International Journal of Molecular Sciences

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Open AccessEditorial

Data Analysis in Chemistry and Bio-Medical Sciences

by Roberto Todeschini, Alejandro Pazos, Sonia Arrasate and Humberto González-Díaz

Int. J. Mol. Sci. 2016, 17(12), 2105; https://doi.org/10.3390/ijms17122105 - 14 Dec 2016

Cited by 2 | Viewed by 4530

Abstract

There is an increasing necessity for multidisciplinary collaborations in molecular science between experimentalists and theoretical scientists, as well as among theoretical scientists from different fields.[...] Full article

(This article belongs to the Special Issue Big Data Analysis and QSAR/QSPR Research in Chemistry, Bio-Medical, and Network Sciences)

Research

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717 KiB

Open AccessArticle

Conformation-Independent QSPR Approach for the Soil Sorption Coefficient of Heterogeneous Compounds

by José F. Aranda, Juan C. Garro Martinez, Eduardo A. Castro and Pablo R. Duchowicz

Int. J. Mol. Sci. 2016, 17(8), 1247; https://doi.org/10.3390/ijms17081247 - 3 Aug 2016

Cited by 21 | Viewed by 4658

Abstract

We predict the soil sorption coefficient for a heterogeneous set of 643 organic non-ionic compounds by means of Quantitative Structure-Property Relationships (QSPR). A conformation-independent representation of the chemical structure is established. The 17,538 molecular descriptors derived with PaDEL and EPI Suite softwares are [...] Read more.

We predict the soil sorption coefficient for a heterogeneous set of 643 organic non-ionic compounds by means of Quantitative Structure-Property Relationships (QSPR). A conformation-independent representation of the chemical structure is established. The 17,538 molecular descriptors derived with PaDEL and EPI Suite softwares are simultaneously analyzed through linear regressions obtained with the Replacement Method variable subset selection technique. The best predictive three-descriptors QSPR is developed on a reduced training set of 93 chemicals, having an acceptable predictive capability on 550 test set compounds. We also establish a model with a single optimal descriptor derived from CORAL freeware. The present approach compares fairly well with a previously reported one that uses Dragon descriptors. Full article

(This article belongs to the Special Issue Big Data Analysis and QSAR/QSPR Research in Chemistry, Bio-Medical, and Network Sciences)

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527 KiB

Open AccessArticle

A Machine Learning Approach for Hot-Spot Detection at Protein-Protein Interfaces

by Rita Melo, Robert Fieldhouse, André Melo, João D. G. Correia, Maria Natália D. S. Cordeiro, Zeynep H. Gümüş, Joaquim Costa, Alexandre M. J. J. Bonvin and Irina S. Moreira

Int. J. Mol. Sci. 2016, 17(8), 1215; https://doi.org/10.3390/ijms17081215 - 27 Jul 2016

Cited by 45 | Viewed by 10079

Abstract

Understanding protein-protein interactions is a key challenge in biochemistry. In this work, we describe a more accurate methodology to predict Hot-Spots (HS) in protein-protein interfaces from their native complex structure compared to previous published Machine Learning (ML) techniques. Our model is trained on [...] Read more.

Understanding protein-protein interactions is a key challenge in biochemistry. In this work, we describe a more accurate methodology to predict Hot-Spots (HS) in protein-protein interfaces from their native complex structure compared to previous published Machine Learning (ML) techniques. Our model is trained on a large number of complexes and on a significantly larger number of different structural- and evolutionary sequence-based features. In particular, we added interface size, type of interaction between residues at the interface of the complex, number of different types of residues at the interface and the Position-Specific Scoring Matrix (PSSM), for a total of 79 features. We used twenty-seven algorithms from a simple linear-based function to support-vector machine models with different cost functions. The best model was achieved by the use of the conditional inference random forest (c-forest) algorithm with a dataset pre-processed by the normalization of features and with up-sampling of the minor class. The method has an overall accuracy of 0.80, an F1-score of 0.73, a sensitivity of 0.76 and a specificity of 0.82 for the independent test set. Full article

(This article belongs to the Special Issue Big Data Analysis and QSAR/QSPR Research in Chemistry, Bio-Medical, and Network Sciences)

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927 KiB

Open AccessArticle

Molecular Rearrangement of an Aza-Scorpiand Macrocycle Induced by pH: A Computational Study

by Jesus Vicente De Julián-Ortiz, Begoña Verdejo, Víctor Polo, Emili Besalú and Enrique García-España

Int. J. Mol. Sci. 2016, 17(7), 1131; https://doi.org/10.3390/ijms17071131 - 14 Jul 2016

Cited by 5 | Viewed by 4304

Abstract

Rearrangements and their control are a hot topic in supramolecular chemistry due to the possibilities that these phenomena open in the design of synthetic receptors and molecular machines. Macrocycle aza-scorpiands constitute an interesting system that can reorganize their spatial structure depending on pH [...] Read more.

