Protein-protein interactions govern virtually all biological processes, making accurate prediction of binding modes a fundamental challenge in computational structural biology. Traditional protein-protein docking algorithms typically employ rigid-body approximations followed by limited flexibility refinement, an approach that has proven insufficient for systems exhibiting significant conformational dynamics [1,2].

The limitations of conventional docking methods become particularly apparent when dealing with intrinsically flexible proteins or those undergoing conformational changes upon binding. These systems often exhibit multiple low-energy conformations, making it challenging to identify native binding modes using standard approaches [3].

Recent advances in artificial intelligence and deep learning have shown promise in protein-protein docking, with methods such as diffusion models and transformer-based architectures demonstrating improved accuracy in certain scenarios [4,5]. However, these AI-driven approaches often require extensive training datasets, may not generalize well to novel protein families, and can struggle with systems requiring significant conformational flexibility [1].

Coarse-grained molecular dynamics simulations have emerged as powerful tools for studying large-scale protein dynamics and interactions. By reducing computational complexity while preserving essential physical properties, CG models enable longer timescale simulations and enhanced conformational sampling [6]. The Martini force field, in particular, has demonstrated remarkable success in capturing protein-protein binding thermodynamics and kinetics, with successful applications including coarse-grained protein-protein docking with HADDOCK and large-scale protein assembly simulations [7,8].

The concept of systematic orientation sampling addresses a fundamental challenge in protein-protein docking: the vast conformational space that must be explored to identify native binding modes. While exhaustive sampling is computationally prohibitive, systematic approaches can efficiently cover essential regions of the binding landscape [9].

It is important to distinguish between global and local docking approaches in this context. Global docking methods attempt to explore the entire surface of both proteins without prior knowledge of binding sites, while local docking approaches focus on specific regions where binding is expected to occur based on experimental evidence or computational predictions. The systematic approach presented in this study falls into the local docking category, as it requires prior identification of putative bioactive surfaces on at least one binding partner.

This study introduces the Coarse-Grained Cubic Orientation Approach (CG-COA), a novel methodology that combines systematic orientation sampling with efficient CG-MD simulations and rigorous free energy calculations.

As a local docking approach, CG-COA is designed to work most effectively when the approximate location of binding interfaces is known or can be predicted, making it particularly suitable for structure-function studies and cases where experimental constraints are available. The primary objectives are to: (a) develop a systematic approach for protein-protein docking validation, (b) demonstrate the effectiveness of cubic orientation sampling, (c) validate the methodology using diverse protein complexes, and (d) provide a computationally efficient alternative to traditional docking approaches with clear applicability boundaries. The innovation lies in the systematic cubic orientation framework, which ensures comprehensive sampling while maintaining computational tractability, combined with rigorous validation that includes both successful applications and clear identification of method limitations.