The use of Xuanbai Chengqi decoction on monkeypox disease through the estrone-target AR interaction.

Introduction: After COVID-19, there was an outbreak of a new infectious disease caused by monkeypox virus. So far, no specific drug has been found to treat it. Xuanbai Chengqi decoction (XBCQD) has shown effects against a variety of viruses in China. Methods: We searched for the active compounds and potential targets for XBCQD from multiple open databases and literature. Monkeypox related targets were searched out from the OMIM and GeneCards databases. After determining the assumed targets of XBCQD for monkeypox treatment, we built the PPI network and used R for GO enrichment and KEGG pathway analysis. The interactions between the active compounds and the hub targets were investigated by molecular docking and molecular dynamics (MD) simulations. Results: In total, 5 active compounds and 10 hub targets of XBCQD were screened out. GO enrichment and KEGG analysis demonstrated that XBCQD plays a therapeutic role in monkeypox mainly by regulating signaling pathways related to viral infection and inflammatory response. The main active compound estrone binding to target AR was confirmed to be the best therapy choice for monkeypox. Discussion: This study systematically explored the interactions between the bioactive compounds of XBCQD and the monkeypox-specific XBCQD targets using network pharmacological methods, bioinformatics analyses and molecular simulations, suggesting that XBCQD could have a beneficial therapeutic effect on monkeypox by reducing the inflammatory damage and viral replication via multiple pathways. The use of XBCQD on monkeypox disease was confirmed to be best worked through the estrone-target AR interaction. Our work could provide evidence and guidance for further research on the treatment of monkeypox disease.

Fast end-to-end surface interpretation of SARS-CoV-2 variants by differentiable molecular surface interaction fingerprinting method.

Confronting the challenge of persistent mutations of SARS-CoV-2, researchers have turned to deep learning methods to predict the mutated structures of spike proteins and to hypothesize potential changes in their structures and drug efficacies. However, limited works are focused on the surface learning of spike proteins even though their biological functions are usually defined by the geometric and chemical features of 3D molecular surfaces. In addition, the current used geometric deep learning methods are based on mesh representations of proteins to identify potential binding targets for drugs. However, the use of meshes has limitations and is not applicable for many important tasks in molecular biology. To address these limitations, we adopt the differentiable molecular surface interaction fingerprinting (dMaSIF) method which is based on the 3D point clouds and a novel efficient geometric convolutional layer to fast predict the interaction sites on the protein surface. The different binding site patterns for Delta, Omicron and its subvariants are clearly visualized. We find that Delta and Omicron show the similar surface binding patterns while BA.2, BA.2.13, BA.3 and BA.4 present similar ones. BA.4 possesses higher positive interaction site ratio than the others which may account for its higher transmission and infection among humans. In addition, the positive interaction site ratios of BA.2, BA.2.13, BA.3 are higher than Delta and Omicron, which are accordant with their transmission and infection rates. Hopefully our work offers a new effective route to analyze the protein-protein interaction for the SARS-CoV-2 variants.

Impact of E484Q and L452R Mutations on Structure and Binding Behavior of SARS-CoV-2 B.1.617.1 Using Deep Learning AlphaFold2, Molecular Docking and Dynamics Simulation.

During the outbreak of COVID-19, many SARS-CoV-2 variants presented key amino acid mutations that influenced their binding abilities with angiotensin-converting enzyme 2 (hACE2) and neutralizing antibodies. For the B.1.617 lineage, there had been fears that two key mutations, i.e., L452R and E484Q, would have additive effects on the evasion of neutralizing antibodies. In this paper, we systematically investigated the impact of the L452R and E484Q mutations on the structure and binding behavior of B.1.617.1 using deep learning AlphaFold2, molecular docking and dynamics simulation. We firstly predicted and verified the structure of the S protein containing L452R and E484Q mutations via the AlphaFold2-calculated pLDDT value and compared it with the experimental structure. Next, a molecular simulation was performed to reveal the structural and interaction stabilities of the S protein of the double mutant variant with hACE2. We found that the double mutations, L452R and E484Q, could lead to a decrease in hydrogen bonds and higher interaction energy between the S protein and hACE2, demonstrating the lower structural stability and the worse binding affinity in the long dynamic evolutional process, even though the molecular docking showed the lower binding energy score of the S1 RBD of the double mutant variant with hACE2 than that of the wild type (WT) with hACE2. In addition, docking to three approved neutralizing monoclonal antibodies (mAbs) showed a reduced binding affinity of the double mutant variant, suggesting a lower neutralization ability of the mAbs against the double mutant variant. Our study helps lay the foundation for further SARS-CoV-2 studies and provides bioinformatics and computational insights into how the double mutations lead to immune evasion, which could offer guidance for subsequent biomedical studies.

