Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Disulfide connectivity prediction from one chain of protein helps determine protein tertiary structure. The more accuracy of prediction it reaches the more precise three dimensional structures  can obtain through computational methods. Previous methods only use local sequence or secondary structure information or global sequence information or combination of the above descriptors to predict the disulfide bond pattern. Instead of using those descriptors,  take an alternative descriptor of global secondary structure to make prediction, and the highest performance among all pattern-wise methods is obtained. Cysteine separation profiles on protein secondary structure have been used to predict the disulfide connectivity of proteins. The cysteine separation profiles on secondary structure(CSPSS) represent a vector encoded from the sepeartions beten any two consecutive cysteine-corresponding positions in a predicted protein secondary structure sequence. Through comparisons of their CSPSS, the disulfide connectivity of a test protein is inferred from a template set. In 4-fold of SP39, any two proteins from different groups share less than 30% sequence identity. The result shows a prediction accuracy (54%), which proves again a disulfide bond pattern is highly related to protein secondary structure.
['Guantao Chen', 'Hai Deng', 'Yufeng Gui', 'Yi Pan', 'Xue Wang']
I am writing a research paper on a idea Cysteine separations profiles on protein secondary structure infer disulfide connectivity
[{"from": "human", "value": "I am writing a research paper on a idea Cysteine separations profiles on protein secondary structure infer disulfide connectivity"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Disulfide connectivity prediction from one chain of protein helps determine protein tertiary structure. The more accuracy of prediction it reaches the more precise three dimensional structures  can obtain through computational methods. Previous methods only use local sequence or secondary structure information or global sequence information or combination of the above descriptors to predict the disulfide bond pattern. Instead of using those descriptors,  take an alternative descriptor of global secondary structure to make prediction, and the highest performance among all pattern-wise methods is obtained. Cysteine separation profiles on protein secondary structure have been used to predict the disulfide connectivity of proteins. The cysteine separation profiles on secondary structure(CSPSS) represent a vector encoded from the sepeartions beten any two consecutive cysteine-corresponding positions in a predicted protein secondary structure sequence. Through comparisons of their CSPSS, the disulfide connectivity of a test protein is inferred from a template set. In 4-fold of SP39, any two proteins from different groups share less than 30% sequence identity. The result shows a prediction accuracy (54%), which proves again a disulfide bond pattern is highly related to protein secondary structure."}]