A deep learning model integrating a wind direction-based dynamic graph network for ozone prediction.

Ozone pollution is an important environmental issue in many countries. Accurate forecasting of ozone concentration enables relevant authorities to enact timely policies to mitigate adverse impacts. This study develops a novel hybrid deep learning model, named wind direction-based dynamic spatio-temporal graph network (WDDSTG-Net), for hourly ozone concentration prediction. The model uses a dynamic directed graph structure based on hourly changing wind direction data to capture evolving spatial relationships between air quality monitoring stations. It applied the graph attention mechanism to compute dynamic weights between connected stations, thereby aggregating neighborhood information adaptively. For temporal modeling, it utilized a sequence-to-sequence model with attention mechanism to extract long-range temporal dependencies. Additionally, it integrated meteorological predictions to guide the ozone forecasting. The model achieves a mean absolute error of 6.69 µg/m3 and 18.63 µg/m3 for 1-h prediction and 24-h prediction, outperforming several classic models. The model's IAQI accuracy predictions at all stations are above 75 %, with a maximum of 81.74 %. It also exhibits strong capabilities in predicting severe ozone pollution events, with a 24-h true positive rate of 0.77. Compared to traditional static graph models, WDDSTG-Net demonstrates the importance of incorporating short-term wind fluctuations and transport dynamics for data-driven air quality modeling. In principle, it may serve as an effective data-driven approach for the concentration prediction of other airborne pollutants.

Molecular dynamics simulation of the effect of temperature on the conformation of ubiquitin protein.

CONTENT: Ubiquitin, a ubiquitous small protein found in all living organisms, is crucial for tagging proteins earmarked for degradation and holds pivotal importance in biomedicine. Protein functionality is intricately linked to its structure. To comprehend the impact of diverse temperatures on ubiquitin protein structure, our study delved into the energy landscape, hydrogen bonding, and overall structural stability of ubiquitin protein at varying temperatures. Through meticulous analysis of root mean square deviation and root mean square fluctuation, we validated the robustness of the simulation conditions employed. Within our simulated system, the bonding energy and electrostatic potential energy exhibited linear augmentation, while the van der Waals energy demonstrated a linear decline. Additionally, our findings highlighted that the α-Helix secondary structure of the ubiquitin protein gradually transitions toward helix destabilization under high-temperature conditions. The secondary structure of ubiquitin protein experiences distinct changes under varying temperatures. The outcomes of our molecular simulations offer a theoretical framework that enhances our comprehension of how temperature impacts the structural stability of ubiquitin protein. These insights contribute not only to a deeper understanding of iniquity's behavior but also hold broader implications in the realm of biomedicine and beyond. METHODS: All the MD simulations were performed using the GROMACS software with GROMOS96 force field and SPC for water. The ubiquitin protein was put in the center of a cubic box with a length of 8 nm, a setting that allowed > 0.8 nm in the minimal distance between the protein surface and the box wall. To remove the possible coordinate collision of the configurations, in the beginning, the steepest descent method was used until the maximum force between atoms was under 100 kJ/mol·nm with a 0.01 nm step size. Minimization was followed by 30 ps of position-restrained MD simulation. The protein was restrained to its initial position, and the solvent was freely equilibrated. The product phase was obtained with the whole system simulated for 10 ns without any restraint using an integral time step of 1 fs with different temperatures. The cutoff for short-range electronic interaction was set to 1.5 nm. The long-range interactions were treated with a particle-mesh Ewald (PME) method with a grid width of 1.2 nm.

Integrated Computational Pipeline for the High-Throughput Discovery of Cell Adhesion Peptides.

Cell adhesion peptides (CAPs) often play a critical role in tissue engineering research. However, the discovery of novel CAPs for diverse applications remains a challenging and time-intensive process. This study presents an efficient computational pipeline integrating sequence embeddings, binding predictors, and molecular dynamics simulations to expedite the discovery of new CAPs. A Pro2vec model, trained on vast CAP data sets, was built to identify RGD-similar tripeptide candidates. These candidates were further evaluated for their binding affinity with integrin receptors using the Mutabind2 machine learning model. Additionally, molecular dynamics simulations were applied to model receptor-peptide interactions and calculate their binding free energies, providing a quantitative assessment of the binding strength for further screening. The resulting peptide demonstrated performance comparable to that of RGD in endothelial cell adhesion and spreading experimental assays, validating the efficacy of the integrated computational pipeline.

Density Functional Theory Study of CO2 Hydrogenation on Transition-Metal-Doped Cu(211) Surfaces.

The massive emission of CO2 has caused a series of environmental problems, including global warming, which exacerbates natural disasters and human health. Cu-based catalysts have shown great activity in the reduction of CO2, but the mechanism of CO2 activation remains ambiguous. In this work, we performed density functional theory (DFT) calculations to investigate the hydrogenation of CO2 on Cu(211)-Rh, Cu(211)-Ni, Cu(211)-Co, and Cu(211)-Ru surfaces. The doping of Rh, Ni, Co, and Ru was found to enhance CO2 hydrogenation to produce COOH. For CO2 hydrogenation to produce HCOO, Ru plays a positive role in promoting CO dissociation, while Rh, Ni, and Co increase the barriers. These results indicate that Ru is the most effective additive for CO2 reduction in Cu-based catalysts. In addition, the doping of Rh, Ni, Co, and Ru alters the electronic properties of Cu, and the activity of Cu-based catalysts was subsequently affected according to differential charge analysis. The analysis of Bader charge shows good predictions for CO2 reduction over Cu-based catalysts. This study provides some fundamental aids for the rational design of efficient and stable CO2-reducing agents to mitigate CO2 emission.

