{ "abstract": "Microtubules are dynamic cytoskeletal polymers assembled from α/β-tubulin heterodimers. Microtubules are validated targets for novel therapeutic drugs, yet therapeutic efficacy targeting them is often compromised by multidrug resistance (MDR). 5-(3-chlorophenyl)-N-(3-pyridinyl)-2-furamide (CPPF) is a novel microtubule-targeting anticancer agent which was found to disrupt microtubule growth in cells and inhibit tubulin polymerization in vitro. CPPF could suppress the growth of multidrug-resistant cell lines and demonstrate anti-tumor efficacy in animal models. However, the fundamental mechanism of how CPPF disrupts microtubule assembly remains unclear. To investigate this, we performed structure prediction using Protenix, RoseTTAFold All-Atom (RFAA) and Umol, as well as molecular dynamics (MD) simulations, using the human α/β-tubulin heterodimer (PDB ID: 5IJ0) for analyzing the interaction between CPPF and tubulin. Our results showed that CPPF binds at the α/β interface with dominant contributions from β-tubulin residues, particularly VAL236 and LEU253. In isolated-monomer comparisons, β-tubulin exhibited more favorable binding energetics and deeper, broader free-energy minima than α-tubulin. Supplementary simulations on the alternative β-tubulin conformational state (PDB: 6E7B; straight microtubule-lattice) indicated that CPPF binding is preserved across the two major β-tubulin conformations, with comparable MM-PBSA binding free energies. Taken together, these findings establish a plausible binding mode for CPPF at the α/β-tubulin interface, providing a structural hypothesis for understanding its anticancer potential against multidrug-resistant cancers. Experimental validation through binding assays or crystallography is warranted to further substantiate these computational insights. Author summary: Microtubules are essential for cell division and intracellular transport, which makes tubulin a major target for anticancer drugs. However, many tumors become resistant to commonly used microtubule-targeting agents, creating a need for new compounds with distinct binding mechanisms. CPPF is a recently reported microtubule inhibitor that remains active in multidrug-resistant cancer cell lines, but how it interacts with tubulin at the molecular level is still unclear. Here, we used an integrated computational framework that combines AI-based protein–ligand structure prediction (Protenix, RoseTTAFold All-Atom (RFAA), and Umol), binding-pocket analysis, residue-level interaction profiling, and all-atom molecular dynamics simulations to characterize CPPF–tubulin recognition. Across multiple models, CPPF showed more consistent binding poses and recurrent contact residues on β-tubulin than on α-tubulin, with key hotspots including VAL236 and LEU253. Simulations further suggested that the CPPF–β-tubulin complex is dynamically more stable and occupies deeper free-energy minima than the α-tubulin complex, with an ensemble-averaged MM-PBSA binding free energy indicating a substantially favorable interaction. Complementary simulations on an alternative β-tubulin conformational state (the microtubule-lattice-related straight form) indicated that CPPF binding is preserved across both major β-tubulin conformations, suggesting the pocket is accessible at multiple stages of microtubule dynamics. Together, our results propose a plausible binding mode for CPPF at a composite α/β interface pocket with dominant contributions from β-tubulin residues, providing structural hypotheses to guide future biochemical validation and the rational optimization of tubulin inhibitors that may help overcome drug resistance.", "abstract_shown": true, "affiliations": [ "Data Science and Analytics Thrust, Information Hub, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China", "Bioscience and Biomedical Engineering Thrust, Systems Hub, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China", "Department of Biostatistics, Virginia Commonwealth University, Richmond, Virginia, United States of America", "Massey Comprehensive Cancer Center, Virginia Commonwealth University, Richmond, Virginia, United States of America", "Shenzhen Key Laboratory of Synthetic Genomics, Guangdong Provincial Key Laboratory of Synthetic Genomics, State Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China", "Faculty of Synthetic Biology and Institute of Emerging Agricultural Technology, Shenzhen University of Advanced Technology, Shenzhen, China" ], "article_type": "Research Article", "arxiv": null, "author_total": 7, "authors": [ { "affs": [ 0 ], "name": "Jixin Yang" }, { "affs": [ 1 ], "name": "Lisha Liang" }, { "affs": [ 2 ], "name": "Dengchao Zhu" }, { "affs": [ 3 ], "name": "Xuzhe Yin" }, { "affs": [ 4 ], "name": "Yizhuo Feng" }, { "affs": [ 5 ], "name": "Shaojun Tang" }, { "name": "Muzi Li" } ], "bibtex": "https://research.draconitic.com/paper/8r8750v73d8nq.bib", "cited_by": 0, "doi": "10.1371/journal.pcbi.1014804", "fulltext_url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014804", "id": "8r8750v73d8nq", "issns": [ "1553-7358" ], "landing_url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014804", "language": null, "licence": "CC-BY-4.0", "licence_label": "CC BY 4.0", "licence_source": "journal", "licence_url": "https://creativecommons.org/licenses/by/4.0/", "metadata_licence": null, "native_subjects": [ [ "openalex", "1703|Computational Drug Discovery Methods" ], [ "plos", "/Biology and life sciences/Biochemistry/Biochemical simulations" ], [ "plos", "/Biology and life sciences/Biochemistry/Proteins/Cytoskeletal proteins/Tubulins" ], [ "plos", "/Biology and life sciences/Biochemistry/Proteins/Protein structure/Protein structure prediction" ], [ "plos", "/Biology and life sciences/Cell biology/Cellular structures and organelles/Cytoskeleton/Microtubules" ], [ "plos", "/Biology and life sciences/Computational biology/Biochemical simulations" ], [ "plos", "/Biology and life sciences/Molecular biology/Macromolecular structure analysis/Protein structure/Protein structure prediction" ], [ "plos", "/Medicine and health sciences/Pharmacology/Drug interactions" ], [ "plos", "/Physical sciences/Chemistry/Polymer chemistry/Dimers" ], [ "plos", "/Physical sciences/Chemistry/Polymer chemistry/Monomers" ], [ "plos", "/Physical sciences/Materials science/Materials/Oligomers/Dimers" ], [ "plos", "/Physical sciences/Physics/Thermodynamics/Free energy" ] ], "openalex": "W7213544130", "pdf_url": "https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1014804&type=printable", "pmcid": null, "pmid": null, "published": "2026-09-18", "publisher": "Public Library of Science", "ris": "https://research.draconitic.com/paper/8r8750v73d8nq.ris", "sources": [ { "first_seen": "2026-10-30T12:17:49Z", "native_id": "10.1371/journal.pcbi.1014804", "source": "plos", "url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014804" } ], "subjects": [ { "field": "chemistry", "origin": "plos:polymer chemistry", "subfield": "materials-polymers" } ], "title": "Integrative AI-assisted modeling suggests CPPF binding at a composite α/β-tubulin interface pocket dominated by β-tubulin contacts", "title_text": "Integrative AI-assisted modeling suggests CPPF binding at a composite α/β-tubulin interface pocket dominated by β-tubulin contacts", "type": "peer-reviewed", "updated_at": "2026-10-30T12:17:49Z", "url": "https://research.draconitic.com/paper/8r8750v73d8nq", "venue": "PLOS Computational Biology", "versions": [] }