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.

Alchemical approach performance in calculating the ligand-binding free energy.

Alchemical binding free energy calculations are one of the most accurate methods for estimating ligand-binding affinity. Assessing the accuracy of the approach over protein targets is one of the most interesting issues. The free energy difference of binding between a protein and a ligand was calculated via the alchemical approach. The alchemical approach exhibits satisfactory accuracy over four targets, including AmpC beta-lactamase (AmpC); glutamate receptor, ionotropic kainate 1 (GluK1); heat shock protein 90 (Hsp90); and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease (Mpro). In particular, the correlation coefficients between calculated binding free energies and the respective experiments over four targets range from 0.56 to 0.86. The affinity computed via free energy perturbation (FEP) simulations is overestimated over the experimental value. Particularly, the electrostatic interaction free energy rules the binding process of ligands to AmpC and GluK1. However, the van der Waals (vdW) interaction free energy plays an important role in the ligand-binding processes of HSP90 and SARS-CoV-2 Mpro. The obtained results associate with the hydrophilic or hydrophobic properties of the ligands. This observation may enhance computer-aided drug design.

Unraveling Hydrogen Adsorption on Transition Metal-Doped [Mo3S13]2- Clusters: Insights from Density Functional Theory Calculations.

Molecular and dissociative hydrogen adsorption of transition metal (TM)-doped [Mo3S13]2- atomic clusters were investigated using density functional theory calculations. The introduced TM dopants form stable bonds with S atoms, preserving the geometric structure. The S-TM-S bridging bond emerges as the most stable configuration. The preferred adsorption sites were found to be influenced by various factors, such as the relative electronegativity, coordination number, and charge of the TM atom. Notably, the presence of these TM atoms remarkably improved the hydrogen adsorption activity. The dissociation of a single hydrogen molecule on TM[Mo3S13]2- clusters (TM = Sc, Cr, Mn, Fe, Co, and Ni) is thermodynamically and kinetically favorable compared to their bare counterparts. The extent of favorability monotonically depends on the TM impurity, with a maximum activation barrier energy ranging from 0.62 to 1.58 eV, lower than that of the bare cluster (1.69 eV). Findings provide insights for experimental research on hydrogen adsorption using TM-doped molybdenum sulfide nanoclusters, with potential applications in the field of hydrogen energy.

Natural compounds inhibit Monkeypox virus methyltransferase VP39 in silico studies.

VP39, an essential 2'-O-RNA methyltransferase enzyme discovered in Monkeypox virus (MPXV), plays a vital role in viral RNA replication and transcription. Inhibition of the enzyme may prevent viral replication. In this context, using a combination of molecular docking and molecular dynamics (MDs) simulations, the inhibitory ability of NCI Diversity Set VII natural compounds to VP39 protein was investigated. It should be noted that the computed binding free energy of ligand via molecular docking and linear interaction energy (LIE) approaches are in good agreement with the corresponding experiments with coefficients of R=0.72 and 0.75, respectively. NSC 319990, NSC 196515 and NSC 376254 compounds were demonstrated that can inhibit MPVX methyltransferase VP39 protein with the similar affinity compared to available inhibitor sinefungin. Moreover, nine residues involving Gln39, Gly68, Gly72, Asp95, Arg97, Val116, Asp138, Arg140 and Asn156 may be argued that they play an important role in binding process of inhibitors to VP39.Communicated by Ramaswamy H. Sarma.

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: 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.

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.

Density Functional Study of Size-Dependent Hydrogen Adsorption on Ag n Cr (n = 1-12) Clusters.

Increasing interest has been paid for hydrogen adsorption on atomically controlled nanoalloys due to their potential applications in catalytic processes and energy storage. In this work, we investigate the interaction of H2 with small-sized Ag n Cr (n = 1-12) using density functional theory calculations. It is found that the cluster structures are preserved during the adsorption of H2 either molecularly or dissociatively. Ag3Cr-H2, Ag6Cr-H2, and Ag9Cr-H2 clusters are identified to be relatively more stable from computed binding energies and second-order energy difference. The dissociation of adsorbed H2 on Ag2Cr, Ag3Cr, Ag6Cr, and Ag7Cr clusters is favored both thermodynamically and kinetically. The dissociative adsorption is unlikely to occur because of a considerable energy barrier before reaching the final state for Ag4Cr or due to energetic preferences for n = 1, 5, and 8-12 species. Comprehensive analysis shows that the geometric structure of clusters, the relative electronegativity, and the coordination number of the Cr impurity play a decisive role in determining the preferred adsorption configuration.

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.

Some metaheuristic algorithms for solving multiple cross-functional team selection problems.

We can find solutions to the team selection problem in many different areas. The problem solver needs to scan across a large array of available solutions during their search. This problem belongs to a class of combinatorial and NP-Hard problems that requires an efficient search algorithm to maintain the quality of solutions and a reasonable execution time. The team selection problem has become more complicated in order to achieve multiple goals in its decision-making process. This study introduces a multiple cross-functional team (CFT) selection model with different skill requirements for candidates who meet the maximum required skills in both deep and wide aspects. We introduced a method that combines a compromise programming (CP) approach and metaheuristic algorithms, including the genetic algorithm (GA) and ant colony optimization (ACO), to solve the proposed optimization problem. We compared the developed algorithms with the MIQP-CPLEX solver on 500 programming contestants with 37 skills and several randomized distribution datasets. Our experimental results show that the proposed algorithms outperformed CPLEX across several assessment aspects, including solution quality and execution time. The developed method also demonstrated the effectiveness of the multi-criteria decision-making process when compared with the multi-objective evolutionary algorithm (MOEA).

