Morphology of a Transmembrane Aß42 Tetramer via REMD Simulations.

The folding/misfolding of membrane-permiable Amyloid beta (Aß) peptides is likely associated with the advancing stage of Alzheimer's disease (AD) by disrupting Ca2+ homeostasis. In this context, the aggregation of four transmembrane Aß17-42 peptides was investigated using temperature replica-exchange molecular dynamics (REMD) simulations. The obtained results indicated that the secondary structure of transmembrane Aß peptides tends to have different propensities compared to those in solution. Interestingly, the residues favorably forming ß-structure were interleaved by residues rigidly adopting turn-structure. A combination of ß and turn regions likely forms a pore structure. Six morphologies of 4Aß were found over the free energy landscape and clustering analyses. Among these, the morphologies include (1) Aß binding onto the membrane surface and three transmembrane Aß; (2) three helical and coil transmembrane Aß; (3) four helical transmembrane Aß; (4) three helical and one ß-hairpin transmembrane Aß; (5) two helical and two ß-strand transmembrane Aß; and (6) three ß-strand and one helical transmembrane Aß. Although the formation of the ß-barrel structure was not observed during the 0.28 ms─long MD simulation, the structure is likely to form when the simulation time is further extended.

Amyloid Oligomers: A Joint Experimental/Computational Perspective on Alzheimer's Disease, Parkinson's Disease, Type II Diabetes, and Amyotrophic Lateral Sclerosis.

Protein misfolding and aggregation is observed in many amyloidogenic diseases affecting either the central nervous system or a variety of peripheral tissues. Structural and dynamic characterization of all species along the pathways from monomers to fibrils is challenging by experimental and computational means because they involve intrinsically disordered proteins in most diseases. Yet understanding how amyloid species become toxic is the challenge in developing a treatment for these diseases. Here we review what computer, in vitro, in vivo, and pharmacological experiments tell us about the accumulation and deposition of the oligomers of the (Aß, tau), α-synuclein, IAPP, and superoxide dismutase 1 proteins, which have been the mainstream concept underlying Alzheimer's disease (AD), Parkinson's disease (PD), type II diabetes (T2D), and amyotrophic lateral sclerosis (ALS) research, respectively, for many years.

Searching for potential inhibitors of SARS-COV-2 main protease using supervised learning and perturbation calculations.

Inhibiting the biological activity of SARS-CoV-2 Mpro can prevent viral replication. In this context, a hybrid approach using knowledge- and physics-based methods was proposed to characterize potential inhibitors for SARS-CoV-2 Mpro. Initially, supervised machine learning (ML) models were trained to predict a ligand-binding affinity of ca. 2 million compounds with the correlation on a test set of R = 0.748 ± 0.044 . Atomistic simulations were then used to refine the outcome of the ML model. Using LIE/FEP calculations, nine compounds from the top 100 ML inhibitors were suggested to bind well to the protease with the domination of van der Waals interactions. Furthermore, the binding affinity of these compounds is also higher than that of nirmatrelvir, which was recently approved by the US FDA to treat COVID-19. In addition, the ligands altered the catalytic triad Cys145 - His41 - Asp187, possibly disturbing the biological activity of SARS-CoV-2.

Improving ligand-ranking of AutoDock Vina by changing the empirical parameters.

AutoDock Vina (Vina) achieved a very high docking-success rate, p^ , but give a rather low correlation coefficient, R , for binding affinity with respect to experiments. This low correlation can be an obstacle for ranking of ligand-binding affinity, which is the main objective of docking simulations. In this context, we evaluated the dependence of Vina R coefficient upon its empirical parameters. R is affected more by changing the gauss2 and rotation than other terms. The docking-success rate p^ is sensitive to the alterations of the gauss1, gauss2, repulsion, and hydrogen bond parameters. Based on our benchmarks, the parameter set1 has been suggested to be the most optimal. The testing study over 800 complexes indicated that the modified Vina provided higher correlation with experiment Rset1=0.556±0.025 compared with RDefault=0.493±0.028 obtained by the original Vina and RVina1.2=0.503±0.029 by Vina version 1.2. Besides, the modified Vina can be also applied more widely, giving R≥0.500 for 32/48 targets, compared with the default package, giving R≥0.500 for 31/48 targets. In addition, validation calculations for 1036 complexes obtained from version 2019 of PDBbind refined structures showed that the set1 of parameters gave higher correlation coefficient ( Rset1=0.617±0.017 ) than the default package ( RDefault=0.543±0.020 ) and Vina version 1.2 ( RVina1.2=0.540±0.020 ). The version of Vina with set1 of parameters can be downloaded at https://github.com/sontungngo/mvina. The outcomes would enhance the ranking of ligand-binding affinity using Autodock Vina.

