Bridging functional annotation gaps in non-model plant genes with AlphaFold, DeepFRI and small molecule docking

Background: Functional annotation assigns descriptive biological meaning to genetic sequences. Limited availability of manually curated or experimentally validated plant genes from a diverse range of taxa poses a significant challenge for functional annotation in non-model organisms. Accurate computational approaches are required. We argue that recent breakthroughs in deep learning have the potential to not only narrow the functional annotation gap between non-model and model plant organisms, but also annotate and reveal novel functions even for genes with no homologs in public databases. Results: Deep learning models were applied to functionally annotate a set of previously published differentially expressed genes. Predicted protein structures and functional annotations were generated using the AlphaFold protein structure and DeepFRI protein language inference models respectively. The resulting structures and functional annotations were validated using small molecule docking experiments. DeepFRI and AlphaFold models not only correctly annotated differentially expressed genes, but also revealed detailed mechanisms involving protein-protein interactions. Conclusions: Deep learning models are capable of inferring novel functions and achieving high accuracy in functional annotation. Their increased use in plant research will result in major improvements in annotations for non-model plants that are underrepresented in genome databases. We illustrate how integrating protein structure prediction, functional residue prediction, and small molecule docking can infer plausible protein-protein interactions and yield additional mechanistic insights. This approach will aid in the selection of candidate genes for further study from differential expression studies that generate large gene lists.

Network Pharmacology-Based Study on the Mechanism of Aloe Vera for Treating Cancer

Background. Aloe vera has long been considered an anticancer herb in different parts of the world. Objective. To explore the potential mechanism of aloe vera in the treatment of cancer using network pharmacology and molecule docking approaches. Methods. The active ingredients and corresponding protein targets of aloe vera were identified from the TCMSP database. Targets related to cancer were obtained from GeneCards and OMIM databases. The anticancer targets of aloe vera were obtained by intersecting the drug targets with the disease targets, and the process was presented in the form of a Venn plot. These targets were uploaded to the String database for protein-protein interaction (PPI) analysis, and the result was visualized by Cytoscape software. Go and KEGG enrichment were used to analyze the biological process of the target proteins. Molecular docking was used to verify the relationship between the active ingredients of aloe vera and predicted targets. Results. By screening and analyzing, 8 active ingredients and 174 anticancer targets of aloe vera were obtained. The active ingredient-anticancer target network constructed by Cytoscape software indicated that quercetin, arachidonic acid, aloe-emodin, and beta-carotene, which have more than 4 gene targets, may play crucial roles. In the PPI network, AKT1, TP53, and VEGFA have the top 3 highest values. The anticancer targets of aloe vera were mainly involved in pathways in cancer, prostate cancer, bladder cancer, pancreatic cancer, and non-small-cell lung cancer and the TNF signaling pathway. The results of molecular docking suggested that the binding ability between TP53 and quercetin was the strongest. Conclusion. This study revealed the active ingredients of aloe vera and the potential mechanism underlying its anticancer effect based on network pharmacology and provided ideas for further research.

Exploring the Comorbidity Mechanisms Between Asthma and Idiopathic Pulmonary Fibrosis and the Pharmacological Mechanisms of Bu-Shen-Yi-Qi Decoction Therapy via Network Pharmacology

Backgrounds. Asthma and idiopathic pulmonary fibrosis (IPF) are common chronic diseases of the respiratory system in clinical practice. However, the relationship and molecular links between them remain unclear, and the current treatment's efficacy is disappointing. Bu-Shen-Yi-Qi (BSYQ) decoction has clinically proved to be effective in treating various chronic airway inflammatory diseases, including asthma and IPF. But the underlying pharmacological mechanisms are still to be elucidated. Methods. This study searched the proteins related to asthma and IPF via TTD, CTD, and DisGeNET database. We then submitted them to the STRING database to establish the protein-protein interaction (PPI) network. The co-bioinformatics analysis was conducted by Metascape. The active ingredients of BSYQ decoction were screened from TCMSP，ETCM，BATMAN-TCM database and HPLC/MS experiment. Then we predicted the corresponding targets based on TCMSP，ETCM, and BATMAN-TCM database. The common targets for asthma and IPF treatment were recognized, and further GO and KEGG analyses were conducted with the DAVID platform. Finally, molecule docking via Autodock Vina was employed to predict the potential binding mode between core potential compounds and targets.Results. One thousand three hundred thirty-three asthma-related targets and 404 IPF-related proteins were retrieved, 120 were overlapped between them, and much of the asthma-related proteins fall into the same statistically significant GO terms with IPF. One hundred sixteen active ingredients of BSYQ decoction were acquired, and 1535 corresponding targets were retrieved. Eighty-three potential compounds and 56 potential targets were recognized for both asthma and IPF treatment. GO and KEGG analysis indicated that the inflammation response, cytokine production, leukocyte differentiation, oxygen level response, etc., were the common pathological processes in asthma and IPF, which were regulated by BSYQ decoction. Molecule docking further predicted the potential binding modes between the core potential compounds and targets.Conclusion. The current study successfully clarified the complex molecule links between asthma and IPF and found the potential common targets between them. Then we demonstrated the efficacy of BSYQ decoction for asthma and IPF treatment from the angle of network pharmacology, which may provide valuable references for further studies and clinical use.