Identifying Possible AChE Inhibitors from Drug-like Molecules via Machine Learning and Experimental Studies.

Acetylcholinesterase (AChE) is one of the most important drug targets for Alzheimer's disease (AD) treatment. In this work, a machine learning model was trained to rapidly and accurately screen large chemical databases for the potential inhibitors of AChE. The obtained results were then validated via in vitro enzyme assay. Moreover, atomistic simulations including molecular docking and molecular dynamics simulations were then used to understand molecular insights into the binding process of ligands to AChE. In particular, two compounds including benzyl trifluoromethyl ketone and trifluoromethylstyryl ketone were indicated as highly potent inhibitors of AChE because they established IC50 values of 0.51 and 0.33 µM, respectively. The obtained IC50 of two compounds is significantly lower than that of galantamine (2.10 µM). The predicted log(BB) suggests that the compounds may be able to traverse the blood-brain barrier. A good agreement between computational and experimental studies was observed, indicating that the hybrid approach can enhance AD therapy.

Searching for AChE inhibitors from natural compounds by using machine learning and atomistic simulations.

Acetylcholinesterase (AChE) is one of the most important drug targets for Alzheimer's disease treatment. In this work, a combined approach involving machine-learning (ML) model and atomistic simulations was established to predict the ligand-binding affinity to AChE of the natural compounds from VIETHERB database. The trained ML model was first utilized to rapidly and accurately screen the natural compound database for potential AChE inhibitors. Atomistic simulations including molecular docking and steered-molecular dynamics simulations were then used to confirm the ML outcome. Good agreement between ML and atomistic simulations was observed. Twenty compounds were suggested to be able to inhibit AChE. Especially, four of them including geranylgeranyl diphosphate, 2-phosphoglyceric acid, and 2-carboxy-d-arabinitol 1-phosphate, and farnesyl diphosphate are highly potent inhibitors with sub-nanomolar affinities.

Insights into the binding and covalent inhibition mechanism of PF-07321332 to SARS-CoV-2 Mpro.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been causing the COVID-19 pandemic, resulting in several million deaths being reported. Numerous investigations have been carried out to discover a compound that can inhibit the biological activity of the SARS-CoV-2 main protease, which is an enzyme related to the viral replication. Among these, PF-07321332 (Nirmatrelvir) is currently under clinical trials for COVID-19 therapy. Therefore, in this work, atomistic and electronic simulations were performed to unravel the binding and covalent inhibition mechanism of the compound to Mpro. Initially, 5 µs of steered-molecular dynamics simulations were carried out to evaluate the ligand-binding process to SARS-CoV-2 Mpro. The successfully generated bound state between the two molecules showed the important role of the PF-07321332 pyrrolidinyl group and the residues Glu166 and Gln189 in the ligand-binding process. Moreover, from the MD-refined structure, quantum mechanics/molecular mechanics (QM/MM) calculations were carried out to unravel the reaction mechanism for the formation of the thioimidate product from SARS-CoV-2 Mpro and the PF-07321332 inhibitor. We found that the catalytic triad Cys145-His41-Asp187 of SARS-CoV-2 Mpro plays an important role in the activation of the PF-07321332 covalent inhibitor, which renders the deprotonation of Cys145 and, thus, facilitates further reaction. Our results are definitely beneficial for a better understanding of the inhibition mechanism and designing new effective inhibitors for SARS-CoV-2 Mpro.

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.

Unbinding ligands from SARS-CoV-2 Mpro via umbrella sampling simulations.

The umbrella sampling (US) simulation is demonstrated to be an efficient approach for determining the unbinding pathway and binding affinity to the SARS-CoV-2 Mpro of small molecule inhibitors. The accuracy of US is in the same range as the linear interaction energy (LIE) and fast pulling of ligand (FPL) methods. In detail, the correlation coefficient between US and experiments does not differ from FPL and is slightly smaller than LIE. The root mean square error of US simulations is smaller than that of LIE. Moreover, US is better than FPL and poorer than LIE in classifying SARS-CoV-2 Mpro inhibitors owing to the reciever operating characteristic-area under the curve analysis. Furthermore, the US simulations also provide detailed insights on unbinding pathways of ligands from the binding cleft of SARS-CoV-2 Mpro. The residues Cys44, Thr45, Ser46, Leu141, Asn142, Gly143, Glu166, Leu167, Pro168, Ala191, Gln192 and Ala193 probably play an important role in the ligand dissociation. Therefore, substitutions at these points may change the mechanism of binding of inhibitors to SARS-CoV-2 Mpro.

501Y.V2 spike protein resists the neutralizing antibody in atomistic simulations.

SARS-CoV-2 outbreaks worldwide caused COVID-19 pandemic, which is related to several million deaths. In particular, SARS-CoV-2 Spike (S) protein is a major biological target for COVID-19 vaccine design. Unfortunately, recent reports indicated that Spike (S) protein mutations can lead to antibody resistance. However, understanding the process is limited, especially at the atomic scale. The structural change of S protein and neutralizing antibody fragment (FAb) complexes was thus probed using molecular dynamics (MD) simulations. In particular, the backbone RMSD of the 501Y.V2 complex was significantly larger than that of the wild-type one implying a large structural change of the mutation system. Moreover, the mean of Rg, CCS, and SASA are almost the same when compared two complexes, but the distributions of these values are absolutely different. Furthermore, the free energy landscape of the complexes was significantly changed when the 501Y.V2 variant was induced. The binding pose between S protein and FAb was thus altered. The FAb-binding affinity to S protein was thus reduced due to revealing over steered-MD (SMD) simulations. The observation is in good agreement with the respective experiment that the 501Y.V2 SARS-CoV-2 variant can escape from neutralizing antibody (NAb).

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.