Bourns College of Engineering

Gaussian accelerated molecular dynamics (GaMD) is a robust computational method for simultaneous unconstrained enhanced sampling and free energy calculations of biomolecules. It works by adding a harmonic boost potential to smooth biomolecular potential energy surface and reduce energy barriers. GaMD greatly accelerates biomolecular simulations by orders of magnitude. Without the need to set predefined reaction coordinates or collective variables, GaMD provides unconstrained enhanced sampling and is advantageous for simulating complex biological processes. The GaMD boost potential exhibits a Gaussian distribution, thereby allowing for energetic reweighting via cumulant expansion to the second order (i.e., "Gaussian approximation"). This leads to accurate reconstruction of free energy landscapes of biomolecules. Hybrid schemes with other enhanced sampling methods, such as the replica exchange GaMD (rex-GaMD) and replica exchange umbrella sampling GaMD (GaREUS), have also been introduced, further improving sampling and free energy calculations. Recently, new "selective GaMD" algorithms including the ligand GaMD (LiGaMD) and peptide GaMD (Pep-GaMD) enabled microsecond simulations to capture repetitive dissociation and binding of small-molecule ligands and highly flexible peptides. The simulations then allowed highly efficient quantitative characterization of the ligand/peptide binding thermodynamics and kinetics. Taken together, GaMD and its innovative variants are applicable to simulate a wide variety of biomolecular dynamics, including protein folding, conformational changes and allostery, ligand binding, peptide binding, protein-protein/nucleic acid/carbohydrate interactions, and carbohydrate/nucleic acid interactions. In this review, we present principles of the GaMD algorithms and recent applications in biomolecular simulations and drug design.

Heterogeneous networks, HetNets, are known to enhance the bandwidth efficiency and throughput of wireless networks by more effectively utilizing the network resources. However, the higher density of users and access points in HetNets introduces significant inter-user interference that needs to be mitigated through complex and sophisticated interference cancellation schemes. Moreover, due to significant channel attenuation and the presence of hardware impairments, e.g. phase noise and amplifier nonlinearities, the vast bandwidth in the millimeterwave band has not been fully utilized to date. In order to enable the development of multi-Gigabit per second wireless networks, we introduce a novel millimeter-wave HetNet paradigm, termed hybrid HetNet, which exploits the vast bandwidth and propagation characteristics in the 60 GHz and 70-80 GHz bands to reduce the impact of interference in HetNets. Simulation results are presented to illustrate the performance advantage of hybrid HetNets with respect to traditional networks. Next, two specific transceiver structures that enable hand-offs from the 60 GHz band, i.e. the V-band to the 70-80 GHz band, i.e. the E-band, and vice versa are proposed. Finally, the practical and regulatory challenges for establishing a hybrid HetNet are outlined.

The antimalarial drug-resistance conundrum which threatens to reverse the great strides taken to curb the malaria scourge warrants an urgent need to find novel chemical scaffolds to serve as templates for the development of new antimalarial drugs. Plants represent a viable alternative source for the discovery of unique potential antiplasmodial chemical scaffolds. To expedite the discovery of new antiplasmodial compounds from plants, the aim of this study was to use phylogenetic analysis to identify higher plant orders and families that can be rationally prioritised for antimalarial drug discovery. We queried the PubMed database for publications documenting antiplasmodial properties of natural compounds isolated from higher plants. Thereafter, we manually collated compounds reported along with plant species of origin and relevant pharmacological data. We systematically assigned antiplasmodial-associated plant species into recognised families and orders, and then computed the resistance index, selectivity index and physicochemical properties of the compounds from each taxonomic group. Correlating the generated phylogenetic trees and the biological data of each clade allowed for the identification of 3 hot plant orders and families. The top 3 ranked plant orders were the (i) Caryophyllales, (ii) Buxales, and (iii) Chloranthales. The top 3 ranked plant families were the (i) Ancistrocladaceae, (ii) Simaroubaceae, and (iii) Buxaceae. The highly active natural compounds (IC50 ≤ 1 µM) isolated from these plant orders and families are structurally unique to the legacy antimalarial drugs. Our study was able to identify the most prolific taxa at order and family rank that we propose be prioritised in the search for potent, safe and drug-like antimalarial molecules.

