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.

Upgrading nirmatrelvir to inhibit SARS-CoV-2 Mpro via DeepFrag and free energy calculations.

The first oral drug for the treatment of COVID-19, Paxlovid, has been authorized; however, nirmatrelvir, a major component of the drug, is reported to be associated with some side effects. Moreover, the appearance of many novel variants raises concerns about drug resistance, and designing new potent inhibitors to prevent viral replication is thus urgent. In this context, using a hybrid approach combining machine learning (ML) and free energy simulations, 6 compounds obtained by modifying nirmatrelvir were proposed to bind strongly to SARS-CoV-2 Mpro. The structural modification of nirmatrelvir significantly enhances the electrostatic interaction free energy between the protein and ligand and slightly decreases the vdW term. However, the vdW term is the most important factor in controlling the ligand-binding affinity. In addition, the modified nirmatrelvir might be less toxic to the human body than the original inhibitor.

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.

Correction: Rapid prediction of possible inhibitors for SARS-CoV-2 main protease using docking and FPL simulations.

[This corrects the article DOI: 10.1039/D0RA06212J.].

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.

Distal Hydrophobic Loop Modulates the Copper Active Site and Reaction of AA13 Polysaccharide Monooxygenases.

Polysaccharide monooxygenases (PMOs) use a type-2 copper center to activate O2 for the selective hydroxylation of one of the two C-H bonds of glycosidic linkages. Our electron paramagnetic resonance (EPR) analysis and molecular dynamics (MD) simulations suggest the unprecedented dynamic roles of the loop containing the residue G89 (G89 loop) on the active site structure and reaction cycle of starch-active PMOs (AA13 PMOs). In the Cu(II) state, the G89 loop could switch between an "open" and "closed" conformation, which is associated with the binding and dissociation of an aqueous ligand in the distal site, respectively. The conformation of the G89 loop influences the positioning of the copper center on the preferred substrate of AA13 PMOs. The dissociation of the distal ligand results in the bending of the T-shaped core of the Cu(II) active site, which could help facilitate its reduction to the active Cu(I) state. In the Cu(I) state, the G89 loop is in the "closed" conformation with a confined copper center, which could allow for efficient O2 binding. In addition, the G89 loop remains in the "closed" conformation in the Cu(II)-superoxo intermediate, which could prevent off-pathway superoxide release via exchange with the distal aqueous ligand. Finally, at the end of the reaction cycle, aqueous ligand binding to the distal site could switch the G89 loop to the "open" conformation and facilitate product release.

Transcriptomic and Ultrastructural Analyses of Pyricularia Oryzae Treated With Fungicidal Peptaibol Analogs of Trichoderma Trichogin.

Eco-friendly analogs of Trichogin GA IV, a short peptaibol produced by Trichoderma longibrachiatum, were assayed against Pyricularia oryzae, the causal agent of rice blast disease. In vitro and in vivo screenings allowed us to identify six peptides able to reduce by about 70% rice blast symptoms. One of the most active peptides was selected for further studies. Microscopy analyses highlighted that the treated fungal spores could not germinate and the fluorescein-labeled peptide localized on the spore cell wall and in the agglutinated cytoplasm. Transcriptomic analysis was carried out on P. oryzae mycelium 3 h after the peptide treatment. We identified 1,410 differentially expressed genes, two-thirds of which upregulated. Among these, we found genes involved in oxidative stress response, detoxification, autophagic cell death, cell wall biogenesis, degradation and remodeling, melanin and fatty acid biosynthesis, and ion efflux transporters. Molecular data suggest that the trichogin analogs cause cell wall and membrane damages and induce autophagic cell death. Ultrastructure observations on treated conidia and hyphae confirmed the molecular data. In conclusion, these selected peptides seem to be promising alternative molecules for developing effective bio-pesticides able to control rice blast disease.

Binding of inhibitors to the monomeric and dimeric SARS-CoV-2 Mpro.