Characterizing the ligand-binding affinity toward SARS-CoV-2 Mpro via physics- and knowledge-based approaches.

Computational approaches, including physics- and knowledge-based methods, have commonly been used to determine the ligand-binding affinity toward SARS-CoV-2 main protease (Mpro or 3CLpro). Strong binding ligands can thus be suggested as potential inhibitors for blocking the biological activity of the protease. In this context, this paper aims to provide a short review of computational approaches that have recently been applied in the search for inhibitor candidates of Mpro. In particular, molecular docking and molecular dynamics (MD) simulations are usually combined to predict the binding affinity of thousands of compounds. Quantitative structure-activity relationship (QSAR) is the least computationally demanding and therefore can be used for large chemical collections of ligands. However, its accuracy may not be high. Moreover, the quantum mechanics/molecular mechanics (QM/MM) method is most commonly used for covalently binding inhibitors, which also play an important role in inhibiting the activity of SARS-CoV-2. Furthermore, machine learning (ML) models can significantly increase the searching space of ligands with high accuracy for binding affinity prediction. Physical insights into the binding process can then be confirmed via physics-based calculations. Integration of ML models into computational chemistry provides many more benefits and can lead to new therapies sooner.

Correction: Characterizing the ligand-binding affinity toward SARS-CoV-2 Mpro via physics- and knowledge-based approaches.

Correction for 'Characterizing the ligand-binding affinity toward SARS-CoV-2 Mpro via physics- and knowledge-based approaches' by Son Tung Ngo et al., Phys. Chem. Chem. Phys., 2022, https://doi.org/10.1039/d2cp04476e.

Effect of Cholesterol Molecules on Aß1-42 Wild-Type and Mutants Trimers.

Alzheimer's disease displays aggregates of the amyloid-beta (Aß) peptide in the brain, and there is increasing evidence that cholesterol may contribute to the pathogenesis of the disease. Though many experimental and theoretical studies have focused on the interactions of Aß oligomers with membrane models containing cholesterol, an understanding of the effect of free cholesterol on small Aß42 oligomers is not fully established. To address this question, we report on replica exchange with a solute tempering simulation of an Aß42 trimer with cholesterol and compare it with a previous replica exchange molecular dynamics simulation. We show that the binding hot spots of cholesterol are rather complex, involving hydrophobic residues L17-F20 and L30-M35 with a non-negligible contribution of loop residues D22-K28 and N-terminus residues. We also examine the effects of cholesterol on the trimers of the disease-causing A21G and disease-protective A2T mutations by molecular dynamics simulations. We show that these two mutations moderately impact cholesterol-binding modes. In our REST2 simulations, we find that cholesterol is rarely inserted into aggregates but rather attached as dimers and trimers at the surface of Aß42 oligomers. We propose that cholesterol acts as a glue to speed up the formation of larger aggregates; this provides a mechanistic link between cholesterol and Alzheimer's disease.

Estimating the ligand-binding affinity via λ-dependent umbrella sampling simulations.

