The purpose of this quick guide is to help new modelers who have little or no background in comparative modeling yet are keen to produce high-resolution protein 3D structures for their study by following systematic good modeling practices, using affordable personal computers or online computational resources. Through the available experimental 3D-structure repositories, the modeler should be able to access and use the atomic coordinates for building homology models. We also aim to provide the modeler with a rationale behind making a simple list of atomic coordinates suitable for computational analysis abiding to principles of physics (e.g., molecular mechanics). Keeping that objective in mind, these quick tips cover the process of homology modeling and some postmodeling computations such as molecular docking and molecular dynamics (MD). A brief section was left for modeling nonprotein molecules, and a short case study of homology modeling is discussed.
- MeSH
- Algorithms MeSH
- Amino Acids chemistry MeSH
- Models, Biological MeSH
- Databases, Protein MeSH
- Internet MeSH
- Ions MeSH
- Hydrogen-Ion Concentration MeSH
- Ligands MeSH
- Computer Simulation MeSH
- Protein Processing, Post-Translational MeSH
- Proteins chemistry MeSH
- Solvents MeSH
- Protein Folding MeSH
- Molecular Dynamics Simulation MeSH
- Molecular Docking Simulation MeSH
- Software MeSH
- Machine Learning MeSH
- Structural Homology, Protein MeSH
- Water MeSH
- Computational Biology methods MeSH
- Imaging, Three-Dimensional methods MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Names of Substances
- Amino Acids MeSH
- Ions MeSH
- Ligands MeSH
- Proteins MeSH
- Solvents MeSH
- Water MeSH
Pharmacophore modeling is a widely used strategy for finding new hit molecules. Since not all protein targets have available 3D structures, ligand-based approaches are still useful. Currently, there are just a few free ligand-based pharmacophore modeling tools, and these have a lot of restrictions, e.g., using a template molecule for alignment. We developed a new approach to 3D pharmacophore representation and matching which does not require pharmacophore alignment. This representation can be used to quickly find identical pharmacophores in a given set. Based on this representation, a 3D pharmacophore ligand-based modeling approach to search for pharmacophores which preferably match active compounds and do not match inactive ones was developed. The approach searches for 3D pharmacophore models starting from 2D structures of available active and inactive compounds. The implemented approach was successfully applied for several retrospective studies. The results were compared to a 2D similarity search, demonstrating some of the advantages of the developed 3D pharmacophore models. Also, the generated 3D pharmacophore models were able to match the 3D poses of known ligands from their protein-ligand complexes, confirming the validity of the models. The developed approach is available as an open-source software tool: http://www.qsar4u.com/pages/pmapper.php and https://github.com/meddwl/psearch.
- Keywords
- 3D pharmacophore hash, 3D pharmacophore signatures, ligand-based modeling, pharmacophore modeling,
- MeSH
- Adenosine A2 Receptor Antagonists chemistry MeSH
- Cholinesterase Inhibitors chemistry MeSH
- Cytochrome P-450 CYP3A Inhibitors chemistry MeSH
- Ligands MeSH
- Models, Molecular * MeSH
- Publication type
- Journal Article MeSH
- Names of Substances
- Adenosine A2 Receptor Antagonists MeSH
- Cholinesterase Inhibitors MeSH
- Cytochrome P-450 CYP3A Inhibitors MeSH
- Ligands MeSH
The objective of the studies was to synthesize and characterize new mono- and diesters with an imidazoquinolin-2-one ring with the use of 2,3-dihydro-2-thioxo-1H-imidazo[4 ,5-c]-quinolin-4(5H)-ones and ethyl bromoacetate. The products were isolated at high yield and characterized by instrumental methods (IR, 1H-, 13C-, and 15N- NMR, MS-ESI, HR-MS, EA). In order to clarify the places of substitution and the structure of the derivatives obtained, molecular modeling of substrates and products was performed. Consideration of the possible tautomeric structures of the substrates confirmed the existence only the most stable keto form. Based on the free energy of monosubstituted ester derivatives, the most stable form were derivatives substituted at sulfur atom of enolic form the used imidazoquinolones. Enolic form referred only to nitrogen atom no 1. The modeling results were consistent with the experimental data. The HOMO electron densities at selected atoms of each substrate has shown that the most reactive atom is sulfur atom. It explained the formation of monoderivatives substituted at sulfur atom. The diester derivatives of the used imidazoquinolones had second substituent at nitrogen atom no. 3. The new diesters can be used as raw material for synthesis of thermally stable polymers, and they can also have biological activity.
