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Prediction of Inhibitors Against Alpha-Synuclein Fibrils Formed in Parkinson’s Disease
No full textParkinson\u27s disease (PD) is a chronic and progressive neurological disorder that significantly impairs a person\u27s ability to control their movements. Currently, nearly one million people in the USA are living with PD. After Alzheimer’s disease, PD is the second most common neurodegenerative disease in the USA. The disease is characterized by the aggregation of the alpha-synuclein protein, which forms fibril-like structures called Lewy bodies. The current work aims to develop therapeutic strategies against the disease by inhibiting this fibril formation. We hypothesize that chemical compounds that can bind at the interface can block the binding and prevent fibril formation. We have utilized molecular docking techniques to screen 3450 chemical compounds against the alpha-synuclein protein, to bind to and prevent alpha-synuclein clumping, thereby inhibiting fibril formation. Based on our docking simulations, we have selected the top five compounds that bind strongly to the protein. All these ligands bind to the hydrophobic region of the protein, suggesting that hydrophobic drugs (capable of crossing the blood-brain barrier) will be more effective in treating this disease. We validated our hypothesis by docking inhibitor-bound fibrils and free fibrils together and found that the inhibitor blocks the fibril interface interaction. In addition, we have also used machine learning and graph neural network tools to propose the druggable site on the fibril surface which will help in designing inhibitors against the fibrils. The work opens new avenues for novel treatment of Parkinson’s disease and offers hope for improved therapeutic options in the future
Capacity Decay in LiNiO2: An Atomistic Kinetic Picture
No full textHigh-Ni layered oxides experience significant capacity decay over cycling, but the underlying mechanisms remain controversial. Using atomistic simulations, the electrochemical behavior of the fatigue phase is reproduced: a surface densified phase traps the last 25% of Li the end of charge, while discharge remains unimpeded. When the Li content falls to 25%, the remaining Li are locked into a superlattice, making the creation of vacancies the rate-limiting step for further delithiation. After cycling, the surface densified phase resembles Ni5O8 , with 25% Ni in the Li layer forming a similar superlattice. These Ni pin nearby Li, suppressing vacancy formation at the surface and kinetically trapping Li inside. Meanwhile, the Ni5O8 phase exhibits high diffusivity for Li interstitials in the superlattice, which explains the minimal resistance increase during discharge at the same Li content. Further densification leads to a surface phase that hinders both charge and discharge across the entire voltage range
Evaluating Cost and Accuracy in Two-Point Complete Basis Set Extrapolation Schemes Using Efficient Diffuse Basis Sets
No full textA widely used procedure for obtaining the complete-basis set (CBS) limit of an electronic structure method is extrapolating results from a sequence of correlation-consistent basis sets. A recent study by Xi et al. trained two-point extrapolation schemes against a new extensive dataset using aug-cc-pVXZ (X = D, T, Q, 5, and 6) basis set pairs. Their results were very promising, providing a significant improvement over previous two-point extrapolation schemes. The present work shows that equally good results can be obtained at lower cost by using the smaller jun-cc-pVXZ or jul-cc-pVXZ basis sets, which contain fewer diffuse functions. Using the smaller jun and jul basis sets to extrapolate to the CBS limit provides a good compromise between accuracy and computational cost
Thioesters support efficient protein biosynthesis by the ribosome
No full textThioesters are critical chemical intermediates in numerous extant biochemical reactions and are invoked as key reagents during prebiotic peptide synthesis on an evolving Earth. Here we asked if a thioester could replace the native oxo-ester in acyl-tRNA substrates during protein biosynthesis by the ribosome. We prepared 3′-thio-3′-deoxyadenosine triphosphate in 10 steps from xylose and demonstrated that it is an effective substrate for the Escherichia coli CCA-adding enzyme, which appends 3′-thio-3′-deoxyadenosine to truncated tRNAs ending with 3′-CC. Using a variety of aminoacyl-tRNA synthetases, flexizymes, or a direct thioester exchange reaction, we prepared a suite of 3′-thio-tRNAs acylated with α- and non-α-amino acids. All were recognized and utilized by wild-type E. coli ribosomes during in vitro translation reactions to generate oligopeptides in yields commensurate with native oxo-ester tRNAs. These results indicate that thioester intermediates widely used in Nature can be co-opted to support the incorporation of natural α-amino acids as well as non-canonical monomers by the extant translational machinery for sequence-defined polymer synthesis
Nature of Reactive Sites in TS-1 from 15N solid-state NMR and Ti K-edge X-Ray Absorption Spectroscopic Signatures upon Pyridine Adsorption
No full textTi-containing zeotypes, notably titanosilicalite-1 (TS-1), are prominent examples of heterogeneous catalysts that have found applications in selective oxidation processes with hydrogen peroxide. Despite extensive characterization studies including using various probe molecules to interrogate the nature and the local environment of Ti sites, their detailed structure (as well as reactivity) remains elusive. Here, we demonstrate that using low temperature 15N magic angle spinning (MAS) ssNMR spectroscopy of adsorbed pyridine on TS-1 combined with Ti K-edge XANES on a range of samples (dehydrated, hydrated, contacted with H2O2 and pyridine) provides unique information regarding the Ti sites, highlighting their reactivity and dynamic nature. While dehydrated TS-1 shows only Lewis acid sites, the presence of H2O generates Brønsted acid sites, whose amount correlate with water loading. Moreover, the methodology – based on 15N ssNMR and Ti K-edge XANES – applied to a library of samples with various Ti-loadings and absence of extraframework TiO2 also enables quantification of the amount of Lewis acid sites and to establish a structure-activity descriptor (ratio of pyridine adsorbed on silanols vs. titanium). Complementary analysis including computational modelling reveals that the reaction of Ti sites with H2O generates an acidic bridging silanol Ti-(OH)-Si, upon hydrolysis of one Ti-O-Si linkage, where Ti expands its coordination from four to pentacoordinated according to XAS
