International Journal of Molecular Sciences

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15 pages, 825 KiB

Open AccessArticle

De Novo Prediction of Drug Targets and Candidates by Chemical Similarity-Guided Network-Based Inference

by Carlos Vigil-Vásquez and Andreas Schüller

Int. J. Mol. Sci. 2022, 23(17), 9666; https://doi.org/10.3390/ijms23179666 - 26 Aug 2022

Cited by 1 | Viewed by 2512

Identifying drug–target interactions is a crucial step in discovering novel drugs and for drug repositioning. Network-based methods have shown great potential thanks to the straightforward integration of information from different sources and the possibility of extracting novel information from the graph topology. However, [...] Read more.

Identifying drug–target interactions is a crucial step in discovering novel drugs and for drug repositioning. Network-based methods have shown great potential thanks to the straightforward integration of information from different sources and the possibility of extracting novel information from the graph topology. However, despite recent advances, there is still an urgent need for efficient and robust prediction methods. Here, we present SimSpread, a novel method that combines network-based inference with chemical similarity. This method employs a tripartite drug–drug–target network constructed from protein–ligand interaction annotations and drug–drug chemical similarity on which a resource-spreading algorithm predicts potential biological targets for both known or failed drugs and novel compounds. We describe small molecules as vectors of similarity indices to other compounds, thereby providing a flexible means to explore diverse molecular representations. We show that our proposed method achieves high prediction performance through multiple cross-validation and time-split validation procedures over a series of datasets. In addition, we demonstrate that our method performed a balanced exploration of both chemical ligand space (scaffold hopping) and biological target space (target hopping). Our results suggest robust and balanced performance, and our method may be useful for predicting drug targets, virtual screening, and drug repositioning. Full article

(This article belongs to the Special Issue Computational Methods in Drug Design)

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25 pages, 7581 KiB

Open AccessArticle

Identification of Potential Cytochrome P450 3A5 Inhibitors: An Extensive Virtual Screening through Molecular Docking, Negative Image-Based Screening, Machine Learning and Molecular Dynamics Simulation Studies

by Md Ataul Islam, Dawood Babu Dudekula, V. P. Subramanyam Rallabandi, Sridhar Srinivasan, Sathishkumar Natarajan, Hoyong Chung and Junhyung Park

Int. J. Mol. Sci. 2022, 23(16), 9374; https://doi.org/10.3390/ijms23169374 - 19 Aug 2022

Cited by 3 | Viewed by 2725

Abstract

Cytochrome P450 3A5 (CYP3A5) is one of the crucial CYP family members and has already proven to be an important drug target for cardiovascular diseases. In the current study, the PubChem database was screened through molecular docking and high-affinity molecules were adopted for [...] Read more.

Cytochrome P450 3A5 (CYP3A5) is one of the crucial CYP family members and has already proven to be an important drug target for cardiovascular diseases. In the current study, the PubChem database was screened through molecular docking and high-affinity molecules were adopted for further assessment. A negative image-based (NIB) model was used for a similarity search by considering the complementary shape and electrostatics of the target and small molecules. Further, the molecules were segregated into active and inactive groups through six machine learning (ML) matrices. The active molecules found in each ML model were used for in silico pharmacokinetics and toxicity assessments. A total of five molecules followed the acceptable pharmacokinetics and toxicity profiles. Several potential binding interactions between the proposed molecules and CYP3A5 were observed. The dynamic behavior of the selected molecules in the CYP3A5 was explored through a molecular dynamics (MD) simulation study. Several parameters obtained from the MD simulation trajectory explained the stability of the protein–ligand complexes in dynamic states. The high binding affinity of each molecule was revealed by the binding free energy calculation through the MM-GBSA methods. Therefore, it can be concluded that the proposed molecules might be potential CYP3A5 molecules for therapeutic application in cardiovascular diseases subjected to in vitro/in vivo validations. Full article