The umbrella sampling (US) approach has been demonstrated to be a very efficient method for estimating the ligand-binding affinity. However, most of the calculated values overestimate experimental ones that are probably caused by the inaccurate representation of the interaction between the ligand and the surrounding molecules. The issue can be resolved via the implementation aspects of λ-alteration simulation into the US approach, which we call the λ-dependent umbrella sampling (λUS) scheme. In particular, the electrostatic and van der Waals interactions were simultaneously changed by using the coupling parameter λ during λUS simulations. The mean value of obtained results, ∆GUSλ=0.20=-11.59±1.51 kcal mol-1 , is in good fitting to the mean value of respective experiments, ∆GEXP = - 11.26 ± 0.89 kcal mol-1 . Moreover, the correlation between the proposed approach and experiment is quite good with a value of RUSλ=0.20=0.82±0.10 . The λUS scheme significantly enhances the calculated accuracy since the RMSE of the proposed scheme is smaller than traditional US simulations, RMSEUSλ=0.20=2.99±0.82 kcal mol-1 versus RMSEUSλ=0.00=5.48±0.81 kcal mol-1 . Furthermore, the precision is increased since the computed error via λUS approach, δUSλ=0.20=1.51 kcal mol-1 , was smaller than those of the US simulation, δUSλ=0.00=1.78 kcal mol-1 . Overall, the proposed approach perhaps provides an efficient way to accurately and precisely estimate the ligand-binding free energy.

Benchmark of Popular Free Energy Approaches Revealing the Inhibitors Binding to SARS-CoV-2 Mpro.

The COVID-19 pandemic has killed millions of people worldwide since its outbreak in December 2019. The pandemic is caused by the SARS-CoV-2 virus whose main protease (Mpro) is a promising drug target since it plays a key role in viral proliferation and replication. Currently, developing an effective therapy is an urgent task, which requires accurately estimating the ligand-binding free energy to SARS-CoV-2 Mpro. However, it should be noted that the accuracy of a free energy method probably depends on the protein target. A highly accurate approach for some targets may fail to produce a reasonable correlation with the experiment when a novel enzyme is considered as a drug target. Therefore, in this context, the ligand-binding affinity to SARS-CoV-2 Mpro was calculated via various approaches. The molecular docking approach was manipulated using Autodock Vina (Vina) and Autodock4 (AD4) protocols to preliminarily investigate the ligand-binding affinity and pose to SARS-CoV-2 Mpro. The binding free energy was then refined using the fast pulling of ligand (FPL), linear interaction energy (LIE), molecular mechanics-Poisson-Boltzmann surface area (MM-PBSA), and free energy perturbation (FEP) methods. The benchmark results indicated that for docking calculations, Vina is more accurate than AD4, and for free energy methods, FEP is the most accurate method, followed by LIE, FPL, and MM-PBSA (FEP > LIE ≈ FPL > MM-PBSA). Moreover, atomistic simulations revealed that the van der Waals interaction is the dominant factor. The residues Thr26, His41, Ser46, Asn142, Gly143, Cys145, His164, Glu166, and Gln189 are essential elements affecting the binding process. Our benchmark provides guidelines for further investigations using computational approaches.

Substrate selectivity in starch polysaccharide monooxygenases.

Degradation of polysaccharides is central to numerous biological and industrial processes. Starch-active polysaccharide monooxygenases (AA13 PMOs) oxidatively degrade starch and can potentially be used with industrial amylases to convert starch into a fermentable carbohydrate. The oxidative activities of the starch-active PMOs from the fungi Neurospora crassa and Myceliophthora thermophila, NcAA13 and MtAA13, respectively, on three different starch substrates are reported here. Using high-performance anion-exchange chromatography coupled with pulsed amperometry detection, we observed that both enzymes have significantly higher oxidative activity on amylose than on amylopectin and cornstarch. Analysis of the product distribution revealed that NcAA13 and MtAA13 more frequently oxidize glycosidic linkages separated by multiples of a helical turn consisting of six glucose units on the same amylose helix. Docking studies identified important residues that are involved in amylose binding and suggest that the shallow groove that spans the active-site surface of AA13 PMOs favors the binding of helical amylose substrates over nonhelical substrates. Truncations of NcAA13 that removed its native carbohydrate-binding module resulted in diminished binding to amylose, but truncated NcAA13 still favored amylose oxidation over other starch substrates. These findings establish that AA13 PMOs preferentially bind and oxidize the helical starch substrate amylose. Moreover, the product distributions of these two enzymes suggest a unique interaction with starch substrates.

Oversampling Free Energy Perturbation Simulation in Determination of the Ligand-Binding Free Energy.

