Combining Deep Learning and Structural Modeling to Identify Potential Acetylcholinesterase Inhibitors from Hericium erinaceus

Alzheimer's disease (AD) is the most common type of dementia, affecting over 50 million people worldwide. Currently, most approved medications for AD inhibit the activity of acetylcholinesterase (AChE), but these treatments often come with harmful side effects. There is growing interest in the use of natural compounds for disease prevention, alleviation, and treatment. This trend is driven by the anticipation that these substances may incur fewer side effects than existing medications. This research presents a computational approach combining machine learning with structural modeling to discover compounds from medicinal mushrooms with a high potential to inhibit the activity of AChE. First, we developed a deep neural network capable of rapidly screening a vast number of compounds to indicate their potential to inhibit AChE activity. Subsequently, we applied deep learning models to screen the compounds in the BACMUSHBASE database, which catalogs the bioactive compounds from cultivated and wild mushroom varieties local to Thailand, resulting in the identification of five promising compounds. Next, the five identified compounds underwent molecular docking techniques to calculate the binding energy between the compounds and AChE. This allowed us to refine the selection to two compounds, erinacerin A and hericenone B. Further analysis of the binding energy patterns between these compounds and the target protein revealed that both compounds displayed binding energy profiles similar to the combined characteristics of donepezil and galanthamine, the prescription drugs for AD. We propose that these two compounds, derived from Hericium erinaceus (also known as lion's mane mushroom), are suitable candidates for further research and development into symptom-alleviating AD medications.

Interpreting drug synergy in breast cancer with deep learning using target-protein inhibition profiles

BACKGROUND

Breast cancer is the most common malignancy among women worldwide. Despite advances in treating breast cancer over the past decades, drug resistance and adverse effects remain challenging. Recent therapeutic progress has shifted toward using drug combinations for better treatment efficiency. However, with a growing number of potential small-molecule cancer inhibitors, in silico strategies to predict pharmacological synergy before experimental trials are required to compensate for time and cost restrictions. Many deep learning models have been previously proposed to predict the synergistic effects of drug combinations with high performance. However, these models heavily relied on a large number of drug chemical structural fingerprints as their main features, which made model interpretation a challenge.

RESULTS

This study developed a deep neural network model that predicts synergy between small-molecule pairs based on their inhibitory activities against 13 selected key proteins. The synergy prediction model achieved a Pearson correlation coefficient between model predictions and experimental data of 0.63 across five breast cancer cell lines. BT-549 and MCF-7 achieved the highest correlation of 0.67 when considering individual cell lines. Despite achieving a moderate correlation compared to previous deep learning models, our model offers a distinctive advantage in terms of interpretability. Using the inhibitory activities against key protein targets as the main features allowed a straightforward interpretation of the model since the individual features had direct biological meaning. By tracing the synergistic interactions of compounds through their target proteins, we gained insights into the patterns our model recognized as indicative of synergistic effects.

CONCLUSIONS

The framework employed in the present study lays the groundwork for future advancements, especially in model interpretation. By combining deep learning techniques and target-specific models, this study shed light on potential patterns of target-protein inhibition profiles that could be exploited in breast cancer treatment.

Boolean modeling of breast cancer signaling pathways uncovers mechanisms of drug synergy