Rearrangements and their control are a hot topic in supramolecular chemistry due to the possibilities that these phenomena open in the design of synthetic receptors and molecular machines. Macrocycle aza-scorpiands constitute an interesting system that can reorganize their spatial structure depending on pH variations or the presence of metal cations. In this study, the relative stabilities of these conformations were predicted computationally by semi-empirical and density functional theory approximations, and the reorganization from closed to open conformations was simulated by using the Monte Carlo multiple minimum method. Full article

(This article belongs to the Special Issue Big Data Analysis and QSAR/QSPR Research in Chemistry, Bio-Medical, and Network Sciences)

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1745 KiB

Open AccessArticle

Molecular Dynamics Simulation Study of the Selectivity of a Silica Polymer for Ibuprofen

by Riccardo Concu and M. Natalia D. S. Cordeiro

Int. J. Mol. Sci. 2016, 17(7), 1083; https://doi.org/10.3390/ijms17071083 - 7 Jul 2016

Cited by 7 | Viewed by 6038

Abstract

In the past few years, the sol-gel polycondensation technique has been increasingly employed with great success as an alternative approach to the preparation of molecularly imprinted materials (MIMs). The main aim of this study was to study, through a series of molecular dynamics [...] Read more.

In the past few years, the sol-gel polycondensation technique has been increasingly employed with great success as an alternative approach to the preparation of molecularly imprinted materials (MIMs). The main aim of this study was to study, through a series of molecular dynamics (MD) simulations, the selectivity of an imprinted silica xerogel towards a new template—the (±)-2-(P-Isobutylphenyl) propionic acid (Ibuprofen, IBU). We have previously demonstrated the affinity of this silica xerogel toward a similar molecule. In the present study, we simulated the imprinting process occurring in a sol-gel mixture using the Optimized Potentials for Liquid Simulations-All Atom (OPLS-AA) force field, in order to evaluate the selectivity of this xerogel for a template molecule. In addition, for the first time, we have developed and verified a new parameterisation for the Ibuprofen^® based on the OPLS-AA framework. To evaluate the selectivity of the polymer, we have employed both the radial distribution functions, interaction energies and cluster analyses. Full article

(This article belongs to the Special Issue Big Data Analysis and QSAR/QSPR Research in Chemistry, Bio-Medical, and Network Sciences)

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201 KiB

Open AccessArticle

Prognostic Value of Affective Symptoms in First-Admission Psychotic Patients

by Marta Arrasate, Itxaso González-Ortega, Adriana García-Alocén, Susana Alberich, Iñaki Zorrilla and Ana González-Pinto

Int. J. Mol. Sci. 2016, 17(7), 1039; https://doi.org/10.3390/ijms17071039 - 30 Jun 2016

Cited by 16 | Viewed by 5161

Abstract

Background: Very little research has been conducted in patients with first-episode psychosis using a dimensional approach. Affective dimensional representations might be useful to predict the clinical course and treatment needs in such patients. Methods: Weincluded 112 patients with first-episode psychosis in a longitudinal-prospective [...] Read more.

Background: Very little research has been conducted in patients with first-episode psychosis using a dimensional approach. Affective dimensional representations might be useful to predict the clinical course and treatment needs in such patients. Methods: Weincluded 112 patients with first-episode psychosis in a longitudinal-prospective study with a five-year follow-up (N = 82). Logistic analyses were performed to determine the predictive factors associated with depressive, manic, activation, and dysphoric dimensions. Results: High scores on the depressive dimension were associated with the best prognosis. On the other hand, high scores on the activation dimension and the manic dimension were associated with a poorer prognosis in terms of relapses. Only the dysphoric dimension was not associated with syndromic or functional prognosis. Conclusion: Ourresults suggest that the pattern of baseline affective symptoms helps to predict the course of psychotic illness. Therefore, the systematic assessment of affective symptoms would enable us to draw important conclusions regarding patients’ prognosis. Interventions for patients with high scores on manic or activation dimensions could be beneficial in decreasing relapses in first-episode psychosis. Full article

(This article belongs to the Special Issue Big Data Analysis and QSAR/QSPR Research in Chemistry, Bio-Medical, and Network Sciences)

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5012 KiB

Open AccessArticle

Genome-Wide Discriminatory Information Patterns of Cytosine DNA Methylation

by Robersy Sanchez and Sally A. Mackenzie

Int. J. Mol. Sci. 2016, 17(6), 938; https://doi.org/10.3390/ijms17060938 - 17 Jun 2016

Cited by 11 | Viewed by 6187

Abstract

Cytosine DNA methylation (CDM) is a highly abundant, heritable but reversible chemical modification to the genome. Herein, a machine learning approach was applied to analyze the accumulation of epigenetic marks in methylomes of 152 ecotypes and 85 silencing mutants of Arabidopsis thaliana. [...] Read more.