- Keywords
- 3-hydroxyquinolinediones, ammonium thiocyanate, debenzylation, molecular modeling, thioxoimidazoquinolinone ring,
- MeSH
- Quinolones chemical synthesis chemistry MeSH
- Esters chemistry MeSH
- Imidazoles chemistry MeSH
- Quantum Theory MeSH
- Molecular Conformation MeSH
- Models, Molecular * MeSH
- Thiocyanates chemistry MeSH
- Publication type
- Journal Article MeSH
- Names of Substances
- Quinolones MeSH
- Esters MeSH
- imidazole MeSH Browser
- Imidazoles MeSH
- thiocyanic acid MeSH Browser
- Thiocyanates MeSH
The most widely used QSAR approaches are mainly based on 2D molecular representation which ignores stereoconfiguration and conformational flexibility of compounds. 3D QSAR uses a single conformer of each compound which is difficult to choose reasonably. 4D QSAR uses multiple conformers to overcome the issues of 2D and 3D methods. However, many of existing 4D QSAR models suffer from the necessity to pre-align conformers, while alignment-independent approaches often ignore stereoconfiguration of compounds. In this study we propose a QSAR modeling approach based on transforming chirality-aware 3D pharmacophore descriptors of individual conformers into a set of latent variables representing the whole conformer set of a molecule. This is achieved by clustering together all conformers of all training set compounds. The final representation of a compound is a bit string encoding cluster membership of its conformers. In our study we used Random Forest, but this representation can be used in combination with any machine learning method. We compared this approach with conventional 2D and 3D approaches using multiple data sets and investigated the sensitivity of the approach proposed to tuning parameters: number of conformers and clusters.
Beta-site APP cleaving enzyme1 (BACE1) catalyzes the rate determining step in the generation of Aβ peptide and is widely considered as a potential therapeutic drug target for Alzheimer's disease (AD). Active site of BACE1 contains catalytic aspartic (Asp) dyad and flap. Asp dyad cleaves the substrate amyloid precursor protein with the help of flap. Currently, there are no marketed drugs available against BACE1 and existing inhibitors are mostly pseudopeptide or synthetic derivatives. There is a need to search for a potent inhibitor with natural scaffold interacting with flap and Asp dyad. This study screens the natural database InterBioScreen, followed by three-dimensional (3D) QSAR pharmacophore modeling, mapping, in silico ADME/T predictions to find the potential BACE1 inhibitors. Further, molecular dynamics of selected inhibitors were performed to observe the dynamic structure of protein after ligand binding. All conformations and the residues of binding region were stable but the flap adopted a closed conformation after binding with the ligand. Bond oligosaccharide interacted with the flap as well as catalytic dyad via hydrogen bond throughout the simulation. This led to stabilize the flap in closed conformation and restricted the entry of substrate. Carbohydrates have been earlier used in the treatment of AD because of their low toxicity, high efficiency, good biocompatibility, and easy permeability through the blood-brain barrier. Our finding will be helpful in identify the potential leads to design novel BACE1 inhibitors for AD therapy.
