Bioactivity modeling
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An affinity fingerprint is the vector consisting of compound's affinity or potency against the reference panel of protein targets. Here, we present the QAFFP fingerprint, 440 elements long in silico QSAR-based affinity fingerprint, components of which are predicted by Random Forest regression models trained on bioactivity data from the ChEMBL database. Both real-valued (rv-QAFFP) and binary (b-QAFFP) versions of the QAFFP fingerprint were implemented and their performance in similarity searching, biological activity classification and scaffold hopping was assessed and compared to that of the 1024 bits long Morgan2 fingerprint (the RDKit implementation of the ECFP4 fingerprint). In both similarity searching and biological activity classification, the QAFFP fingerprint yields retrieval rates, measured by AUC (~ 0.65 and ~ 0.70 for similarity searching depending on data sets, and ~ 0.85 for classification) and EF5 (~ 4.67 and ~ 5.82 for similarity searching depending on data sets, and ~ 2.10 for classification), comparable to that of the Morgan2 fingerprint (similarity searching AUC of ~ 0.57 and ~ 0.66, and EF5 of ~ 4.09 and ~ 6.41, depending on data sets, classification AUC of ~ 0.87, and EF5 of ~ 2.16). However, the QAFFP fingerprint outperforms the Morgan2 fingerprint in scaffold hopping as it is able to retrieve 1146 out of existing 1749 scaffolds, while the Morgan2 fingerprint reveals only 864 scaffolds.
- Klíčová slova
- Affinity fingerprint, Bioactivity modeling, Biological fingerprint, QSAR, Scaffold hopping, Similarity searching,
- Publikační typ
- časopisecké články MeSH
Affinity fingerprints report the activity of small molecules across a set of assays, and thus permit to gather information about the bioactivities of structurally dissimilar compounds, where models based on chemical structure alone are often limited, and model complex biological endpoints, such as human toxicity and in vitro cancer cell line sensitivity. Here, we propose to model in vitro compound activity using computationally predicted bioactivity profiles as compound descriptors. To this aim, we apply and validate a framework for the calculation of QSAR-derived affinity fingerprints (QAFFP) using a set of 1360 QSAR models generated using Ki, Kd, IC50 and EC50 data from ChEMBL database. QAFFP thus represent a method to encode and relate compounds on the basis of their similarity in bioactivity space. To benchmark the predictive power of QAFFP we assembled IC50 data from ChEMBL database for 18 diverse cancer cell lines widely used in preclinical drug discovery, and 25 diverse protein target data sets. This study complements part 1 where the performance of QAFFP in similarity searching, scaffold hopping, and bioactivity classification is evaluated. Despite being inherently noisy, we show that using QAFFP as descriptors leads to errors in prediction on the test set in the ~ 0.65-0.95 pIC50 units range, which are comparable to the estimated uncertainty of bioactivity data in ChEMBL (0.76-1.00 pIC50 units). We find that the predictive power of QAFFP is slightly worse than that of Morgan2 fingerprints and 1D and 2D physicochemical descriptors, with an effect size in the 0.02-0.08 pIC50 units range. Including QSAR models with low predictive power in the generation of QAFFP does not lead to improved predictive power. Given that the QSAR models we used to compute the QAFFP were selected on the basis of data availability alone, we anticipate better modeling results for QAFFP generated using more diverse and biologically meaningful targets. Data sets and Python code are publicly available at https://github.com/isidroc/QAFFP_regression .
