Computational Methods Research Articles

AIUPred: combining energy estimation with deep learning for the enhanced prediction of protein disorder.

Intrinsically disordered proteins and protein regions (IDPs/IDRs) carry out important biological functions without relying on a single well-defined conformation. As these proteins are a challenge to study experimentally, computational methods play important roles in their characterization. One of the commonly used tools is the IUPred web server which provides prediction of disordered regions and their binding sites. IUPred is rooted in a simple biophysical model and uses a limited number of parameters largely derived on globular protein structures only. This enabled an incredibly fast and robust prediction method, however, its limitations have also become apparent in light of recent breakthrough methods using deep learning techniques. Here, we present AIUPred, a novel version of IUPred which incorporates deep learning techniques into the energy estimation framework. It achieves improved performance while keeping the robustness of the original method. Based on the evaluation of recent benchmark datasets, AIUPred scored amongst the top three single sequence based methods. With a new web server we offer fast and reliable visual analysis for users as well as options to analyze whole genomes in mere seconds with the downloadable package. AIUPred is available at https://aiupred.elte.hu.

PCSK9 inhibitors as safer therapeutics for atherosclerotic cardiovascular disease (ASCVD): Pharmacophore design and molecular dynamics analysis.

Cardiovascular disorders are still challenging and are among the deadly diseases. As a major risk factor for atherosclerotic cardiovascular disease, dyslipidemia, and high low-density lipoprotein cholesterol in particular, can be prevented primary and secondary by lipid-lowering medications. Therefore, insights are still needed into designing new drugs with minimal side effects. Proprotein convertase subtilisin/kexin 9 (PCSK9) enzyme catalyses protein-protein interactions with low-density lipoprotein, making it a critical target for designing promising inhibitors compared to statins. Therefore, we screened for potential compounds using a redesigned PCSK9 conformational behaviour to search for a significantly extensive chemical library and investigated the inhibitory mechanisms of the final compounds using integrated computational methods, from ligand essential functional group screening to all-atoms MD simulations and MMGBSA-based binding free energy. The inhibitory mechanisms of the screened compounds compared with the standard inhibitor. K31 and K34 molecules showed stronger interactions for PCSK9, having binding energy (kcal/mol) of -33.39 and -63.51, respectively, against -27.97 of control. The final molecules showed suitable drug-likeness, non-mutagenesis, permeability, and high solubility values. The C-α atoms root mean square deviation and root mean square fluctuation of the bound-PCSK9 complexes showed stable and lower fluctuations compared to apo PCSK9. The findings present a model that unravels the mechanism by which the final molecules proposedly inhibit the PCSK9 function and could further improve the design of novel drugs against cardiovascular diseases.

Advancements in machine learning for predicting phases in high-entropy alloys: a comprehensive review

High entropy alloys (HEAs) are distinguished by their enhanced physicochemical properties, attributed to the formation of various phases such as solid solution (SS), intermetallic (IM), or a combination (SS + IM). These phases contribute distinctively to the microstructure of the alloys. A critical aspect of alloy design revolves around accurately predicting these phases, which has led to the integration of sophisticated data vetting methods and Machine Learning (ML) algorithms in recent research. This review paper aims to provide a comprehensive analysis of the advancements in phase prediction accuracy within HEAs, an essential component in the development of these alloys. HEAs are known for their intricate compositions, offering a wide spectrum of material properties, making them particularly relevant for applications aimed at future sustainability. Phase engineering in HEAs unlocks the potential for creating materials tailored to eco-friendly technologies and energy-efficient solutions. The challenge in predicting phase selection in HEAs is accentuated by the limited data available on these complex materials. This review delves into how advanced data vetting techniques and ML algorithms are being employed to overcome these challenges, thus contributing significantly to sustainable material design. The paper examines various algorithms used in HEA phase prediction, including KNN (K-Nearest Neighbors), SVM (Support Vector Machines), ANN (Artificial Neural Networks), GNB (Gaussian Naive Bayes), and RF (Random Forest). It discusses the testing accuracy of these algorithms in classifying HEA phases, revealing variations in their effectiveness. The review highlights the superior accuracy of ANNs, followed closely by KNN and SVM, while noting the comparatively lower accuracy of GNB. This comprehensive review synthesizes current research efforts in utilizing computational methods to design HEAs, underlining their broader implications in expediting the discovery and development of diverse metal alloys. These efforts are pivotal in meeting the evolving demands of modern engineering applications, thereby contributing to the advancement of materials science.