Determination of the ligand-binding affinity is an extremely interesting problem. Normally, the free energy perturbation (FEP) method provides an appropriate result. However, it is of great interest to improve the accuracy and precision of this method. In this context, temperature replica exchange molecular dynamics implementation of the FEP computational approach, which we call replica exchange free energy perturbation (REP) was proposed. In particular, during REP simulations, the system can easily escape from being trapped in local minima by exchanging configurations with high temperatures, resulting in significant improvement in the accuracy and precision of protein-ligand binding affinity calculations. The distribution of the decoupling free energy was enlarged, and its mean values were decreased. This results in changes in the magnitude of the calculated binding free energies as well as in alteration in the binding mechanism. Moreover, the REP correlation coefficient with respect to experiment ( RREP = 0.85 ± 0.15) is significantly boosted in comparison with the FEP one ( RFEP = 0.64 ± 0.30). Furthermore, the root-mean-square error (RMSE) of REP is also smaller than FEP, RMSEREP = 4.28 ± 0.69 versus RMSEFEP = 5.80 ± 1.11 kcal/mol, respectively. © 2019 Wiley Periodicals, Inc.

Computational Determination of Potential Inhibitors of SARS-CoV-2 Main Protease.

The novel coronavirus (SARS-CoV-2) has infected several million people and caused thousands of deaths worldwide since December 2019. As the disease is spreading rapidly all over the world, it is urgent to find effective drugs to treat the virus. The main protease (Mpro) of SARS-CoV-2 is one of the potential drug targets. Therefore, in this context, we used rigorous computational methods, including molecular docking, fast pulling of ligand (FPL), and free energy perturbation (FEP), to investigate potential inhibitors of SARS-CoV-2 Mpro. We first tested our approach with three reported inhibitors of SARS-CoV-2 Mpro, and our computational results are in good agreement with the respective experimental data. Subsequently, we applied our approach on a database of ∼4600 natural compounds, as well as 8 available HIV-1 protease (PR) inhibitors and an aza-peptide epoxide. Molecular docking resulted in a short list of 35 natural compounds, which was subsequently refined using the FPL scheme. FPL simulations resulted in five potential inhibitors, including three natural compounds and two available HIV-1 PR inhibitors. Finally, FEP, the most accurate and precise method, was used to determine the absolute binding free energy of these five compounds. FEP results indicate that two natural compounds, cannabisin A and isoacteoside, and an HIV-1 PR inhibitor, darunavir, exhibit a large binding free energy to SARS-CoV-2 Mpro, which is larger than that of 13b, the most reliable SARS-CoV-2 Mpro inhibitor recently reported. The binding free energy largely arises from van der Waals interaction. We also found that Glu166 forms H-bonds to all of the inhibitors. Replacing Glu166 by an alanine residue leads to ∼2.0 kcal/mol decreases in the affinity of darunavir to SARS-CoV-2 Mpro. Our results could contribute to the development of potential drugs inhibiting SARS-CoV-2.

Impact of the Astaxanthin, Betanin, and EGCG Compounds on Small Oligomers of Amyloid Aß40 Peptide.

There is experimental evidence that the astaxanthin, betanin, and epigallocatechin-3-gallate (EGCG) compounds slow down the aggregation kinetics and the toxicity of the amyloid-ß (Aß) peptide. How these inhibitors affect the self-assembly at the atomic level remains elusive. To address this issue, we have performed for each ligand atomistic replica exchange molecular dynamic (REMD) simulations in an explicit solvent of the Aß11-40 trimer from the U-shape conformation and MD simulations starting from Aß1-40 dimer and tetramer structures characterized by different intra- and interpeptide conformations. We find that the three ligands have similar binding free energies on small Aß40 oligomers but very distinct transient binding sites that will affect the aggregation of larger assemblies and fibril elongation of the Aß40 peptide.

Autodock Vina Adopts More Accurate Binding Poses but Autodock4 Forms Better Binding Affinity.

