Statistical Analysis And Prediction Of Human Acetylation Proteins

To date,more than 450 unique protein modifications have been identified,including phosphorylation,acetylation,ubiquitination,and small ubiquitination,which are regulatory mechanisms of cellular proteins with many biological functions,and in many prokaryotic and eukaryotic It plays a role in the regulation of protein function.In these PTM among them,acetylation is a dynamic and highly conserved post-translational modification that plays a key role in the regulation of various cellular processes.It is one of the most important post-translational modifications of reversible proteins due to its important role in certain related biological processes.Abnormal acetylated proteins with many pathological diseases.For example,cancer,neurological diseases and metabolic diseases,so further understanding of acetylated proteins for future analysis of acetylation mechanisms and related experimental validation,drug development provides guiding significance.The function of proteins is often labeled KW,GO,Smart,Pfam,Inter Pro,PRINT,PROSITE,SUPFAM.The acetylated protein has a large gap between the positive and negative samples,and the functional labeling information of the positive sample contains the amount of protein.It is much larger than the negative sample,and KW,GO information is much larger than other Smart,Pfam,Inter Pro,PRINT,PROSITE,SUPFAM information.Among the 6832 proteins of human protein,there are 3404 positive samples and 3428 negative samples.Articles,positive and negative samples are basically flat.The keywords in the keyword acetylation have 3392 in the positive sample.In the statistical analysis of GO information,it is found that acetylation plays a key role in the nuclear and cytoplasmic processes,and in the three major categories of GO databases,the positive sample cell fraction contains much more protein than the rest.Process and molecular function.The amount of information in Smart,Pfam,Inter Pro,PRINT,PROSITE and SUPFAM has very little protein in the entire positive and negative samples,and plays a minor role in the overall analysis results.Acetylation is a type of post-translational modification.It usually reacts with acetic acid to introduce an acetyl group into an organic compound.In order to understand the mechanism of acetylation,it is necessary to correctly recognize the acetylated protein in a biological system.Although high-throughput experimental studies using mass spectrometry have identified many acetylation sites,most of the acetylation sites have not been discovered.In order to reduce the cost of experiments and improve the effectiveness and efficiency of acetylation sites,an information technology-based calculation method is introduced.Based on the functional domain annotation(FDA)and subcellular localization information,this study preserves the sequence from the gray system model and the KNN score.Extracting features from interest,established a new calculation method for predicting acetylated proteins.Combined with the detailed feature analysis and application of the relief feature selection algorithm,the results of five cross-validation of three data sets are given.The accuracy obtained is satisfactory,as the average performance,the accuracy is 77.10%,horse the correlation coefficient is 0.5457% and the AUC value is 0.8389.These work have a certain guiding effect on the relevant experimental verification,which provides useful insights for studying the mechanism of acetylation and provides a powerful help for further research on other PTM processes.In this paper,the statistical analysis of human acetylated protein function annotation information and fusion of acetylated protein characteristics,functional domain annotation identification of acetylated protein can improve the efficiency of acetylation experiments,provide reference and help for basic research and drug development experimenters.