Destabilization and Degradation of a Disease-Linked PGM1 Protein Variant.

PGM1-linked congenital disorder of glycosylation (PGM1-CDG) is an autosomal recessive disease characterized by several phenotypes, some of which are life-threatening. Research focusing on the disease-related variants of the α-D-phosphoglucomutase 1 (PGM1) protein has shown that several are insoluble in vitro and expressed at low levels in patient fibroblasts. Due to these observations, we hypothesized that some disease-linked PGM1 protein variants are structurally destabilized and subject to protein quality control (PQC) and rapid intracellular degradation. Employing yeast-based assays, we show that a disease-associated human variant, PGM1 L516P, is insoluble, inactive, and highly susceptible to ubiquitylation and rapid degradation by the proteasome. In addition, we show that PGM1 L516P forms aggregates in S. cerevisiae and that both the aggregation pattern and the abundance of PGM1 L516P are chaperone-dependent. Finally, using computational methods, we perform saturation mutagenesis to assess the impact of all possible single residue substitutions in the PGM1 protein. These analyses identify numerous missense variants with predicted detrimental effects on protein function and stability. We suggest that many disease-linked PGM1 variants are subject to PQC-linked degradation and that our in silico site-saturated data set may assist in the mechanistic interpretation of PGM1 variants.

Contemporary Assessment of Energy Degeneracy in Orbital Mixing with Tetravalent f-Block Compounds.

The f block is a comparatively understudied group of elements that find applications in many areas. Continued development of technologies involving the lanthanides (Ln) and actinides (An) requires a better fundamental understanding of their chemistry. Specifically, characterizing the electronic structure of the f elements presents a significant challenge due to the spatially core-like but energetically valence-like nature of the f orbitals. This duality led f-block scientists to hypothesize for decades that f-block chemistry is dominated by ionic metal-ligand interactions with little covalency because canonical covalent interactions require both spatial orbital overlap and orbital energy degeneracy. Recent studies on An compounds have suggested that An ions can engage in appreciable orbital mixing between An 5f and ligand orbitals, which was attributed to "energy-degeneracy-driven covalency". This model of bonding has since been a topic of debate because different computational methods have yielded results that support and refute the energy-degeneracy-driven covalency model. In this Viewpoint, literatures concerning the metal- and ligand-edge X-ray absorption near-edge structure (XANES) of five tetravalent f-block systems─MO2 (M = Ln, An), LnF4, MCl62-, and [Ln(NP(pip)3)4]─are compiled and discussed to explore metal-ligand bonding in f-block compounds through experimental metrics. Based on spectral assignments from a variety of theoretical models, covalency is seen to decrease from CeO2 and PrO2 to TbO2 through weaker ligand-to-metal charge-transfer (LMCT) interactions, while these LMCT interactions are not observed in the trivalent Ln sesquixodes until Yb. In comparison, while XANES characterization of AnO2 compounds is scarce, computational modeling of available X-ray absorption spectra suggests that covalency among AnO2 reaches a maximum between Am and Cm. Moreover, a decrease in covalency is observed upon changing ligands while maintaining an isostructural coordination environment from CeO2 to CeF4. These results could allude to the importance of orbital energy degeneracy in f-block bonding, but there are a variety of data gaps and conflicting results from different modeling techniques that need to be addressed before broad conclusions can be drawn.

Theoretical Perspective on the Design of Surface Frustrated Lewis Pairs for Small-Molecule Activation.

The excellent reactivity of frustrated Lewis pairs (FLP) to activate small molecules has gained increasing attention in recent decades. Though the development of surface FLP (SFLP) is prompting the application of FLP in the chemical industry, the design of SFLP with superior activity, high density, and excellent stability for small-molecule activation is still challenging. Herein, we review the progress of designing SFLP by surface engineering, screening natural SFLP, and the dynamic formation of SFLP from theoretical perspectives. We highlight the breakthrough in fine-tuning the activity, density, and stability of the designed SFLP studied by using computational methods. We also discuss future challenges and directions in designing SFLP with outstanding capabilities for small-molecule activation.

