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Pharmaceuticals
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Chemoinformatics and Drug Design
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Chemoinformatics and Drug Design

A special issue of Pharmaceuticals (ISSN 1424-8247).

Deadline for manuscript submissions: closed (30 November 2018) | Viewed by 54106

Special Issue Editor

Prof. Dr. Osvaldo Andrade Santos-Filho

E-Mail Website
Guest Editor
Center of Health Sciences, Laboratory of Molecular Modeling and Computational Structural Biology, Federal University of Rio de Janeiro, IPPN, Av. Carlos Chagas Filho 373, Bloco H, Rio de Janeiro 21941-599, Brazil
Interests: molecular modeling; computational and medicinal chemistry; molecular simulations; structural biology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Chemoinformatics is the branch of chemical sciences regarding the design, creation, organization, retrieval, analysis, management, and dissemination of chemical information. In this context, one could say that chemoinformatics is the application of information science methods to solve chemical issues. Due to the development of hardware and software technologies, chemoinformatics is today a mature scientific field. Moreover, it is well known that, during the drug design process, a huge amount of chemical data is generated and, consequently, chemoinformatics have been successfully applied by the pharmaceutical community. To celebrate the success story and the advances on this important field, the journal Pharmaceuticals invites fellow scientists to submit original papers or reviews, which will be published as a Special Issue on “Chemoinformatics and Drug Design”.

Looking forward to your contribution.

Prof. Dr. Osvaldo Andrade Santos-Filho
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Pharmaceuticals is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Chemoinformatics
  • Drug design
  • Machine learning
  • Data mining
  • Pharmacophore-based virtual screening
  • In silico databases
  • Molecular descriptors
  • Combinatorial chemistry
  • QSAR
  • ADMET

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Published Papers (7 papers)

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Research

19 pages, 10502 KiB  
Article
In Silico Study to Identify New Antituberculosis Molecules from Natural Sources by Hierarchical Virtual Screening and Molecular Dynamics Simulations
by Vinícius de S. Pinto, Janay S. C. Araújo, Rai C. Silva, Glauber V. da Costa, Jorddy N. Cruz, Moysés F. De A. Neto, Joaquín M. Campos, Cleydson B. R. Santos, Franco H. A. Leite and Manoelito C. S. Junior
Pharmaceuticals 2019, 12(1), 36; https://doi.org/10.3390/ph12010036 - 12 Mar 2019
Cited by 54 | Viewed by 7818
Abstract
Tuberculosis (TB) is an infection caused by Mycobacterium tuberculosis, responsible for 1.5 million documented deaths in 2016. The increase in reported cases of M. tuberculosis resistance to the main drugs show the need for the development of new and efficient drugs for [...] Read more.
Tuberculosis (TB) is an infection caused by Mycobacterium tuberculosis, responsible for 1.5 million documented deaths in 2016. The increase in reported cases of M. tuberculosis resistance to the main drugs show the need for the development of new and efficient drugs for better TB control. Based on these facts, this work aimed to use combined in silico techniques for the discovery of potential inhibitors to β-ketoacyl-ACP synthase (MtKasA). Initially compounds from natural sources present in the ZINC database were selected, then filters were sequentially applied by virtual screening, initially with pharmacophoric modeling, and later the selected compounds (based on QFIT scores) were submitted to the DOCK 6.5 program. After recategorization of the variables (QFIT score and GRID score), compounds ZINC35465970 and ZINC31170017 were selected. These compounds showed great hydrophobic contributions and for each established system 100 ns of molecular dynamics simulations were performed and the binding free energy was calculated. ZINC35465970 demonstrated a greater capacity for the KasA enzyme inhibition, with a ΔGbind = −30.90 kcal/mol and ZINC31170017 presented a ΔGbind = −27.49 kcal/mol. These data can be used in other studies that aim at the inhibition of the same biological targets through drugs with a dual action. Full article
(This article belongs to the Special Issue Chemoinformatics and Drug Design)
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Figure 1

Figure 1
<p>2D and 3D structures of thiolactomycin (TLM).</p> Full article ">Figure 2
<p>Representation of the best pharmacophore model for KasA inihibitors. Pink: Hbond donor; green: Hbond acceptors; cyan: hydrophobic centers. The size of the spheres represents the tolerance. The distances are shown in angstroms (Å).</p> Full article ">Figure 3
<p>Result of the redocking. Crystallographic ligand in cyan and the best docking pose in orange.</p> Full article ">Figure 4
<p>ROC curves for evaluation of Grid-Hawkins GB/SA and Grid Score.</p> Full article ">Figure 5
<p>Enrichment factor (EF) for scoring functions used in MtKasA in 1, 5, 10 and 25% of the database.</p> Full article ">Figure 6
<p>Distribution of compounds according to their affinity energy against MtKasA.</p> Full article ">Figure 7
<p>Interactions of compounds against the active site of <span class="html-italic">Mycobacterium tuberculosis</span> KasA, In (<b>A</b>) ZINC35465970 and (<b>B</b>) ZINC31170017.</p> Full article ">Figure 8
<p>Evaluation of RMSD plots during MD simulations. The KasA protein backbone has been represented in black, while the ligands graphs have been represented in different colors. (<b>a</b>) RMSDs of the KasA- ZINC35465970 system and (<b>b</b>) RMSDs of the KasA-ZINC31170017 system.</p> Full article ">Figure 9
<p>Protein backbone RMSF plots.</p> Full article ">Figure 10
<p>Regions of protein that showed greater residue fluctuations.</p> Full article ">
20 pages, 2641 KiB  
Article
An Antioxidant Potential, Quantum-Chemical and Molecular Docking Study of the Major Chemical Constituents Present in the Leaves of Curatella americana Linn
by Mayara Amoras Teles Fujishima, Nayara Dos Santos Raulino da Silva, Ryan Da Silva Ramos, Elenilze Figueiredo Batista Ferreira, Kelton Luís Belém dos Santos, Carlos Henrique Tomich de Paula da Silva, Jocivania Oliveira da Silva, Joaquín Maria Campos Rosa and Cleydson Breno Rodrigues dos Santos
Pharmaceuticals 2018, 11(3), 72; https://doi.org/10.3390/ph11030072 - 20 Jul 2018
Cited by 39 | Viewed by 6325
Abstract
Reactive oxygen species (ROS) are continuously generated in the normal biological systems, primarily by enzymes as xanthine oxidase (XO). The inappropriate scavenging or inhibition of ROS has been considered to be linked with aging, inflammatory disorders, and chronic diseases. Therefore, many plants and [...] Read more.