SARS-CoV-2 rapidly infects millions of people worldwide since December 2019. There is still no effective treatment for the virus, resulting in the death of more than one million patients. Inhibiting the activity of SARS-CoV-2 main protease (Mpro), 3C-like protease (3CLP), is able to block the viral replication and proliferation. In this context, our study has revealed that in silico screening for inhibitors of SARS-CoV-2 Mpro can be reliably done using the monomeric structure of the Mpro instead of the dimeric one. Docking and fast pulling of ligand (FPL) simulations for both monomeric and dimeric forms correlate well with the corresponding experimental binding affinity data of 24 compounds. The obtained results were also confirmed via binding pose and noncovalent contact analyses. Our study results show that it is possible to speed up computer-aided drug design for SARS-CoV-2 Mpro by focusing on the monomeric form instead of the larger dimeric one.

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.

Nano-plastics and their analytical characterisation and fate in the marine environment: From source to sea.

Polymer contamination is a major pollutant in all waterways and a significant concern of the 21st Century, gaining extensive research, media, and public attention. The polymer pollution problem is so vast; plastics are now observed in some of the Earth's most remote regions such as the Mariana trench. These polymers enter the waterways, migrate, breakdown; albeit slowly, and then interact with the environment and the surrounding biodiversity. It is these biodiversity and ecosystem interactions that are causing the most nervousness, where health researchers have demonstrated that plastics have entered the human food chain, also showing that plastics are damaging organisms, animals, and plants. Many researchers have focused on reviewing the macro and micro-forms of these polymer contaminants, demonstrating a lack of scientific data and also a lack of investigation regarding nano-sized polymers. It is these nano-polymers that have the greatest potential to cause the most harm to our oceans, waterways, and wildlife. This review has been especially ruthless in discussing nano-sized polymers, their ability to interact with organisms, and the potential for these nano-polymers to cause environmental damage in the marine environment. This review details the breakdown of macro-, micro-, and nano-polymer contamination, examining the sources, the interactions, and the fates of all of these polymer sizes in the environment. The main focus of this review is to perform a comprehensive examination of the literature of the interaction of nanoplastics with organisms, soils, and waters; followed by the discussion of toxicological issues. A significant focus of the review is also on current analytical characterisation techniques for nanoplastics, which will enable researchers to develop protocols for nanopolymer analysis and enhance understanding of nanoplastics in the marine environment.

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.

Fine Tuning of the Copper Active Site in Polysaccharide Monooxygenases.

Type 2 copper active sites, one of the several important copper active sites in biology, were recently found in the novel superfamily of polysaccharide monooxygenases (PMOs) that cleave recalcitrant polysaccharides via an unprecedented oxidative mechanism. The copper center in PMOs is ligated by the bidentate N-terminal histidine residue and another conserved histidine residue, forming a unique T-shaped core termed as Histidine brace. This core serves as the foundation for diverse structures and electronic properties among PMO families and subfamilies. Understanding of the copper active site in PMOs is limited to the static solid structures obtained with X-ray diffraction (XRD), whereas in several families, the copper center exists as a mixture of species in solution as indicated by electron paramagnetic resonance (EPR) spectroscopy. To obtain further details on the copper active sites in PMOs, we carried out density functional theory calculations and molecular dynamics simulations on MtPMO3* that were previously studied with XRD, EPR, mutagenesis, and activity assays. The results reveal the fine-tuning of the binding of the distal ligands by both proximal and distal H-bond-forming residues. Q167 forms H bonds with the proximal OTyr ligand of Y167 and the equatorial aqueous ligand (Oeq). T74 forms a H bond with the distal aqueous ligand (Odis). Removing these H bonds by mutating Q167 or T74 to alanine results in great fluctuations of the axial ligands. Strengthening the proximal H bonds by mutating Q167 to glutamate confines Y167 to the copper centers. In all mutants, the residence time of Odis is significantly reduced. Q167A, Q167E, and T74A mutants were previously shown to have a significantly reduced activity. Our results indicate that well-tuned H bonds are required for the activity of PMOs.

