Abstract
Prediction of intrinsic disordered proteins is a hot area in the field of bio-information. Due to the high cost of evaluating the disordered regions of protein sequences using experimental methods, we used a low-complexity prediction scheme. Sequence complexity is used in this scheme to calculate five features for each residue of the protein sequence, including the Shannon entropy, the Topo-logical entropy, the Permutation entropy and the weighted average values of two propensities. Particularly, this is the first time that permutation entropy has been applied to the field of protein sequencing. In addition, in the data preprocessing stage, an appropriately sized sliding window and a comprehensive oversampling scheme can be used to improve the prediction performance of our scheme, and two ensemble learning algorithms are also used to verify the prediction results before and after. The results show that adding permutation entropy improves the performance of the prediction algorithm, in which the MCC value can be improved from the original 0.465 to 0.526 in our scheme, proving its universality. Finally, we compare the simulation results of our scheme with those of some existing schemes to demonstrate its effectiveness.
Highlights
Academic Editor: Mircea-BogdanAs the highest content of organic compounds in human body, protein is the main bearer of human life activities, The “Amino Acid Sequence—3-Dimensional Structure—Protein Function” paradigm of protein was generally accepted [1]
A considerable number of biophysical studies have shown, type-2 diabetic islets are characterized by islet amyloid protein derived from islet amyloid peptide (IAPP), a protein co-expressed by beta cells with insulin that, when misfolded
Disordered proteins often have repetitive regions in their amino acid sequences, so they have lower sequence complexity than ordered proteins [25], We propose a new feature extraction scheme based on sequence complexity, which uses five features including Shannon entropy, topological entropy, permutation entropy and two amino acid preferences
Summary
As the highest content of organic compounds in human body, protein is the main bearer of human life activities, The “Amino Acid Sequence—3-Dimensional Structure—. Compared with the physicochemical-based method, The second method distinguishes ordered and disordered proteins with positive samples and negative samples, effectively combines various features, and uses machine learning to make predictions, such as support vector machines (SVM), Naive Bayes (NB), K nearest neighbors (KNN) and decision trees (DT). These schemes include DISOPRED3 [16], SPINE-D [17], ESpritz [18] and MetaDisorder [19]. Step 3: select a suitable sliding window, calculate the Shannon entropy, topological entropy, permutation entropy and two amino acid preferences of each residue, and acquire a 1,217,223 × 5 data set DIS2209.
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