Nanodroplets of Metal Carbonate Conform to Critical Nuclei of Classical Nucleation Theory
No full textMineral nucleation is a fundamental process of paramount importance to various fields, including geology, biomineralization, and industrial manufacturing. Recently, studies on the biominerals of calcium carbonate and phosphate have revealed the presence of a dense liquid phase prior to the formation of an amorphous solid phase. However, there is vigorous debate on whether the nucleation of calcium carbonate clusters can be adequately described by the classical nucleation theory (CNT) or if one must turn to the non-classical nucleation theory. Here, we show that liquid-liquid phase separation (LLPS) occurs in a non-aqueous solution where magnesium and calcium carbonates are dissolved in ethanol with excess triethylamine. The nanodroplets can be kinetically trapped by triethylamine. The size and number of the nanodroplets, in the vicinity of critical nuclei, can be adequately described within the framework of CNT. We found that bicarbonate-like species formed by triethylamine, CO2, and ethanol molecules are in constant exchange with sizable carbonate clusters. Our results demonstrate that the concepts of critical nuclei and the energy barrier of nucleation are indeed physically relevant. We argue that nanodroplets comprising highly solvated carbonate clusters are the key entities in the nucleation process, which falls within the realm of CNT
Data-efficient modeling of catalytic reactions via enhanced sampling and on-the-fly learning of machine learning potentials
No full textSimulating catalytic reactivity under operative conditions poses a significant challenge due to the dynamic nature of the catalysts and the high computational cost of electronic structure calculations. Machine learning potentials offer a promising avenue to simulate dynamics at a fraction of the cost, but they require datasets containing all relevant configurations, particularly reactive ones. Here we present a scheme to construct reactive potentials in a data-efficient manner. This is achieved by combining enhanced sampling methods first with Gaussian processes to discover transition paths and then with graph neural networks to obtain a uniformly accurate description. The necessary configurations are extracted via a Data-Efficient Active Learning (DEAL) procedure based on local environment uncertainty. We validated our approach by studying several reactions related to the decomposition of ammonia on iron-cobalt alloy catalysts. Our scheme proved efficient, requiring only ~1,000 DFT calculations per reaction, and robust, sampling reactive configurations from the different accessible pathways. Using this potential, we calculated free energy profiles and characterized reaction mechanisms, showing the ability to provide microscopic insights into complex processes under dynamic conditions
Alarming structural error rates in MOF databases used in data driven workflows identified via a novel metal oxidation state-based method
No full textMetal-organic frameworks (MOFs) are a diverse class of porous materials composed of inorganic nodes joined by organic linkers, currently under investigation for a wide range of applications including gas storage and separation where they have been commercialized. Given the labor-intensive nature of synthesizing and testing individual MOFs, high-throughput computational screening and machine learning (ML) methods are increasingly viewed as essential for facilitating MOF development. However, the structural fidelity of the “computation-ready” MOF databases used in such studies remains largely unquantified. We introduce MOSAEC, an algorithm that detects chemically invalid structures on the basis of metal oxidation states. MOSAEC was manually validated against ~16k MOF structures from the popular CoRE database, and was found to flag erroneous structures with 95% accuracy. Systematic examination of 14 leading experimental and hypothetical MOF databases containing >1.9 million MOFs reveals concerning structural error rates, exceeding 40% in most cases
CO2 utilization in a micellar system: synthesis of cyclic carbonates
No full textFixation of carbon dioxide from its waste streams into value added products, in organic synthesis processes, whilst challenging, would lead to great ecological benefits. To this end, a CO2 cycloaddition to epoxides seems most promising as it is a 100% atom economic reaction leading to valuable cyclic carbonates. However, many of the reaction systems utilized for their synthesis employ organic solvents, high temperatures, and high pressure. Thus, the micellar conditions reported here achieve a metal and organic solvent free approach, giving access to not only carbonates but also carbamates without the need for pressurization or heating. Extensive studies of interactions between micelles and carbon dioxide opens up pathways to use this gaseous reagent in aqueous media
Polariton Spectra under the Collective Coupling Regime. I. Efficient Simulation of Linear Spectra and Quantum Dynamics
No full textWe outline two general theoretical techniques to simulate polariton quantum dynamics and optical spectra under the collective coupling regimes described by a Holestein-Tavis-Cummings (HTC) model Hamiltonian. The first one takes the advantage of sparsity of the HTC Hamiltonian, which allows one to reduce the cost of acting polariton Hamiltonian onto a state vector to the linear order of the number of states, instead of the quadratic order. The second one is applying the well-known Chebyshev series expansion approach for quantum dynamics propagation and applying them to simulate the polariton dynamics in the HTC system, allowing one to use a much larger time step for propagation and only requires a few recursive operations of the Polariton Hamiltonian acting on state vectors. These two theoretical approaches are general and can be applied to any trajectory-based non-adiabatic quantum dynamics methods. We apply these two techniques with our previously developed Lindblad-Partially Linearized Density Matrix (L-PLDM) approach to simulating the linear absorption spectra of the HTC model system, with both inhomogeneous site energy disorder as well as dipolar orientational disorders. Our numerical results agree well with the previous analytic and numerical work