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Protein-protein interactions are central to understanding how cells carry out their wide array of functions and metabolic procedures. Conventional studies on specific protein interactions focus either on details of one-to-one binding interfaces, or on large networks that require a priori knowledge of binding strengths. Moreover, specific protein interactions, occurring within a crowded macromolecular environment, which is precisely the case for interactions in a real cell, are often under-investigated. A macromolecular simulation package, called BioSimz, has been developed to perform Langevin dynamics simulations on multiple protein-protein interactions at atomic resolution, aimed at bridging the gaps between structural, kinetic and crowding studies on protein-protein interactions. Simulations on twenty-seven experimentally determined protein-protein interactions, indicated that the use of contact frequency information of proteins forming specific encounters can guide docking algorithms towards the most likely binding regions. Further evidence from eleven benchmarked protein interactions showed that the association rate constant of a complex, kon, can be estimated, with good agreement to experimental values, based on the retention time of its specific encounter. Performing these simulations with ten types of environmental protein crowders, it suggests, from the change of kon, that macromolecular crowding improves the association kinetics of slower-binding proteins, while it damps the association kinetics of fast, electrostatics-driven protein-protein interactions. It is hypothesised, based on evidence from docking, kinetics and crowding, that the dynamics of specific protein-protein encounters is vitally important in determining their association affinity. There are multiple factors by which encounter dynamics, and subsequently the kon, can be influenced, such as anchor residues, long-range forces, and environmental steering via crowders’ electrostatics and/or volume exclusion. The capacity of emulating these conditions on a common platform not only provides a holistic view of interacting dynamics, but also offers the possibility of evaluating and engineering protein-protein interactions from aspects that have never been opened before.