"Dividing and Conquering" and "Caching" in Molecular Modeling.

Molecular modeling is widely utilized in subjects including but not limited to physics, chemistry, biology, materials science and engineering. Impressive progress has been made in development of theories, algorithms and software packages. To divide and conquer, and to cache intermediate results have been long standing principles in development of algorithms. Not surprisingly, most important methodological advancements in more than half century of molecular modeling are various implementations of these two fundamental principles. In the mainstream classical computational molecular science, tremendous efforts have been invested on two lines of algorithm development. The first is coarse graining, which is to represent multiple basic particles in higher resolution modeling as a single larger and softer particle in lower resolution counterpart, with resulting force fields of partial transferability at the expense of some information loss. The second is enhanced sampling, which realizes "dividing and conquering" and/or "caching" in configurational space with focus either on reaction coordinates and collective variables as in metadynamics and related algorithms, or on the transition matrix and state discretization as in Markov state models. For this line of algorithms, spatial resolution is maintained but results are not transferable. Deep learning has been utilized to realize more efficient and accurate ways of "dividing and conquering" and "caching" along these two lines of algorithmic research. We proposed and demonstrated the local free energy landscape approach, a new framework for classical computational molecular science. This framework is based on a third class of algorithm that facilitates molecular modeling through partially transferable in resolution "caching" of distributions for local clusters of molecular degrees of freedom. Differences, connections and potential interactions among these three algorithmic directions are discussed, with the hope to stimulate development of more elegant, efficient and reliable formulations and algorithms for "dividing and conquering" and "caching" in complex molecular systems.

Molecular free energy optimization on a computational graph.

Free energy is arguably the most important property of molecular systems. Despite great progress in both its efficient estimation by scoring functions/potentials and more rigorous computation based on extensive sampling, we remain far from accurately predicting and manipulating biomolecular structures and their interactions. There are fundamental limitations, including accuracy of interaction description and difficulty of sampling in high dimensional space, to be tackled. Computational graph underlies major artificial intelligence platforms and is proven to facilitate training, optimization and learning. Combining autodifferentiation, coordinates transformation and generalized solvation free energy theory, we construct a computational graph infrastructure to realize seamless integration of fully trainable local free energy landscape with end to end differentiable iterative free energy optimization. This new framework drastically improves efficiency by replacing local sampling with differentiation. Its specific implementation in protein structure refinement achieves superb efficiency and competitive accuracy when compared with state of the art all-atom mainstream methods.

Identification of metal ion binding sites based on amino acid sequences.

The identification of metal ion binding sites is important for protein function annotation and the design of new drug molecules. This study presents an effective method of analyzing and identifying the binding residues of metal ions based solely on sequence information. Ten metal ions were extracted from the BioLip database: Zn2+, Cu2+, Fe2+, Fe3+, Ca2+, Mg2+, Mn2+, Na+, K+ and Co2+. The analysis showed that Zn2+, Cu2+, Fe2+, Fe3+, and Co2+ were sensitive to the conservation of amino acids at binding sites, and promising results can be achieved using the Position Weight Scoring Matrix algorithm, with an accuracy of over 79.9% and a Matthews correlation coefficient of over 0.6. The binding sites of other metals can also be accurately identified using the Support Vector Machine algorithm with multifeature parameters as input. In addition, we found that Ca2+ was insensitive to hydrophobicity and hydrophilicity information and Mn2+ was insensitive to polarization charge information. An online server was constructed based on the framework of the proposed method and is freely available at http://60.31.198.140:8081/metal/HomePage/HomePage.html.

Facile synthesis of monodisperse of hollow mesoporous SiO2 nanoparticles and in-situ growth of Ag nanoparticles for antibacterial.

Monodispersed hollow mesoporous silica nanoparticles (HMSNs) are successfully synthesized via a facile dual template method, in which poly(styrene-co-methyl methacrylate-co-methacrylic acid) (PS-PMMA-PMAA) particles are used as hard template for producing the hollow structure and cetyltrimethylammonium bromide (CTAB) used for introducing the mesopores in the silica shells. The obtained HMSNs possess uniform diameter and morphology, and the shell of which could be adjusted by changing the addition of silicon precursor. The synthesized HMSNs have been characterized by transmission electron microscopy (TEM) and nitrogen physisorption. Furthermore, the HMSNs are used as support for in-situ deposition of silver nanoparticles (Ag NPs) using n-butylamine as reducing agent for AgNO3 in ethanol. Significantly, Ag NPs were successfully supported in the HMSNs without any aggregation. The Ag-deposited HMSNs showed excellent dispersibility in ethanol and water, and their antibacterial activity against Escherichia coli (E. coli) ATCC 25922 and Staphylococcus aureus (S. aureus) ATCC 6538 have been demonstrated. Therefore, the unique nanostructure based on the HMSNs provided a useful platform for the fabrication of antibacterial agent with superior activity and accessibility. And also, it is expected to be a significant template for the synthesis of other novel nanostructures.