Pseudo amino acid feature-based protein function prediction using support vector machine and K-nearest neighbors

Bioinformatics facing the vital challenge in protein function prediction due to protein data are available in primary structure, an amino acid sequence. Every protein cell sequence length and size are in different sequence order. Protein is available in 20 amino acid sequence alphabetic order; however, the corresponding information of the membrane protein sequence is insufficient to capture the function and structures of a protein from primary sequence datasets. A challenging task to correctly identify protein structure and function from amino acid sequence. The basic principle of PseAAC (Pseudo Amino Acid Composition) is to generate a discrete number of every protein samples. In each protein, sequence length varies due to protein functions. Some protein sequence length is less than 50, and some are large. Due to this, different sizes of the amino acid sample are chances to lose sequence order information. PseAAC feature generates a fixed size descriptor value in vector space to overcome sequence information loss and is used to further systematic evolution. Therefore machine learning computational tool synthesizes accurate identification of structure and function class of membrane protein. In this study, SVM (Support Vector Machine) and KNN (K-nearest neighbors) based prediction classifier used to identifying membrane protein and their types. © 2020, Science and Information Organization.