A Computational Approach to Predicting Distance Maps from Contact Maps
Kuo, Tony Chien-Yen
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One approach to protein structure prediction is to first predict from sequence, a thresholded and binary 2D representation of a protein's topology known as a contact map. Then, the predicted contact map can be used as distance constraints to construct a 3D structure. We focus on the latter half of the process and aim to obtain a set of non-binary distance constraints from contacts maps. This thesis proposes an approach to extend the traditional binary definition of “in contact” by incorporating fuzzy logic to construct fuzzy contact maps from a set of contact maps at different thresholds, providing a vehicle for error handling. Then, a novel template-based similarity search and distance geometry methods were applied to predict distance constraints in the form of a distance map. The three-dimensional coordinates were then calculated from the predicted distance constraints. Experiments were conducted to test our approach for various levels of noise. As well, we compare the performance of fuzzy contact maps to binary contact maps in the framework of our methodology. Our results showed that fuzzy contact map similarity was indicative of distance map similarity. Thus, we were able to retrieved similar distance map regions using fuzzy contact map similarity. The retrieved distance map regions provided a good starting point for adaptation and allowed for the extrapolation of missing distance values. We were thus able to predict distance maps from which, the three-dimensional coordinates were able to be calculated. Testing of this framework on binary contact maps revealed that fuzzy contact maps had better performance with or without noise due to a stronger correlation between fuzzy contact map similarity and distance map similarity. Thus, the methodology described in this thesis is able to predict good distance maps from fuzzy contact maps in the presence of noise and the resulting coordinates were highly correlated to the performance of the predicted distance maps.