CoDock-Ligand: combined template-based docking and CNN-based scoring in ligand binding prediction

Essential oil-derived compounds target core fatigue-related genes: A network pharmacology and molecular Docking approach

Fatigue is a widespread condition associated with various health issues, yet identifying specific bioactive compounds for its management remains challenging. This study integrates network pharmacology and molecular docking to uncover essential oil-derived compounds with potential antifatigue properties by targeting key genes and molecular pathways. A comprehensive analysis of 872 essential oil compounds was conducted using PubChem, with target prediction via SwissTargetPrediction. The protein-protein interaction (PPI) network and KEGG pathway analysis identified core fatigue-related targets, including ALB, BCL2, EGFR, IL-6, and STAT3, in metabolic dysregulation and inflammatory responses linked to fatigue. Molecular docking exhibits strong binding affinity between key compounds such as Calamenene, T-cadinol, and Bornyl acetate and core targets, suggesting their potential antifatigue effects. However, ADMET analysis confirmed T-cadinol's drug-likeness, suggesting good bioavailability and minimal toxicity risks. Thus, molecular docking revealed high binding affinity, which was further validated through a 100 ns MD simulation and demonstrated stable interactions with low root mean square deviation (RMSD). Additionally, hydrogen bond analysis confirmed that T-cadinol maintained consistent interactions with key residues such as Thr-790 in EGFR, Arg-222 in ALB, and Arg-104 in IL-6, indicating strong binding stability. While this study provides valuable computational insights, further in vitro and in vivo validation is necessary to confirm these findings and explore potential therapeutic applications.

E3Docker: a docking server for potential E3 binder discovery

Targeted protein degradation (TPD) has emerged as a promising therapeutic strategy for modulating protein levels in cells. Proteolysis-targeting chimeras and molecular glues facilitate the formation of a complex between the protein of interest (POI) and a specific E3 ligase, leading to POI ubiquitination and subsequent degradation by the proteasome. Considering over 600 E3s in the human genome, it is of great potential to find novel E3 binders and recruit new E3 ligase for TPD related drug discovery. Here we introduce E3Docker, an online computational tool for E3 binder discovery. A total of 1075 Homo sapiens E3 ligases are collected from databases and literature, and 4474 three-dimensional structures of these E3 ligases, in either apo or complex forms, are integrated into the web server. The druggable pockets for each E3 ligase are defined by experimentally bound ligand from PDB or predicted by using DeepPocket. CoDock-Ligand is employed as docking engine for potential E3 binder estimation. With a user-friendly interface, E3Docker facilitates the generation of binding poses and affinity scores for compounds with over 1000 kinds of E3 ligases and may benefit for novel E3 binder discovery. The E3Docker server and tutorials are freely available at https://e3docker.schanglab.org.cn/.

Deep Learning for Protein-Ligand Docking: Are We There Yet?

The effects of ligand binding on protein structures and their in vivo functions carry numerous implications for modern biomedical research and biotechnology development efforts such as drug discovery. Although several deep learning (DL) methods and benchmarks designed for protein-ligand docking have recently been introduced, to date no prior works have systematically studied the behavior of the latest docking and structure prediction methods within the broadly applicable context of (1) using predicted (apo) protein structures for docking (e.g., for applicability to new proteins); (2) binding multiple (cofactor) ligands concurrently to a given target protein (e.g., for enzyme design); and (3) having no prior knowledge of binding pockets (e.g., for generalization to unknown pockets). To enable a deeper understanding of docking methods' real-world utility, we introduce PoseBench, the first comprehensive benchmark for broadly applicable protein-ligand docking. PoseBench enables researchers to rigorously and systematically evaluate DL methods for apo-to-holo protein-ligand docking and protein-ligand structure prediction using both primary ligand and multi-ligand benchmark datasets, the latter of which we introduce for the first time to the DL community. Empirically, using PoseBench, we find that (1) DL co-folding methods generally outperform comparable conventional and DL docking baselines, yet popular methods such as AlphaFold 3 are still challenged by prediction targets with novel protein sequences; (2) certain DL co-folding methods are highly sensitive to their input multiple sequence alignments, while others are not; and (3) DL methods struggle to strike a balance between structural accuracy and chemical specificity when predicting novel or multi-ligand protein targets. Code, data, tutorials, and benchmark results are available at https://github.com/BioinfoMachineLearning/PoseBench.