(This article belongs to the Special Issue Computational Methods in Drug Design)

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16 pages, 2087 KiB

Open AccessArticle

VenomPred: A Machine Learning Based Platform for Molecular Toxicity Predictions

by Salvatore Galati, Miriana Di Stefano, Elisa Martinelli, Marco Macchia, Adriano Martinelli, Giulio Poli and Tiziano Tuccinardi

Int. J. Mol. Sci. 2022, 23(4), 2105; https://doi.org/10.3390/ijms23042105 - 14 Feb 2022

Cited by 24 | Viewed by 4801

Abstract

The use of in silico toxicity prediction methods plays an important role in the selection of lead compounds and in ADMET studies since in vitro and in vivo methods are often limited by ethics, time, budget and other resources. In this context, we [...] Read more.

The use of in silico toxicity prediction methods plays an important role in the selection of lead compounds and in ADMET studies since in vitro and in vivo methods are often limited by ethics, time, budget and other resources. In this context, we present our new web tool VenomPred, a user-friendly platform for evaluating the potential mutagenic, hepatotoxic, carcinogenic and estrogenic effects of small molecules. VenomPred platform employs several in-house Machine Learning (ML) models developed with datasets derived from VEGA QSAR, a software that includes a comprehensive collection of different toxicity models and has been used as a reference for building and evaluating our ML models. The results showed that our models achieved equal or better performance than those obtained with the reference models included in VEGA QSAR. In order to improve the predictive performance of our platform, we adopted a consensus approach combining the results of different ML models, which was able to predict chemical toxicity better than the single models. This improved method was thus implemented in the VenomPred platform, a freely accessible webserver that takes the SMILES (Simplified Molecular-Input Line-Entry System) strings of the compounds as input and sends the prediction results providing a probability score about their potential toxicity. Full article

(This article belongs to the Special Issue Computational Methods in Drug Design)

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18 pages, 2065 KiB

Open AccessArticle

Development of Computational Approaches with a Fragment-Based Drug Design Strategy: In Silico Hsp90 Inhibitors Discovery

by Roberto León, Jorge Soto-Delgado, Elizabeth Montero and Matías Vargas

Int. J. Mol. Sci. 2021, 22(24), 13226; https://doi.org/10.3390/ijms222413226 - 8 Dec 2021

Cited by 5 | Viewed by 3021

Abstract

A semi-exhaustive approach and a heuristic search algorithm use a fragment-based drug design (FBDD) strategy for designing new inhibitors in an in silico process. A deconstruction reconstruction process uses a set of known Hsp90 ligands for generating new ones. The deconstruction process consists [...] Read more.

A semi-exhaustive approach and a heuristic search algorithm use a fragment-based drug design (FBDD) strategy for designing new inhibitors in an in silico process. A deconstruction reconstruction process uses a set of known Hsp90 ligands for generating new ones. The deconstruction process consists of cutting off a known ligand in fragments. The reconstruction process consists of coupling fragments to develop a new set of ligands. For evaluating the approaches, we compare the binding energy of the new ligands with the known ligands. Full article

(This article belongs to the Special Issue Computational Methods in Drug Design)

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22 pages, 2464 KiB

Open AccessArticle

Sources of Variability in the Response of Labeled Microspheres and B Cells during the Analysis by a Flow Cytometer

by Adolfas K. Gaigalas, Yu-Zhong Zhang, Linhua Tian and Lili Wang

Int. J. Mol. Sci. 2021, 22(15), 8256; https://doi.org/10.3390/ijms22158256 - 31 Jul 2021

Cited by 4 | Viewed by 1818

Abstract

A stochastic model of the flow cytometer measurement process was developed to assess the nature of the observed coefficient of variation (CV%) of the mean fluorescence intensity (MFI) from a population of labeled microspheres (beads). Several sources of variability were considered: the total [...] Read more.