New sexually transmitted HIV infections from 2016 to 2050 in Guangdong Province, China: a study based on a dynamic compartmental model

BackgroundIn Guangdong Province, China, there is lack of information on the HIV epidemic among high-risk groups and the general population, particularly in relation to sexual transmission, which is a predominant route. The new HIV infections each year is also uncertain owing to HIV transmission from men who have sex with men (MSM) to women, as a substantial proportion of MSM also have female sexual partnerships to comply with social demands in China.MethodsA deterministic compartmental model was developed to predict new HIV infections in four risk groups, including heterosexual men and women and low- and high-risk MSM, in Guangdong Province from 2016 to 2050, considering HIV transmission from MSM to women. The new HIV infections and its 95% credible interval (CrI) were predicted. An adaptive sequential Monte Carlo method for approximate Bayesian computation (ABC-SMC) was used to estimate the unknown parameter, a mixing index. We calibrated our results based on new HIV diagnoses and proportions of late diagnoses. The Morris and Sobol methods were applied in the sensitivity analysis.ResultsNew HIV infections increased during and 2 years after the COVID-19 pandemic, then declined until 2050. New infections rose from 8,828 [95% credible interval (CrI): 6,435–10,451] in 2016 to 9,652 (95% CrI: 7,027–11,434) in 2019, peaking at 11,152 (95% CrI: 8,337–13,062) in 2024 before declining to 7,084 (95% CrI: 5,165–8,385) in 2035 and 4,849 (95% CrI: 3,524–5,747) in 2050. Women accounted for approximately 25.0% of new HIV infections, MSM accounted for 40.0% (approximately 55.0% of men), and high-risk MSM accounted for approximately 25.0% of the total. The ABC-SMC mixing index was 0.504 (95% CrI: 0.239–0.894).ConclusionsGiven that new HIV infections and the proportion of women were relatively high in our calibrated model, to some extent, the HIV epidemic in Guangdong Province remains serious, and services for HIV prevention and control are urgently needed to return to the levels before the COVID-19 epidemic, especially in promoting condom-based safe sex and increasing awareness of HIV prevention to general population.

A bio-ionic liquid based self-healable and adhesive ionic hydrogel for the on-demand transdermal delivery of a chemotherapeutic drug.

The non-invasive nature and potential for sustained release make transdermal drug administration an appealing treatment option for cancer therapy. However, the strong barrier of the stratum corneum (SC) poses a challenge for the penetration of hydrophilic chemotherapy drugs such as 5-fluorouracil (5-FU). Due to its biocompatibility and capacity to increase drug solubility and permeability, especially when paired with chemical enhancers, such as oleic acid (OA), which is used in this work, choline glycinate ([Cho][Gly]) has emerged as a potential substance for transdermal drug delivery. In this work, we examined the possibility of transdermal delivery of 5-FU for the treatment of breast cancer using an ionic hydrogel formulation consisting of [Cho][Gly] with OA. Small angle neutron scattering, rheological analysis, field emission scanning electron microscopy, and dynamic light scattering analysis were used to characterize the ionic hydrogel. The non-covalent interactions present between [Cho][Gly] and OA were investigated by computational simulations and FTIR spectroscopy methods. When subjected to in vitro drug permeation using goat skin in a Franz diffusion cell, the hydrogel demonstrated sustained release of 5-FU and effective permeability in the order: [Cho][Gly]-OA gel > [Cho][Gly] > PBS (control). The hydrogel also demonstrated 92% cell viability after 48 hours for the human keratinocyte cell line (HaCaT cells) as well as the normal human cell line L-132. The breast cancer cell line MCF-7 and the cervical cancer cell line HeLa were used to study in vitro cytotoxicity that was considerably affected by the 5-FU-loaded hydrogel. These results indicate the potential of the hydrogel as a transdermal drug delivery vehicle for the treatment of breast cancer.