Reactive oxygen species (ROS) are continuously generated in the normal biological systems, primarily by enzymes as xanthine oxidase (XO). The inappropriate scavenging or inhibition of ROS has been considered to be linked with aging, inflammatory disorders, and chronic diseases. Therefore, many plants and their products have been investigated as natural antioxidants for their potential use in preventive medicine. The leaves and bark extracts of Curatella americana Linn. were described in scientific research as anti-inflammatory, vasodilator, anti-ulcerogenic, and hypolipidemic effects. So, the aim of this study was to evaluate the antioxidant potentials of leaf hydroalcoholic extract from C. americana (HECA) through the scavenging DPPH assay and their main chemical constituents, evaluated by the following quantum chemical approaches (DFT B3LYP/6-31G**): Maps of Molecular Electrostatic Potential (MEP), Frontier Orbital’s (HOMO and LUMO) followed by multivariate analysis and molecular docking simulations with the xanthine oxidase enzyme. The hydroalcoholic extract showed significant antioxidant activity by free radical scavenging probably due to the great presence of flavonoids, which were grouped in the PCA and HCA analysis with the standard gallic acid. In the molecular docking study, the compounds studied presented the binding free energy (ΔG) values close each other, due to the similar interactions with amino acids residues at the activity site. The descriptors Gap and softness were important to characterize the molecules with antioxidant potential by capturing oxygen radicals. Full article
(This article belongs to the Special Issue Chemoinformatics and Drug Design)
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Figure 1

Figure 1
<p>Chemical constituents of the leaves the <span class="html-italic">C. americana</span> L<span class="html-italic">.</span></p> Full article ">Figure 2
<p>DPPH radical scavenging potential of gallic acid as the reference compound and hydroalcoholic extract of <span class="html-italic">Curatella americana</span> (HECA). The mean of scavenging percentage was significative different in all concentrations at <span class="html-italic">p</span> &lt; 0.01.</p> Full article ">Figure 3
<p>The plot of PC1–PC2 scores for <span class="html-italic">Curatella americana</span> chemical constituents. Red color indicates less reactive compounds and blue color indicate more reactive compounds.</p> Full article ">Figure 4
<p>The plot of the PC1–PC2 loadings with the five descriptors selected.</p> Full article ">Figure 5
<p>HCA dendrogram for <span class="html-italic">Curatella americana</span> constituents showing them separated into two main classes (red color indicates less reactive and blue color indicate more reactive compounds).</p> Full article ">Figure 6
<p>Spin densities in the cation free-radical of selected compounds.</p> Full article ">Figure 7
<p>Control Ligands 2D structure: (<b>A</b>) hypoxanthine (HPX) and (<b>B</b>) Febuxostat (FBX).</p> Full article ">Figure 8
<p>Binding affinity provided by AutoDock/Vina software of the compounds <b>11</b> (Gallic acid), <b>2</b> (Quercetin) and <b>12</b> (Foeniculin) and control ligand febuxostat (FBX). Ligand complexed hypoxanthine (HPX) for XO (organism <span class="html-italic">Bos taurus</span>).</p> Full article ">Figure 9
<p>Interactions of the compounds with XO enzyme. Nominal interactions, aminoacids, and distances can be seen in <a href="#pharmaceuticals-11-00072-t003" class="html-table">Table 3</a>.</p> Full article ">
26 pages, 4180 KiB  
Article
Heterodimer Binding Scaffolds Recognition via the Analysis of Kinetically Hot Residues
by Ognjen Perišić
Pharmaceuticals 2018, 11(1), 29; https://doi.org/10.3390/ph11010029 - 16 Mar 2018
Cited by 3 | Viewed by 5233
Abstract
Physical interactions between proteins are often difficult to decipher. The aim of this paper is to present an algorithm that is designed to recognize binding patches and supporting structural scaffolds of interacting heterodimer proteins using the Gaussian Network Model (GNM). The recognition is [...] Read more.
