Identification of Catechins' Binding Sites in Monomeric Aß42 through Ensemble Docking and MD Simulations.

The assembly of the amyloid-ß peptide (Aß) into toxic oligomers and fibrils is associated with Alzheimer's disease and dementia. Therefore, disrupting amyloid assembly by direct targeting of the Aß monomeric form with small molecules or antibodies is a promising therapeutic strategy. However, given the dynamic nature of Aß, standard computational tools cannot be easily applied for high-throughput structure-based virtual screening in drug discovery projects. In the current study, we propose a computational pipeline-in the framework of the ensemble docking strategy-to identify catechins' binding sites in monomeric Aß42. It is shown that both hydrophobic aromatic interactions and hydrogen bonding are crucial for the binding of catechins to Aß42. Additionally, it has been found that all the studied ligands, especially EGCG, can act as potent inhibitors against amyloid aggregation by blocking the central hydrophobic region of Aß. Our findings are evaluated and confirmed with multi-microsecond MD simulations. Finally, it is suggested that our proposed pipeline, with low computational cost in comparison with MD simulations, is a suitable approach for the virtual screening of ligand libraries against Aß.

Identification of key stabilizing interactions of amyloid-ß oligomers based on fragment molecular orbital calculations on macrocyclic ß-hairpin peptides.

Analyzing the electronic states and inter-/intra-molecular interactions of amyloid oligomers expand our understanding of the molecular basis of Alzheimer's disease and other amyloid diseases. In the current study, several high-resolution crystal structures of oligomeric assemblies of Aß-derived peptides have been studied by the ab initio fragment molecular orbital (FMO) method. The FMO method provides comprehensive details of the molecular interactions between the residues of the amyloid oligomers at the quantum mechanical level. Based on the calculations, two sequential aromatic residues (F19 and F20) and negatively charged E22 on the central region of Aß have been identified as key residues in oligomer stabilization and potential interesting pharmacophores for preventing oligomer formation.

Structural insights into the substrate-binding site of main protease for the structure-based COVID-19 drug discovery.

An attractive drug target to combat COVID-19 is the main protease (Mpro ) because of its key role in the viral life cycle by processing the polyproteins translated from the viral RNA. Studying the crystal structures of the protease is important to enhance our understanding of its mechanism of action at the atomic-level resolution, and consequently may provide crucial structural insights for structure-based drug discovery. In the current study, we report a comparative structural analysis of the Mpro substrate binding site for both apo and holo forms to identify key interacting residues and conserved water molecules during the ligand-binding process. It is shown that in addition to the catalytic dyad residues (His41 and Cys145), the oxyanion hole residues (Asn142-Ser144) and residues His164-Glu166 form essential parts of the substrate-binding pocket of the protease in the binding process. Furthermore, we address the issue of the substrate-binding pocket flexibility and show that two adjacent loops in the Mpro structures (residues Thr45-Met49 and Arg188-Ala191) with high flexibility can regulate the binding cavity' accessibility for different ligand sizes. Moreover, we discuss in detail the various structural and functional roles of several important conserved and mobile water molecules within and around the binding site in the proper enzymatic function of Mpro . We also present a new docking protocol in the framework of the ensemble docking strategy. The performance of the docking protocol has been evaluated in predicting ligand binding pose and affinity ranking for two popular docking programs; AutoDock4 and AutoDock Vina. Our docking results suggest that the top-ranked poses of the most populated clusters obtained by AutoDock Vina are the most important representative docking runs that show a very good performance in estimating experimental binding poses and affinity ranking.

Electrostatically induced pKa shifts in oligopeptides: the upshot of neighboring side chains.

pKa values of homorepeat hexapeptides with a 2,3-diazabicyclo[2.2.2]oct-2-ene (DBO) chromophore attached at the peptide C termini, through an asparagine derivative (Dbo), namely His6-Dbo (H6), Lys6-Dbo (K6), and Arg6-Dbo (R6), were determined by a novel fluorescence-based method. The fluorescence lifetime of Dbo in the peptides (τ) was measured as a function of pH. The side chains collide with Dbo intramolecularly and quench it efficiently only when they are deprotonated (i.e., pH ≥ side chain pKa). The pKa values of the H6, K6, and R6 peptides, attributable to side chain ionization, were found to be depressed compared to the pKa values of the His, Lys, and Arg residues in their free amino acid forms. We further looked into the structural changes of the peptides by molecular dynamics (MD) simulations; the peptides were structurally more expanded when their side chains are protonated. The structural expansion of the peptides reflects an electrostatic repulsion between the protonated side chain residues, which also accounts for the observed decrease in pKa values, which corresponds to a facilitated deprotonation, assisted by electrostatic repulsion.

Histidine substitution in the most flexible fragments of firefly luciferase modifies its thermal stability.