SeamDock: An Interactive and Collaborative Online Docking Resource to Assist Small Compound Molecular Docking

In silico assessment of protein receptor interactions with small ligands is now part of the standard pipeline for drug discovery, and numerous tools and protocols have been developed for this purpose. With the SeamDock web server, we propose a new approach to facilitate access to small molecule docking for nonspecialists, including students. The SeamDock online service integrates different docking tools in a common framework that allows ligand global and/or local docking and a hierarchical approach combining the two for easy interaction site identification. This service does not require advanced computer knowledge, and it works without the installation of any programs with the exception of a common web browser. The use of the Seamless framework linking the RPBS calculation server to the user’s browser allows the user to navigate smoothly and interactively on the SeamDock web page. A major effort has been put into the 3D visualization of ligand, receptor, and docking poses and their interactions with the receptor. The advanced visualization features combined with the seamless library allow a user to share with an unlimited number of collaborators, a docking session, and its full visualization states. As a result, SeamDock can be seen as a free, simple, didactic, evolving online docking resource best suited for education and training.

Crocetin Exerts Its Anti-inflammatory Property in LPS-Induced RAW264.7 Cells Potentially via Modulation on the Crosstalk between MEK1/JNK/NF-κB/iNOS Pathway and Nrf2/HO-1 Pathway

Crocetin is a main bioactive component with a carotenoid skeleton in Gardenia jasminoides, a typical traditional Chinese medicine with a long history in Southeast Asia. Crocetin is being commonly consumed as spices, dyes, and food colorants. Recent pharmacological studies had implied that crocetin may possess potent anti-inflammatory properties; however, the underlying molecular mechanism is not fully elucidated. In the present study, the regulatory effect of crocetin on redox balance was systematically investigated in lipopolysaccharide- (LPS-) stimulated RAW264.7 cells. The results showed that crocetin dose-dependently inhibited LPS-induced nitric oxide production and inducible nitric oxide synthase (iNOS) expression in RAW264.7 cells. Molecular data revealed that crocetin exerted its anti-inflammatory property by inhibiting the MEK1/JNK/NF-κB/iNOS pathway and activating the Nrf2/HO-1 pathway. The shRNA-knockdown (KD) of MEK1 and ERK1 confirmed that the activation of MEK1 and inhibition of JNK mediated the anti-inflammatory effect of crocetin. Moreover, the pull-down assay and computational molecule docking showed that crocetin could directly bind to MEK1 and JNK1/2. It is noticed that both KD and knockout (KO) of HO-1 gene blocked this action. More detailed data have shown that HO-1-KO blocked the inhibition of p-IκB-α by crocetin. These data indicated that crocetin exerted its anti-inflammatory property via modulating the crosstalk between the MEK1/JNK/NF-κB/iNOS pathway and the Nrf2/HO-1 pathway, highlighting HO-1 as a major player. Therefore, the present study reveals that crocetin can act as a potential candidate for redox-balancing modulation in charge of its anti-inflammatory and chemopreventive effect, which strengthens its potency in the subsequent clinic application in the near future.

Shape-restrained modelling of protein-small molecule complexes with HADDOCK

Small molecule docking remains one of the most valuable computational techniques for the structure prediction of protein-small molecule complexes. It allows us to study the interactions between compounds and the protein receptors they target at atomic detail, in a timely and efficient manner. Here we present a new protocol in HADDOCK, our integrative modelling platform, which incorporates homology information for both receptor and compounds. It makes use of HADDOCK's unique ability to integrate information in the simulation to drive it toward conformations which agree with the provided data. The focal point is the use of shape restraints derived from homologous compounds bound to the target receptors. We have developed two protocols: In the first, the shape is composed of fake atom beads based on the position of the heavy atoms of the homologous template compound, whereas in the second the shape is additionally annotated with pharmacophore data, for some or all beads. For both protocols, ambiguous distance restraints are subsequently defined between those beads and the heavy atoms of the ligand to be docked. We have benchmarked the performance of these protocols with a fully unbound version of the widely used DUD-E dataset. In this unbound docking scenario, our template/shape-based docking protocol reaches an overall success rate of 81% on 99 complexes, which is close to the best results reported for bound docking on the DUD-E dataset.

