent antimalarial bisbenzamidines.
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Affiliation(s)
- Maykel Cruz-Monteagudo
- Applied Chemistry Research Centre, Faculty of Chemistry and Pharmacy, Central University of Las Villas, Santa Clara, Cuba
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17
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Fiuza SM, Gomes C, Teixeira LJ, Girão da Cruz MT, Cordeiro MNDS, Milhazes N, Borges F, Marques MPM. Phenolic acid derivatives with potential anticancer properties--a structure-activity relationship study. Part 1: methyl, propyl and octyl esters of caffeic and gallic acids. Bioorg Med Chem 2005; 12:3581-9. [PMID: 15186842 DOI: 10.1016/j.bmc.2004.04.026] [Citation(s) in RCA: 207] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2004] [Accepted: 04/19/2004] [Indexed: 11/25/2022]
Abstract
The antiproliferative and cytotoxic properties of polyphenolic acid derivatives, structurally related with the natural models caffeic and gallic acids, have been tested in human cervix adenocarcinoma cells (HeLa). Simultaneous structural information was obtained for these compounds through theoretical ab initio methods. This study was conducted for the following esters: methyl caffeate (MC, 1), propyl caffeate (PC, 2), octyl caffeate (OC, 3), methyl gallate (MG, 4), propyl gallate (PG, 5) and octyl gallate (OG, 6). A significant growth-inhibition effect was assessed for some of these compounds, clearly dependent on their structural characteristics. Marked structure-activity relationships (SARs)--namely the number of hydroxyl ring substituents--were found to rule the biological effect of such systems.
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Affiliation(s)
- S M Fiuza
- Research Unit Molecular Physical-Chemistry, Coimbra University, Portugal
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Yao SW, Lopes VHC, Fernández F, García-Mera X, Morales M, Rodríguez-Borges JE, Cordeiro MNDS. Synthesis and QSAR study of the anticancer activity of some novel indane carbocyclic nucleosides. Bioorg Med Chem 2003; 11:4999-5006. [PMID: 14604662 DOI: 10.1016/j.bmc.2003.09.005] [Citation(s) in RCA: 49] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
A set of 14 indane carbocyclic nucleosides were synthesized and experimentally assayed for their inhibitory effects in the proliferation of murine leukemia (L1210/0) and human T-lymphocyte (Molt4/C8, CEM/0) cells. The compounds have promising inhibitory activity judging from the IC(50) values obtained for all these cellular lines. Multiple linear regression analysis was then applied to build up consistent QSAR models based on quantum mechanics-derived molecular descriptors. The derived models reproduce well the experimental data of both three cells (r(2) >/=0.90), display a good predictive power and are, above all, easily interpretable. They show that frontier-orbital energies and hydrophobicity are mainly responsible for the activity of the synthesized compounds and also, suggest similar mechanisms of action. The final QSAR-models involve only two descriptors: the lowest unoccupied molecular orbital energy and the solvent accessible-hydrophobic surface area, but describe a sound correlation between predicted and experimental activity data (r(2)=0.931, r(2)=0.936 and r(2)=0.931 for the cells L1210/0, Molt4/C8 and CEM/0, respectively).
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Affiliation(s)
- S-W Yao
- REQUIMTE/Departamento de Qui;mica, Faculdade de Ciências, Universidade do Porto, Rua do Campo Alegre 687, 4169-007, Porto, Portugal
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