DeepPepPI: A deep cross-dependent framework with information sharing mechanism for predicting plant peptide-protein interactions

Peptide-protein interactions (PepPIs) play a crucial role in various fundamental biological activities in plants. As traditional experimental technologies are usually time-consuming and labor-intensive, it is indispensable to develop computational methods for PepPI identification, which has potential applications across diverse fields including food safety and peptide drug discovery. However, existing methods have received relatively little attention in adequately representing the interaction information between peptides and proteins, consequently limiting the prediction performance. To this end, we present DeepPepPI, a novel deep cross-dependent framework for accurate prediction of plant PepPIs. Concretely, a data-driven context-embedded representation (DCR) module is developed as the peptide feature extractor that can capture rich contextual semantic information, even from short sequences. To fully characterize inherent properties of proteins, we propose a bi-level self-correlation search (BSS) module, which integrates the primary sequence and secondary structure into a unified space to learn their potential relationships. In addition, a cross-dependent feature integration (CFI) module with information sharing mechanism is introduced, aimed at providing a comprehensive feature representation to portray the intricate interaction patterns between peptides and proteins from a global perspective. Extensive experimental results conducted on the benchmark datasets show that DeepPepPI achieves superior prediction performance compared to other state-of-the-art methods.

Mechanism of Z-Selective Allylic Functionalization via Thianthrenium Salts.

A detailed mechanistic study of the Z-selective allylic functionalization via thianthrenium salts is presented. Kinetic analyses, deuterium labeling experiments, and computational methods are used to rationalize the observed reactivity and selectivity. We find that the reaction proceeds via a rate-determining and stereodetermining allylic deprotonation of an alkenylthianthrenium species. The Z-configuration of the resultant allylic ylide is translated into the Z-allylic amine product through a sequence of subsequent fast and irreversible steps: protonation to form a Z-allylic thianthrenium electrophile and then regioselective substitution by the nucleophile. In the stereodetermining deprotonation step, computational studies identified a series of stabilizing nonbonding interactions in the Z-alkene-forming transition state that contribute to the stereoselectivity.

Optimal solving of a binary knapsack problem on a D-Wave quantum machine and its implementation in production systems

AbstractThe efficient management of complex production systems is a challenge in today’s logistics. In the field of intelligent and sustainable logistics, the optimization of production batches, especially in the context of a rapidly changing product range, requires fast and precise computational solutions. This paper explores the potential of quantum computers for solving these problems. Traditional computational methods are often limited when it comes to optimizing complex logistics systems. In response to these challenges, the paper proposes the use of a hybrid algorithm that combines quantum technologies with classical computational methods. Such integration allows the computational power of both types of technologies to be harnessed, leading to faster and more efficient identification of optimal solutions. In this work, we consider the knapsack problem, a classic NP-hard optimization problem that is commonly used to verify the effectiveness of new algorithm construction methods. The algorithm presented is based on the Branch and Bound method and aims to ensure solution optimality in the context of the non-determinism of quantum computers. Within the algorithm, computations are performed alternately on a classical processor and a quantum processor. In addition, the lower and upper bounds of the objective function are computed in constant time using the D-Wave quantum machine.

Prediction of Age-Related MicroRNA Signature in Mesenchymal Stem Cells by using Computational Methods.

Aging is a phenomenon which occurs over time and leads to the decay of living organisms. During the progression of aging, some age-associated diseases including cardiovascular disease, cancers, and neurological, mental, and physical disorders could develop. Genetic and epigenetic factors like microRNAs, as one of the post-transcriptional regulators of genes, play important roles in senescence. The self-renewal and differentiation capacity of mesenchymal stem cells makes them good candidates for regenerative medicine. The objective of this study is to evaluate senescence-related miRNAs in human MSCs using bioinformatics analysis. In this study, the Gene Expression Omnibus (GEO) database was used to investigate the senescence-related genome profile. Then, down-regulated genes were selected for further bioinformatics analysis with the assumption that their decreased expression is associated with an increased aging process. Considering that miRNAs can interfere in gene expression, miRNAs complementary to these genes were identified using bioinformatics software. Through bioinformatics analysis, we predicted hsa-miR-590-3p, hsa-miR-10b-3p, hsamiR- 548 family, hsa-miR-144-3p, and hsa-miR-30b-5p which involve in cellular senescence and the aging of human MSCs. miRNA mimics or anti-miRNA agents have the potential to be used as anti-aging tools for MSCs.