Laser-based diagnostics and therapeutics show promise for many neurological disorders. However, the poor transparency of cranial bone (calvaria) limits the spatial resolution and interaction depth that can be achieved, thus constraining opportunity in this regard. Herein, we report preliminary results from efforts seeking to address this limitation through use of novel transparent cranial implants made from nanocrystalline yttria-stabilized zirconia (nc-YSZ). Using optical coherence tomography (OCT) imaging of underlying brain in an acute murine model, we show that signal strength is improved when imaging through nc-YSZ implants relative to native cranium. As such, this provides initial evidence supporting the feasibility of nc-YSZ as a transparent cranial implant material. Furthermore, it represents a crucial first step towards realization of an innovative new concept we are developing, which seeks to eventually provide a clinically-viable means for optically accessing the brain, on-demand, over large areas, and on a chronically-recurring basis, without need for repeated craniectomies.

From the clinical editor

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Noncoding RNA (ncRNA) has a key role in regulating gene expression, mediating fundamental processes and diseases via a variety of yet unknown mechanisms. Here, we review recent applications of conventional and enhanced Molecular Dynamics (MD) simulations methods to address the mechanistic function of large biomolecular systems that are tightly involved in the ncRNA function and that are of key importance in life sciences. This compendium focuses of three biomolecular systems, namely the CRISPR-Cas9 genome editing machinery, group II intron ribozyme and the ribonucleoprotein complex of the spliceosome, which edit and process ncRNA. We show how the application of a novel accelerated MD simulations method has been key in disclosing the conformational transitions underlying RNA binding in the CRISPR-Cas9 complex, suggesting a mechanism for RNA recruitment and clarifying the conformational changes required for attaining genome editing. As well, we discuss the use of mixed quantum-classical MD simulations in deciphering the catalytic mechanism of RNA splicing as operated by group II intron ribozyme, one of the largest ncRNA structures crystallized so far. Finally, we debate the future challenges and opportunities in the field, discussing the recent application of MD simulations for unraveling the functional biophysics of the spliceosome, a multi-mega Dalton complex of proteins and small nuclear RNAs that performs RNA splicing in humans. This showcase of applications highlights the current talent of MD simulations to dissect atomic-level details of complex biomolecular systems instrumental for the design of finely engineered genome editing machines. As well, this review aims at inspiring future investigations of several other ncRNA regulatory systems, such as micro and small interfering RNAs, which achieve their function and specificity using RNA-based recognition and targeting strategies.

Certain organophosphorus molecules are infamous due to their use as highly toxic nerve agents. The filtration materials currently in common use for protection against chemical warfare agents were designed before organophosphorus compounds were used as chemical weapons. A better understanding of the surface chemistry between simulant molecules and the individual filtration-material components is a critical precursor to the development of more effective materials for filtration, destruction, decontamination, and/or sensing of nerve agents. Here, we report on the surface adsorption and reactions of a sarin simulant molecule, dimethyl methylphosphonate (DMMP), with cupric oxide surfaces. In situ ambient pressure X-ray photoelectron and infrared spectroscopies are coupled with density functional calculations to propose mechanisms for DMMP decomposition on CuO. We find extensive room temperature decomposition of DMMP on CuO, with the majority of decomposition fragments bound to the CuO surface. We observe breaking of PO-CH3, P-OCH3, and P-CH3 bonds at room temperature. On the basis of these results, we identify specific DMMP decomposition mechanisms not seen on other metal oxides. Participation of lattice oxygen in the decomposition mechanism leads to significant changes in chemical and electronic surface environment, which are manifest in the spectroscopic and computational data. This study establishes a computational baseline for the study of highly toxic organophosphorous compounds on metal oxide surfaces.