- Klíčová slova
- Affinity fingerprints, Bioactivity modeling, ChEMBL, Cytotoxicity, Drug sensitivity, Drug sensitivity prediction, QSAR,
- Publikační typ
- časopisecké články MeSH
Mammalian cell perfusion cultures are gaining renewed interest as an alternative to traditional fed-batch processes for the production of therapeutic proteins, such as monoclonal antibodies (mAb). The steady state operation at high viable cell density allows the continuous delivery of antibody product with increased space-time yield and reduced in-process variability of critical product quality attributes (CQA). In particular, the production of a confined mAb N-linked glycosylation pattern has the potential to increase therapeutic efficacy and bioactivity. In this study, we show that accurate control of flow rates, media composition and cell density of a Chinese hamster ovary (CHO) cell perfusion bioreactor allowed the production of a constant glycosylation profile for over 20 days. Steady state was reached after an initial transition phase of 6 days required for the stabilization of extra- and intracellular processes. The possibility to modulate the glycosylation profile was further investigated in a Design of Experiment (DoE), at different viable cell density and media supplement concentrations. This strategy was implemented in a sequential screening approach, where various steady states were achieved sequentially during one culture. It was found that, whereas high ammonia levels reached at high viable cell densities (VCD) values inhibited the processing to complex glycan structures, the supplementation of either galactose, or manganese as well as their synergy significantly increased the proportion of complex forms. The obtained experimental data set was used to compare the reliability of a statistical response surface model (RSM) to a mechanistic model of N-linked glycosylation. The latter outperformed the response surface predictions with respect to its capability and reliability in predicting the system behavior (i.e., glycosylation pattern) outside the experimental space covered by the DoE design used for the model parameter estimation. Therefore, we can conclude that the modulation of glycosylation in a sequential steady state approach in combination with mechanistic model represents an efficient and rational strategy to develop continuous processes with desired N-linked glycosylation patterns. Biotechnol. Bioeng. 2017;114: 1978-1990. © 2017 Wiley Periodicals, Inc.
- Klíčová slova
- N-linked glycosylation, mammalian cell culture, mathematical model, monoclonal antibody, perfusion bioreactor,
- MeSH
- analýza selhání vybavení MeSH
- biologické modely * MeSH
- bioreaktory * MeSH
- CHO buňky MeSH
- Cricetulus MeSH
- design s pomocí počítače MeSH
- design vybavení MeSH
- glykosylace MeSH
- monoklonální protilátky izolace a purifikace metabolismus MeSH
- perfuze přístrojové vybavení metody MeSH
- počítačová simulace MeSH
- polysacharidy metabolismus MeSH
- zvířata MeSH
- Check Tag
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Názvy látek
- monoklonální protilátky MeSH
- polysacharidy MeSH
Modern QSAR approaches have wide practical applications in drug discovery for designing potentially bioactive molecules. If such models are based on the use of 2D descriptors, important information contained in the spatial structures of molecules is lost. The major problem in constructing models using 3D descriptors is the choice of a putative bioactive conformation, which affects the predictive performance. The multi-instance (MI) learning approach considering multiple conformations in model training could be a reasonable solution to the above problem. In this study, we implemented several multi-instance algorithms, both conventional and based on deep learning, and investigated their performance. We compared the performance of MI-QSAR models with those based on the classical single-instance QSAR (SI-QSAR) approach in which each molecule is encoded by either 2D descriptors computed for the corresponding molecular graph or 3D descriptors issued for a single lowest energy conformation. The calculations were carried out on 175 data sets extracted from the ChEMBL23 database. It is demonstrated that (i) MI-QSAR outperforms SI-QSAR in numerous cases and (ii) MI algorithms can automatically identify plausible bioactive conformations.
The development of new biomaterials for musculoskeletal tissue repair is currently an important branch in biomedicine research. The approach presented here is centered around the development of a prototypic synthetic glycerogel scaffold for bone regeneration, which simultaneously features therapeutic activity. The main novelty of this work lies in the combination of an open meso and macroporous nanocrystalline cellulose (NCC)-based glycerogel with a fully biocompatible microporous bioMOF system (CaSyr-1) composed of calcium ions and syringic acid. The bioMOF framework is further impregnated with a third bioactive component, i.e., ibuprofen (ibu), to generate a multifold bioactive system. The integrated CaSyr-1(ibu) serves as a reservoir for bioactive compounds delivery, while the NCC scaffold is the proposed matrix for cell ingrowth, proliferation and differentiation. The measured drug delivery profiles, studied in a phosphate-buffered saline solution at 310 K, indicate that the bioactive components are released concurrently with bioMOF dissolution after ca. 30 min following a pseudo-first-order kinetic model. Furthermore, according to the semi-empirical Korsmeyer-Peppas kinetic model, this release is governed by a case-II mechanism, suggesting that the molecular transport is influenced by the relaxation of the NCC matrix. Preliminary in vitro results denote that the initial high concentration of glycerol in the NCC scaffold can be toxic in direct contact with human osteoblasts (HObs). However, when the excess of glycerol is diluted in the system (after the second day of the experiment), the direct and indirect assays confirm full biocompatibility and suitability for HOb proliferation.