Physical interactions between proteins are often difficult to decipher. The aim of this paper is to present an algorithm that is designed to recognize binding patches and supporting structural scaffolds of interacting heterodimer proteins using the Gaussian Network Model (GNM). The recognition is based on the (self) adjustable identification of kinetically hot residues and their connection to possible binding scaffolds. The kinetically hot residues are residues with the lowest entropy, i.e., the highest contribution to the weighted sum of the fastest modes per chain extracted via GNM. The algorithm adjusts the number of fast modes in the GNM’s weighted sum calculation using the ratio of predicted and expected numbers of target residues (contact and the neighboring first-layer residues). This approach produces very good results when applied to dimers with high protein sequence length ratios. The protocol’s ability to recognize near native decoys was compared to the ability of the residue-level statistical potential of Lu and Skolnick using the Sternberg and Vakser decoy dimers sets. The statistical potential produced better overall results, but in a number of cases its predicting ability was comparable, or even inferior, to the prediction ability of the adjustable GNM approach. The results presented in this paper suggest that in heterodimers at least one protein has interacting scaffold determined by the immovable, kinetically hot residues. In many cases, interacting proteins (especially if being of noticeably different sizes) either behave as a rigid lock and key or, presumably, exhibit the opposite dynamic behavior. While the binding surface of one protein is rigid and stable, its partner’s interacting scaffold is more flexible and adaptable. Full article
(This article belongs to the Special Issue Chemoinformatics and Drug Design)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>(<b>a</b>) Prediction output (ratio of true vs. false predictions depicted as a scatterplot) for a simple prediction approach based on the 5 fastest modes, for each protein chain in our list (433 dimers in total). The diagonal line separates area where true positives outpace false positives from the area where false positives are dominant. The square in the upper left quadrant is the area of good predictions (ratio of true predictions is greater than 0.5, and ratio of bad predictions is lass or equal than 0.5). The true positives mean is 43.09%, and the false positives mean is 40.77%. There is 22.17% of good predictions (192 chains, they are in the upper left quadrant) and 12.70% of very bad predictions (110 chains). The very bad predictions are in the lower right quadrant, which is not depicted as rectangle to emphasize the importance of good predictions. (<b>b</b>) Prediction output for 278 heterodimers chains only, for the basic approach based on the 5 fastest modes. The true positives mean is 50.74%, and the false positives mean is 42.68%. There is 31.29% of good predictions (87 chains) and 11.15% of very bad predictions (31 chains). (<b>c</b>) Prediction output for the simple approach based on the fastest 10% of modes per chain for all heterodimers (278 chains). The true positives mean is 52.52%, and the false positives mean is 46.27%. There is 23.02% of good predictions (64 chains) and 14.39% of very bad predictions (40 chains). (<b>d</b>) Prediction output for the simple approach based on the modes that correspond to top 10% of the eigenvalues range, for heterodimer chains with high sequence length ratios (the chain length ratio &gt;2, the individual chain lengths longer than 80 residues). The true positives mean is 52.03%, and the false positives mean is 40.67%. There is 33.01% of good predictions (34 chains) and 6.80% of very bad predictions (7 chains).</p> Full article ">Figure 1 Cont.
<p>(<b>a</b>) Prediction output (ratio of true vs. false predictions depicted as a scatterplot) for a simple prediction approach based on the 5 fastest modes, for each protein chain in our list (433 dimers in total). The diagonal line separates area where true positives outpace false positives from the area where false positives are dominant. The square in the upper left quadrant is the area of good predictions (ratio of true predictions is greater than 0.5, and ratio of bad predictions is lass or equal than 0.5). The true positives mean is 43.09%, and the false positives mean is 40.77%. There is 22.17% of good predictions (192 chains, they are in the upper left quadrant) and 12.70% of very bad predictions (110 chains). The very bad predictions are in the lower right quadrant, which is not depicted as rectangle to emphasize the importance of good predictions. (<b>b</b>) Prediction output for 278 heterodimers chains only, for the basic approach based on the 5 fastest modes. The true positives mean is 50.74%, and the false positives mean is 42.68%. There is 31.29% of good predictions (87 chains) and 11.15% of very bad predictions (31 chains). (<b>c</b>) Prediction output for the simple approach based on the fastest 10% of modes per chain for all heterodimers (278 chains). The true positives mean is 52.52%, and the false positives mean is 46.27%. There is 23.02% of good predictions (64 chains) and 14.39% of very bad predictions (40 chains). (<b>d</b>) Prediction output for the simple approach based on the modes that correspond to top 10% of the eigenvalues range, for heterodimer chains with high sequence length ratios (the chain length ratio &gt;2, the individual chain lengths longer than 80 residues). The true positives mean is 52.03%, and the false positives mean is 40.67%. There is 33.01% of good predictions (34 chains) and 6.80% of very bad predictions (7 chains).</p> Full article ">Figure 2
<p>Distribution of targets per sequence length for 414 dimers that belong to the training set depicted as a heat map. The burgundy square designates a length/percent pair with a highest concentration of chains. Yellow and light green squares are length/percent pairs with a medium number of chains. The dark blue squares are length/percent pairs with low occupancy. The navy areas designate zero chain occupancy. It is obvious that the percent of targets is a decreasing function of the sequence length.</p> Full article ">Figure 3
<p>(<b>a</b>) Algorithm output for the prediction based on the adjustable number of fastest modes per chain and sequential influence of hot residues, for high sequence-length ratio dimer chains (length ratio greater than two, chain length greater than 80 residues). The true positives mean true is 53.27%, and the false positives mean is 42.05%. There is 56.31% of good predictions (58 of 103 chains) and only 14.56% of very bad predictions (15 chains). (<b>b</b>) Algorithm output for the prediction based on the adjustable number of fastest modes per chain and the variable 3D influence per hot residue (the influence of a hot residue is spread to spatial neighbors closer than 6 or 8 Å), for chains in dimers with high sequence length ratios (Length ratio &gt; 2, length &gt; 80 residues). The true positives mean true is 53.77%, and false positives mean is 41.29%. There is 56.31% of good predictions (58 chains) and 8.74% of very bad predictions (9 chains). (<b>c</b>) Algorithm output for the prediction based on the adjustable number of fastest modes per chain and combined 1D &amp; 3D influences of hot residues, for chains in dimers with high sequence length ratio (Length ratio &gt; 2, length &gt; 80 residues). The true positives mean is 56.77%, and the false positives mean is 43.21%. There is 63.11% of good predictions (65 chains) and 11.65% of very bad predictions (12 chains).</p> Full article ">Figure 3 Cont.