Cytosine DNA methylation (CDM) is a highly abundant, heritable but reversible chemical modification to the genome. Herein, a machine learning approach was applied to analyze the accumulation of epigenetic marks in methylomes of 152 ecotypes and 85 silencing mutants of Arabidopsis thaliana. In an information-thermodynamics framework, two measurements were used: (1) the amount of information gained/lost with the CDM changes

I_{R}

and (2) the uncertainty of not observing a SNP

L C_{R}

. We hypothesize that epigenetic marks are chromosomal footprints accounting for different ontogenetic and phylogenetic histories of individual populations. A machine learning approach is proposed to verify this hypothesis. Results support the hypothesis by the existence of discriminatory information (DI) patterns of CDM able to discriminate between individuals and between individual subpopulations. The statistical analyses revealed a strong association between the topologies of the structured population of Arabidopsis ecotypes based on

I_{R}

and on LC_R, respectively. A statistical-physical relationship between

I_{R}

and

L C_{R}

was also found. Results to date imply that the genome-wide distribution of CDM changes is not only part of the biological signal created by the methylation regulatory machinery, but ensures the stability of the DNA molecule, preserving the integrity of the genetic message under continuous stress from thermal fluctuations in the cell environment. Full article

(This article belongs to the Special Issue Big Data Analysis and QSAR/QSPR Research in Chemistry, Bio-Medical, and Network Sciences)

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Open AccessArticle

In Silico Prediction of Cytochrome P450-Drug Interaction: QSARs for CYP3A4 and CYP2C9

by Serena Nembri, Francesca Grisoni, Viviana Consonni and Roberto Todeschini

Int. J. Mol. Sci. 2016, 17(6), 914; https://doi.org/10.3390/ijms17060914 - 9 Jun 2016

Cited by 56 | Viewed by 7917

Abstract

Cytochromes P450 (CYP) are the main actors in the oxidation of xenobiotics and play a crucial role in drug safety, persistence, bioactivation, and drug-drug/food-drug interaction. This work aims to develop Quantitative Structure-Activity Relationship (QSAR) models to predict the drug interaction with two of [...] Read more.

Cytochromes P450 (CYP) are the main actors in the oxidation of xenobiotics and play a crucial role in drug safety, persistence, bioactivation, and drug-drug/food-drug interaction. This work aims to develop Quantitative Structure-Activity Relationship (QSAR) models to predict the drug interaction with two of the most important CYP isoforms, namely 2C9 and 3A4. The presented models are calibrated on 9122 drug-like compounds, using three different modelling approaches and two types of molecular description (classical molecular descriptors and binary fingerprints). For each isoform, three classification models are presented, based on a different approach and with different advantages: (1) a very simple and interpretable classification tree; (2) a local (k-Nearest Neighbor) model based classical descriptors and; (3) a model based on a recently proposed local classifier (N-Nearest Neighbor) on binary fingerprints. The salient features of the work are (1) the thorough model validation and the applicability domain assessment; (2) the descriptor interpretation, which highlighted the crucial aspects of P450-drug interaction; and (3) the consensus aggregation of models, which largely increased the prediction accuracy. Full article

(This article belongs to the Special Issue Big Data Analysis and QSAR/QSPR Research in Chemistry, Bio-Medical, and Network Sciences)

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Full article ">Figure 1
Scheme of the data splitting. Full article ">Figure 2
Representation of the molecular descriptor (MD)-based models for CYP3A4: (a) Classification and Regression Trees (CART) model; (b) Score plot of the training molecules described by the k-Nearest Neighbours (k-NN) descriptors, coloured according to their activity; (c) Loading plot of the k-NN descriptors. Full article ">Figure 3
Occurrence frequency of the 19 selected fragments for CYP3A4 within the active/inactive compounds. Symbols associated with the fragments (according to SMARTS language) have the following meaning: X + number = number of total bonds in which the considered atom is involved; a = aromatic atom; A = aliphatic atom. Full article ">Figure 3 Cont.
Occurrence frequency of the 19 selected fragments for CYP3A4 within the active/inactive compounds. Symbols associated with the fragments (according to SMARTS language) have the following meaning: X + number = number of total bonds in which the considered atom is involved; a = aromatic atom; A = aliphatic atom. Full article ">Figure 4
Representation of the MD-based models for CYP2C9: (a) CART model; (b) Score plot of the k-NN descriptors, colored according to their activity; (c) Loading plot of the k-NN descriptors. Full article ">Figure 5
Occurrence frequency of the 16 selected fragments for CYP2C9 within the active/inactive compounds. Symbols associated with the fragments (according to SMARTS language) have the following meaning: X + number = number of total bonds in which the considered atom is involved; a = aromatic atom; A = aliphatic atom. Full article ">

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Open AccessArticle

Development of an in Silico Model of DPPH• Free Radical Scavenging Capacity: Prediction of Antioxidant Activity of Coumarin Type Compounds

by Elizabeth Goya Jorge, Anita Maria Rayar, Stephen J. Barigye, María Elisa Jorge Rodríguez and Maité Sylla-Iyarreta Veitía

Int. J. Mol. Sci. 2016, 17(6), 881; https://doi.org/10.3390/ijms17060881 - 7 Jun 2016

Cited by 18 | Viewed by 7655

Abstract

A quantitative structure-activity relationship (QSAR) study of the 2,2-diphenyl-l-picrylhydrazyl (DPPH•) radical scavenging ability of 1373 chemical compounds, using DRAGON molecular descriptors (MD) and the neural network technique, a technique based on the multilayer multilayer perceptron (MLP), was developed. The built model demonstrated a [...] Read more.

