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Over the past decade, iterative projection algorithms, an effective approach to recovering phases from a single intensity measurement, have found application in protein crystallography to directly surmount the `phase problem'. However, previous studies have always assumed that some prior knowledge constraints (i.e. a low-resolution envelope about the protein structure in the crystal cell or histogram matching requiring a similar density distribution to the target crystal) must be known for successful phase retrieval, thus hindering its widespread application. In this study, a novel phase-retrieval workflow is proposed that eliminates the need for a reference density distribution by utilizing low-resolution diffraction data in phasing algorithms. The approach involves randomly assigning one out of 12 possible phases at 30° intervals (or two for centric reflections) to produce an initial envelope, which is then refined through density modification after each run of phase retrieval. To evaluate the success of the phase-retrieval procedure, information entropy is introduced as a new metric. This approach was validated using ten protein structures with high solvent content, demonstrating its effectiveness and robustness.

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