Cannabis as a source of novel therapeutics: A computational study combining LBDD and structure-based docking/dynamics to identify novel drug-like compounds for colorectal cancer
Jul 2026· Journal of Genetic Engineering and Biotechnology· Vol 24, pp. 100763· 0 citations· 75 references
TL;DR
This study investigates the potential of cannabis-derived compounds to inhibit cyclin-dependent kinase 2 (CDK2), a crucial regulator of cell-cycle progression and colorectal cancer cell proliferation.
Abstract
Cannabis represents a valuable source of bioactive compounds with significant therapeutic potential, thanks to its rich phytochemical profile, making it a prime candidate for cancer research. Our study investigates the potential of cannabis-derived compounds to inhibit cyclin-dependent kinase 2 (CDK2), a crucial regulator of cell-cycle progression and colorectal cancer cell proliferation. A comprehensive computational workflow integrating 3D-QSAR modeling, molecular docking, molecular dynamics simulations, drug-likeness assessment, and ADMET prediction was employed to investigate a dataset of 33 cannabis-derived compounds. The robustness and predictive performance of the generated CoMFA and CoMSIA models were confirmed through internal and external validation, leave-one-out cross-validation (LOOCV), and Y-randomization tests, yielding excellent statistical parameters for CoMFA (Q2=0.651, R2=0.986, SEE=0.069) and CoMSIA (Q2=0.665, R2=0.985, SEE=0.071). Using contour map analysis, twenty new molecules (V1–V20) with enhanced antiproliferative activity against the HCT-116 colorectal cancer cell line were developed, and outperformed the original dataset’s most active one. Following drug-likeness screening and ADMET profiling, compounds V2, V5, V7, and V8 emerged as the most promising candidates, exhibiting favorable pharmacokinetic properties and drug-like characteristics. Molecular docking studies revealed that V7 and V8 exhibit high stability within the CDK2 active site and possess stronger binding affinity than the reference compound. Furthermore, 100 ns molecular dynamics simulations demonstrated that both protein–ligand complexes reached stable conformational states, as confirmed by converged backbone RMSD profiles, low residue fluctuations (RMSF), persistent protein–ligand interactions, and complementary structural descriptors including radius of gyration (Rg), solvent-accessible surface area (SASA), and molecular surface area (MolSA). Collectively, these analyses confirmed the structural stability and compactness of the investigated complexes throughout the simulation period, with V8 exhibiting the most favorable dynamic behavior.
Caspase-1 is a crucial inflammatory cysteine protease that facilitates the maturation of pro-inflammatory cytokines such as interleukin-1β and interleukin-18, making it a significant therapeutic target for inflammatory diseases. However, existing caspase-1 inhibitors often face challenges like toxicity and suboptimal drug-like properties, underscoring the need for new inhibitors. This study employed an integrated computational strategy, combining quantitative structure–activity relationship (QSAR) modeling and application of this validated model to a large natural product database followed by molecular docking, rigorous binding free energy analysis and extended molecular dynamics simulations. Initially, a dataset of 185 caspase-1 inhibitors with experimentally reported pKi values (ranging from 4.05 to 9.24) was used to construct a QSAR model using Partial Least Squares (PLS) regression. The PLS-based QSAR model was developed with 18 descriptors out of 5799 calculated descriptors for each compound and 10 latent variables, demonstrating strong statistical performance with R2 values of 0.870 and 0.838 for the training and test sets, respectively, and leave-one-out cross-validation coefficient Q2LOO and 5-fold cross-validation (Q25-fold) values of 0.819 and 0.814, respectively. Y-randomization tests further confirmed the model’s robustness, as the randomized models exhibited significantly lower statistical parameters than the original model. The validated QSAR model was applied to 276,518 natural products in the LOTUS database. Subsequent molecular docking, Molecular Mechanics/General Born Surface Area (MM/GBSA) scoring, and Pan-Assay INterference Compounds (PAINS) and Chemical Frequent Hitter (ChemFH) filtering identified 14 candidate compounds, which were further evaluated using 300 ns molecular dynamics simulations. Among these, four natural products (LTS0162325, LTS0221286, LTS0016840, and LTS0070407) showed the most stable binding behavior and maintained persistent interactions with key catalytic and substrate-binding residues of caspase-1 in a mimicked physiological condition. Overall, this study highlights natural diterpenoids and coumarin glycosides as promising scaffolds for caspase-1 inhibition and demonstrates that integrating QSAR modeling with structure-based approaches provides an efficient strategy for discovering potential anti-inflammatory drug candidates.
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