Assessment of the Anti-adipogenic Effect of Crateva religiosa Bark Extract for Molecular Regulation of Adipogenesis: In Silico and In Vitro Approaches for Management of Hyperlipidemia Through the 3T3-L1 Cell Line

AIMS

This study aimed to determine the phytoconstituents of Crateva religiosa bark (CRB) and evaluate the hypolipidemic effect of bioactive CRB extract by preventing adipocyte differentiation and lipogenesis.

BACKGROUND

After performing the preliminary phytochemicals screening, the antioxidant activity of CRB extracts was determined through a DPPH (2, 2-diphenyl-1-picrylhydrazyl) assay. Ethyl acetate extract (CREAE) and ethanol extract (CRETE) of CRB were selected for chromatographic evaluation.

METHODS

The antihyperlipidemic potential was analyzed by molecular docking through the PKCMS software platform. Further, a 3T3-L1 cell line study via in vitro sulforhodamine B assay and western blotting was performed to confirm the prevention of adipocyte differentiation and lipogenesis.

RESULTS

The total phenolic contents in CREAE and CRETE were estimated as 29.47 and 81.19 μg/mg equivalent to gallic acid, respectively. The total flavonoid content was found to be 8.78 and 49.08 μg/mg, equivalent to quercetin in CREAE and CRETE, respectively. CRETE exhibited greater scavenging activity with the IC50 value of 61.05 μg/ mL. GC-MS analysis confirmed the presence of three bioactive molecules, stigmasterol, gamma sitosterol, and lupeol, in CRETE. Molecular docking studies predicted that the bioactive molecules interact with HMG-CoA reductase, PPARγ, and CCAAT/EBP, which are responsible for lipid metabolism. In vitro, Sulforhodamine B assays revealed that CRETE dose-dependently reduced cell differentiation and viability. Cellular staining using 'Oil Red O' revealed a decreased lipid content in the CRETE-treated cell lines. CRETE significantly inhibited the induction of PPARγ and CCAAT/EBP expression, as determined through protein expression via western blotting.

CONCLUSION

The influence of CRETE on lipid metabolism in 3T3-L1 cells is potentially suggesting a new approach to managing hyperlipidemia.

A proteome-wide structural systems approach reveals insights into protein families of all human herpesviruses

Structure predictions have become invaluable tools, but viral proteins are absent from the EMBL/DeepMind AlphaFold database. Here, we provide proteome-wide structure predictions for all nine human herpesviruses and analyze them in depth with explicit scoring thresholds. By clustering these predictions into structural similarity groups, we identified new families, such as the HCMV UL112-113 cluster, which is conserved in alpha- and betaherpesviruses. A domain-level search found protein families consisting of subgroups with varying numbers of duplicated folds. Using large-scale structural similarity searches, we identified viral proteins with cellular folds, such as the HSV-1 US2 cluster possessing dihydrofolate reductase folds and the EBV BMRF2 cluster that might have emerged from cellular equilibrative nucleoside transporters. Our HerpesFolds database is available at https://www.herpesfolds.org/herpesfolds and displays all models and clusters through an interactive web interface. Here, we show that system-wide structure predictions can reveal homology between viral species and identify potential protein functions.

Recent Progress of Protein Tertiary Structure Prediction

The prediction of three-dimensional (3D) protein structure from amino acid sequences has stood as a significant challenge in computational and structural bioinformatics for decades. Recently, the widespread integration of artificial intelligence (AI) algorithms has substantially expedited advancements in protein structure prediction, yielding numerous significant milestones. In particular, the end-to-end deep learning method AlphaFold2 has facilitated the rise of structure prediction performance to new heights, regularly competitive with experimental structures in the 14th Critical Assessment of Protein Structure Prediction (CASP14). To provide a comprehensive understanding and guide future research in the field of protein structure prediction for researchers, this review describes various methodologies, assessments, and databases in protein structure prediction, including traditionally used protein structure prediction methods, such as template-based modeling (TBM) and template-free modeling (FM) approaches; recently developed deep learning-based methods, such as contact/distance-guided methods, end-to-end folding methods, and protein language model (PLM)-based methods; multi-domain protein structure prediction methods; the CASP experiments and related assessments; and the recently released AlphaFold Protein Structure Database (AlphaFold DB). We discuss their advantages, disadvantages, and application scopes, aiming to provide researchers with insights through which to understand the limitations, contexts, and effective selections of protein structure prediction methods in protein-related fields.