The binding pose and affinity between a ligand and enzyme are very important pieces of information for computer-aided drug design. In the initial stage of a drug discovery project, this information is often obtained by using molecular docking methods. Autodock4 and Autodock Vina are two commonly used open-source and free software tools to perform this task, and each has been cited more than 6000 times in the last ten years. It is of great interest to compare the success rate of the two docking software programs for a large and diverse set of protein-ligand complexes. In this study, we selected 800 protein-ligand complexes for which both PDB structures and experimental binding affinity are available. Docking calculations were performed for these complexes using both Autodock4 and Autodock Vina with different docking options related to computing resource consumption and accuracy. Our calculation results are in good agreement with a previous study that the Vina approach converges much faster than AD4 one. However, interestingly, AD4 shows a better performance than Vina over 21 considered targets, whereas the Vina protocol is better than the AD4 package for 10 other targets. There are 16 complexes for which both the AD4 and Vina protocols fail to produce a reasonable correlation with respected experiments so both are not suitable to use to estimate binding free energies for these cases. In addition, the best docking option for performing the AD4 approach is the long option. However, the short option is the best solution for carrying out Vina docking. The obtained results probably will be useful for future docking studies in deciding which program to use.

Photofragmentation Patterns of Cobalt Oxide Cations ConOm+ (n = 5-9, m = 4-13): From Oxygen-Deficient to Oxygen-Rich Species.

Cobalt oxide clusters, ConOm+ (5 ≤ n ≤ 9 and 4 ≤ m ≤ 13), are produced by laser vaporization and studied by time-of-flight mass spectrometry. Specific stoichiometries are mass separated and photofragmented using 355 nm laser light. The preferred fragmentation channels of m = n-1, m = n-2, and m ≥ n species are investigated. Loss of oxygen molecules is the favorable dissociation channel of m ≥ n clusters. While ConOn-2+ clusters decay via the loss of a Co atom, the photofragmentation behavior of ConOn-1+ species interestingly can be divided into two regimes: the n ≤ 6 clusters tend to lose an oxygen atom, but for n > 6 they favorably dissociate via the loss of a cobalt atom. The geometric structures of selected m = n - 2 species are studied using density functional theory calculations. Dissociation energies for different evaporation channels are calculated and thermodynamically favorable channels are found to correspond to the experimental observations.

Replica exchange molecular dynamics study of the truncated amyloid beta (11-40) trimer in solution.

Amyloid beta (Aß) oligomers are neurotoxic compounds that destroy the brain of Alzheimer's disease patients. Recent studies indicated that the trimer is one of the most cytotoxic forms of low molecular weight Aß oligomers. As there was limited information about the structure of the Aß trimer, either by experiment or by computation, we determined in this work the structure of the 3Aß11-40 oligomer for the first time using the temperature replica exchange molecular dynamics simulations in the presence of an explicit solvent. More than 20.0 µs of MD simulations were performed. The probability of the ß-content and random coil structure of the solvated trimer amounts to 42 ± 6 and 49 ± 7% which is in good agreement with experiments. Intermolecular interactions in central hydrophobic cores play a key role in stabilizing the oligomer. Intermolecular polar contacts between D23 and residues 24-29 replace the salt bridge D23-K28 to secure the loop region. The hydrophilic region of the N-terminus is maintained by the intermolecular polar crossing contacts H13A-Q15B and H13B-Q15C. The difference in the free energy of binding between the constituting monomers and the others amounts to -36 ± 8 kcal mol-1. The collision cross section of the representative structures of the trimer was computed to be 1330 ± 47 Å2, which is in good agreement with previous experiments.

Fast and accurate determination of the relative binding affinities of small compounds to HIV-1 protease using non-equilibrium work.

The fast pulling ligand (FPL) out of binding cavity using non-equilibrium molecular dynamics (MD) simulations was demonstrated to be a rapid, accurate and low CPU demand method for the determination of the relative binding affinities of a large number of HIV-1 protease (PR) inhibitors. In this approach, the ligand is pulled out of the binding cavity of the protein using external harmonic forces, and the work of pulling force corresponds to the relative binding affinity of HIV-1 PR inhibitor. The correlation coefficient between the pulling work and the experimental binding free energy of R=-0.95 shows that FPL results are in good agreement with experiment. It is thus easier to rank the binding affinities of HIV-1 PR inhibitors, that have similar binding affinities because the mean error bar of pulling work amounts to δW=7%. The nature of binding is discovered using the FPL approach. © 2016 Wiley Periodicals, Inc.