Rapid prediction of possible inhibitors for SARS-CoV-2 main protease using docking and FPL simulations.

Originating for the first time in Wuhan, China, the outbreak of SARS-CoV-2 has caused a serious global health issue. An effective treatment for SARS-CoV-2 is still unavailable. Therefore, in this study, we have tried to predict a list of potential inhibitors for SARS-CoV-2 main protease (Mpro) using a combination of molecular docking and fast pulling of ligand (FPL) simulations. The approaches were initially validated over a set of eleven available inhibitors. Both Autodock Vina and FPL calculations produced consistent results with the experiments with correlation coefficients of R Dock = 0.72 ± 0.14 and R W = -0.76 ± 0.10, respectively. The combined approaches were then utilized to predict possible inhibitors that were selected from a ZINC15 sub-database for SARS-CoV-2 Mpro. Twenty compounds were suggested to be able to bind well to SARS-CoV-2 Mpro. Among them, five top-leads are periandrin V, penimocycline, cis-p-Coumaroylcorosolic acid, glycyrrhizin, and uralsaponin B. The obtained results could probably lead to enhance the COVID-19 therapy.

Assessing potential inhibitors of SARS-CoV-2 main protease from available drugs using free energy perturbation simulations.

The main protease (Mpro) of the novel coronavirus SARS-CoV-2, which has caused the COVID-19 pandemic, is responsible for the maturation of its key proteins. Thus, inhibiting SARS-CoV-2 Mpro could prevent SARS-CoV-2 from multiplying. Because new inhibitors require thorough validation, repurposing current drugs could help reduce the validation process. Many recent studies used molecular docking to screen large databases for potential inhibitors of SARS-CoV-2 Mpro. However, molecular docking does not consider molecular dynamics and thus can be prone to error. In this work, we developed a protocol using free energy perturbation (FEP) to assess the potential inhibitors of SARS-CoV-2 Mpro. First, we validated both molecular docking and FEP on a set of 11 inhibitors of SARS-CoV-2 Mpro with experimentally determined inhibitory data. The experimentally deduced binding free energy exhibits significantly stronger correlation with that predicted by FEP (R = 0.94 ± 0.04) than with that predicted by molecular docking (R = 0.82 ± 0.08). This result clearly shows that FEP is the most accurate method available to predict the binding affinity of SARS-CoV-2 Mpro + ligand complexes. We subsequently used FEP to validate the top 33 compounds screened with molecular docking from the ZINC15 database. Thirteen of these compounds were predicted to bind strongly to SARS-CoV-2 Mpro, most of which are currently used as drugs for various diseases in humans. Notably, delamanid, an anti-tuberculosis drug, was predicted to inhibit SARS-CoV-2 Mpro in the nanomolar range. Because both COVID-19 and tuberculosis are lung diseases, delamanid has higher probability to be suitable for treating COVID-19 than other predicted compounds. Analysis of the complexes of SARS-CoV-2 Mpro and the top inhibitors revealed the key residues involved in the binding, including the catalytic dyad His14 and Cys145, which is consistent with the structural studies reported recently.

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.

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.

Effective Estimation of Ligand-Binding Affinity Using Biased Sampling Method.