Breast cancer is one of the most common types of cancer in females. While drug combinations have shown potential in breast cancer treatments, identifying new effective drug pairs is challenging due to the vast number of possible combinations among available compounds. Efforts have been made to accelerate the process with in silico predictions. Here, we developed a Boolean model of signaling pathways in breast cancer. The model was tailored to represent five breast cancer cell lines by integrating information about cell-line specific mutations, gene expression, and drug treatments. The models reproduced cell-line specific protein activities and drug-response behaviors in agreement with experimental data. Next, we proposed a calculation of protein synergy scores (PSSs), determining the effect of drug combinations on individual proteins' activities. The PSSs of selected proteins were used to investigate the synergistic effects of 150 drug combinations across five cancer cell lines. The comparison of the highest single agent (HSA) synergy scores between experiments and model predictions from the MDA-MB-231 cell line achieved the highest Pearson's correlation coefficient of 0.58 with a great balance among the classification metrics (AUC = 0.74, sensitivity = 0.63, and specificity = 0.64). Finally, we clustered drug pairs into groups based on the selected PSSs to gain further insights into the mechanisms underlying the observed synergistic effects of drug pairs. Clustering analysis allowed us to identify distinct patterns in the protein activities that correspond to five different modes of synergy: 1) synergistic activation of FADD and BID (extrinsic apoptosis pathway), 2) synergistic inhibition of BCL2 (intrinsic apoptosis pathway), 3) synergistic inhibition of MTORC1, 4) synergistic inhibition of ESR1, and 5) synergistic inhibition of CYCLIN D. Our approach offers a mechanistic understanding of the efficacy of drug combinations and provides direction for selecting potential drug pairs worthy of further laboratory investigation.

Combined Deep Learning and Molecular Modeling Techniques on the Virtual Screening of New mTOR Inhibitors from the Thai Mushroom Database

The mammalian target of rapamycin (mTOR) is a protein kinase of the PI3K/Akt signaling pathway that regulates cell growth and division and is an attractive target for cancer therapy. Many reports on finding alternative mTOR inhibitors available in a database contain a mixture of active compound data with different mechanisms, which results in an increased complexity for training the machine learning models based on the chemical features of active compounds. In this study, a deep learning model supported by principal component analysis (PCA) and structural methods was used to search for an alternative mTOR inhibitor from mushrooms. The mTORC1 active compound data set from the PubChem database was first filtered for only the compounds resided near the first-generation inhibitors (rapalogs) within the first two PCA coordinates of chemical features. A deep learning model trained by the filtered data set captured the main characteristics of rapalogs and displayed the importance of steroid cores. After that, another layer of virtual screening by molecular docking calculations was performed on ternary complexes of FKBP12-FRB domains and six compound candidates with high "active" probability scores predicted by the deep learning models. Finally, all-atom molecular dynamics simulations and MMPBSA binding energy analysis were performed on two selected candidates in comparison to rapamycin, which confirmed the importance of ring groups and steroid cores for interaction networks. Trihydroxysterol from Lentinus polychrous Lev. was predicted as an interesting candidate due to the small but effective interaction network that facilitated FKBP12-FRB interactions and further stabilized the ternary complex.

A model of infection in honeybee colonies with social immunity

Honeybees (Apis mellifera) play a significant role in the pollination of various food crops and plants. In the past decades, honeybee management has been challenged with increased pathogen and environmental pressure associating with increased beekeeping costs, having a marked economic impact on the beekeeping industry. Pathogens have been identified as a contributing cause of colony losses. Evidence suggested a possible route of pathogen transmission among bees via oral-oral contacts through trophallaxis. Here we propose a model that describes the transmission of an infection within a colony when bee members engage in the trophallactic activity to distribute nectar. In addition, we examine two important features of social immunity, defined as collective disease defenses organized by honeybee society. First, our model considers the social segregation of worker bees. The segregation limits foragers, which are highly exposed to pathogens during foraging outside the nest, from interacting with bees residing in the inner parts of the nest. Second, our model includes a hygienic response, by which healthy nurse bees exterminate infected bees to mitigate horizontal transmission of the infection to other bee members. We propose that the social segregation forms the first line of defense in reducing the uptake of pathogens into the colony. If the first line of defense fails, the hygienic behavior provides a second mechanism in preventing disease spread. Our study identifies the rate of egg-laying as a critical factor in maintaining the colony's health against an infection. We propose that winter conditions which cease or reduce the egg-laying activity combined with an infection in early spring can compromise the social immunity defenses and potentially cause colony losses.