A stochastic model of the flow cytometer measurement process was developed to assess the nature of the observed coefficient of variation (CV%) of the mean fluorescence intensity (MFI) from a population of labeled microspheres (beads). Several sources of variability were considered: the total number of labels on a bead, the path through the laser beam, the optical absorption cross-section, the quantum yield, the numerical aperture of the collection optics, and the photoelectron conversion efficiency of the photomultiplier (PMT) cathode. The variation in the number of labels on a bead had the largest effect on the CV% of the MFI of the bead population. The variation in the path of the bead through the laser beam was minimized using flat-top lasers. The variability in the average optical properties of the labels was of minor importance for beads with sufficiently large number of labels. The application of the bead results to the measured CV% of labeled B cells indicated that the measured CV% was a reliable measure of the variability of antibodies bound per cell. With some modifications, the model can be extended to multicolor flow cytometers and to the study of CV% from cells with low fluorescence signal. Full article

(This article belongs to the Special Issue Computational Methods in Drug Design)

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13 pages, 4803 KiB

Open AccessArticle

Discovery of a Novel Triazolopyridine Derivative as a Tankyrase Inhibitor

by Hwani Ryu, Ky-Youb Nam, Hyo Jeong Kim, Jie-Young Song, Sang-Gu Hwang, Jae Sung Kim, Joon Kim and Jiyeon Ahn

Int. J. Mol. Sci. 2021, 22(14), 7330; https://doi.org/10.3390/ijms22147330 - 8 Jul 2021

Cited by 7 | Viewed by 2865

Abstract

More than 80% of colorectal cancer patients have adenomatous polyposis coli (APC) mutations, which induce abnormal WNT/β-catenin activation. Tankyrase (TNKS) mediates the release of active β-catenin, which occurs regardless of the ligand that translocates into the nucleus by AXIN degradation via the ubiquitin-proteasome [...] Read more.

More than 80% of colorectal cancer patients have adenomatous polyposis coli (APC) mutations, which induce abnormal WNT/β-catenin activation. Tankyrase (TNKS) mediates the release of active β-catenin, which occurs regardless of the ligand that translocates into the nucleus by AXIN degradation via the ubiquitin-proteasome pathway. Therefore, TNKS inhibition has emerged as an attractive strategy for cancer therapy. In this study, we identified pyridine derivatives by evaluating in vitro TNKS enzyme activity and investigated N-([1,2,4]triazolo[4,3-a]pyridin-3-yl)-1-(2-cyanophenyl)piperidine-4-carboxamide (TI-12403) as a novel TNKS inhibitor. TI-12403 stabilized AXIN2, reduced active β-catenin, and downregulated β-catenin target genes in COLO320DM and DLD-1 cells. The antitumor activities of TI-12403 were confirmed by the viability of the colorectal cancer cells and its lack of visible toxicity in DLD-1 xenograft mouse model. In addition, combined 5-FU and TI-12403 treatment synergistically inhibited proliferation to a greater extent than that in a single drug treatment. Our observations suggest that TI-12403, a novel selective TNKS1 inhibitor, may be a suitable compound for anticancer drug development. Full article

(This article belongs to the Special Issue Computational Methods in Drug Design)

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19 pages, 8573 KiB

Open AccessArticle

Structure-Based Design, Docking and Binding Free Energy Calculations of A366 Derivatives as Spindlin1 Inhibitors

by Chiara Luise, Dina Robaa, Pierre Regenass, David Maurer, Dmytro Ostrovskyi, Ludwig Seifert, Johannes Bacher, Teresa Burgahn, Tobias Wagner, Johannes Seitz, Holger Greschik, Kwang-Su Park, Yan Xiong, Jian Jin, Roland Schüle, Bernhard Breit, Manfred Jung and Wolfgang Sippl

Int. J. Mol. Sci. 2021, 22(11), 5910; https://doi.org/10.3390/ijms22115910 - 31 May 2021

Cited by 8 | Viewed by 4785

Abstract

The chromatin reader protein Spindlin1 plays an important role in epigenetic regulation, through which it has been linked to several types of malignant tumors. In the current work, we report on the development of novel analogs of the previously published lead inhibitor A366 [...] Read more.