- Klíčová slova
- bioMOF, cellulose, composite, glycerogel, scaffold, tissue repair,
- Publikační typ
- časopisecké články MeSH
Insertion of a heterocyclic ring into the steroid core could enhance bioactivity, improve selectivity and reduce side effects in potential drugs for cancer therapy. The present study describes the synthesis of new thiazoline, thiadiazoline and thiazolidinone steroid compounds combined with lactone, lactam or pyridine moieties. These steroid hybrid molecules may be potential candidates for drug design, with improved biological activity and bioavailability. The starting androstenedione or dehydroepiandrosterone were modified by multiphase synthesis into thiosemicarbazone androstane derivatives, direct precursors for the synthesis of new heterocyclic compounds. Their cytotoxicity was tested against five cancer cell lines: breast adenocarcinoma cells (MCF7), acute lymphoblastic leukemia (CCRF-CEM), cervical carcinoma cells (HeLa), hormone-independent (DU 145) and hormone-sensitive prostate cancer cells (LNCaP), as well as against normal skin fibroblasts (BJ). Compounds 5 and 16 were found to be the most selective, with both inducing apoptosis in HeLa cells. New compounds were also evaluated for their relative binding affinities for the ligand-binding domains (LBDs) of estrogen receptor α (ERα), estrogen receptor β (ERβ), androgen receptor (AR) or glucocorticoid receptor (GR) using a fluorescent assay in yeast cells, where thiazole derivative 13 exhibited the highest binding affinity for ERα, while thiazolidinone 7 showed strong selective affinity for ERβ. Furthermore, inhibition potential against human aldo-keto reductase 1C3 and 1C4 (AKR1C3 and AKR1C4) was evaluated by fluorescence spectroscopy, with acetamido thiadiazoline 21 displaying an IC50 value for AKR1C3 slightly higher than the reference inhibitor ibuprofen. Molecular docking studies were used to propose protein-ligand binding models for compounds showing the strongest affinity toward specific proteins based on in vitro experiments. In summary, our results suggest that the tested heterocyclic derivatives are active against hormone-dependent cancer cells and represent promising leads for the development of novel therapeutics.
- Klíčová slova
- Androstane, Cytotoxic activity, Hormone receptors, Molecular docking, Molecular hybridization, Thiadiazoline, Thiazolidinone, Thiazoline,
- MeSH
- androgenní receptory metabolismus MeSH
- androstany * farmakologie chemie chemická syntéza MeSH
- apoptóza účinky léků MeSH
- heterocyklické sloučeniny * chemie farmakologie chemická syntéza MeSH
- lidé MeSH
- molekulární struktura MeSH
- nádorové buněčné linie MeSH
- proliferace buněk účinky léků MeSH
- protinádorové látky * farmakologie chemická syntéza chemie MeSH
- screeningové testy protinádorových léčiv MeSH
- simulace molekulového dockingu * MeSH
- vztah mezi dávkou a účinkem léčiva MeSH
- vztahy mezi strukturou a aktivitou MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- Názvy látek
- androgenní receptory MeSH
- androstane MeSH Prohlížeč
- androstany * MeSH
- heterocyklické sloučeniny * MeSH
- protinádorové látky * MeSH
The nuclear receptors peroxisome proliferator-activated receptor γ (PPARγ) and its hetero-dimerization partner retinoid X receptor α (RXRα) are considered as drug targets in the treatment of diseases like the metabolic syndrome and diabetes mellitus type 2. Effort has been made to develop new agonists for PPARγ to obtain ligands with more favorable properties than currently used drugs. Magnolol was previously described as dual agonist of PPARγ and RXRα. Here we show the structure-based rational design of a linked magnolol dimer within the ligand binding domain of PPARγ and its synthesis. Furthermore, we evaluated its binding properties and functionality as a PPARγ agonist in vitro with the purified PPARγ ligand binding domain (LBD) and in a cell-based nuclear receptor transactivation model in HEK293 cells. We determined the synthesized magnolol dimer to bind with much higher affinity to the purified PPARγ ligand binding domain than magnolol (K i values of 5.03 and 64.42 nM, respectively). Regarding their potency to transactivate a PPARγ-dependent luciferase gene both compounds were equally effective. This is likely due to the PPARγ specificity of the newly designed magnolol dimer and lack of RXRα-driven transactivation activity by this dimeric compound.
