proCCK

proCCK is a computational tool designed for the analysis of protein crystallization data. It focuses on predicting the probability of protein crystallization success based on various experimental parameters. The software utilizes machine learning algorithms trained on a large dataset of past crystallization experiments, encompassing factors such as protein properties, buffer conditions, and additive concentrations. proCCK aims to streamline the early stages of the protein crystallization workflow by identifying potentially promising conditions, thereby reducing the time and resources spent on unproductive trials.

The input for proCCK typically includes a list of experimental conditions, often in a structured format, along with relevant information about the protein being crystallized. The output is a ranked list of conditions, each associated with a predicted crystallization probability score. This score can help researchers prioritize which conditions to test first. The underlying models within proCCK are continuously refined as new crystallization data becomes available, aiming to improve prediction accuracy over time. The tool is intended to be used by structural biologists and researchers involved in protein crystallography.