Molecular dynamics (MD) at two temperatures of 300 and 340 K identified two histidine residues, His461 and His489, in the most flexible regions of firefly luciferase, a light emitting enzyme. We therefore designed four protein mutants H461D, H489K, H489D and H489M to investigate their enzyme kinetic and thermodynamic stability changes. Substitution of His461 by aspartate (H461D) decreased ATP binding affinity, reduced the melting temperature of protein by around 25 °C and shifted its optimum temperature of activity to 10 °C. In line with the common feature of psychrophilic enzymes, the MD data showed that the overall flexibility of H461D was relatively high at low temperature, probably due to a decrease in the number of salt bridges around the mutation site. On the other hand, substitution of His489 by aspartate (H489D) introduced a new salt bridge between the C-terminal and N-terminal domains and increased protein rigidity but only slightly improved its thermal stability. Similar changes were observed for H489K and, to a lesser degree, H489M mutations. Based on our results we conclude that the MD simulation-based rational substitution of histidines by salt-bridge forming residues can modulate conformational dynamics in luciferase and shift its optimal temperature activity.

Seeking Extremes in Molecular Design: To What Extent May Two "Non-Bonded" Hydrogen Atoms be Squeezed in a Hydrocarbon?

A series of novel and possibly synthetically accessible rigid hydrocarbon structures is computationally introduced, maintaining ultrashort non-bonded hydrogen-hydrogen (H⋅⋅⋅H) contacts smaller than 1.2 Å. These are the shortest non-bonded H⋅⋅⋅H contacts reported to date, bypassing previous world records of both experimentally observed, 1.56 Å, and computationally derived, 1.4 Å, H⋅⋅⋅H contacts in any stable molecular structure.

Description of heteroaromaticity on the basis of π-electron density anisotropy.

It is demonstrated that there is a direct connection between aromaticity and the anisotropy of the π-electron density on planes parallel to the molecular ring. The electron density anisotropy on the plane is measured through the ratio of the in-plane Hessian eigenvalues associated with the eigenvectors lying in the plane. Computations on a wide-ranging set of well-characterized monocyclic systems containing heteroatoms validate the correlation between this one-electron density-based descriptor and aromaticity; in aromatic compounds, the in-plane Hessian eigenvalues are degenerate (or near degenerate) and the anisotropy of the π-electron density is undirected, whereas the results for antiaromatic rings are reversed and the degeneracy of the eigenvalues completely disappears. This finding is in line with our very recent study on [n]annulenes and provides further evidence that the anisotropy of the π-electron density should be considered as a new manifestation of aromaticity.

Improving protein-ligand docking results using the Semiempirical quantum mechanics: testing on the PDBbind 2016 core set.

Molecular docking techniques are routinely employed for predicting ligand binding conformations and affinities in the in silico phase of the drug design and development process. In this study, a reliable semiempirical quantum mechanics (SQM) method, PM7, was employed for geometry optimization of top-ranked poses obtained from two widely used docking programs, AutoDock4 and AutoDock Vina. The PDBbind core set (version 2016), which contains high-quality crystal protein - ligand complexes with their corresponding experimental binding affinities, was used as an initial dataset in this research. It was shown that docking pose optimization improves the accuracy of pose predictions and is very useful for the refinement of docked complexes via removing clashes between ligands and proteins. It was also demonstrated that AutoDock Vina achieves a higher sampling power than AutoDock4 in generating accurate ligand poses (RMSD ≤ 2.0 Å), while AutoDock4 exhibits a better ranking power than AutoDock Vina. Finally, a new protocol based on a combination of the results obtained from the two docking programs was proposed for structure-based virtual screening studies, which benefits from the robust sampling abilities of AutoDock Vina and the reliable ranking performance of AutoDock4.Communicated by Ramaswamy H. Sarma.

GB-score: Minimally designed machine learning scoring function based on distance-weighted interatomic contact features.

In recent years, thanks to advances in computer hardware and dataset availability, data-driven approaches (like machine learning) have become one of the essential parts of the drug design framework to accelerate drug discovery procedures. Constructing a new scoring function, a function that can predict the binding score for a generated protein-ligand pose during docking procedure or a crystal complex, based on machine and deep learning has become an active research area in computer-aided drug design. GB-Score is a state-of-the-art machine learning-based scoring function that utilizes distance-weighted interatomic contact features, PDBbind-v2019 general set, and Gradient Boosting Trees algorithm to the binding affinity prediction. The distance-weighted interatomic contact featurization method used the distance between different ligand and protein atom types for numerical representation of the protein-ligand complex. GB-Score attains Pearson's correlation 0.862 and RMSE 1.190 on the CASF-2016 benchmark test in the scoring power metric. GB-Score's codes are freely available on the web at https://github.com/miladrayka/GB_Score.