A quantitative structure-activity relationship (QSAR) study of the 2,2-diphenyl-l-picrylhydrazyl (DPPH•) radical scavenging ability of 1373 chemical compounds, using DRAGON molecular descriptors (MD) and the neural network technique, a technique based on the multilayer multilayer perceptron (MLP), was developed. The built model demonstrated a satisfactory performance for the training

(R^{2} = 0.713)

and test set

(Q_{ext}^{2} = 0.654)

, respectively. To gain greater insight on the relevance of the MD contained in the MLP model, sensitivity and principal component analyses were performed. Moreover, structural and mechanistic interpretation was carried out to comprehend the relationship of the variables in the model with the modeled property. The constructed MLP model was employed to predict the radical scavenging ability for a group of coumarin-type compounds. Finally, in order to validate the model’s predictions, an in vitro assay for one of the compounds (4-hydroxycoumarin) was performed, showing a satisfactory proximity between the experimental and predicted pIC₅₀ values. Full article

(This article belongs to the Special Issue Big Data Analysis and QSAR/QSPR Research in Chemistry, Bio-Medical, and Network Sciences)

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15608 KiB

Open AccessArticle

Physico-Chemical and Structural Interpretation of Discrete Derivative Indices on N-Tuples Atoms

by Oscar Martínez-Santiago, Yovani Marrero-Ponce, Stephen J. Barigye, Huong Le Thi Thu, F. Javier Torres, Cesar H. Zambrano, Jorge L. Muñiz Olite, Maykel Cruz-Monteagudo, Ricardo Vivas-Reyes, Liliana Vázquez Infante and Luis M. Artiles Martínez

Int. J. Mol. Sci. 2016, 17(6), 812; https://doi.org/10.3390/ijms17060812 - 27 May 2016

Cited by 7 | Viewed by 6038

Abstract

This report examines the interpretation of the Graph Derivative Indices (GDIs) from three different perspectives (i.e., in structural, steric and electronic terms). It is found that the individual vertex frequencies may be expressed in terms of the geometrical and electronic reactivity [...] Read more.

This report examines the interpretation of the Graph Derivative Indices (GDIs) from three different perspectives (i.e., in structural, steric and electronic terms). It is found that the individual vertex frequencies may be expressed in terms of the geometrical and electronic reactivity of the atoms and bonds, respectively. On the other hand, it is demonstrated that the GDIs are sensitive to progressive structural modifications in terms of: size, ramifications, electronic richness, conjugation effects and molecular symmetry. Moreover, it is observed that the GDIs quantify the interaction capacity among molecules and codify information on the activation entropy. A structure property relationship study reveals that there exists a direct correspondence between the individual frequencies of atoms and Hückel’s Free Valence, as well as between the atomic GDIs and the chemical shift in NMR, which collectively validates the theory that these indices codify steric and electronic information of the atoms in a molecule. Taking in consideration the regularity and coherence found in experiments performed with the GDIs, it is possible to say that GDIs possess plausible interpretation in structural and physicochemical terms. Full article

(This article belongs to the Special Issue Big Data Analysis and QSAR/QSPR Research in Chemistry, Bio-Medical, and Network Sciences)

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1247 KiB

Open AccessArticle

Fast Modeling of Binding Affinities by Means of Superposing Significant Interaction Rules (SSIR) Method

by Emili Besalú

Int. J. Mol. Sci. 2016, 17(6), 827; https://doi.org/10.3390/ijms17060827 - 26 May 2016

Cited by 7 | Viewed by 4789

Abstract

The Superposing Significant Interaction Rules (SSIR) method is described. It is a general combinatorial and symbolic procedure able to rank compounds belonging to combinatorial analogue series. The procedure generates structure-activity relationship (SAR) models and also serves as an inverse SAR tool. The method [...] Read more.

