Recep Adiyaman
bioinformatics

Issue #2: Meeko: Molecule Parametrization and Software Interoperability for Docking and Beyond.

December 21, 2025 Daily Intelligence
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Meeko: Molecule Parametrization and Software Interoperability for Docking and Beyond.

🧬 Abstract

Molecule parametrization is an essential requirement to guarantee the accuracy of docking calculations. Parametrization includes a proper perception of chemical properties such as bonds, formal charges and protonation states. This includes large biological macromolecules, such as proteins and nucleic acids, and small molecules, such as ligands and cofactors. The structures of proteins and nucleic acids are challenging due to omission of several atoms from the structural model, and from the lack of connectivity and bond order information in the PDB and mmCIF file formats. For small molecules, the very large chemical diversity poses challenges for both validating correctness and providing accurate parameters. These challenges affect various modeling approaches like molecular docking and molecular dynamics. Moreover, several specialized methods (particularly in molecular docking) leverage specific chemical properties to add custom potentials, pseudoatoms, or manipulate atomic connectivity. To address these challenges, we developed Meeko, a molecular parametrization Python package that leverages the widely used RDKit cheminformatics library for a chemically accurate description of the molecular representation. Small molecules are modeled as single RDKit molecules, and biological macromolecules as multiple RDKit molecules, one for each residue. Meeko is highly customizable and designed to be easily scriptable for high-throughput processing, replacing MGLTools for receptor and ligand preparation.

Why it matters: Expands the searchable sequence space for novel folds and high-affinity binders.

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⚡ Quick Reads

From sweetener to risk factor: Network toxicology, molecular docking and molecular dynamics reveal the mechanism of aspartame in promoting coronary heart disease.

Aspartame, a widely used non-nutritive sweetener, has been epidemiologically linked to coronary heart disease (CHD), although the underlying mechanisms remain unclear. This study employed an integrative computational strategy combining network toxicology, molecular docking, and molecular dynamics to decode aspartame’s CHD-promoting mechanisms. Initially, the toxicity profile of aspartame was predicted using ProTox 3.0 and ADMETlab 3.0, which highlighted significant cardiotoxicity. Through multi-source target screening of aspartame (PharmMapper, SEA, etc.) and CHD (GeneCards, OMIM), 216 shared targets were identified. Protein-protein interaction network analysis revealed 10 hub targets (INS, PPARGC1A, TNF, AKT1, IL6, MMP9, IGF1, PTGS2, SIRT1, PPARG). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses revealed significant enrichment in lipid metabolism, inflammatory responses, insulin resistance, and atherosclerosis-related pathways. Molecular docking and molecular dynamics simulations (MDS) demonstrated high-affinity binding of aspartame to three core targets (PTGS2, TNF, and PPARGC1A), with a binding energy ≤ -7.0 kcal/mol, and confirmed high binding stability. This study reveals that aspartame may promote the pathogenesis of CHD by disrupting cardiovascular homeostasis through multi-target interactions, including inflammatory response, metabolic dysregulation, and vascular remodeling. These findings provide molecular evidence for re-evaluating the safety profile of aspartame and establish a computational framework to guide experimental validation and preventive strategies.

Enzyme Engineering Database (EnzEngDB): a platform for sharing and interpreting sequence-function relationships across protein engineering campaigns.

The discovery and engineering of new enzymes is important across the bioeconomy, with diverse applications from foods to pharmaceuticals, sensors to agriculture. However, enzyme engineering, in particular machine learning-guided engineering, is hampered by a lack of data. Currently there exists no database designed to capture and interpret datasets created in this domain, nor are there easy analysis and visualisation tools. We developed the Enzyme Engineering Database to provide a centralized resource and an online analysis tool to consolidate sequence-function data from enzyme engineering campaigns, thereby making three contributions: (i) a database into which researchers can deposit public data, (ii) visualisation and analysis tools for protein engineers to analyse their own data or compare enzyme variants to other engineering campaigns, and (iii) a gold-standard dataset for benchmarking automated extraction along with the first large language model extraction pipeline specific for enzyme engineering campaigns. The Enzyme Engineering Database is accessible at http://enzengdb.org/.

Multi-target exploration of newly synthesized pyrazoline-quinoline derivatives via in vitro screening, QSAR, molecular docking, MD simulations, and DFT analysis.

The development of multifunctional therapeutic agents remains a promising strategy in modern drug discovery, particularly for diseases associated with oxidative stress, bacterial infections, and cancer progression. In this study, a new series of [5-(substituted phenyl)-3-(substituted phenyl)-4,5-dihydro-pyrazol-1-yl]-(2-methyl-quinolin-4-yl)-methanones (9a-o) has been synthesized and evaluated for anticancer, antibacterial, and antioxidant activities through established in vitro and in silico screening models. The in vitro cytotoxic evaluation conducted against the human lung cancer cell line (A549) using the MTT assay revealed that all synthesized compounds have significant inhibitory potential. Among them, compounds 9i, 9b, 9h, 9d, and 9o demonstrated superior potency, showing IC₅₀ values of 3.68 ± 0.45 μM, 4.06 ± 0.35 μM, 4.33 ± 0.68 μM, 6.32 ± 0.89 μM, and 7.82 ± 0.52 μM compared to the reference drug doxorubicin, which showed an IC₅₀ of 9.48 ± 0.35 μM under identical experimental conditions. The same compounds also possess the best antibacterial (MIC value 12.5-25 μg/mL) and antioxidant potential. The in silico studies encompassed ADMET analysis, QSAR, molecular docking, and molecular dynamics simulations, which were carried out using Molsoft LLC, pkCSM, ChemDes, AutoDock 4.2, and GROMACS software. Their electronic and reactivity features were also analyzed through DFT calculations based on HOMO, LUMO, electron affinity, ionization potential, chemical potential, and global softness. The results of computational studies reinforced the findings in all dimensions. In summary, this study introduces a promising class of pyrazoline-quinoline conjugates with significant multifunctional efficacy.

💡 Pipeline Tip

Index your BigWig files before visualization to save memory.

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The protein structure is the language of life; design is its poetry. — Recep Adiyaman

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