Exploring the activation mechanism of metabotropic glutamate receptor 2

Homomeric dimerization of metabotropic glutamate receptors (mGlus) is essential for the modulation of their functions and represents a promising avenue for the development of novel therapeutic approaches to address central nervous system diseases. Yet, the scarcity of detailed molecular and energetic data on mGlu2 impedes our in-depth comprehension of their activation process. Here, we employ computational simulation methods to elucidate the activation process and key events associated with the mGlu2, including a detailed analysis of its conformational transitions, the binding of agonists, Gi protein coupling, and the guanosine diphosphate (GDP) release. Our results demonstrate that the activation of mGlu2 is a stepwise process and several energy barriers need to be overcome. Moreover, we also identify the rate-determining step of the mGlu2's transition from the agonist-bound state to its active state. From the perspective of free-energy analysis, we find that the conformational dynamics of mGlu2's subunit follow coupled rather than discrete, independent actions. Asymmetric dimerization is critical for receptor activation. Our calculation results are consistent with the observation of cross-linking and fluorescent-labeled blot experiments, thus illustrating the reliability of our calculations. Besides, we also identify potential key residues in the Gi protein binding position on mGlu2, mGlu2 dimer's TM6-TM6 interface, and Gi α5 helix by the change of energy barriers after mutation. The implications of our findings could lead to a more comprehensive grasp of class C G protein-coupled receptor activation.

Predicting the properties of perovskite materials by improved compositionally restricted attention-based networks and explainable machine learning

Understanding the unique properties of perovskite materials is crucial in advancing solar energy technologies. Factors like heat of formation and bandgap significantly influence the light absorption capability and stability of perovskite solar cells. However, it is time-consuming and labor-intensive to obtain the properties of perovskites using traditional experimental or high-throughput computational methods. As a prospective method, machine learning can find regularities in the given training data and give accurate prediction results. In this article, we use deep learning models based on attention mechanisms and elemental features to predict the heat of formation and bandgap of perovskite materials. Random Forest and Gradient Boosted Regression Tree models have also been used for interpretable predictions of properties. The compositionally restricted attention-based network was improved by introducing a densely connected network and optimizing the network structure to increase data processing capabilities. The experiment results show that the mean absolute errors of the heat of formation and bandgap on the test sets are decreased by 5.77% and 3.37% respectively. The optimized model also shows better performance when used for classification tasks. In addition, we use the gradient boosting regression tree model and the shapley additive explanations tool to conduct an interpretable analysis, explaining the impact of different features on the predictions of the properties.

Combined Computational and Experimental Study Reveals Complex Mechanistic Landscape of Brønsted Acid-Catalyzed Silane-Dependent P═O Reduction.

The reaction mechanism of Brønsted acid-catalyzed silane-dependent P═O reduction has been elucidated through combined computational and experimental methods. Due to its remarkable chemo- and stereoselective nature, the Brønsted acid/silane reduction system has been widely employed in organophosphine-catalyzed transformations involving P(V)/P(III) redox cycle. However, the full mechanistic profile of this type of P═O reduction has yet to be clearly established to date. Supported by both DFT and experimental studies, our research reveals that the reaction likely proceeds through mechanisms other than the widely accepted "dual activation mode by silyl ester" or "acid-mediated direct P═O activation" mechanism. We propose that although the reduction mechanisms may vary with the substitution patterns of silane species, Brønsted acid generally activates the silane rather than the P═O group in transition structures. The proposed activation mode differs significantly from that associated with traditional Brønsted acid-catalyzed C═O reduction. The uniqueness of P═O reduction originates from the dominant Si/O═P orbital interactions in transition structures rather than the P/H-Si interactions. The comprehensive mechanistic landscape provided by us will serve as a guidance for the rational design and development of more efficient P═O reduction systems as well as novel organophosphine-catalyzed reactions involving P(V)/P(III) redox cycle.

Cyclization Reactions of 1,5-Diynes: Mechanisms and the Role of the Central Linker.

This study employed computational methods to elucidate the influence of structural features on the cyclization pathways of 1,5-diynes through the 5-endo-dig and 6-endo-dig mechanisms. The results revealed that the nature of the central linker played a significant role in dictating the preferred cyclization pathway. Notably, the capacity of this linker to extend delocalization appears to be the key factor governing the reaction pathway preference.