<p>(<b>a</b>) Algorithm output for the prediction based on the adjustable number of fastest modes per chain and sequential influence of hot residues, for high sequence-length ratio dimer chains (length ratio greater than two, chain length greater than 80 residues). The true positives mean true is 53.27%, and the false positives mean is 42.05%. There is 56.31% of good predictions (58 of 103 chains) and only 14.56% of very bad predictions (15 chains). (<b>b</b>) Algorithm output for the prediction based on the adjustable number of fastest modes per chain and the variable 3D influence per hot residue (the influence of a hot residue is spread to spatial neighbors closer than 6 or 8 Å), for chains in dimers with high sequence length ratios (Length ratio &gt; 2, length &gt; 80 residues). The true positives mean true is 53.77%, and false positives mean is 41.29%. There is 56.31% of good predictions (58 chains) and 8.74% of very bad predictions (9 chains). (<b>c</b>) Algorithm output for the prediction based on the adjustable number of fastest modes per chain and combined 1D &amp; 3D influences of hot residues, for chains in dimers with high sequence length ratio (Length ratio &gt; 2, length &gt; 80 residues). The true positives mean is 56.77%, and the false positives mean is 43.21%. There is 63.11% of good predictions (65 chains) and 11.65% of very bad predictions (12 chains).</p> Full article ">Figure 4
<p>Number of correctly predicted chains per heterodimer using the combined (1D and 3D) adjustable approach, for dimers in which both chains are longer than 80 residues. Two cases are analyzed: heterodimers with long sequence length ratios (&gt;2) and heterodimers with short sequence length ratio (≤2). (<b>a</b>) Number of chains per dimer in the upper left quadrant. (<b>b</b>) Number of chains per heterodimer above the main diagonal (the diagoanal that passes through the lower left and upper right quadrants).</p> Full article ">Figure 5
<p>(<b>A</b>) Prediction algorithms comparison expressed as a plot of the true positives mean and the false positives mean percentages for each algorithm described previously. The first two algorithms were applied on all heterodimer chains. In all other cases, algorithms were applied on the heterodimer chains with high sequence-length ratios. The algorithms are (a) all heterodimers, 5 fastest modes; (b) all heterodimers, fastest modes corresponding to top 10% of eigenvalues range; (c) high sequence length ratio, fastest modes corresponding to top 10% of eigenvalues range; (d) adjustable number of modes, 1D influence; (e) adjustable modes, 3D influence, within a sphere with a radius of 6 or 8 Å; and (f) algorithms <b>d</b> and <b>e</b> combined. (<b>B</b>) Prediction algorithms comparison expressed as a percentage of good and very bad chains. The first two algorithms were applied on all heterodimer chains. In all other cases, algorithms were applied on the chains with high sequence-length ratios. The algorithms are (a) all heterodimers, 5 fastest modes; (b) all heterodimers, with fastest modes corresponding to top 10% of eigenvalues range; (c) high sequence length ratio, with fastest modes corresponding to top 10% of eigenvalues range; (d) adjustable number of modes, 1D influence; (e) adjustable modes, 3D influence, within a sphere with a radius of 6 or 8 Å; (f) algorithms <b>d</b> and <b>e</b> combined.</p> Full article ">Figure 6
<p>Ability of the adjustable 1D&amp;3D GNM algorithm to predict binding scaffolds. It is depicted via four heterodimers (PDB ID codes 1BRC, 1DTD, 1WEJ, and 1QGK). The analyzed chains are blue, depicted using the whole atom representation, with the adjustable GNM predictions colored yellow. Partnering chains are red and depicted as ribbons. (<b>a</b>) Chains E and I from the protein 1BRC. The chain E was analyzed with the adjustable GNM. This is a very good prediction. There is 75.29% true positives, with 47.41% false positives. (<b>b</b>) Chains A and B from the protein 1DTD. The chain A was analyzed with the adjustable GNM. This is a very good prediction. There is 71.08% true positives, and only 30.45% false positives. For the chain A, only residues 363 to 665 are given in the PDB file. There is a Zinc atom and four water molecules embedded in the interface (not shown). The binding interface is defined only using the weighted sum (Equation (1)). (<b>c</b>) Chains L and F from the protein 1WEJ. The chain L was analyzed with the adjustable GNM. This is a very good prediction. There is 92.73% true positives, and 42.67% false positives. (<b>d</b>) Chains A and B from 1QGK. The chain A was analyzed with the adjustable GNM. This is a very good prediction. There is 88.58% true positives, and only 36.83% false positives.</p> Full article ">Figure 7
<p>Protein dimer decoys recognition using the adjustable GNM protocol. The influence of hot residues is spread to spatial neighbors closer than 6 or 8 Å. Subplots (<b>a</b>) and (<b>b</b>) are from the Vakser decoy sets (PDB ID 1CHO). Blue circles depict neutral decoys regardless (neither native nor far from it). Decoys far from native structure are green, and near native ones are red. Subplots (<b>c</b>) and (<b>d</b>) are from the Sternberg decoy sets (PDB ID 2SIC). Green dots depict far from native decoys. Near native decoys are blue, and the native structure is a red circle.</p> Full article ">Figure 8