- Keywords
- 3D QSAR pharmacophore modeling, Alzheimer’s disease, Asp dyad, flap, molecular dynamics, oligosaccharide, virtual screening, β-secretase,
- MeSH
- Algorithms MeSH
- Aspartic Acid Endopeptidases antagonists & inhibitors metabolism MeSH
- Biological Products chemistry pharmacology MeSH
- Enoxaparin pharmacology MeSH
- Heparitin Sulfate pharmacology MeSH
- Inhibitory Concentration 50 MeSH
- Enzyme Inhibitors chemistry pharmacology MeSH
- Crystallography, X-Ray MeSH
- Quantitative Structure-Activity Relationship * MeSH
- Humans MeSH
- Ligands MeSH
- Oligosaccharides chemistry MeSH
- Drug Evaluation, Preclinical * MeSH
- Amyloid Precursor Protein Secretases antagonists & inhibitors metabolism MeSH
- Molecular Dynamics Simulation * MeSH
- Hydrogen Bonding MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Names of Substances
- Aspartic Acid Endopeptidases MeSH
- BACE1 protein, human MeSH Browser
- Biological Products MeSH
- Enoxaparin MeSH
- Heparitin Sulfate MeSH
- Enzyme Inhibitors MeSH
- Ligands MeSH
- Oligosaccharides MeSH
- Amyloid Precursor Protein Secretases MeSH
The acid dissociation constant is an important molecular property, and it can be successfully predicted by Quantitative Structure-Property Relationship (QSPR) models, even for in silico designed molecules. We analyzed how the methodology of in silico 3D structure preparation influences the quality of QSPR models. Specifically, we evaluated and compared QSPR models based on six different 3D structure sources (DTP NCI, Pubchem, Balloon, Frog2, OpenBabel, and RDKit) combined with four different types of optimization. These analyses were performed for three classes of molecules (phenols, carboxylic acids, anilines), and the QSPR model descriptors were quantum mechanical (QM) and empirical partial atomic charges. Specifically, we developed 516 QSPR models and afterward systematically analyzed the influence of the 3D structure source and other factors on their quality. Our results confirmed that QSPR models based on partial atomic charges are able to predict pKa with high accuracy. We also confirmed that ab initio and semiempirical QM charges provide very accurate QSPR models and using empirical charges based on electronegativity equalization is also acceptable, as well as advantageous, because their calculation is very fast. On the other hand, Gasteiger-Marsili empirical charges are not applicable for pKa prediction. We later found that QSPR models for some classes of molecules (carboxylic acids) are less accurate. In this context, we compared the influence of different 3D structure sources. We found that an appropriate selection of 3D structure source and optimization method is essential for the successful QSPR modeling of pKa. Specifically, the 3D structures from the DTP NCI and Pubchem databases performed the best, as they provided very accurate QSPR models for all the tested molecular classes and charge calculation approaches, and they do not require optimization. Also, Frog2 performed very well. Other 3D structure sources can also be used but are not so robust, and an unfortunate combination of molecular class and charge calculation approach can produce weak QSPR models. Additionally, these 3D structures generally need optimization in order to produce good quality QSPR models.
- MeSH
- Chemical Phenomena * MeSH
- Quantitative Structure-Activity Relationship * MeSH
- Quantum Theory MeSH
- Molecular Conformation * MeSH
- Models, Molecular * MeSH
- Computer Simulation MeSH
- Drug Design MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Research Support, N.I.H., Extramural MeSH
In this study, we have carried out a combined experimental and computational investigation to elucidate several bred-in-the-bone ideas standing out in rational design of novel cationic surfactants as antibacterial agents. Five 3-hydroxypyridinium salts differing in the length of N-alkyl side chain have been synthesized, analyzed by high performance liquid chromatography, tested for in vitro activity against a panel of pathogenic bacterial and fungal strains, computationally modeled in water by a SCRF B3LYP/6-311++G(d,p) method, and evaluated by a systematic QSAR analysis. Given the results of this work, the hypothesis suggesting that higher positive charge of the quaternary nitrogen should increase antimicrobial efficacy can be rejected since 3-hydroxyl group does increase the positive charge on the nitrogen but, simultaneously, it significantly derogates the antimicrobial activity by lowering the lipophilicity and by escalating the desolvation energy of the compounds in comparison with non-hydroxylated analogues. Herein, the majority of the prepared 3-hydroxylated substances showed notably lower potency than the parent pyridinium structures, although compound 8 with C12 alkyl chain proved a distinctly better antimicrobial activity in submicromolar range. Focusing on this anomaly, we have made an effort to reveal the reason of the observed activity through a molecular dynamics simulation of the interaction between the bacterial membrane and compound 8 in GROMACS software.
