Prediction Of Protein Folding Rates Based On Structure Characteristics

Protein folding rate is as important to folding the mechanism as protein is to life activity.Protein and life are closely related.The mechanism of protein folding is intricate.Only when it is folded into a specific natural structure,the protein can perform its biological function.Therefore,the analysis of the mechanism of protein folding is not limited to biolog,ical engineering field or medical field.The mechanism of protein folding plays a decisive role in the development of human beings.In recent years,many researchers proposed a number of methods for the prediction of protein folding.The characteristic values of protein are extracted in this paper,and it is based on the structural characteristics.We use a mathematical model to predict the folding rate.The main study includes the following two aspects:1.Analysising structure and understanding functional feature of protein.Integrate characteristic information of primary and secondary structure.Build mathematical model to predict protein folding rate.We calculate the correlation coefficient between the predicted and experimental values of the data set containing 62 proteins.Using Jack-knife test and comparing with the other four methods that have been mature.Our method can get better results.It can be proved that the extracted characteristic value has a certain effect on the protein folding rates.2.According to the folding mechanism of the protein,it can be divided into two-state and multi-state.Because of the different folding types of proteins,the folding rates are different.Then we take the targeted prediction research.We establish a multiple linear regression function between the eigenvalues of two-state multi-state and mixed-state and the experimental value.After calculating the correlation coefficient and Jack-knife test,we found that the mixed characteristic value of the selected has a great influence on the protein folding rate.And the folding rate of different types of protein is influenced by the mixed characteristic value.