<p>Comparison of the abilities of the adjustable spatial GNM approach and the statistical potential to distinguish near native decoys/structures from false decoys. Blue dots depict neutral decoys. Decoys far from native structure are green, and near native ones are red. Two decoys sets are depicted (1CHO from Vakser set, and 2SIC from Sternberg set), with two chains per example. The left plot in each example corresponds to the longer chain, and the right plot to its shorter pair. The circular segments in the lower left corners correspond to the distances of the <span class="html-italic">n</span>-th best chain according to the combined approach of the adjustable GNM and the statistical potential, in which <span class="html-italic">n</span> is the number of near native structures. It is a good measure of the concordance between the two methods.</p> Full article ">
18 pages, 7327 KiB  
Article
In Silico Study, Synthesis, and Cytotoxic Activities of Porphyrin Derivatives
by Fransiska Kurniawan, Youhei Miura, Rahmana Emran Kartasasmita, Naoki Yoshioka, Abdul Mutalib and Daryono Hadi Tjahjono
Pharmaceuticals 2018, 11(1), 8; https://doi.org/10.3390/ph11010008 - 20 Jan 2018
Cited by 16 | Viewed by 8751
Abstract
Five known porphyrins, 5,10,15,20-tetrakis(p-tolyl)porphyrin (TTP), 5,10,15,20-tetrakis(p-bromophenyl)porphyrin (TBrPP), 5,10,15,20-tetrakis(p-aminophenyl)porphyrin (TAPP), 5,10,15-tris(tolyl)-20-mono(p-nitrophenyl)porphyrin (TrTMNP), 5,10,15-tris(tolyl)-20-mono(p-aminophenyl)porphyrin (TrTMAP), and three novel porphyrin derivatives, 5,15-di-[bis(3,4-ethylcarboxymethylenoxy)phenyl]-10,20-di(p-tolyl)porphyrin [...] Read more.
Five known porphyrins, 5,10,15,20-tetrakis(p-tolyl)porphyrin (TTP), 5,10,15,20-tetrakis(p-bromophenyl)porphyrin (TBrPP), 5,10,15,20-tetrakis(p-aminophenyl)porphyrin (TAPP), 5,10,15-tris(tolyl)-20-mono(p-nitrophenyl)porphyrin (TrTMNP), 5,10,15-tris(tolyl)-20-mono(p-aminophenyl)porphyrin (TrTMAP), and three novel porphyrin derivatives, 5,15-di-[bis(3,4-ethylcarboxymethylenoxy)phenyl]-10,20-di(p-tolyl)porphyrin (DBECPDTP), 5,10-di-[bis(3,4-ethylcarboxymethylenoxy)phenyl]-15,20-di-(methylpyrazole-4-yl)porphyrin (cDBECPDPzP), 5,15-di-[bis(3,4-ethylcarboxymethylenoxy)phenyl]-10,20-di-(methylpyrazole-4-yl)porphyrin (DBECPDPzP), were used to study their interaction with protein targets (in silico study), and were synthesized. Their cytotoxic activities against cancer cell lines were tested using 3-(4,5-dimetiltiazol-2-il)-2,5-difeniltetrazolium bromide (MTT) assay. The interaction of porphyrin derivatives with carbonic anhydrase IX (CAIX) and REV-ERBβ proteins were studied by molecular docking and molecular dynamic simulation. In silico study results reveal that DBECPDPzP and TrTMNP showed the highest binding interaction with REV- ERBβ and CAIX, respectively, and both complexes of DBECPDPzP-REV-ERBβ and TrTMNP-CAIX showed good and comparable stability during molecular dynamic simulation. The studied porphyrins have selective growth inhibition activities against tested cancer cells and are categorized as marginally active compounds based on their IC50. Full article
(This article belongs to the Special Issue Chemoinformatics and Drug Design)
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Figure 1

Figure 1
<p>Plot of the RMSD value of the 5FL6-TrTMNP complex (red) and the 4N73-DBECPDPzP complex (blue) during the molecular dynamic simulation.</p> Full article ">Figure 2
<p>Trajectory of TrTMNP against 5FL6.</p> Full article ">Figure 3
<p>Trajectory of DBECPDPzP against 4N73.</p> Full article ">Figure 4
<p>2D interaction between TrTMNP and CAIX.</p> Full article ">Figure 5
<p>Overlay the docking pose of TrTMNP (green) and DBECPDPzP (red) in the binding site of 5FL6.</p> Full article ">Figure 6
<p>Overlay the docking pose of cDBECPDPzP (green) and DBECPDPzP (yellow) in the binding site of 4N73.</p> Full article ">Figure 7
<p>2D interaction between cDBECPDPzP and REV-ERBβ, which contains an unfavorable bond (red interaction) due to steric effects.</p> Full article ">Figure 8
<p>2D interaction between DBECPDPzP and REV-ERBβ.</p> Full article ">Scheme 1
<p>General Adler method to synthesize porphyrin derivatives.</p> Full article ">Scheme 2
<p>Synthetic route of BECB.</p> Full article ">
8432 KiB  
Article
Propagation of Fibrillar Structural Forms in Proteins Stopped by Naturally Occurring Short Polypeptide Chain Fragments
by Irena Roterman, Mateusz Banach and Leszek Konieczny
Pharmaceuticals 2017, 10(4), 89; https://doi.org/10.3390/ph10040089 - 16 Nov 2017
Cited by 7 | Viewed by 4917
Abstract
Amyloids characterized by unbounded growth of fibrillar structures cause many pathological processes. Such unbounded propagation is due to the presence of a propagating hydrophobicity field around the fibril’s main axis, preventing its closure (unlike in globular proteins). Interestingly, similar fragments, commonly referred to [...] Read more.
