Recognizing Proteins with Binding Function in Elymus nutans Based on Machine Learning Methods

Abstract

: Recognition for proteins is essential for study of biology. In order to obtain the function proteins of Elymus nutans, we sequenced their transcriptomes in Inner Mongolia of China. Then, we used BLAST software for their function annotations. Besides, we used machine learning methods to recognize proteins which are not annotated by the software. In the process, we focused on identify the proteins with binding functions. In our research, features are extracted by four algorithms and selected by mutual information estimator. Meanwhile, a total of three types of classifiers are constructed based on K-nearest neighbor algorithm and gradient boosting algorithm. Results show that there are 848 proteins with ATP binding function, 113 proteins with heme binding function, 315 proteins with zinc-ion binding function, 135 proteins with GTP binding function and 21 proteins with ADP binding function. Furthermore, we have successfully predicted the functions of 10 special protein sequences whose function annotations cannot be obtained by making sequence alignment with seven famous protein databases. Among them, seven sequences have ATP binding functions, one sequence has heme binding function, one sequence has zinc-ion binding function and the other one has GTP binding function.

Autoregressive Modeling and Prediction of the Activity of Antihypertensive Peptides

“…Some research studies focus on feature representation ( Tong, et al, 2008 ; Manavalan et al, 2019 ), and some research studies focus on identification ( Majumder, and Wu, 2010 ). Machine learning (ML) approaches are becoming more and more popular in bioinformatics ( Baldi et al, 2001 ; Libbrecht and Noble, 2015 ; Zou and Qiliu, 2019 ; Yang et al, 2020 ; Zhang et al, 2021 ). Some research studies are associated with classification, and some are associated with regression.…”