Reconnoitering imidazopyridazines as anticancer agents based on virtual modelling approach: quantitative structure activity relationship, molecular docking and molecular dynamics

Cancer is an unimpeded growth of cells leading to metathesis of cancer and eventually spread throughout the body. PIM kinases are the members of the serine threonine kinase playing role in cancer progression, differentiation and proliferation. Till date there is no single drug targeting PIM-1 kinase in the market, that has made itself a target in limelight for the discover of new anticancer agents. The contemporary research focusses on the development of new inhibitors of PIM-1 kinase by application of ligand-based and structure-based perspective of drug discovery namely 3D-QSAR, molecular docking and dynamics. The following study stated the correlation amid structural and biological activity of the compounds employing 3D-QSAR analysis. Three 3D-QSAR models were generated using 33 molecules from which the excellent model stated an encouraging conventional correlation coefficient (r2) 0.8651, cross validation coefficient (q2) 0.7609. Furthermore, the predicted correlation coefficient (r2 pred) 0.6274, respectively. Molecular docking studies revealed that the most active compound 26 resided in the active pocket of PIM-1 kinase establishing hydrogen bond interactions with Asp186 in the DFG motif; similarly, all other molecules were engaged within the active site of the PIM-1 kinase. Moreover, molecular dynamics simulation study stated the stability of the ligand in the active site of PIM-1 kinase protein by developing two hydrogen bonds throughout the trajectory of 100 ns. In nutshell, the output stated the successful application of ligand and structure-based strategy for the development of novel PIM-1 kinase inhibitors as anticancer agents.Communicated by Ramaswamy H. Sarma.

A potential implication of UDP-glucuronosyltransferase 2B10 in the detoxification of drugs used in pediatric hematopoietic stem cell transplantation setting: an in silico investigation

Background

Sinusoidal occlusion syndrome (SOS) is a potentially severe complication following hematopoietic stem cell transplantation (HSCT) in pediatric patients. Treatment related risk factors such as intensity of conditioning, hepatotoxic co-medication and patient related factors such as genetic variants predispose individuals to develop SOS. The variant allele for SNP rs17146905 in UDP-glucuronosyl transferase 2B10 (UGT2B10) gene was correlated with the occurrence of SOS in an exome-wide association study. UGT2B10 is a phase II drug metabolizing enzyme involved in the N-glucuronidation of tertiary amine containing drugs.

Methods

To shed light on the functionality of UGT2B10 enzyme in the metabolism of drugs used in pediatric HSCT setting, we performed in silico screening against custom based library of putative ligands. First, a list of potential substrates for in silico analysis was prepared using a systematic consensus-based strategy. The list comprised of drugs and their metabolites used in pediatric HSCT setting. The three-dimensional structure of UGT2B10 was not available from the Research Collaboratory Structural Bioinformatics - Protein Data Bank (RCSB - PDB) repository and thus we predicted the first human UGT2B10 3D model by using multiple template homology modeling with MODELLER Version 9.2 and molecular docking calculations with AutoDock Vina Version 1.2 were implemented to quantify the estimated binding affinity between selected putative substrates or ligands and UGT2B10. Finally, we performed molecular dynamics simulations using GROMACS Version 5.1.4 to confirm the potential UGT2B10 ligands prioritized after molecular docking (exhibiting negative free binding energy).

Results

Four potential ligands for UGT2B10 namely acetaminophen, lorazepam, mycophenolic acid and voriconazole n-oxide intermediate were identified. Other metabolites of voriconazole satisfied the criteria of being possible ligands of UGT2B10. Except for bilirubin and 4-Hydroxy Voriconazole, all the ligands (particularly voriconazole and hydroxy voriconazole) are oriented in substrate binding site close to the co-factor UDP (mean ± SD; 0.72 ± 0.33 nm). Further in vitro screening of the putative ligands prioritized by in silico pipeline is warranted to understand the nature of the ligands either as inhibitors or substrates of UGT2B10.

Conclusions

These results may indicate the clinical and pharmacological relevance UGT2B10 in pediatric HSCT setting. With this systematic computational methodology, we provide a rational-, time-, and cost-effective way to identify and prioritize the interesting putative substrates or inhibitors of UGT2B10 for further testing in in vitro experiments.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12860-021-00402-5.

Multi-scale simulation reveals that an amino acid substitution increases photosensitizing reaction inputs in Rhodopsins

Evaluating the availability of molecular oxygen (O₂ ) and energy of excited states in the retinal binding site of rhodopsin is a crucial challenging first step to understand photosensitizing reactions in wild-type (WT) and mutant rhodopsins by absorbing visible light. In the present work, energies of the ground and excited states related to 11-cis-retinal and the O₂ accessibility to the β-ionone ring are evaluated inside WT and human M207R mutant rhodopsins. Putative O₂ pathways within rhodopsins are identified by using molecular dynamics simulations, Voronoi-diagram analysis, and implicit ligand sampling while retinal energetic properties are investigated through density functional theory, and quantum mechanical/molecular mechanical methods. Here, the predictions reveal that an amino acid substitution can lead to enough energy and O₂ accessibility in the core hosting retinal of mutant rhodopsins to favor the photosensitized singlet oxygen generation, which can be useful in understanding retinal degeneration mechanisms and in designing blue-lighting-absorbing proteic photosensitizers.