Amyloids characterized by unbounded growth of fibrillar structures cause many pathological processes. Such unbounded propagation is due to the presence of a propagating hydrophobicity field around the fibril’s main axis, preventing its closure (unlike in globular proteins). Interestingly, similar fragments, commonly referred to as solenoids, are present in many naturally occurring proteins, where their propagation is arrested by suitably located “stopper” fragments. In this work, we analyze the distribution of hydrophobicity in solenoids and in their corresponding “stoppers” from the point of view of the fuzzy oil drop model (called FOD in this paper). This model characterizes the unique linear propagation of local hydrophobicity in the solenoid fragment and allows us to pinpoint “stopper” sequences, where local hydrophobicity quite closely resembles conditions encountered in globular proteins. Consequently, such fragments perform their function by mediating entropically advantageous contact with the water environment. We discuss examples of amyloid-like structures in solenoids, with particular attention to “stop” segments present in properly folded proteins found in living organisms. Full article
(This article belongs to the Special Issue Chemoinformatics and Drug Design)
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Figure 1

Figure 1
<p>Selected proteins viewed from two different angles. Left column: visualization of linear propagation of hydrophobic residues responsible for local maxima. Right column: visualization of “stop” fragments. (<b>A</b>,<b>B</b>): 3UYV; (<b>C</b>,<b>D</b>): 4YZA; (<b>E</b>,<b>F</b>): 2A0Z. In 2A0Z, the only residue directed toward the center is ILE 510. This protein is not included in our analysis due to its peculiar structural form which does not permit analysis based on the fuzzy oil drop model; however, it is visualized in the figure to show linear propagation of hydrophobic residues. Green fragments: stop fragments. Red space filling presentation: the positions of residues identified by fuzzy oil drop model as highly discordant versus the idealized distribution. This aims to show that they generate the linear propagation accordant to long axis of the solenoid. Gray fragments: additional chain fragments increasing the solubility of protein under consideration. However, this subject is not the object of analysis in this paper. Left column: solenoid axis—parallel to the paper sheet plane (<b>A</b>,<b>C</b>,<b>E</b>), right column—solenoid axis perpendicular to the paper sheet plane (<b>B</b>,<b>D</b>,<b>F</b>).</p> Full article ">Figure 2
<p>Example of an amyloid fibril (prion protein 2KJ3) in two projections, showing linear distribution of hydrophobicity (red: hydrophobic residues; blue: hydrophilic residues). Since no “stop” signal is present, the structure is susceptible to further linear aggregation. The regularity and of fibril without any polypeptide chain fragment disturbing the linear organization. (<b>A</b>,<b>B</b>)—different perspective: (<b>A</b>)—the fibril long axis—perpendiculat ro the paper sheet, (<b>B</b>)—the fibril axis parallel to the paper sheet.</p> Full article ">Figure 3
<p>Theoretical (blue), observed (red) and intrinsic (green) hydrophobicity distributions for successive β-sheets forming the lyase solenoid fragment (1DBG). Labels (<b>A</b>,<b>B</b>,<b>C</b>) follow the convention used to identify sheets in PDB. The blue line (T) shows the theoretical concentration of hydrophobicity in the central part, along with low hydrophobicity in the N- and C-terminal section, consistent with the 3D Gaussian. Proteins which exhibit such distribution are water-soluble. In contrast, the red line represents a distribution where no central peak can be observed and where hydrophobicity does not taper off in the terminal section—such as in the case of solenoids. Actual distribution (green) is closely aligned with the red profile, suggesting that the solenoid does not generate a central hydrophobic core and that it is moreover capable of complexing additional hydrophobic molecules in its terminal sections. This phenomenon is thought to be responsible for structural changes leading to formation of fibrillar forms rather than monocentric globules.</p> Full article ">Figure 4
<p>Hydrophobic (<b>A</b>) and hydrophilic (<b>B</b>) residues in 1DBG. VdW presentation has been applied in both images to highlight selected residues. Note the linear arrangement of hydrophobic and hydrophilic bands. The green helix at the bottom acts as a “stopper”.</p> Full article ">Figure 5
<p>“Stop” signals which accompany solenoids. (<b>A</b>) lyase (1DBG): red fragments: helix preventing further propagation of the fibrillar structure; green: residues engaged in enzymatic activity; yellow: β-sheet comprising the solenoid; (<b>B</b>) cell adhesion protein (1DAB): red fragments which constitute “stop” signals—an uncoiled loop (front) and a beta fold (red)—preventing propagation of fibrillar forms in either direction; blue: fragment (limited by two blue dots) believed to mediate interaction with epithelial cells [<a href="#B13-pharmaceuticals-10-00089" class="html-bibr">13</a>] (GGXXP)5.</p> Full article ">Figure 6
<p>Examples of “stoppers” adjacent to the N- and C-terminal sections of the solenoid. Blue—theoretical distribution, red—observed one. (<b>A</b>) N-term 1L0S, (<b>B</b>) C-term 3UYV, (<b>C</b>) N-term 1DBG, (<b>D</b>) C-term 1L1I, (<b>E</b>) N-term 1EWW, (<b>F</b>) C-term 4YZA.</p> Full article ">Figure 7