The binding between two biomolecules is one of the most critical factors controlling many bioprocesses. Therefore, it is of great interest to derive a reliable method to calculate the free binding energy between two biomolecules. In this work, we have demonstrated that the binding affinity of ligands to proteins can be determined through biased sampling simulations. The umbrella sampling (US) method was applied on 20 protein-ligand complexes, including the cathepsin K (CTSK), type II dehydroquinase (DHQase), heat shock protein 90 (HSP90), and factor Xa (FXa) systems. The ligand-binding affinity was evaluated as the difference between the largest and smallest values of the free-energy curve, which was obtained via a potential of mean force analysis. The calculated affinities differ sizably from the previously reported experimental values, with an average difference of ∼3.14 kcal/mol. However, the calculated results are in good correlation with the experimental data, with correlation coefficients of 0.76, 0.87, 0.96, and 0.97 for CTSK, DHQase, HSP90, and FXa, respectively. Thus, the binding free energy of a new ligand can be reliably estimated using our US approach. Furthermore, the root-mean-square errors (RMSEs) of binding affinity of these systems are 1.13, 0.90, 0.37, and 0.25 kcal/mol, for CTSK, DHQase, HSP90, and FXa, respectively. The small RMSE values indicate the good precision of the biased sampling method that can distinguish the ligands exhibiting similar binding affinities.

C-Terminal Plays as the Possible Nucleation of the Self-Aggregation of the S-Shape Aß11-42 Tetramer in Solution: Intensive MD Study.

Amyloid beta (Aß) peptides are characterized as the major factors associated with neuron death in Alzheimer's disease, which is listed as the most common form of neurodegeneration. Disordered Aß peptides are released from proteolysis of the amyloid precursor protein. The Aß self-assembly process roughly takes place via five steps: disordered forms → oligomers → photofibrils → mature fibrils → plaques. Although Aß fibrils are often observed in patient brains, oligomers were recently indicated to be major neurotoxic elements. In this work, the neurotoxic compound S-shape Aß11-42 tetramer (S4Aß11-42) was investigated over 10 µs of unbiased MD simulations. In particular, the S4Aß11-42 oligomer adopted a high dynamics structure, resulting in unsuccessful determination of their structures in experiments. The C-terminal was suggested as the possible nucleation of the Aß42 aggregation. The sequences 27-35 and 39-40 formed rich ß-content, whereas other residues mostly adopted coil structures. The mean value of the ß-content over the equilibrium interval is ∼42 ± 3%. Furthermore, the dissociation free energy of the S4Aß11-42 peptide was predicted using a biased sampling method. The obtained free energy is ΔG US = -58.44 kcal/mol which is roughly the same level as the corresponding value of the U-shape Aß17-42 peptide. We anticipate that the obtained S4Aß11-42 structures could be used as targets for AD inhibitor screening over the in silico study.

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.

Probable Transmembrane Amyloid α-Helix Bundles Capable of Conducting Ca2+ Ions.

Amyloid ß (Aß) peptides are considered the major causative agents of Alzheimer's disease (AD). In a widely accepted mechanism for AD pathogenesis, Aß peptides are proposed to play multiple roles in damaging brain cells and their synaptic communications. Due to the heterogeneous nature of Aß oligomers, their in vivo structures have not been understood. Most experimental and computational studies favored ß-rich structures of Aß as observed in Aß fibrils. In this in silico study, we investigated an alternative perspective on the structures and function of Aß oligomers in the cell membrane. Transmembrane α-helix bundles of the Aß17-42 tetramer and trimer were observed in extensive temperature replica exchange molecular dynamics (REMD) simulations. We observed three minima on the free-energy landscape of each oligomer, namely, A, B, and C for the tetramer and D, E, and F for the trimer. Except for F, the minima consist of 4 or 3 parallel helices spanning across the membrane model dipalmitoylphosphatidylcholine. Replica exchange molecular dynamics-umbrella sampling (REMD-US) simulation was applied to study the process of a Ca2+ crossing the pore formed by the α-helix bundles in A-E in comparison to that in a calcium channel and a proton channel. REMD-US reveals that A, C, and D allow Ca2+ to cross their pore with a free-energy barrier comparable to that found for the calcium channel. In contrast, the free-energy barrier of a Ca2+ ion crossing B, E, and the proton channel is significantly higher. This result suggests that Aß peptide oligomers could form transmembrane α-helix bundles that provide feasible pathways for Ca2+ transport. This is an intriguing observation that will stimulate further studies.