- Keywords
- Antimicrobials, Molecular dynamics, Molecular modeling, QSAR, Quaternary ammoniums salts, Surfactants,
- MeSH
- Anti-Bacterial Agents chemistry pharmacology toxicity MeSH
- Bacteria drug effects MeSH
- CHO Cells MeSH
- Cricetulus MeSH
- Fungi drug effects MeSH
- Hydrophobic and Hydrophilic Interactions MeSH
- Cricetinae MeSH
- Quantitative Structure-Activity Relationship * MeSH
- Molecular Conformation MeSH
- Pyridines chemistry pharmacology toxicity MeSH
- Molecular Dynamics Simulation * MeSH
- Cell Survival drug effects MeSH
- Animals MeSH
- Check Tag
- Cricetinae MeSH
- Animals MeSH
- Publication type
- Journal Article MeSH
- Names of Substances
- 3-hydroxypyridine MeSH Browser
- Anti-Bacterial Agents MeSH
- Pyridines MeSH
A novel series of 7-methoxytacrine (7-MEOTA)-donepezil like compounds was synthesized and tested for their ability to inhibit electric eel acetylcholinesterase (EeAChE), human recombinant AChE (hAChE), equine serum butyrylcholinesterase (eqBChE) and human plasmatic BChE (hBChE). New hybrids consist of a 7-MEOTA unit, representing less toxic tacrine (THA) derivative, connected with analogues of N-benzylpiperazine moieties mimicking N-benzylpiperidine fragment from donepezil. 7-MEOTA-donepezil like compounds exerted mostly non-selective profile in inhibiting cholinesterases of different origin with IC50 ranging from micromolar to sub-micromolar concentration scale. Kinetic analysis confirmed mixed-type inhibition presuming that these inhibitors are capable to simultaneously bind peripheral anionic site (PAS) as well as catalytic anionic site (CAS) of AChE. Molecular modeling studies and QSAR studies were performed to rationalize studies from in vitro. Overall, 7-MEOTA-donepezil like derivatives can be considered as interesting candidates for Alzheimer's disease treatment.
- Keywords
- 7-MEOTA, AChE/BChE inhibitors, Alzheimer's disease, Molecular modeling, QSAR, Tacrine,
- MeSH
- Acetylcholinesterase metabolism MeSH
- Butyrylcholinesterase blood metabolism MeSH
- Cholinesterase Inhibitors chemical synthesis chemistry pharmacology MeSH
- Donepezil MeSH
- Electrophorus MeSH
- Indans chemistry pharmacology MeSH
- Horses MeSH
- Quantitative Structure-Activity Relationship * MeSH
- Humans MeSH
- Models, Molecular MeSH
- Molecular Structure MeSH
- Piperidines chemistry pharmacology MeSH
- Recombinant Proteins metabolism MeSH
- Tacrine analogs & derivatives chemistry pharmacology MeSH
- Dose-Response Relationship, Drug MeSH
- Animals MeSH
- Check Tag
- Humans MeSH
- Animals MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Names of Substances
- 7-methoxytacrine MeSH Browser
- Acetylcholinesterase MeSH
- Butyrylcholinesterase MeSH
- Cholinesterase Inhibitors MeSH
- Donepezil MeSH
- Indans MeSH
- Piperidines MeSH
- Recombinant Proteins MeSH
- Tacrine MeSH
A set of 4-benzylsulfanyl derivatives of pyridine-2-carbonitriles and pyridine-2-carbothioamides, previously tested for their antimycobacterial activity, were analysed by quantitative structure-activity relationship (QSAR) techniques, using some physicochemical and quantum-chemical parameters. The resulting QSAR revealed that the activity increases with electron withdrawing substituents in the benzyl moiety of studied compounds. HOMO orbitals can play an important role in the description of the mechanism of interactions at the molecular level. Additionally, the results of multiple linear regression indicate the differences between Mycobacterium tuberculosis and M. avium. The hydrophobicity of studied compounds is important for activity against M. avium.
- MeSH
- Anti-Infective Agents pharmacology MeSH
- Quantitative Structure-Activity Relationship * MeSH
- Models, Molecular MeSH
- Pyridines pharmacology MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Names of Substances
- Anti-Infective Agents MeSH
- Pyridines 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.