Fluoroalkylether substances (ether PFAS) constitute a large group of emerging PFAS with uncertain environmental fate. Among them, GenX is the well-known alternative to perfluorooctanoic acid and one of the six proposed PFAS to be regulated by the U.S. Environmental Protection Agency. This study investigated the structure-biodegradability relationship for 12 different ether PFAS with a carboxylic acid headgroup in activated sludge communities. Only polyfluorinated ethers with at least one -CH₂- moiety adjacent to or a C=C bond in the proximity of the ether bond underwent active biotransformation via oxidative and hydrolytic O-dealkylation. The bioreactions at ether bonds led to the formation of unstable fluoroalcohol intermediates subject to spontaneous defluorination. We further demonstrated that this aerobic biotransformation/defluorination could complement the advanced reduction process in a treatment train system to achieve more cost-effective treatment for GenX and other recalcitrant perfluorinated ether PFAS. These findings provide essential insights into the environmental fate of ether PFAS, the design of biodegradable alternative PFAS, and the development of cost-effective ether PFAS treatment strategies.

Crassulacean acid metabolism (CAM) plants exhibit elevated drought and heat tolerance compared to C₃ and C₄ plants through an inverted pattern of day/night stomatal closure and opening for CO₂ assimilation. However, the molecular responses to water-deficit conditions remain unclear in obligate CAM species. In this study, we presented genome-wide transcription sequencing analysis using leaf samples of an obligate CAM species Kalanchoë fedtschenkoi under moderate and severe drought treatments at two-time points of dawn (2-h before the start of light period) and dusk (2-h before the dark period). Differentially expressed genes were identified in response to environmental drought stress and a whole genome wide co-expression network was created as well. We found that the expression of CAM-related genes was not regulated by drought stimuli in K. fedtschenkoi. Our comparative analysis revealed that CAM species (K. fedtschenkoi) and C₃ species (Arabidopsis thaliana, Populus deltoides 'WV94') share some common transcriptional changes in genes involved in multiple biological processes in response to drought stress, including ABA signaling and biosynthesis of secondary metabolites.

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Access to web-based platforms has enabled scientists to perform research remotely. A critical aspect of mass spectrometry data analysis is the inspection, analysis, and visualization of the raw data to validate data quality and confirm statistical observations. We developed the GNPS Dashboard, a web-based data visualization tool, to facilitate synchronous collaborative inspection, visualization, and analysis of private and public mass spectrometry data remotely.

We suggest a technique for constructing lower (existence) bounds for the fault-tolerant threshold to scalable quantum computation applicable to degenerate quantum codes with sublinear distance scaling. We give explicit analytic expressions combining probabilities of erasures, depolarizing errors, and phenomenological syndrome measurement errors for quantum low-density parity-check codes with logarithmic or larger distances. These threshold estimates are parametrically better than the existing analytical bound based on percolation.

With advanced image journaling tools, one can easily alter the semantic meaning of an image by exploiting certain manipulation techniques such as copy clone, object splicing, and removal, which mislead the viewers. In contrast, the identification of these manipulations becomes a very challenging task as manipulated regions are not visually apparent. This paper proposes a high-confidence manipulation localization architecture that utilizes resampling features, long short-term memory (LSTM) cells, and an encoder-decoder network to segment out manipulated regions from non-manipulated ones. Resampling features are used to capture artifacts, such as JPEG quality loss, upsampling, downsampling, rotation, and shearing. The proposed network exploits larger receptive fields (spatial maps) and frequency-domain correlation to analyze the discriminative characteristics between the manipulated and non-manipulated regions by incorporating the encoder and LSTM network. Finally, the decoder network learns the mapping from low-resolution feature maps to pixel-wise predictions for image tamper localization. With the predicted mask provided by the final layer (softmax) of the proposed architecture, end-to-end training is performed to learn the network parameters through back-propagation using the ground-truth masks. Furthermore, a large image splicing dataset is introduced to guide the training process. The proposed method is capable of localizing image manipulations at the pixel level with high precision, which is demonstrated through rigorous experimentation on three diverse datasets.

