Biology, Purdue Cancer

This paper presents an integrated computational-experimental method to determine the fold of a target protein by probing it with a set of planned disulfide cross-links. We start with predicted structural models obtained by standard fold recog-nition techniques. In a first stage, we characterize the fold-level differences between the models in terms of topological (contact) patterns of secondary structure elements (SSEs), and select a small set of SSE pairs that differentiate the folds. In a second stage, we determine a set of residue-level cross-links to probe the selected SSE pairs. Each stage em-ploys an information-theoretic planning algorithm to maxi-mize information gain while minimizing experimental com-plexity, along with a Bayes error plan assessment framework to characterize the probability of making a correct decision once data for the plan are collected. By focusing on over-all topological differences and planning cross-linking exper-iments to probe them, our fold determination approach is robust to noise and uncertainty in the models (e.g., thread-ing misalignment) and in the actual structure (e.g., flexibil-ity). We demonstrate the effectiveness of our approach in case studies for a number of CASP targets, showing that the optimized plans have low risk of error while testing only a small portion of the quadratic number of possible cross-link candidates. Simulation studies with these plans further show that they do a very good job of selecting the correct model, according to cross-links simulated from the actual crystal structures. Fold determination can overcome scoring limitations in purely computational fold recognition meth-ods, while requiring less experimental effort than traditional protein structure determination approaches. ∗Corresponding author