Refining Genetic Algorithm Twin Removal for High-Resolution Protein Structure Prediction

To gain a better understanding of how proteins function a process known as protein structure prediction (PSP) is carried out. However, experimental PSP methods, such as X-ray crystallography and Nuclear Magnetic Resonance (NMR), can be time-consuming and inaccurate. This has given rise to numerous computational PSP approaches to try and elicit a protein's three-dimensional conformation. A popular PSP search strategy is Genetic Algorithms (GA). GAs allow for a generic search approach, which can provide a generic improvement to alleviate the need to redefine the search strategies for separate sequences. Though GA's working principles are remarkable, a serious problem that is inherent in the GA search process is the growth of twins or identical chromosomes. Therefore, enhanced twin removal strategies are crucial for any GA search solving hard-optimisation problems like PSP. In this paper we explain our high-resolution GA feature-based resampling PSP approach and propose a twin removal strategy to further enhance its prediction accuracy. This includes investigating the optimal chromosome correlation factor (CCF) for our approach and defining a pre-built structure library for twin removal. We have also compared our GA approach with the popular Monte Carlo (MC) method for PSP. Our results indicate that out of all the CCF values we tested a CCF value of 0.8 provided the best level of diversity within our GA population. It also generated, on average, more native-like structures than any of the other CCF values, and clearly demonstrated that twin removal is needed in PSP when using GAs to obtain more accurate results.