Sinapic Acid Reduces Oxidative Stress and Pyroptosis via Inhibition of BRD4 in Alcoholic Liver Disease

Alcoholic liver disease (ALD) is one of the main causes of death in chronic liver disease. Oxidative stress and pyroptosis are important factors leading to ALD. Bromodomain-containing protein 4 (BRD4) is a factor that we have confirmed to regulate ALD. As a phenolic acid compound, sinapic acid (SA) has significant effects in antioxidant, anti-inflammatory and liver protection. In this study, we explored whether SA regulates oxidative stress and pyroptosis through BRD4 to play a protective effect in ALD. Male C57BL/6 mice and AML-12 cells were used for experiments. We found that SA treatment largely abolished the up-regulation of BRD4 and key proteins of the canonical pyroptosis signalling in the liver of mice fed with alcohol, while conversely enhanced the antioxidant response. Consistantly, both SA pretreatment and BRD4 knockdown inhibited oxidative stress, pyroptosis, and liver cell damage in vitro. More importantly, the expression levels of BRD4 and pyroptosis indicators increased significantly in ALD patients. Molecule docking analysis revealed a potent binding of SA with BRD4. In conclusion, this study demonstrates that SA reduces ALD through BRD4, which is a valuable lead compound that prevents the ALD process.

Anti-inflammatory activity of psoralen in human periodontal ligament cells via estrogen receptor signaling pathway

Psoralen is one of the most effective ingredients extracted from the Chinese herb, Psoralea corylifolia L. Studies have found that psoralen has anti-inflammatory and estrogen-like effects; however, little research has been conducted to elucidate the mechanisms underlying these effects. Through the molecule docking assay, psoralen was found to have a better combination with ERα than ERβ. In human periodontal ligament cells, psoralen was found to upregulate the estrogen target genes (e.g., CTSD, PGR, TFF1) and down-regulate the expression of inflammatory cytokines (TNF-α, IL-1β, IL-6 and IL-8) stimulated by P. gingivalis LPS, as well as TLR4-IRAK4-NF-κb signaling pathway proteins. These effects were reversed by the ER antagonist ICI 182780. These results indicated that psoralen may exert anti-inflammatory effects as an agonist to ER, which could provide a theoretical basis for the use of psoralen for adjuvant therapy and prevention of periodontitis.

PoseFilter: a PyMOL plugin for filtering and analyzing small molecule docking in symmetric binding sites

Summary ‘PoseFilter’ is a PyMOL plugin that assists in analyses and filtering of docked poses. PoseFilter enables automatic detection of symmetric poses from docking outputs and can be accessed using both graphical user interface and command-line options within the PyMOL program. Two methods of analyses, root mean square deviations and interaction fingerprints, are available from this plugin. The capabilities of the plugin are demonstrated using docking outputs from different oligomeric protein-ligand complexes. Availability and implementation The plugin can be downloaded from the GitHub page, https://github.com/skalyaanamoorthy/PoseFilter. Supplementary information Supplementary data are available at Bioinformatics online.

Piroxicam Analogs: Design, Synthesis, Docking Study and Biological Evaluation as Promising Anti-HIV-1 agents

Background: Taking the well-known drug, Piroxicam as a lead compound, we designed and synthesized two series of 1,2-benzothiazines 1,1-dioxide derivatives to assay their ability in inhibition of HIV-1 replication in cell culture. Objective: In this study, we describe the synthesis, docking study and biological evaluation of 1,2-benzothiazines 1,1- dioxide derivatives. Results: Most of the new compounds were active in the cell-based anti-HIV-1 assay with EC50 < 50 M. Among them, compounds 7g was found to be the most active molecule. Docking study using 3OYA pdb code on the most active molecule 7g with EC50 values of 10 M showed a similar binding mode to the HIV integrase inhibitors. Conclusion: Since all the compounds showed no remarkable cytotoxicity (CC50> 500 M), the designed scaffold is promising structure for development of new anti-HIV-1 agents.