The influence of single-point mutation D614G on the binding process between human angiotensin-converting enzyme 2 and the SARS-CoV-2 spike protein-an atomistic simulation study.

SARS-CoV-2 has continuously evolved as changes in the genetic code occur during replication of the genome, with some of the mutations leading to higher transmission among human beings. The spike aspartic acid-614 to glycine (D614G) substitution in the spike represents a "more transmissible form of SARS-CoV-2" and occurs in all SARS-CoV-2 mutants. However, the underlying mechanism of the D614G substitution in virus infectivity has remained unclear. In this paper, we adopt molecular simulations to study the contact processes of the D614G mutant and wild-type (WT) spikes with hACE2. The interaction areas with hACE2 for the two spikes are completely different by visualizing the whole binding processes. The D614G mutant spike moves towards the hACE2 faster than the WT spike. We have also found that the receptor-binding domain (RBD) and N-terminal domain (NTD) of the D614G mutant extend more outwards than those of the WT spike. By analyzing the distances between the spikes and hACE2, the changes of number of hydrogen bonds and interaction energy, we suggest that the increased infectivity of the D614G mutant is not possibly related to the binding strength, but to the binding velocity and conformational change of the mutant spike. This work reveals the impact of D614G substitution on the infectivity of the SARS-CoV-2, and hopefully could provide a rational explanation of interaction mechanisms for all the SARS-CoV-2 mutants.

Unraveling the Role of Scutellaria baicalensis for the Treatment of Breast Cancer Using Network Pharmacology, Molecular Docking, and Molecular Dynamics Simulation.

Scutellaria baicalensis is often used to treat breast cancer, but the molecular mechanism behind the action is unclear. In this study, network pharmacology, molecular docking, and molecular dynamics simulation are combined to reveal the most active compound in Scutellaria baicalensis and to explore the interaction between the compound molecule and the target protein in the treatment of breast cancer. In total, 25 active compounds and 91 targets were screened out, mainly enriched in lipids in atherosclerosis, the AGE-RAGE signal pathway of diabetes complications, human cytomegalovirus infection, Kaposi-sarcoma-associated herpesvirus infection, the IL-17 signaling pathway, small-cell lung cancer, measles, proteoglycans in cancer, human immunodeficiency virus 1 infection, and hepatitis B. Molecular docking shows that the two most active compounds, i.e., stigmasterol and coptisine, could bind well to the target AKT1. According to the MD simulations, the coptisine-AKT1 complex shows higher conformational stability and lower interaction energy than the stigmasterol-AKT1 complex. On the one hand, our study demonstrates that Scutellaria baicalensis has the characteristics of multicomponent and multitarget synergistic effects in the treatment of breast cancer. On the other hand, we suggest that the best effective compound is coptisine targeting AKT1, which can provide a theoretical basis for the further study of the drug-like active compounds and offer molecular mechanisms behind their roles in the treatment of breast cancer.

Deep learning-based molecular dynamics simulation for structure-based drug design against SARS-CoV-2.

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), has led to a global pandemic. Deep learning (DL) technology and molecular dynamics (MD) simulation are two mainstream computational approaches to investigate the geometric, chemical and structural features of protein and guide the relevant drug design. Despite a large amount of research papers focusing on drug design for SARS-COV-2 using DL architectures, it remains unclear how the binding energy of the protein-protein/ligand complex dynamically evolves which is also vital for drug development. In addition, traditional deep neural networks usually have obvious deficiencies in predicting the interaction sites as protein conformation changes. In this review, we introduce the latest progresses of the DL and DL-based MD simulation approaches in structure-based drug design (SBDD) for SARS-CoV-2 which could address the problems of protein structure and binding prediction, drug virtual screening, molecular docking and complex evolution. Furthermore, the current challenges and future directions of DL-based MD simulation for SBDD are also discussed.