The Superposing Significant Interaction Rules (SSIR) method is described. It is a general combinatorial and symbolic procedure able to rank compounds belonging to combinatorial analogue series. The procedure generates structure-activity relationship (SAR) models and also serves as an inverse SAR tool. The method is fast and can deal with large databases. SSIR operates from statistical significances calculated from the available library of compounds and according to the previously attached molecular labels of interest or non-interest. The required symbolic codification allows dealing with almost any combinatorial data set, even in a confidential manner, if desired. The application example categorizes molecules as binding or non-binding, and consensus ranking SAR models are generated from training and two distinct cross-validation methods: leave-one-out and balanced leave-two-out (BL2O), the latter being suited for the treatment of binary properties. Full article

(This article belongs to the Special Issue Big Data Analysis and QSAR/QSPR Research in Chemistry, Bio-Medical, and Network Sciences)

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Open AccessArticle

3D-QSAR Studies on Barbituric Acid Derivatives as Urease Inhibitors and the Effect of Charges on the Quality of a Model

by Zaheer Ul-Haq, Sajda Ashraf, Abdullah Mohammed Al-Majid and Assem Barakat

Int. J. Mol. Sci. 2016, 17(5), 657; https://doi.org/10.3390/ijms17050657 - 30 Apr 2016

Cited by 14 | Viewed by 7190

Abstract

Urease enzyme (EC 3.5.1.5) has been determined as a virulence factor in pathogenic microorganisms that are accountable for the development of different diseases in humans and animals. In continuance of our earlier study on the helicobacter pylori urease inhibition by barbituric acid derivatives, [...] Read more.

Urease enzyme (EC 3.5.1.5) has been determined as a virulence factor in pathogenic microorganisms that are accountable for the development of different diseases in humans and animals. In continuance of our earlier study on the helicobacter pylori urease inhibition by barbituric acid derivatives, 3D-QSAR (three dimensional quantitative structural activity relationship) advance studies were performed by Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) methods. Different partial charges were calculated to examine their consequences on the predictive ability of the developed models. The finest developed model for CoMFA and CoMSIA were achieved by using MMFF94 charges. The developed CoMFA model gives significant results with cross-validation (q²) value of 0.597 and correlation coefficients (r²) of 0.897. Moreover, five different fields i.e., steric, electrostatic, and hydrophobic, H-bond acceptor and H-bond donors were used to produce a CoMSIA model, with q² and r² of 0.602 and 0.98, respectively. The generated models were further validated by using an external test set. Both models display good predictive power with r²_pred ≥ 0.8. The analysis of obtained CoMFA and CoMSIA contour maps provided detailed insight for the promising modification of the barbituric acid derivatives with an enhanced biological activity. Full article

(This article belongs to the Special Issue Big Data Analysis and QSAR/QSPR Research in Chemistry, Bio-Medical, and Network Sciences)

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Graphical abstract
Full article ">Figure 1
Superimposed view of 1–44 compounds by structure-based alignment using the reference ligand of 1E9Y.pdb. Cyan, red, blue, green and white color represent Hydrogen, Oxygen, Nitrogen, Chlorine and Carbon atom, respectively. Full article ">Figure 2
Graphical interpretation of experimental vs. predicted pIC50 of compounds 1–44 developed by CoMFA (a) and CoMSIA (b) models. Full article ">Figure 3
CoMFA steric and electrostatic contour maps. Electronegativity and electropositivity are represented by red and blue contours, while sterically-favored and -disfavored areas are depicted by green and yellow regions, respectively. Panel (a) and (c) are representative of steric and electrostatic contours of most active comp-4i, while (b) and (d) are representative of least active comp-5s, respectively. Full article ">Figure 3 Cont.
CoMFA steric and electrostatic contour maps. Electronegativity and electropositivity are represented by red and blue contours, while sterically-favored and -disfavored areas are depicted by green and yellow regions, respectively. Panel (a) and (c) are representative of steric and electrostatic contours of most active comp-4i, while (b) and (d) are representative of least active comp-5s, respectively. Full article ">Figure 4
CoMSIA contour maps. Figure (a,c,e,g) displayed steric, electrostatic, hydrophobic, as well as donor contour maps of compound 4i claimed as the most active; While, (b,d,f,h) are representative of compounds, 5s claimed as one of the least active compounds within the series. Full article ">Figure 5
The 3D scatter plot of three selected principle components (PCA1, PCA2, and PCA3), each point corresponds to a molecule and is colored according to its activity. Full article ">Scheme 1
Synthesis of compounds 4a-z and 5a-s. Full article ">

412 KiB

Open AccessArticle

Is It Reliable to Use Common Molecular Docking Methods for Comparing the Binding Affinities of Enantiomer Pairs for Their Protein Target?

by David Ramírez and Julio Caballero

Int. J. Mol. Sci. 2016, 17(4), 525; https://doi.org/10.3390/ijms17040525 - 20 Apr 2016

Cited by 117 | Viewed by 9329

Abstract

Molecular docking is a computational chemistry method which has become essential for the rational drug design process. In this context, it has had great impact as a successful tool for the study of ligand–receptor interaction modes, and for the exploration of large chemical [...] Read more.