Toward Ab Initio Protein Folding: Inherent Secondary Structure Propensity of Short Peptides from the Bioinformatics and Quantum-Chemical Perspective

By combining bioinformatics with quantum-chemical calculations, we attempt to address quantitatively some of the physical principles underlying protein folding. The former allowed us to identify tripeptide sequences in existing protein three-dimensional structures with a strong preference for either helical or extended structure. The selected representatives of pro-helical and pro-extended sequences were converted into "isolated" tripeptides-capped at N- and C-termini-and these were subjected to an extensive conformational sampling and geometry optimization (typically thousands to tens of thousands of conformers for each tripeptide). For each conformer, the QM(DFT-D3)/COSMO-RS free-energy value was then calculated, G_conf(solv). The Δ G_conf(solv) is expected to provide an objective, unbiased, and quantitatively accurate measure of the conformational preference of the particular tripeptide sequence. It has been shown that irrespective of the helical vs extended preferences of the selected tripeptide sequences in context of the protein, most of the low-energy conformers of isolated tripeptides prefer the R-helical structure. Nevertheless, pro-helical tripeptides show slightly stronger helix preference than their pro-extended counterparts. Furthermore, when the sampling is repeated in the presence of a partner tripeptide to mimic the situation in a β-sheet, pro-extended tripeptides (exemplified by the VIV) show a larger free-energy benefit than pro-helical tripeptides (exemplified by the EAM). This effect is even more pronounced in a hydrophobic solvent, which mimics the less polar parts of a protein. This is in line with our bioinformatic results showing that the majority of pro-extended tripeptides are hydrophobic. The preference for a specific secondary structure by the studied tripeptides is thus governed by the plasticity to adopt to its environment. In addition, we show that most of the "naturally occurring" conformations of tripeptide sequences, i.e., those found in existing three-dimensional protein structures, are within ∼10 kcal·mol^-1 from their global minima. In summary, our "ab initio" data suggest that complex protein structures may start to emerge already at the level of their small oligopeptidic units, which is in line with a hierarchical nature of protein folding.

Computational Studies of Snake Venom Toxins

Most snake venom toxins are proteins, and participate to envenomation through a diverse array of bioactivities, such as bleeding, inflammation, and pain, cytotoxic, cardiotoxic or neurotoxic effects. The venom of a single snake species contains hundreds of toxins, and the venoms of the 725 species of venomous snakes represent a large pool of potentially bioactive proteins. Despite considerable discovery efforts, most of the snake venom toxins are still uncharacterized. Modern bioinformatics tools have been recently developed to mine snake venoms, helping focus experimental research on the most potentially interesting toxins. Some computational techniques predict toxin molecular targets, and the binding mode to these targets. This review gives an overview of current knowledge on the ~2200 sequences, and more than 400 three-dimensional structures of snake toxins deposited in public repositories, as well as of molecular modeling studies of the interaction between these toxins and their molecular targets. We also describe how modern bioinformatics have been used to study the snake venom protein phospholipase A2, the small basic myotoxin Crotamine, and the three-finger peptide Mambalgin.

An overview of comparative modelling and resources dedicated to large-scale modelling of genome sequences

Computational modelling of proteins has been a major catalyst in structural biology. Bioinformatics groups have exploited the repositories of known structures to predict high-quality structural models with high efficiency at low cost. This article provides an overview of comparative modelling, reviews recent developments and describes resources dedicated to large-scale comparative modelling of genome sequences. The value of subclustering protein domain superfamilies to guide the template-selection process is investigated. Some recent cases in which structural modelling has aided experimental work to determine very large macromolecular complexes are also cited.

Natural Products as New Treatment Options for Trichomoniasis: A Molecular Docking Investigation

Trichomoniasis, caused by the parasitic protozoan Trichomonas vaginalis, is the most common non-viral sexually-transmitted disease, and there can be severe complications from trichomoniasis. Antibiotic resistance in T. vaginalis is increasing, but there are currently no alternatives treatment options. There is a need to discover and develop new chemotherapeutic alternatives. Plant-derived natural products have long served as sources for new medicinal agents, as well as new leads for drug discovery and development. In this work, we have carried out an in silico screening of 952 antiprotozoal phytochemicals with specific protein drug targets of T. vaginalis. A total of 42 compounds showed remarkable docking properties to T. vaginalis methionine gamma-lyase (TvMGL) and to T. vaginalis purine nucleoside phosphorylase (TvPNP). The most promising ligands were polyphenolic compounds, and several of these showed docking properties superior to either co-crystallized ligands or synthetic enzyme inhibitors.

Global informatics and physical property selection in protein sequences

The degree of informatic independence between the physical properties of amino acids as encoded in actual protein sequences is calculated. It is shown that no physical property can be identified that carries significantly less information than others and that the information overlap between different properties and different length scales along the sequence is essentially zero. These observations suggest that bioinformatic models based on arbitrarily selected sets of physical properties are inherently deficient.

Homolog detection using global sequence properties suggests an alternate view of structural encoding in protein sequences

We show that a Fourier-based sequence distance function is able to identify structural homologs of target sequences with high accuracy. It is shown that Fourier distances correlate very strongly with independently determined structural distances between molecules, a property of the method that is not attainable using conventional representations. It is further shown that the ability of the Fourier approach to identify protein folds is statistically far in excess of random expectation. It is then shown that, in actual searches for structural homologs of selected target sequences, the Fourier approach gives excellent results. On the basis of these results, we suggest that the global information detected by the Fourier representation is an essential feature of structure encoding in protein sequences and a key to structural homology detection.