M01 tool: an automated, comprehensive computational tool for generating small molecule-peptide hybrids and docking them into curated protein structures

(2025) M01 tool: an automated, comprehensive computational tool for generating small molecule-peptide hybrids and docking them into curated protein structures. Bmc Bioinformatics. p. 16. ISSN 1471-2105

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Abstract

BackgroundThe field of computational drug design is undergoing rapid advancements, highlighting the need for innovative methods to enhance the efficiency and accuracy of calculating ligand-receptor interactions. In this context, we introduce the M01 tool, a comprehensive computational package designed to facilitate the generation and docking of small molecule-peptide hybrids. M01 integrates several established tools, such as RDKit and EasyDock, into a user-friendly platform that automates the workflow from hybrid generation to docking simulations. This tool is particularly beneficial for researchers with limited chemistry expertise, helping them leverage advanced computational techniques.ResultsThe M01 tool features an intuitive interface for visualizing molecules and selecting connection points in generating new ligands. It also offers automated receptor preparation using UniProt or PDB IDs and generates default docking configuration files. Furthermore, it includes ligand preparation and docking capabilities through EasyDock and calculates molecular descriptors relevant to drug-likeness properties. Validation studies with peptide-alkoxyamine hybrids demonstrated the tool's effectiveness, generating over 14,000 unique hybrid molecules and showcasing its versatility in drug design applications.ConclusionsThe M01 tool represents a significant advancement in computational drug design, streamlining the process of creating hybrid molecules and conducting docking studies. Its ability to automate complex workflows and provide essential molecular insights can empower researchers and enhance the development of novel therapeutics, ultimately contributing to more efficient drug discovery efforts.

Item Type: Article
Keywords: Computational drug design Hybrid molecules Docking simulations Ligand-receptor interactions ADMET properties Peptide-drug conjugates drug design autodock Biochemistry & Molecular Biology Biotechnology & Applied Microbiology Mathematical & Computational Biology
Page Range: p. 16
Journal or Publication Title: Bmc Bioinformatics
Journal Index: ISI
Volume: 26
Number: 1
Identification Number: https://doi.org/10.1186/s12859-025-06120-5
ISSN: 1471-2105
Depositing User: خانم ناهید ضیائی
URI: http://eprints.mui.ac.ir/id/eprint/32918

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