Molecular docking is a computational chemistry method which has become essential for the rational drug design process. In this context, it has had great impact as a successful tool for the study of ligand–receptor interaction modes, and for the exploration of large chemical datasets through virtual screening experiments. Despite their unquestionable merits, docking methods are not reliable for predicting binding energies due to the simple scoring functions they use. However, comparisons between two or three complexes using the predicted binding energies as a criterion are commonly found in the literature. In the present work we tested how wise is it to trust the docking energies when two complexes between a target protein and enantiomer pairs are compared. For this purpose, a ligand library composed by 141 enantiomeric pairs was used, including compounds with biological activities reported against seven protein targets. Docking results using the software Glide (considering extra precision (XP), standard precision (SP), and high-throughput virtual screening (HTVS) modes) and AutoDock Vina were compared with the reported biological activities using a classification scheme. Our test failed for all modes and targets, demonstrating that an accurate prediction when binding energies of enantiomers are compared using docking may be due to chance. We also compared pairs of compounds with different molecular weights and found the same results. Full article

(This article belongs to the Special Issue Big Data Analysis and QSAR/QSPR Research in Chemistry, Bio-Medical, and Network Sciences)

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Open AccessArticle

Structural Investigation for Optimization of Anthranilic Acid Derivatives as Partial FXR Agonists by in Silico Approaches

by Meimei Chen, Xuemei Yang, Xinmei Lai, Jie Kang, Huijuan Gan and Yuxing Gao

Int. J. Mol. Sci. 2016, 17(4), 536; https://doi.org/10.3390/ijms17040536 - 8 Apr 2016

Cited by 13 | Viewed by 5805

Abstract

In this paper, a three level in silico approach was applied to investigate some important structural and physicochemical aspects of a series of anthranilic acid derivatives (AAD) newly identified as potent partial farnesoid X receptor (FXR) agonists. Initially, both two and three-dimensional quantitative [...] Read more.

In this paper, a three level in silico approach was applied to investigate some important structural and physicochemical aspects of a series of anthranilic acid derivatives (AAD) newly identified as potent partial farnesoid X receptor (FXR) agonists. Initially, both two and three-dimensional quantitative structure activity relationship (2D- and 3D-QSAR) studies were performed based on such AAD by a stepwise technology combined with multiple linear regression and comparative molecular field analysis. The obtained 2D-QSAR model gave a high predictive ability (R²_train = 0.935, R²_test = 0.902, Q²_LOO = 0.899). It also uncovered that number of rotatable single bonds (b_rotN), relative negative partial charges (RPC⁻), oprea's lead-like (opr_leadlike), subdivided van der Waal’s surface area (SlogP_VSA2) and accessible surface area (ASA) were important features in defining activity. Additionally, the derived3D-QSAR model presented a higher predictive ability (R²_train = 0.944, R²_test = 0.892, Q²_LOO = 0.802). Meanwhile, the derived contour maps from the 3D-QSAR model revealed the significant structural features (steric and electronic effects) required for improving FXR agonist activity. Finally, nine newly designed AAD with higher predicted EC₅₀ values than the known template compound were docked into the FXR active site. The excellent molecular binding patterns of these molecules also suggested that they can be robust and potent partial FXR agonists in agreement with the QSAR results. Overall, these derived models may help to identify and design novel AAD with better FXR agonist activity. Full article

(This article belongs to the Special Issue Big Data Analysis and QSAR/QSPR Research in Chemistry, Bio-Medical, and Network Sciences)

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Figure 1
Plots of experimental against predicted pEC50 values by (A) multiple linear regression (MLR) and (B) CoMFA models. Full article ">Figure 2
The Williams plot for the MLR model. Full article ">Figure 3
Contour maps of the CoMFA model: (A) steric field based on compound 30; (B) electrostatic field based on compound 30. Color values specify the CoMFA field levels that enclose volumes within which increase or decrease in bulk or positive charge favor higher dependent values. Full article ">Figure 4
Structure of template compound (compound 30). The three regions A, B and C are depicted. Full article ">Figure 5
The best docked conformations and poses of newly designed compounds in the ligand binding domain of FXR. Full article ">Figure 6
The 2D representation of docking of compounds N9 (A) and complexed ligand (B) into the FXR active site. Full article ">Figure 7
Alignment of training and test set compounds on compound 30. Baby blue, red, blue, green, gray and yellow signify hydrogen atom, oxygen atom, nitrogen atom, fluorine atom, carbon atom and sulfur atom, respectively. Full article ">

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Open AccessArticle

Hyaluronidase Inhibitory Activity of Pentacylic Triterpenoids from Prismatomeris tetrandra (Roxb.) K. Schum: Isolation, Synthesis and QSAR Study

by Nor Hayati Abdullah, Noel Francis Thomas, Yasodha Sivasothy, Vannajan Sanghiran Lee, Sook Yee Liew, Ibrahim Ali Noorbatcha and Khalijah Awang

Int. J. Mol. Sci. 2016, 17(2), 143; https://doi.org/10.3390/ijms17020143 - 14 Feb 2016

Cited by 20 | Viewed by 7420

Abstract

The mammalian hyaluronidase degrades hyaluronic acid by the cleavage of the β-1,4-glycosidic bond furnishing a tetrasaccharide molecule as the main product which is a highly angiogenic and potent inducer of inflammatory cytokines. Ursolic acid 1, isolated from Prismatomeris tetrandra, was identified [...] Read more.