<p>One-dimensional representation of fuzzy oil drop model parameters. The leftmost chart (<b>A</b>) presents the idealized Gaussian distribution—<span class="html-italic">T</span>—while the chart on the right (<b>C</b>) corresponds to the uniform distribution—<span class="html-italic">R</span>. Actual hydrophobicity distribution (expressed by the RD parameter) for the target protein is shown in the center (<b>B</b>) and marked on the axis with a pink dot (<b>D</b>). According to the fuzzy oil drop (FOD) model this protein contains a well-defined hydrophobic core. Vertical axes represent hydrophobicity (in arbitrary units), while horizontal axes represent distance (in multiplicities of σx). According to the three-sigma rule, the range between 0 + 3σ and 0 − 3σ covers more than 99% of the entire probability expressed by the Gaussian. The bottom axis shows the full range of the RD coefficient from 0 (perfect Gaussian) to 1 (uniform distribution with no concentration of hydrophobicity at any point in the protein body). In the presented example, <span class="html-italic">RD</span> = 0.318. <span class="html-italic">RD</span> &gt; 0.5 is interpreted as a better match for the unified distribution than the theoretical Gaussian, whereas <span class="html-italic">RD</span> &lt; 0.5 reveals the presence of a FOD-compliant monocentric hydrophobic core encapsulated in a hydrophilic shell.</p> Full article ">
6845 KiB  
Article
Molecular Docking and 3D-Pharmacophore Modeling to Study the Interactions of Chalcone Derivatives with Estrogen Receptor Alpha
by Muchtaridi Muchtaridi, Hasna Nur Syahidah, Anas Subarnas, Muhammad Yusuf, Sharon D. Bryant and Thierry Langer
Pharmaceuticals 2017, 10(4), 81; https://doi.org/10.3390/ph10040081 - 16 Oct 2017
Cited by 66 | Viewed by 13308
Abstract
Tamoxifen is the most frequently used anti-estrogen adjuvant treatment for estrogen receptor-positive breast cancer. However, it is associated with an increased risk of several serious side–effects, such as uterine cancer, stroke, and pulmonary embolism. The 2′,4′-dihydroxy-6-methoxy-3,5-dimethylchalcone (ChalcEA) from plant leaves of Eugenia aquea [...] Read more.
Tamoxifen is the most frequently used anti-estrogen adjuvant treatment for estrogen receptor-positive breast cancer. However, it is associated with an increased risk of several serious side–effects, such as uterine cancer, stroke, and pulmonary embolism. The 2′,4′-dihydroxy-6-methoxy-3,5-dimethylchalcone (ChalcEA) from plant leaves of Eugenia aquea, has been found to inhibit the proliferation of MCF-7 human breast cancer cells in a dose-dependent manner, with an IC50 of 74.5 μg/mL (250 μM). The aim of this work was to study the molecular interactions of new ChalcEA derivatives formed with the Estrogen Receptor α (ERα) using computer aided drug design approaches. Molecular docking using Autodock 4.2 was employed to explore the modes of binding of ChalcEA derivatives with ERα. The 3D structure-based pharmacophore model was derived using LigandScout 4.1 Advanced to investigate the important chemical interactions of the ERα-tamoxifen complex structure. The binding energy and the tamoxifen-pharmacophore fit score of the best ChalcEA derivative (HNS10) were −12.33 kcal/mol and 67.07 kcal/mol, respectively. The HNS10 interacted with Leu346, Thr347, Leu349, Ala350, Glu353, Leu387, Met388, Leu391, Arg394, Met421, and Leu525. These results suggest that the new ChalcEA derivatives could serve as the lead compound for potent ERα inhibitor in the fight against breast cancer. Full article
(This article belongs to the Special Issue Chemoinformatics and Drug Design)
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Figure 1
<p>(<b>a</b>) Pharmacophore-Molecular Docking Based of 4-OHT with ERα derived from the X-ray derived structure (PDB code: 3ERT). Hydrophobic, positive ionizable, hydrogen bond donor and acceptor interactions are depicted as yellow spheres, blue star, green and red arrows, respectively. (<b>b</b>) The 2D-depiction illustrates a hydrophobic pocket with hydrophobic interactions with the binding site residues. Interactions derived and depicted using LigandScout 4.1 Advanced. Hydrogen atoms on the ligand and excluded volumes (restricted areas that define the shape of the binding pocket) are not displayed.</p> Full article ">Figure 2
<p>Best docked pose of 4-hydroxytamoxifen (4-OHT) with estrogen receptor-alpha (ERα) using AutoDock 4.2.</p> Full article ">Figure 3
<p>Best docked pose of ChalcEA with ERα. One hydrogen bond donor, one hydrogen acceptor and three hydrophobic (pi-alkyl) interactions are represented with green, red, and black, colored dashed lines, respectively. This interaction was visualized by LigandScout 4.1.</p> Full article ">Figure 4
<p>Overlay of the docked pose of ChalcEA (gray) and 4-hydroxy-tamoxifen (4-OHT) (blue) in the binding site of estrogen receptor alpha (ERα). Hydrogen bond and pi-alkyl interactions are represented in green and pink colored dashed lines, respectively.</p> Full article ">Figure 5
<p>Receiver operating characteristic (ROC) curve validation of the 3D structure-based pharmacophore model using a set of 626 estrogen receptor alpha active and 20,773 decoy molecules.</p> Full article ">Figure 6
<p>Interaction of (<b>a</b>) HNS9 and (<b>b</b>) NHS10 within binding site of ERα. Hydrogen bond, ion-ion interaction, and pi-alkyl interactions are represented in green, blue and purple colored dashed lines, respectively.</p> Full article ">Figure 7
<p>Fit of the (<b>a</b>) HNS9 (<b>b</b>) HNS10 to the structure-based pharmacophore model derived from 4-OHT with ERα from PDB code: 3ERT. The models were generated using LigandScout 4.1 Advanced. Virtual screening was performed leaving at least two features out. The ligands fit six of the eight features and all of the excluded volumes.</p> Full article ">Figure 7 Cont.