Numerical Simulation Analysis of the Correlation between Hydrodynamic Noise of Hydraulic Turbines and Defects in Runner Blades

To investigate the hydrodynamic noise characterization of hydraulic turbines with runner blade defects, this article establishes the intact machine model and three kinds of models with runner blade defects. Using the Computational Fluid Dynamic (CFD) and Computational Acoustic (CA) hybrid simulation computational methods, the hydrodynamic noise field of the hydraulic turbine is numerically simulated, and the results of the acoustic near field and acoustic far field are shown. 1. The double-row leaf grille and the runner are the primary sound source areas of the hydraulic turbine, and the intensity of sound radiation from these areas is positively correlated with the degree of runner blade defects. 2. As the runner blade defects develop, the sound power level (SWL) increases more significantly in the guide vanes near the nose of the spiral case in the double-row leaf grille. The most pronounced increase in the SWL is observed at the defective craters on the runner blades. 3. The frequency of the defective noise signal is primarily concentrated in the low-frequency band. The dominant frequency amplitude associated with runner blade defects increases and rises after the occurrence of defects. Secondary frequency changes are also observed, and the location of these changes varies at different receiving points.

Turbomachines seal flow resistance enhancement and leakage reduction based on flow control method with bow-shaped auxiliary teeth

Improving sealing performance without clearance reduction is important for the economic and safety operation of modern turbomachines. For this purpose, this study proposes the bow-shaped auxiliary teeth to reduce the leakage. One bow-shaped auxiliary tooth is installed after each seal tooth to guide the leakage jet towards the side wall of seal tooth. Then the leakage jet attaches itself to the inner wall of cavity. The Computational Fluid Dynamics method is used to calculate the flow field in the new seal. Results show that flow impingements, flow deflections, and enhanced vena contraction effect increase the leakage flow resistance and reduce the leakage velocity. The application of the bow-shaped auxiliary teeth reduces the leakage flow rate by 43.8–46.3 % compared with the labyrinth seal. Geometry parameters analysis shows that reducing the axial distance between seal tooth and bow-shaped auxiliary tooth, and increasing the central angle of bow-shaped auxiliary tooth are beneficial for leakage reduction. With the increase in the radius of bow-shaped auxiliary tooth, the leakage firstly decreases and then changes a little.

The Challenges of Engaging African American Communities During a Public Health Crisis: The Role of Government Information, COVID-19 Discourse, and Emotional Content on Social Media

ABSTRACT During a public health crisis, social media platforms play a pivotal role in circulating information and influencing public reactions. This research investigates the dynamics of public engagement with COVID-19-related content and government information sources within African American online communities, a population that has experienced significant health risks and inequities. Using advanced computational research methods, we analyzed 199,542 posts from 1,152 communities created between January 2020 and December 2022. The present research focused on the presence of COVID-19-related content, government information sources, the emotions of anger and fear in these posts, and their associations with user engagement metrics. The results indicated that posts discussing COVID-19 and those incorporating government information sources tend to receive lower levels of engagement. On the other hand, posts with higher levels of anger generated more shares and comments. The findings suggest a “triple disadvantage” in user engagement for social media messages that reference government sources and discuss public health risks without delivering strong negative emotions. These patterns are crucial not only for understanding the challenges faced by the at-risk population but also for aiding researchers and practitioners in developing more effective communication strategies during public health crises.

A computationally efficient gradient-enhanced healing model for soft biological tissues.

Soft biological tissues, such as arterial tissue, have the ability to grow and remodel in response to damage. Computational method plays a critical role in understanding the underlying mechanisms of tissue damage and healing. However, the existing healing model often requires huge computation time and it is inconvenient to implement finite element simulation. In this paper, we propose a computationally efficient gradient-enhanced healing model that combines the advantages of the gradient-enhanced damage model, the homeostatic-driven turnover remodeling model, and the damage-induced growth model. In the proposed model, the evolution of healing-related parameters can be solved explicitly. Additionally, an adaptive time increment method is used to further reduce computation time. The proposed model can be easily implemented in Abaqus, requiring only a user subroutine UMAT. The effectiveness of proposed model is verified through a semi-analytical example, and the influence of the variables in the proposed model is investigated using uniaxial tension and open-hole plate tests. Finally, the long-term development of aneurysms is simulated to demonstrate the potential applications of the proposed model in real biomechanical problems.