The mammalian hyaluronidase degrades hyaluronic acid by the cleavage of the β-1,4-glycosidic bond furnishing a tetrasaccharide molecule as the main product which is a highly angiogenic and potent inducer of inflammatory cytokines. Ursolic acid 1, isolated from Prismatomeris tetrandra, was identified as having the potential to develop inhibitors of hyaluronidase. A series of ursolic acid analogues were either synthesized via structure modification of ursolic acid 1 or commercially obtained. The evaluation of the inhibitory activity of these compounds on the hyaluronidase enzyme was conducted. Several structural, topological and quantum chemical descriptors for these compounds were calculated using semi empirical quantum chemical methods. A quantitative structure activity relationship study (QSAR) was performed to correlate these descriptors with the hyaluronidase inhibitory activity. The statistical characteristics provided by the best multi linear model (BML) (R² = 0.9717, R²_cv = 0.9506) indicated satisfactory stability and predictive ability of the developed model. The in silico molecular docking study which was used to determine the binding interactions revealed that the ursolic acid analog 22 had a strong affinity towards human hyaluronidase. Full article

(This article belongs to the Special Issue Big Data Analysis and QSAR/QSPR Research in Chemistry, Bio-Medical, and Network Sciences)

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Full article ">Figure 1
Synthesis of ursolic acid 1 derivatives with different substituents at C-3. ((C5H6N {ClCrNO3}) = Pyridinium chlorochromate; CH2Cl2 = dichloromethane; (CH3)2CO = acetone; H2NOHHCl = hydroxylamine hydrochloride; (CH3CO)2 = acetic anhydride; (CH3SiCH2N+N) = trimethylsilyl diazomethane (TMS)). Full article ">Figure 2
Structure activity relationship of pentacyclic triterpenes (PTs). Full article ">Figure 3
Comparison of the experimental hyaluronidase activity with the activity presented by the QSAR Equation (1), n = 20, with R2 = 0.8579; s2 = 0.0246; F = 21.13; four descriptors. Full article ">Figure 4
Structure of new PTC A compound. Full article ">Figure 5
Superimposed the complex structures of apigenin and 22 with human hyaluronidase (left). The interactions with residues interaction energy below −4 kcal/mol of apigenin (right, top) and 22 (right, bottom) with human hyaluronidase were illustrated. The π–π and hydrogen bonding interaction are depicted in orange and green dashed, respectively. Full article ">

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Computational Analysis of Structure-Based Interactions for Novel H₁-Antihistamines

by Yinfeng Yang, Yan Li, Yanqiu Pan, Jinghui Wang, Feng Lin, Chao Wang, Shuwei Zhang and Ling Yang

Int. J. Mol. Sci. 2016, 17(1), 129; https://doi.org/10.3390/ijms17010129 - 19 Jan 2016

Cited by 18 | Viewed by 8740

Abstract

As a chronic disorder, insomnia affects approximately 10% of the population at some time during their lives, and its treatment is often challenging. Since the antagonists of the H₁ receptor, a protein prevalent in human central nervous system, have been proven as [...] Read more.

As a chronic disorder, insomnia affects approximately 10% of the population at some time during their lives, and its treatment is often challenging. Since the antagonists of the H₁ receptor, a protein prevalent in human central nervous system, have been proven as effective therapeutic agents for treating insomnia, the H₁ receptor is quite possibly a promising target for developing potent anti-insomnia drugs. For the purpose of understanding the structural actors affecting the antagonism potency, presently a theoretical research of molecular interactions between 129 molecules and the H₁ receptor is performed through three-dimensional quantitative structure-activity relationship (3D-QSAR) techniques. The ligand-based comparative molecular similarity indices analysis (CoMSIA) model (Q² = 0.525, R²_ncv = 0.891, R²_pred = 0.807) has good quality for predicting the bioactivities of new chemicals. The cross-validated result suggests that the developed models have excellent internal and external predictability and consistency. The obtained contour maps were appraised for affinity trends for the investigated compounds, which provides significantly useful information in the rational drug design of novel anti-insomnia agents. Molecular docking was also performed to investigate the mode of interaction between the ligand and the active site of the receptor. Furthermore, as a supplementary tool to study the docking conformation of the antagonists in the H₁ receptor binding pocket, molecular dynamics simulation was also applied, providing insights into the changes in the structure. All of the models and the derived information would, we hope, be of help for developing novel potent histamine H₁ receptor antagonists, as well as exploring the H₁-antihistamines interaction mechanism. Full article

(This article belongs to the Special Issue Big Data Analysis and QSAR/QSPR Research in Chemistry, Bio-Medical, and Network Sciences)

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Open AccessReview

Virtual Screening Approaches towards the Discovery of Toll-Like Receptor Modulators

by Lucía Pérez-Regidor, Malik Zarioh, Laura Ortega and Sonsoles Martín-Santamaría

Int. J. Mol. Sci. 2016, 17(9), 1508; https://doi.org/10.3390/ijms17091508 - 9 Sep 2016

Cited by 33 | Viewed by 12444

Abstract

This review aims to summarize the latest efforts performed in the search for novel chemical entities such as Toll-like receptor (TLR) modulators by means of virtual screening techniques. This is an emergent research field with only very recent (and successful) contributions. Identification of [...] Read more.

