Despite the remarkable advances with AlphaFold2 in predicting protein structure and structure determination by cryogenic electron microscopy (cryoEM), protein crystallography is still more likely to provide higher-resolution data and more reliable structures for drug discovery. The bottleneck in crystallographic studies is the search for the chemical cocktail that promotes crystallization. Each lead condition contains two to seven chemicals and is discovered by sampling hundreds of solutions from sparse matrix screens.
Once a good lead's composition is optimized, protein crystals are grown in large numbers for structural studies. Protein crystal growth often gives quadratic responses to rising levels of some factors, and two-way interactions are abundant. Two-level designs miss these features. New two-level modified one-factor-at-a-time (MOFAT) experiments can detect two-way interactions in simulation studies (Yu 2022). The extension of MOFAT to three levels would interest crystal growers.
We hypothesize that a three-level extended MOFAT (EMOFAT) design can detect two-way and quadratic effects. This design retains the baseline run of standard OFAT designs and adds runs with all factors at their lowest or highest levels (2m + 3 runs, where m is the number of factors). We conducted simulation studies using JSL scripts to explore the ability of these designs to detect main effects and higher-order interactions, including three-way interactions.
These designs may be attractive to protein crystal growers because the three levels will detect nonlinear responses and promise to detect two-way interactions before advancing to more advanced optimization experiments.