Constructing conformational library for amyloid-ß42 dimers as the smallest toxic oligomers using two CHARMM force fields.

The study of amyloid-ß (Aß) dimers as the smallest toxic aggregates in the human brain suffering from Alzheimer's disease is of great interest. The structural characterization of the dimers, which is important to rationally design inhibitors for Aß dimerization, is limited by the low stability of these species and their high tendency to aggregate into protofibrils and amyloid fibrils. Therefore, an efficient sampling method is needed for the computational study of the Aß dimers. In this regard, we build a conformational library of the Aß42 dimers by a new computational protocol; the blockwise excursion sampling (BES); with the CHARMM27 and CHARMM36m force fields. The CHARMM27 overestimates helix content and underestimates ß-sheet content, while secondary structure content for the dimers sampled by the CHARMM36m force field is in reasonably consistent with the circular dichroism data. The CHARMM36m force field also generates more Aß42 dimers in line with experimentally measured collision cross sections values relative to the CHARMM27 force field. Our results demonstrate that the BES is an efficient protocol for fast generating a heterogeneous conformational library of the Aß42 dimers in agreement with experimental data. Having a reliable structural library for the Aß42 dimers is very important to identify binding "hot spots" of the dimers versus potential drug candidates using ensemble docking approach.

Interactions of Curcumin's Degradation Products with the Aß42 Dimer: A Computational Study.

Amyloid-ß (Aß) dimers are the smallest toxic species along the amyloid-aggregation pathway and among the most populated oligomeric accumulations present in the brain affected by Alzheimer's disease (AD). A proposed therapeutic strategy to avoid the aggregation of Aß into higher-order structures is to develop molecules that inhibit the early stages of aggregation, i.e., dimerization. Under physiological conditions, the Aß dimer is highly dynamic and does not attain a single well-defined structure but is rather characterized by an ensemble of conformations. In a recent study, a highly heterogeneous library of conformers of the Aß dimer was generated by an efficient sampling method with constraints based on ion mobility mass spectrometry data. Here, we make use of the Aß dimer library to study the interaction with two curcumin degradation products, ferulic aldehyde and vanillin, by molecular dynamics (MD) simulations. Ensemble docking and MD simulations are used to provide atomistic detail of the interactions between the curcumin degradation products and the Aß dimer. The simulations show that the aromatic residues of Aß, and in particular 19FF20, interact with ferulic aldehyde and vanillin through π-π stacking. The binding of these small molecules induces significant changes on the 16KLVFF20 region.

Ensemble learning from ensemble docking: revisiting the optimum ensemble size problem.

Despite considerable advances obtained by applying machine learning approaches in protein-ligand affinity predictions, the incorporation of receptor flexibility has remained an important bottleneck. While ensemble docking has been used widely as a solution to this problem, the optimum choice of receptor conformations is still an open question considering the issues related to the computational cost and false positive pose predictions. Here, a combination of ensemble learning and ensemble docking is suggested to rank different conformations of the target protein in light of their importance for the final accuracy of the model. Available X-ray structures of cyclin-dependent kinase 2 (CDK2) in complex with different ligands are used as an initial receptor ensemble, and its redundancy is removed through a graph-based redundancy removal, which is shown to be more efficient and less subjective than clustering-based representative selection methods. A set of ligands with available experimental affinity are docked to this nonredundant receptor ensemble, and the energetic features of the best scored poses are used in an ensemble learning procedure based on the random forest method. The importance of receptors is obtained through feature selection measures, and it is shown that a few of the most important conformations are sufficient to reach 1 kcal/mol accuracy in affinity prediction with considerable improvement of the early enrichment power of the models compared to the different ensemble docking without learning strategies. A clear strategy has been provided in which machine learning selects the most important experimental conformers of the receptor among a large set of protein-ligand complexes while simultaneously maintaining the final accuracy of affinity predictions at the highest level possible for available data. Our results could be informative for future attempts to design receptor-specific docking-rescoring strategies.

ET-score: Improving Protein-ligand Binding Affinity Prediction Based on Distance-weighted Interatomic Contact Features Using Extremely Randomized Trees Algorithm.

The molecular docking simulation is a key computational tool in modern drug discovery research that its predictive performance strongly depends on the employed scoring functions. Many recent studies have shown that the application of machine learning algorithms in the development of scoring functions has led to a significant improvement in docking performance. In this work, we introduce a new machine learning (ML) based scoring function called ET-Score, which employs the distance-weighted interatomic contacts between atom type pairs of the ligand and the protein for featurizing protein-ligand complexes and Extremely Randomized Trees algorithm for the training process. The performance of ET-Score is compared with some successful ML-based scoring functions and several popular classical scoring functions on the PDBbind 2016v core set. It is shown that our ET-Score model (with Pearson's correlation of 0.827 and RMSE of 1.332) achieves very good performance in comparison with most of the ML-based scoring functions and all classical scoring functions despite its extremely low computational cost. ET-Score's codes are freely available on the web at https://github.com/miladrayka/ET_Score.

