ECPred: a tool for the prediction of the enzymatic functions of protein sequences based on the EC nomenclature

Download

10.1186s12859-018-2368-y.pdf

Date

2018-09-21

Author

Dalkıran, Alperen
Rifaioğlu, Ahmet Süreyya
Dogan, Tunca
Atalay, Mehmet Volkan

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

238
views

124
downloads

Background: The automated prediction of the enzymatic functions of uncharacterized proteins is a crucial topic in bioinformatics. Although several methods and tools have been proposed to classify enzymes, most of these studies are limited to specific functional classes and levels of the Enzyme Commission (EC) number hierarchy. Besides, most of the previous methods incorporated only a single input feature type, which limits the applicability to the wide functional space. Here, we proposed a novel enzymatic function prediction tool, ECPred, based on ensemble of machine learning classifiers.

Subject Keywords

Protein sequence, EC numbers, Function prediction, Machine learning, Benchmark datasets

URI

https://hdl.handle.net/11511/30698

Journal

BMC BIOINFORMATICS

DOI

https://doi.org/10.1186/s12859-018-2368-y

Collections

Graduate School of Natural and Applied Sciences, Article

Suggestions

OpenMETU
Core

Subsequence-based feature map for protein function classification Sarac, Omer Sinan; Guersoy-Yuezueguellue, Oezge; Atalay, Rengül; Atalay, Mehmet Volkan (2008-04-01) Automated classification of proteins is indispensable for further in vivo investigation of excessive number of unknown sequences generated by large scale molecular biology techniques. This study describes a discriminative system based on feature space mapping, called subsequence profile map (SPMap) for functional classification of protein sequences. SPMap takes into account the information coming from the subsequences of a protein. A group of protein sequences that belong to the same level of classification...
Multi-view subcellular localization prediction of human proteins Özsarı, Gökhan; Atalay, M. Volkan.; Department of Computer Engineering (2019) Determining the subcellular localization of proteins is crucial for Understanding the functions of proteins, drug targeting, systems biology, and proteomics research. Experimental validation of subcellular localization is an expensive and challenging process. There exist several computational methods for automated prediction of protein subcellular localization; however, there is still room for better performance. Here, we propose a multi-view SVM-based approach that provides predictions for human proteins. ...
DEEPred: Automated Protein Function Prediction with Multi-task Feed-forward Deep Neural Networks Rifaioğlu, Ahmet Süreyya; Martin, Maria Jesus; Atalay, Rengül; Atalay, Mehmet Volkan (2019-05-14) Automated protein function prediction is critical for the annotation of uncharacterized protein sequences, where accurate prediction methods are still required. Recently, deep learning based methods have outperformed conventional algorithms in computer vision and natural language processing due to the prevention of overfitting and efficient training. Here, we propose DEEPred, a hierarchical stack of multi-task feed-forward deep neural networks, as a solution to Gene Ontology (GO) based protein function pred...
Prediction of Protein Interactions by Structural Matching: Prediction of PPI Networks and the Effects of Mutations on PPIs that Combines Sequence and Structural Information Tunçbağ, Nurcan; Nussinov, Ruth; Gursoy, Attila (Humana Press Inc., 2017) Structural details of protein interactions are invaluable to the understanding of cellular processes. However, the identification of interactions at atomic resolution is a continuing challenge in the systems biology era. Although the number of structurally resolved complexes in the Protein Databank increases exponentially, the complexes only cover a small portion of the known structural interactome. In this chapter, we review the PRISM system that is a protein–protein interaction (PPI) prediction tool—its r...
FTIR spectroscopic characterization of protein structure in aqueous and non-aqueous media Haris, PI; Severcan, Feride (1999-09-15) With increasing use of proteins in many different applications, ranging from phramaceuticals to biosensors and biomaterials, there has emerged a need for protein structural characterisation in diverse environments. In many cases it is not sufficient to just have the three-dimensional structure of a protein in H2O or in the crystalline state. Often information on the structural properties of a protein is required in the presence of organic solvents, detergent micelles, phospholipid membranes and so on. Fouri...

Citation Formats

A. Dalkıran, A. S. Rifaioğlu, T. Dogan, and M. V. Atalay, “ECPred: a tool for the prediction of the enzymatic functions of protein sequences based on the EC nomenclature,” BMC BIOINFORMATICS, pp. 0–0, 2018, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/30698.