Classical structural methods, such as nuclear magnetic resonance (NMR) and X-ray crystallography often encounter technical difficulties in solving the complex structures. In cases, where structure determination of protein-protein complex with the classical approach seems to be challenging, predictive models can be built based on both experimental input in conjunction with docking studies. Computational docking refers to the modelling or prediction of the three-dimensional structure of a bio-molecular complex, which takes into account the individual protein molecules in their free/unbound form. Constraint-driven docking has been proven to be an efficient approach for docking calculation of unknown protein pairs. Popular protein-protein docking platforms, e.g. HADDOCK (High ambiguity driven biomolecular docking), require the input of atleast one pair of interacting amino acid residues between two complementary proteins in a complex. Among all the docking methods participating in the Critical Assessment of Prediction of Interactions (CAPRI) challenge, HADDOCK is the only true data-driven strategy. Experimental data including site-directed mutagenesis, NMR and mass spectrometry data suggest critical point of protein-protein interaction. These amino acid information, pertaining to anyone/either of the protein/s in a complex, can be incorporated during the docking experiments so as to restrain the docking interface between a pair of proteins. Here we propose that considering known crystal structure (PDB Code: 1YCS), even with a single amino-acid constraint for one of the interacting proteins (p53DBD), constraint-driven docking has been consistently found to be successful. Using varied protein-protein docking algorithms this observation is unfailingly replicated. This validates the constraint-based docking approach in predicting true protein-protein associations and further establishes itself as a robust data-driven docking strategy compared to contemporary structural/docking approaches.