Characterizing the MSMP-CCR2 Interaction through Molecular Dynamics Simulations and Machine Learning Approaches

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Not reported numerically in the abstract; described qualitatively as 'similar energetic profiles' for MSMP and CCL2 despite 'distinct binding modes'

• Primary Outcome: Identification of critical residues at the MSMP-CCR2 interface and conformational rearrangements stabilising the active CCR2 state; ML classification of MD trajectories highlighting structural features of stable MSMP-CCR2-G protein complexes

• Nnt Or Sensitivity: Not applicable (computational mechanistic study); ML model performance metrics (sensitivity, specificity, AUC) not reported in abstract

• Confidence Interval: Not reported

Clinical Application

Direct clinical application is not feasible at this stage. The study provides structural hypotheses for drug discovery. Translation would require: (1) experimental validation of interface residues, (2) small molecule or biologic inhibitor development targeting the identified interface, (3) preclinical efficacy and toxicity studies, (4) clinical trials. This represents a multi-year translational pipeline. CCR2 antagonists are not currently PBS-listed or TGA-approved for oncology indications in Australia. Prostate cancer is the most commonly diagnosed cancer in Australian men (Cancer Australia data), and castration-resistant prostate cancer represents a major unmet need. RACGP and Cancer Council Australia guidelines do not yet address MSMP-CCR2 targeting. This research is relevant to Australian oncology drug discovery programs and could inform future TGA submissions if the therapeutic hypothesis is validated experimentally. Australian researchers at institutions such as the Peter MacCallum Cancer Centre and ANZUP cooperative group would be natural partners for translational follow-up. Patients with MSMP-overexpressing cancers, particularly castration-resistant prostate cancer, hormone-resistant ovarian cancer, and breast cancer with antiangiogenic treatment resistance; also relevant to conditions involving CCR2-mediated monocyte/macrophage recruitment in the tumour microenvironment

Abstract

The MSMP (MicroSeminoProtein, Prostate-associated) protein is overexpressed in several cancers, including prostate, ovarian, and breast cancers. Its overexpression is particularly prevalent in tumors resistant to hormonal therapy, as well as to antiangiogenic treatments (e.g., anti-VEGF therapies). In hypoxic tumor microenvironments, characteristic of solid tumors, MSMP expression increases and facilitates tumor growth. MSMP binds to the transmembrane receptor CCR2 (C-C chemokine receptor type 2), a GPCR present on monocytes and lymphocytes. This interaction stabilizes an active conformation of CCR2, enabling downstream MAP kinase signaling pathways to reactivate androgen synthesis and promote tumor progression. In this study, we employed molecular modeling, molecular dynamics simulations, and machine learning techniques to elucidate the structural basis of the MSMP-CCR2 interaction. High-resolution models of the MSMP-CCR2-G protein complex were generated using AlphaFold2 and refined with MD simulations. Comparative analyses with the CCL2-CCR2-G protein complex and the unbound CCR2-G protein complex revealed key conformational rearrangements that modulate and stabilize the active state of the receptor. Binding free energy calculations revealed similar energetic profiles for MSMP and CCL2, despite distinct binding modes. Residue-level energy decomposition identified critical residues at the MSMP-CCR2 interface, offering insights into the receptor's activation mechanism. Additionally, machine learning models classified molecular dynamics trajectories, highlighting key structural features associated with stable MSMP-CCR2-G protein states. This integrative approach identified residues essential for maintaining a stable conformation of the MSMP-CCR2-G protein complex, providing a basis for targeting MSMP-CCR2 interactions in therapeutic development.

References

1. 1.Quitté, L., Leclercq, M., Moroy, G., & Droit, A. (2026). Characterizing the MSMP-CCR2 Interaction through Molecular Dynamics Simulations and Machine Learning Approaches. Journal of Chemical Information and Modeling. https://doi.org/10.1021/acs.jcim.6c00048