This review aims to summarize the latest efforts performed in the search for novel chemical entities such as Toll-like receptor (TLR) modulators by means of virtual screening techniques. This is an emergent research field with only very recent (and successful) contributions. Identification of drug-like molecules with potential therapeutic applications for the treatment of a variety of TLR-regulated diseases has attracted considerable interest due to the clinical potential. Additionally, the virtual screening databases and computational tools employed have been overviewed in a descriptive way, widening the scope for researchers interested in the field. Full article

(This article belongs to the Special Issue Big Data Analysis and QSAR/QSPR Research in Chemistry, Bio-Medical, and Network Sciences)

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Deep Artificial Neural Networks and Neuromorphic Chips for Big Data Analysis: Pharmaceutical and Bioinformatics Applications

by Lucas Antón Pastur-Romay, Francisco Cedrón, Alejandro Pazos and Ana Belén Porto-Pazos

Int. J. Mol. Sci. 2016, 17(8), 1313; https://doi.org/10.3390/ijms17081313 - 11 Aug 2016

Cited by 73 | Viewed by 21618

Abstract

Over the past decade, Deep Artificial Neural Networks (DNNs) have become the state-of-the-art algorithms in Machine Learning (ML), speech recognition, computer vision, natural language processing and many other tasks. This was made possible by the advancement in Big Data, Deep Learning (DL) and [...] Read more.

Over the past decade, Deep Artificial Neural Networks (DNNs) have become the state-of-the-art algorithms in Machine Learning (ML), speech recognition, computer vision, natural language processing and many other tasks. This was made possible by the advancement in Big Data, Deep Learning (DL) and drastically increased chip processing abilities, especially general-purpose graphical processing units (GPGPUs). All this has created a growing interest in making the most of the potential offered by DNNs in almost every field. An overview of the main architectures of DNNs, and their usefulness in Pharmacology and Bioinformatics are presented in this work. The featured applications are: drug design, virtual screening (VS), Quantitative Structure–Activity Relationship (QSAR) research, protein structure prediction and genomics (and other omics) data mining. The future need of neuromorphic hardware for DNNs is also discussed, and the two most advanced chips are reviewed: IBM TrueNorth and SpiNNaker. In addition, this review points out the importance of considering not only neurons, as DNNs and neuromorphic chips should also include glial cells, given the proven importance of astrocytes, a type of glial cell which contributes to information processing in the brain. The Deep Artificial Neuron–Astrocyte Networks (DANAN) could overcome the difficulties in architecture design, learning process and scalability of the current ML methods. Full article

(This article belongs to the Special Issue Big Data Analysis and QSAR/QSPR Research in Chemistry, Bio-Medical, and Network Sciences)

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A Brief Review of Computer-Assisted Approaches to Rational Design of Peptide Vaccines

by Ashesh Nandy and Subhash C. Basak

Int. J. Mol. Sci. 2016, 17(5), 666; https://doi.org/10.3390/ijms17050666 - 4 May 2016

Cited by 49 | Viewed by 10350

Abstract

The growing incidences of new viral diseases and increasingly frequent viral epidemics have strained therapeutic and preventive measures; the high mutability of viral genes puts additional strains on developmental efforts. Given the high cost and time requirements for new drugs development, vaccines remain [...] Read more.

The growing incidences of new viral diseases and increasingly frequent viral epidemics have strained therapeutic and preventive measures; the high mutability of viral genes puts additional strains on developmental efforts. Given the high cost and time requirements for new drugs development, vaccines remain as a viable alternative, but there too traditional techniques of live-attenuated or inactivated vaccines have the danger of allergenic reactions and others. Peptide vaccines have, over the last several years, begun to be looked on as more appropriate alternatives, which are economically affordable, require less time for development and hold the promise of multi-valent dosages. The developments in bioinformatics, proteomics, immunogenomics, structural biology and other sciences have spurred the growth of vaccinomics where computer assisted approaches serve to identify suitable peptide targets for eventual development of vaccines. In this mini-review we give a brief overview of some of the recent trends in computer assisted vaccine development with emphasis on the primary selection procedures of probable peptide candidates for vaccine development. Full article

(This article belongs to the Special Issue Big Data Analysis and QSAR/QSPR Research in Chemistry, Bio-Medical, and Network Sciences)

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