Self-Assembled Three-Dimensional Bi2Te3 Nanowire-PEDOT:PSS Hybrid Nanofilm Network for Ubiquitous Thermoelectrics

Thermoelectric generation capable of delivering reliable performance in the low-temperature range (<150 °C) for large-scale deployment has been a challenge mainly due to limited properties of thermoelectric materials. However, realizing interdependence of topological insulators and thermoelectricity, a new research dimension on tailoring and using the topological-insulator boundary states for thermoelectric enhancement has emerged. Here, we demonstrate a promising hybrid nanowire of topological bismuth telluride (Bi₂Te₃) within the conductive poly(3,4-ethylenedioxythiophene):polystyrenesulfonate (PEDOT:PSS) matrix using the in situ one-pot synthesis to be incorporated into a three-dimensional network of self-assembled hybrid thermoelectric nanofilms for the scalable thermoelectric application. Significantly, the nanowire-incorporated film network exhibits simultaneous increase in electrical conductivity and Seebeck coefficient as opposed to reduced thermal conductivity, improving thermoelectric performance. Based on comprehensive measurements for electronic transport of individual nanowires revealing an interfacial conduction path along the Bi₂Te₃ core inside the encapsulating layer and that the hybrid nanowire is n-type semiconducting, the enhanced thermoelectricity is ascribed to increased hole mobility due to electron transfer from Bi₂Te₃ to PEDOT:PSS and importantly charge transport via the Bi₂Te₃-PEDOT:PSS interface. Scaling up the nanostructured material to construct a thermoelectric generator having the generic pipeline-insulator geometry, the device exhibits a power factor and a figure of merit of 7.45 μW m^-1 K^-2 and 0.048, respectively, with an unprecedented output power of 130 μW and 15 day operational stability at Δ T = 60 °C. Our findings not only encourage a new approach to cost-effective thermoelectric generation, but they could also provide a route for the enhancement of other applications based on the topological nanowire.

An elastic model of DNA under thermal induced stress

DNAs (deoxyribonucleic acids) have the ability to alter its conformation in response to temperature changes to relieve the internal stress. The mistakenly structured DNA can lead to diseases or deformities. The conformation also influences the binding between DNA and other molecular complexes. In the present paper, we investigate the DNA elasticity under the influence of thermal induced stress by employing Kirchhoff's model of a thin elastic rod. The problem is solved by perturbation method to find equilibrium configurations of DNA at different modes. In addition, the model is validated with existing literature in which DNA is stretched or compressed. The behaviors of the helical structure under various temperatures are investigated with the melting temperature of DNA around 80 °C. This elasticity study of DNA could be a groundwork leading to better understandings on the effects of thermal induced stress to DNA's deformation and relevant biological processes in living cells.

Piezoelectric-Induced Triboelectric Hybrid Nanogenerators Based on the ZnO Nanowire Layer Decorated on the Au/polydimethylsiloxane-Al Structure for Enhanced Triboelectric Performance

Here, we demonstrate a novel device structure design to enhance the electrical conversion output of a triboelectric device through the piezoelectric effect called as the piezo-induced triboelectric (PIT) device. By utilizing the piezopotential of ZnO nanowires embedded into the polydimethylsiloxane (PDMS) layer attached on the top electrode of the conventional triboelectric device (Au/PDMS-Al), the PIT device exhibits an output power density of 50 μW/cm², which is larger than that of the conventional triboelectric device by up to 100 folds under the external applied force of 8.5 N. We found that the effect of the external piezopotential on the top Au electrode of the triboelectric device not only enhances the electron transfer from the Al electrode to PDMS but also boosts the internal built-in potential of the triboelectric device through an external electric field of the piezoelectric layer. Furthermore, 100 light-emitting diodes (LEDs) could be lighted up via the PIT device, whereas the conventional device could illuminate less than 20 LED bulbs. Thus, our results highlight that the enhancement of the triboelectric output can be achieved by using a PIT device structure, which enables us to develop hybrid nanogenerators for various self-power electronics such as wearable and mobile devices.