- MeSH
- bifenylové sloučeniny chemická syntéza chemie farmakologie MeSH
- dimerizace * MeSH
- HEK293 buňky MeSH
- lidé MeSH
- ligandy MeSH
- lignany chemická syntéza chemie farmakologie MeSH
- pioglitazon farmakologie MeSH
- PPAR gama agonisté chemie metabolismus MeSH
- proteinové domény MeSH
- racionální návrh léčiv * MeSH
- retinoidní X receptor alfa metabolismus MeSH
- vztahy mezi strukturou a aktivitou MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Názvy látek
- bifenylové sloučeniny MeSH
- ligandy MeSH
- lignany MeSH
- magnolol MeSH Prohlížeč
- pioglitazon MeSH
- PPAR gama MeSH
- retinoidní X receptor alfa MeSH
Ganoderma lucidum, a medicinal mushroom renowned for its production of a diverse array of compounds, accounts for the pharmacological effects including anti-inflammatory, antioxidant, immunomodulatory, and anticancer characteristics. Thus, it is recognized as a valuable species of interest in the pharmaceutical and nutraceutical industries due to its important medicinal properties. Recent advances in omics technologies such as genomes, transcriptomics, proteomics, and metabolomics have considerably increased our understanding of the bioactives in G. lucidum. This review explores the application of molecular breeding techniques to enhance both the yield and quality of G. lucidum across the food, pharmaceutical, and industrial sectors. The article discusses the current state of research on the use of contemporary omics technologies which studies and highlights future research directions that may increase the production of bioactive compounds for their therapeutic potential. Additionally, predictive methods with computational studies have recently emerged as effective tools for investigating bioactive constituents in G. lucidum, providing an organized and cost-effective strategy for understanding their bioactivity, interactions, and possible therapeutic uses. Omics and machine learning techniques can be applied to identify the candidates for pharmaceutical applications and to enhance the production of bioactive compounds in G. lucidum. The quantification and production of the bioactive compounds can be streamlined by the integrating computational study of bioactive compounds with non-destructive predictive machine learning models of the same. Synergistically, these techniques have the potential to be a promising approach for the future prediction of the bioactive constituents, without compromising the integrity of the fungal organism.
CONTEXT: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of the COVID-19 infection and responsible for millions of victims worldwide, remains a significant threat to public health. Even after the development of vaccines, research interest in the emergence of new variants is still prominent. Currently, the focus is on the search for effective and safe drugs, given the limitations and side effects observed for the synthetic drugs administered so far. In this sense, bioactive natural products that are widely used in the pharmaceutical industry due to their effectiveness and low toxicity have emerged as potential options in the search for safe drugs against COVID-19. Following this line, we screened 10 bioactive compounds derived from cholesterol for molecules capable of interacting with the receptor-binding domain (RBD) of the spike protein from SARS-CoV-2 (SC2Spike), responsible for the virus's invasion of human cells. Rounds of docking followed by molecular dynamics simulations and binding energy calculations enabled the selection of three compounds worth being experimentally evaluated against SARS-CoV-2. METHODS: The 3D structures of the cholesterol derivatives were prepared and optimized using the Spartan 08 software with the semi-empirical method PM3. They were then exported to the Molegro Virtual Docking (MVD®) software, where they were docked onto the RBD of a 3D structure of the SC2Spike protein that was imported from the Protein Data Bank (PDB). The best poses obtained from MVD® were subjected to rounds of molecular dynamics simulations using the GROMACS software, with the OPLS/AA force field. Frames from the MD simulation trajectories were used to calculate the ligand's free binding energies using the molecular mechanics - Poisson-Boltzmann surface area (MM-PBSA) method. All results were analyzed using the xmgrace and Visual Molecular Dynamics (VMD) software.