The recent discovery of ferromagnetism in two-dimensional van der Waals crystals has provoked a surge of interest in the exploration of fundamental spin interaction in reduced dimensions. However, existing material candidates have several limitations, notably lacking intrinsic room-temperature ferromagnetic order and air stability. Here, motivated by the anomalously high Curie temperature observed in bulk diluted magnetic oxides, we demonstrate room-temperature ferromagnetism in Co-doped graphene-like Zinc Oxide, a chemically stable layered material in air, down to single atom thickness. Through the magneto-optic Kerr effect, superconducting quantum interference device and X-ray magnetic circular dichroism measurements, we observe clear evidences of spontaneous magnetization in such exotic material systems at room temperature and above. Transmission electron microscopy and atomic force microscopy results explicitly exclude the existence of metallic Co or cobalt oxides clusters. X-ray characterizations reveal that the substitutional Co atoms form Co²⁺ states in the graphitic lattice of ZnO. By varying the Co doping level, we observe transitions between paramagnetic, ferromagnetic and less ordered phases due to the interplay between impurity-band-exchange and super-exchange interactions. Our discovery opens another path to 2D ferromagnetism at room temperature with the advantage of exceptional tunability and robustness.

Controlling the interactions between macrophages and biomaterials is critical for modulating the response to implants. While it has long been thought that biomaterial surface chemistry regulates the immune response, recent studies have suggested that material geometry may in fact dominate. Our previous work demonstrated that elongation of macrophages regulates their polarization toward a pro-healing phenotype. In this work, we elucidate how surface topology might be leveraged to alter macrophage cell morphology and polarization state. Using a deep etch technique, we fabricated titanium surfaces containing micro- and nanopatterned grooves, which have been previously shown to promote cell elongation. Morphology, phenotypic markers, and cytokine secretion of murine bone marrow derived macrophages on different groove widths were analyzed. The results suggest that micro- and nanopatterned grooves influenced macrophage elongation, which peaked on substrates with 400-500 nm wide grooves. Surface grooves did not affect inflammatory activation but drove macrophages toward an anti-inflammatory, pro-healing phenotype. While secretion of TNF-alpha remained low in macrophages across all conditions, macrophages secreted significantly higher levels of anti-inflammatory cytokine, IL-10, on intermediate groove widths compared to cells on other Ti surfaces. Our findings highlight the potential of using surface topography to regulate macrophage function, and thus control the wound healing and tissue repair response to biomaterials.

Heterostructured materials are an emerging class of materials with superior performances that are unattainable by their conventional homogeneous counterparts. They consist of heterogeneous zones with dramatic (>100%) variations in mechanical and/or physical properties. The interaction in these hetero-zones produces a synergistic effect where the integrated property exceeds the prediction by the rule-of-mixtures. The heterostructured materials field explores heterostructures to control defect distributions, long-range internal stresses, and nonlinear inter-zone interactions for unprecedented performances. This paper is aimed to provide perspectives on this novel field, describe the state-of-the-art of heterostructured materials, and identify and discuss key issues that deserve additional studies.

In this study, we present seasonal atmospheric measurements of δ13CCH4 from dairy farms in the San Joaquin Valley of California. We used δ13CCH4 to characterize emissions from enteric fermentation by measuring downwind of cattle housing (e.g., freestall barns, corrals) and from manure management areas (e.g., anaerobic manure lagoons) with a mobile platform equipped with cavity ring-down spectrometers. Across seasons, the δ13CCH4 from enteric fermentation source areas ranged from −69.7 ± 0.6 per mil (‰) to −51.6 ± 0.1‰ while the δ13CCH4 from manure lagoons ranged from −49.5 ± 0.1‰ to −40.5 ± 0.2‰. Measurements of δ13CCH4 of enteric CH4 suggest a greater than 10‰ difference between cattle production groups in accordance with diet. Isotopic signatures of CH4 were used to characterize enteric and manure CH4 from downwind plume sampling of dairies. Our findings show that δ13CCH4 measurements could improve the attribution of CH4 emissions from dairy sources at scales ranging from individual facilities to regions and help constrain the relative contributions from these different sources of emissions to the CH4 budget.