Anti-arrhythmic Medication Propafenone a Potential Drug for Alzheimer's Disease Inhibiting Aggregation of Aß: In Silico and in Vitro Studies.

Alzheimer's disease (AD) is the most common form of dementia caused by the formation of Aß aggregates. So far, no effective medicine for the treatment of AD is available. Many efforts have been made to find effective medicine to cope with AD. Curcumin is a drug candidate for AD, being a potent anti-amyloidogenic compound, but the results of clinical trials for it were either negative or inclusive. In the present study, we took advantages from accumulated knowledge about curcumin and have screened out four compounds that have chemical and structural similarity with curcumin more than 80% from all FDA-approved oral drugs. Using all-atom molecular dynamics simulation and the free energy perturbation method we showed that among predicted compounds anti-arrhythmic medication propafenone shows the best anti-amyloidogenic activity. The in vitro experiment further revealed that it can inhibit Aß aggregation and protect cells against Aß induced cytotoxicity to almost the same extent as curcumin. Our results suggest that propafenone may be a potent drug for the treatment of Alzheimer's disease.

In silico and in vitro characterization of anti-amyloidogenic activity of vitamin K3 analogues for Alzheimer's disease.

BACKGROUND: Aggregation of amyloid-beta (Aß) has been proposed as the main cause of Alzheimer's disease (AD). Vitamin K deficiency has been linked to the pathogenesis of AD. Therefore, 15 synthesized vitamin K3 (VK3) analogues were studied for their anti-amyloidogenic activity. METHODS: Biological and spectroscopic assays were used to characterize the effect of VK3 analogues on amyloidogenic properties of Aß, such as aggregation, free radical formation, and cell viability. Molecular dynamics simulation was used to calculate the binding affinity and mode of VK3 analogue binding to Aß. RESULTS: Both numerical and experimental results showed that several VK3 analogues, including VK3-6, VK3-8, VK3-9, VK3-10, and VK3-224 could effectively inhibit Aß aggregation and conformational conversion. The calculated inhibition constants were in the µM range for VK3-10, VK3-6, and VK3-9 which was similar to the IC50 of curcumin. Cell viability assays indicated that VK3-9 could effectively reduce free radicals and had a protective effect on cytotoxicity induced by Aß. CONCLUSIONS: The results clearly demonstrated that VK3 analogues could effectively inhibit Aß aggregation and protect cells against Aß induced toxicity. Modified VK3 analogues can possibly be developed as effective anti-amyloidogenic drugs for the treatment of AD. GENERAL SIGNIFICANCE: VK3 analogues effectively inhibit Aß aggregation and are highly potent as anti-amyloidogenic drugs for therapeutic treatment of AD.

Silver nanoparticles alter the dimerization of Aß42 studied by REMD simulations.

The aggregation of amyloid beta (Aß) peptides is associated with the development of Alzheimer's disease (AD). However, there has been a growing belief that the oligomerization of Aß species in different environments has a neurotoxic effect on the patient's brain, causing damage. It is necessary to comprehend the compositions of Aß oligomers in order to develop medications that may effectively inhibit these neurotoxic forms that affect the nervous system of AD patients. Thus, dissociation or inhibition of Aß aggregation may be able to prevent AD. To date, the search for traditional agents and biomolecules has largely been unsuccessful. In this context, nanoparticles have emerged as potential candidates to directly inhibit the formation of Aß oligomers. The oligomerization of the dimeric Aß peptides with or without the influence of a silver nanoparticle was thus investigated using temperature replica-exchange molecular dynamics (REMD) simulations. The physical insights into the dimeric Aß oligomerization were clarified by analyzing intermolecular contact maps, the free energy landscape of the dimeric oligomer, secondary structure terms, etc. The difference in obtained metrics between Aß with or without a silver nanoparticle provides a picture of the influence of silver nanoparticles on the oligomerization process. The underlying mechanisms that are involved in altering Aß oligomerization will be discussed. The obtained results may play an important role in searching for Aß inhibitor pathways.