The chromatin reader protein Spindlin1 plays an important role in epigenetic regulation, through which it has been linked to several types of malignant tumors. In the current work, we report on the development of novel analogs of the previously published lead inhibitor A366. In an effort to improve the activity and explore the structure–activity relationship (SAR), a series of 21 derivatives was synthesized, tested in vitro, and investigated by means of molecular modeling tools. Docking studies and molecular dynamics (MD) simulations were performed to analyze and rationalize the structural differences responsible for the Spindlin1 activity. The analysis of MD simulations shed light on the important interactions. Our study highlighted the main structural features that are required for Spindlin1 inhibitory activity, which include a positively charged pyrrolidine moiety embedded into the aromatic cage connected via a propyloxy linker to the 2-aminoindole core. Of the latter, the amidine group anchor the compounds into the pocket through salt bridge interactions with Asp184. Different protocols were tested to identify a fast in silico method that could help to discriminate between active and inactive compounds within the A366 series. Rescoring the docking poses with MM-GBSA calculations was successful in this regard. Because A366 is known to be a G9a inhibitor, the most active developed Spindlin1 inhibitors were also tested over G9a and GLP to verify the selectivity profile of the A366 analogs. This resulted in the discovery of diverse selective compounds, among which 1s and 1t showed Spindlin1 activity in the nanomolar range and selectivity over G9a and GLP. Finally, future design hypotheses were suggested based on our findings. Full article

(This article belongs to the Special Issue Computational Methods in Drug Design)

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21 pages, 4555 KiB

Open AccessArticle

Synthesis, Biological Activity, and Molecular Dynamics Study of Novel Series of a Trimethoprim Analogs as Multi-Targeted Compounds: Dihydrofolate Reductase (DHFR) Inhibitors and DNA-Binding Agents

by Agnieszka Wróbel, Maciej Baradyn, Artur Ratkiewicz and Danuta Drozdowska

Int. J. Mol. Sci. 2021, 22(7), 3685; https://doi.org/10.3390/ijms22073685 - 1 Apr 2021

Cited by 27 | Viewed by 4295

Abstract

Eighteen previously undescribed trimethoprim (TMP) analogs containing amide bonds (1–18) were synthesized and compared with TMP, methotrexate (MTX), and netropsin (NT). These compounds were designed as potential minor groove binding agents (MGBAs) and inhibitors of human dihydrofolate reductase (hDHFR). [...] Read more.

Eighteen previously undescribed trimethoprim (TMP) analogs containing amide bonds (1–18) were synthesized and compared with TMP, methotrexate (MTX), and netropsin (NT). These compounds were designed as potential minor groove binding agents (MGBAs) and inhibitors of human dihydrofolate reductase (hDHFR). The all-new derivatives were obtained via solid phase synthesis using 4-nitrophenyl Wang resin. Data from the ethidium displacement test confirmed their DNA-binding capacity. Compounds 13–14 (49.89% and 43.85%) and 17–18 (41.68% and 42.99%) showed a higher binding affinity to pBR322 plasmid than NT. The possibility of binding in a minor groove as well as determination of association constants were performed using calf thymus DNA, T4 coliphage DNA, poly (dA-dT)², and poly (dG-dC)². With the exception of compounds 9 (IC50 = 56.05 µM) and 11 (IC50 = 55.32 µM), all of the compounds showed better inhibitory properties against hDHFR than standard, which confirms that the addition of the amide bond into the TMP structures increases affinity towards hDHFR. Derivatives 2, 6, 13, 14, and 16 were found to be the most potent hDHFR inhibitors. This molecular modelling study shows that they interact strongly with a catalytically important residue Glu-30. Full article

(This article belongs to the Special Issue Computational Methods in Drug Design)

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