Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Proteins carry out the majority of functionality on a cellular level. Computational protein structure prediction (PSP) methods have been introduced to speed up the PSP process due to manual methods, like nuclear magnetic resonance (NMR) and x-ray crystallography (XC) taking numerous months even years to produce a predicted structure for a target protein. A lot of work in this area is focused on the type of search strategy to employ. Two popular methods in the literature are: Monte Carlo based algorithms and Genetic Algorithms. Genetic Algorithms (GA) have proven to be quite useful in the PSP field, as they allow for a generic search approach, which alleviates the need to redefine the search strategies for separate sequences. They also lend themselves ll to feature-based resampling techniques. Feature-based resampling works by taking previously computed local minima and combining features from them to create new structures that are more uniformly low in free energy. In this work  present a feature-based resampling genetic algorithm to refine structures that are outputted by PSP software. Our results indicate that our approach performs ll, and produced an average 9.5% root mean square deviation (RMSD) improvement and a 17.36% template modeling score (TM-Score) improvement.
['Trent Benjamin Higgs', 'Bela Stantic', 'Tamjidul Hoque', 'Abdul Sattar']
I am writing a research paper on a idea Genetic algorithm feature-based resampling for protein structure prediction
[{"from": "human", "value": "I am writing a research paper on a idea Genetic algorithm feature-based resampling for protein structure prediction"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Proteins carry out the majority of functionality on a cellular level. Computational protein structure prediction (PSP) methods have been introduced to speed up the PSP process due to manual methods, like nuclear magnetic resonance (NMR) and x-ray crystallography (XC) taking numerous months even years to produce a predicted structure for a target protein. A lot of work in this area is focused on the type of search strategy to employ. Two popular methods in the literature are: Monte Carlo based algorithms and Genetic Algorithms. Genetic Algorithms (GA) have proven to be quite useful in the PSP field, as they allow for a generic search approach, which alleviates the need to redefine the search strategies for separate sequences. They also lend themselves ll to feature-based resampling techniques. Feature-based resampling works by taking previously computed local minima and combining features from them to create new structures that are more uniformly low in free energy. In this work  present a feature-based resampling genetic algorithm to refine structures that are outputted by PSP software. Our results indicate that our approach performs ll, and produced an average 9.5% root mean square deviation (RMSD) improvement and a 17.36% template modeling score (TM-Score) improvement."}]