<p>Fit of the (<b>a</b>) HNS9 (<b>b</b>) HNS10 to the structure-based pharmacophore model derived from 4-OHT with ERα from PDB code: 3ERT. The models were generated using LigandScout 4.1 Advanced. Virtual screening was performed leaving at least two features out. The ligands fit six of the eight features and all of the excluded volumes.</p> Full article ">Figure 8
<p>The general scheme of methodologies in the present work.</p> Full article ">
16820 KiB  
Article
Anti-Mycobacterial Evaluation of 7-Chloro-4-Aminoquinolines and Hologram Quantitative Structure–Activity Relationship (HQSAR) Modeling of Amino–Imino Tautomers
by Marcelle L. F. Bispo, Camilo H. S. Lima, Laura N. F. Cardoso, André L. P. Candéa, Flávio A. F. M. Bezerra, Maria C. S. Lourenço, Maria G. M. O. Henriques, Ricardo B. Alencastro, Carlos R. Kaiser, Marcus V. N. Souza and Magaly G. Albuquerque
Pharmaceuticals 2017, 10(2), 52; https://doi.org/10.3390/ph10020052 - 9 Jun 2017
Cited by 4 | Viewed by 5925
Abstract
In an ongoing research program for the development of new anti-tuberculosis drugs, we synthesized three series (A, B, and C) of 7-chloro-4-aminoquinolines, which were evaluated in vitro against Mycobacterium tuberculosis (MTB). Now, we report the anti-MTB and cytotoxicity evaluations [...] Read more.
In an ongoing research program for the development of new anti-tuberculosis drugs, we synthesized three series (A, B, and C) of 7-chloro-4-aminoquinolines, which were evaluated in vitro against Mycobacterium tuberculosis (MTB). Now, we report the anti-MTB and cytotoxicity evaluations of a new series, D (D01–D21). Considering the active compounds of series A (A01–A13), B (B01–B13), C (C01–C07), and D (D01–D09), we compose a data set of 42 compounds and carried out hologram quantitative structure–activity relationship (HQSAR) analysis. The amino–imino tautomerism of the 4-aminoquinoline moiety was considered using both amino (I) and imino (II) forms as independent datasets. The best HQSAR model from each dataset was internally validated and both models showed significant statistical indexes. Tautomer I model: leave-one-out (LOO) cross-validated correlation coefficient (q2) = 0.80, squared correlation coefficient (r2) = 0.97, standard error (SE) = 0.12, cross-validated standard error (SEcv) = 0.32. Tautomer II model: q2 = 0.77, r2 = 0.98, SE = 0.10, SEcv = 0.35. Both models were externally validated by predicting the activity values of the corresponding test set, and the tautomer II model, which showed the best external prediction performance, was used to predict the biological activity responses of the compounds that were not evaluated in the anti-MTB trials due to poor solubility, pointing out D21 for further solubility studies to attempt to determine its actual biological activity. Full article
(This article belongs to the Special Issue Chemoinformatics and Drug Design)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Series <b>A</b>–<b>D</b> of 7-chloro-4-aminoquinoline derivatives synthesized and evaluated against the <span class="html-italic">Mycobacterium tuberculosis</span> wild-type H37Rv strain (minimum inhibitory concentration, MIC) by our research group and their respective lead compounds.</p> Full article ">Figure 2
<p>Plot of the experimental versus predicted pMIC values of the training and test sets of the tautomers I (amino) (A) and II (imino) (B) datasets.</p> Full article ">Figure 3
<p>The HQSAR contribution maps of the most (<b>A08</b>, <b>B05</b>, <b>C03</b>, and <b>D06</b>) and least (<b>A09</b>, <b>B03</b>, <b>C07</b>, and <b>D03</b>) potent compounds according to the tautomer II model from each series (<b>A</b>, <b>B</b>, <b>C</b>, and <b>D</b>) along with the experimental (and calculated) pMIC values.</p> Full article ">Figure 4
<p>The HQSAR contribution maps of the outliers (<b>A01</b> and <b>D09</b>) and similar compounds that belong to the training set.</p> Full article ">
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