Mutli-Features Prediction of Protein Translational Modification Sites

Abstract

Abstract-The post translational modification plays a significiant role in the biological processing. The potential post translational modification is composed of the center sites and the adjacent amino acid residues which are fundamental protein sequence residues.It can be helpful to perform their biological functions and contribute to understanding the molecular mechanisms that are the foundations of protein design and drug design. The existing algorithms of predicting modified sites often have some shortcomings, such as lower stability and accuracy. In this paper, a combination of physical, chemical, statistical, and biological properties of a protein have been ulitized as the features, and a novel framework is proposed to predict a protein's post translational modification sites. The multi-layer neural network and support vector machine are invoked to predict the potential modified sites with the selected features that include the compositions of amino acid residues, the E-H description of protein segments, and several properties from the AAIndex database. Being aware of the possible redundant information, the feature selection is proposed in the propocessing step in this research. The experimental results show that the proposed method has the ability to improve the accuracy in this classification issue.

Recombinant antibody against Trypanosoma cruzi from patients with chronic Chagas heart disease recognizes mammalian nervous system.

“…These data and the fact that some isotypes of mammal's tubulins are neuron specific (TubB3 and TubB4A) [55] raise the question about the structural motif of tubulin that is recognized by the antibody. Our in silico analysis of all the human and trypanosomatids α- and β- tubulin protein sequences available in public databases [ 44 , 45 , [78] , [79] , [80] ] could not provide conclusive information because most residues subject to PTM (i.e. acetylation, detyrosination/tyrosination, polyglutamylation) are conserved among the different species.…”

LAIPT: Lysine Acetylation Site Identification with Polynomial Tree

“…These relationships have the ability to demonstrate the local information of amino-acid residues in some peptide segments, and can be helpful in constructing more useful information with regards to the identification of modification sites. Some related work was proposed in DNA and RNA analysis [25,26,27,28,29,30,31]; methods such as DeepBind and DeepSea take advantage of deep convolutional neural networks (CNNs) to predict the sequence specificities of DNA-binding proteins [32,33,34,35]. In summary, these sequence analysis methods can be regarded as issues resolved using computational biology.…”

HSCVFNT: Inference of Time-Delayed Gene Regulatory Network Based on Complex-Valued Flexible Neural Tree Model

“…Flexible neural tree (FNT) model was initially introduced by Prof. Chen in 2005 [ 52 ]. Because of its automatic features extraction, cross layer connections and flexible activation functions, the FNT model performed better than many classical neural networks and has been applied widely for solving forecasting and classification problems [ 53 , 54 , 55 , 56 , 57 ]. Due to fact that a complex-valued neural network is more flexible and functional, a complex-valued flexible neural tree (CVFNT) model, as the extension of a real-valued FNT model, is proposed to infer the time-delayed regulations in GRN [ 58 , 59 , 60 ].…”