Using the Semiempirical Quantum Mechanics in Improving the Molecular Docking: A Case Study with CDK2.

In this study, we use some modified semiempirical quantum mechanics (SQM) methods for improving the molecular docking process. To this end, the three popular SQM Hamiltonians, PM6, PM6-D3H4X, and PM7 are employed for geometry optimization of some binding modes of ligands docked into the human cyclin-dependent kinase 2 (CDK2) by two widely used docking tools, AutoDock and AutoDock Vina. The results were analyzed with two different evaluation metrics: the symmetry-corrected heavy-atom RMSD and the fraction of recovered ligand-protein contacts. It is shown that the evaluation of the fraction of recovered contacts is more useful to measure the similarity between two structures when interacting with a protein. It was also found that AutoDock is more successful than AutoDock Vina in producing the correct ligand poses (RMSD≤2.0 Å) and ranking of the poses. It is also demonstrated that the ligand optimization at the SQM level improves the docking results and the SQM structures have a significantly better fit to the observed crystal structures. Finally, the SQM optimizations reduce the number of close contacts in the docking poses and successfully remove most of the clash or bad contacts between ligand and protein.

A highly sensitive "ON-OFF" optical sensor for the selective detection of cyanide ions in 100% aqueous solutions based on hydrogen bonding and water assisted aggregation induced emission.

Nanoparticles N,N'-(pyridine-2,6-diyl)bis(2-(2,4-dichlorophenoxy)acetamide) (1) exhibited an "on-off" emission response toward cyanide (CN-) ions in 100% aqueous solutions based on AIE features. AIEgen 1 is an easy-to-use probe that exhibits rapid response (5 s), extremely high sensitivity (limit of detection = 8.2 nM) and excellent selectivity. The sensing performance of CN- through a test kit and bitter seed solutions was good. The experimental results show that compound 1 is planar and can self-assemble into a supramolecular system and show blue emission. Then, CN- destroys both hydrogen bonds and the aggregates of 1 and quenches the emission. This process is reversible upon the addition of HCl solution.

Efficient construction of a diverse conformational library for amyloid-ß as an intrinsically disordered protein.

Structural characterization of intrinsically disordered proteins (IDPs) is paramount and challenging in structural biology. In this regard, a de novo computational protocol is introduced to build heterogeneous structural libraries for amyloid-ß (Aß) as a critical IDP. This method combines the strength of the simulated annealing - in jumping over energy barriers and escaping from traps - with short conventional molecular dynamics simulations to quickly explore local regions of the conformational space. The protocol efficiency and reliability in building Aß conformational library is compared with two widely used simulation methods, replica exchange molecular dynamics and multiple trajectory sampling. The probability distribution functions of various structural and energetic features are constructed for each library, and also the diversity and convergence rates in these protocols were compared. Our results show that the suggested protocol is a successful computational method in the generation of a diverse conformational library of the Aß monomer in agreement with experimental data. This method focuses on visiting more conformations in less computational time without paying attention to the statistical weight of each state in the library. We believe that the suggested computational technique can be used for generating a reasonable starting pool for subsequent reweighting with experimental data to obtain a statistical ensemble.

Regioselectivity in Sonogashira synthesis of 6-(4-nitrobenzyl)-2-phenylthiazolo[3,2-b]1,2,4-triazole: a quantum chemistry study.

In the present research, the experimentally observed regioselectivity in Sonogashira synthesis of 6-(4-nitrobenzyl)-2-phenylthiazolo[3,2-b]1,2,4triazole has been modeled by means of density functional theory (DFT) employed to investigate the structural and thermochemical aspects of this synthesis in the gas and solution phases. Comparison of our calculated structural parameters of the title compound with the available X-ray crystallographical data demonstrate a reliable agreement. Then, the effect of two different solvents, DMF and ethanol, are examined via polarized continuum model calculations, showing a significant decrease in the computed values of the reaction enthalpy and free energy changes compared with the gas phase results. We have also considered two tautomeric structures of the intermediate species that it seems the mode of its intermolecular cyclization has an important role in regioselectivity of the final products. Moreover, all obtained results in the gas and solution phases also confirm that the synthesis of the title compound is thermodynamically more favorable than the other regioisomeric product. We also discuss the thermodynamical feasibility of this reaction at higher temperatures. Finally, we concentrate on the survey of substituent effect by choosing electron-withdrawing and electron-donating groups on the aryl iodide. Our calculated thermochemical data in the gas and solution phases indicate that the use of electron-withdrawing moieties is more favorable thermodynamically than electron-donating ones which has been previously concluded via the experimental elucidations.