- Klíčová slova
- Molecular dynamics, Natural products, SARS-CoV-2, Spike protein,
- MeSH
- antivirové látky farmakologie MeSH
- biologické přípravky * farmakologie MeSH
- COVID-19 * MeSH
- databáze proteinů MeSH
- lidé MeSH
- SARS-CoV-2 MeSH
- simulace molekulární dynamiky MeSH
- simulace molekulového dockingu MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- Názvy látek
- antivirové látky MeSH
- biologické přípravky * MeSH
A series of twenty-six methoxylated and methylated N-aryl-1-hydroxynaphthalene- 2-carboxanilides was prepared and characterized as potential anti-invasive agents. The molecular structure of N-(2,5-dimethylphenyl)-1-hydroxynaphthalene-2-carboxamide as a model compound was determined by single-crystal X-ray diffraction. All the analysed compounds were tested against the reference strain Staphylococcus aureus and three clinical isolates of methicillin-resistant S. aureus as well as against Mycobacterium tuberculosis and M. kansasii. In addition, the inhibitory profile of photosynthetic electron transport in spinach (Spinacia oleracea L.) chloroplasts was specified. In vitro cytotoxicity of the most effective compounds was tested on the human monocytic leukaemia THP-1 cell line. The activities of N-(3,5-dimethylphenyl)-, N-(3-fluoro-5-methoxy-phenyl)- and N-(3,5-dimethoxyphenyl)-1-hydroxynaphthalene-2-carbox- amide were comparable with or even better than the commonly used standards ampicillin and isoniazid. All promising compounds did not show any cytotoxic effect at the concentration >30 µM. Moreover, an in silico evaluation of clogP features was performed for the entire set of the carboxamides using a range of software lipophilicity predictors, and cross-comparison with the experimentally determined lipophilicity (log k), in consensus lipophilicity estimation, was conducted as well. Principal component analysis was employed to illustrate noticeable variations with respect to the molecular lipophilicity (theoretical/experimental) and rule-of-five violations. Additionally, ligand-oriented studies for the assessment of the three-dimensional quantitative structure-activity relationship profile were carried out with the comparative molecular surface analysis to determine electron and/or steric factors that potentially contribute to the biological activities of the investigated compounds.
- Klíčová slova
- 3D-QSAR, CoMSA, MTT assay, PET inhibition, X-Ray structure, antimycobacterial activity, antistaphylococcal activity, cytotoxicity, hydroxynaphthalenecarboxamides, lipophilicity,
- MeSH
- ampicilin farmakologie MeSH
- analýza hlavních komponent MeSH
- anilidy chemická syntéza chemie farmakologie MeSH
- antibakteriální látky chemická syntéza chemie farmakologie MeSH
- chloroplasty účinky léků fyziologie MeSH
- fotosyntéza účinky léků MeSH
- isoniazid farmakologie MeSH
- lidé MeSH
- methicilin rezistentní Staphylococcus aureus účinky léků růst a vývoj MeSH
- metylace MeSH
- mikrobiální testy citlivosti MeSH
- Mycobacterium kansasii účinky léků růst a vývoj MeSH
- Mycobacterium tuberculosis účinky léků růst a vývoj MeSH
- naftoly chemická syntéza chemie farmakologie MeSH
- Spinacia oleracea chemie účinky léků metabolismus MeSH
- THP-1 buňky MeSH
- transport elektronů účinky léků MeSH
- vztahy mezi strukturou a aktivitou MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- srovnávací studie MeSH
- Názvy látek
- ampicilin MeSH
- anilidy MeSH
- antibakteriální látky MeSH
- isoniazid MeSH
- naftoly MeSH
