Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
2.7
3.2
Journals
Life
Special Issues
The Applications and Advancements of the Methods for Protein Subcellular...
share announcement

The Applications and Advancements of the Methods for Protein Subcellular Localization

A special issue of Life (ISSN 2075-1729). This special issue belongs to the section "Proteins and Proteomics".

Deadline for manuscript submissions: closed (29 January 2021) | Viewed by 8830

Special Issue Editors

Dr. Ravindra Kumar

E-Mail Website
Guest Editor
Department of Developmental Biology, School of Medicine, Washington University in St. Louis, St. Louis, MO 63130, USA
Interests: bioinformatics; computational biology; machine learning; deep learning; big data analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Sandeep Kumar Dhanda

E-Mail Website
Guest Editor
Saint Jude Children’s Research Hospital, Memphis, TN 38105, USA
Interests: bioinformatics; immunology; vaccine design; oncology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The identification of the unexplored functions/properties of a protein has been one of the most challenging problems in the field of biological science. The key factor that describes the function of a protein is its annotation, subcellular localization, and classification. This information can help in understanding and exploring the function, structure, and probable interacting partners of a protein. Since proteins located in their respective cellular and subcellular compartments are involved in their assigned cellular processes, mislocalization of proteins may lead to several devastating states, which may result in many diseases. The function of a protein is also closely linked to the class to which it belongs. Therefore, the prediction and annotation of subcellular localization and protein classification can be considered as the pivot of modern biology. Additionally, many of the proteins are of single location, but some may simultaneously exist at two or more different subcellular locations. Proteins with multiple locations are particularly interesting because they may be involved in very special biological function. This is a widespread problem, and a method to address this problem is urgently needed, because such behavior is exhibited by a large number of proteins. Protein subcellular localization also provides useful hints about the function of the proteins, increasing our understanding of the mechanism of certain diseases, and ultimately helps new drug developments and other applications.

Annotating a protein is increasingly attracting more attention with the acquisition of genomic as well as proteomic sequences becoming easier. However, the time required for characterization of individual proteins is not able to keep pace with the rate at which new sequences are becoming available. This has led to the accumulation of a large number of sequences waiting for characterization. We believe in silico methods using the integration of multiple sources of information hold the key to this problem.

In order to acknowledge the progress of this field, we are inviting the manuscripts related to the bioinformatics tools and application of predicting the subcellular localizations of proteins.

The Special Issue will cover but not be limited to the following research topics:

  1. Novel computational methods for protein annotation in terms of subcellular localizations and protein family classification;
  2. Advancements in feature extractions for the development of bioinformatics tools;
  3. Application of subcellular localization in drug designing;
  4. Resources for advancement of the field;
  5. Review articles summarizing the progress of protein localizations research.

Dr. Ravindra Kumar
Dr. Sandeep Kumar Dhanda
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Life is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • subcellular localization
  • protein classification
  • prediction methods
  • machine learning

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

15 pages, 1147 KiB  
Article
Ensemble of Multiple Classifiers for Multilabel Classification of Plant Protein Subcellular Localization
by Warin Wattanapornprom, Chinae Thammarongtham, Apiradee Hongsthong and Supatcha Lertampaiporn
Life 2021, 11(4), 293; https://doi.org/10.3390/life11040293 - 30 Mar 2021
Cited by 13 | Viewed by 2354
Abstract
The accurate prediction of protein localization is a critical step in any functional genome annotation process. This paper proposes an improved strategy for protein subcellular localization prediction in plants based on multiple classifiers, to improve prediction results in terms of both accuracy and [...] Read more.
The accurate prediction of protein localization is a critical step in any functional genome annotation process. This paper proposes an improved strategy for protein subcellular localization prediction in plants based on multiple classifiers, to improve prediction results in terms of both accuracy and reliability. The prediction of plant protein subcellular localization is challenging because the underlying problem is not only a multiclass, but also a multilabel problem. Generally, plant proteins can be found in 10–14 locations/compartments. The number of proteins in some compartments (nucleus, cytoplasm, and mitochondria) is generally much greater than that in other compartments (vacuole, peroxisome, Golgi, and cell wall). Therefore, the problem of imbalanced data usually arises. Therefore, we propose an ensemble machine learning method based on average voting among heterogeneous classifiers. We first extracted various types of features suitable for each type of protein localization to form a total of 479 feature spaces. Then, feature selection methods were used to reduce the dimensions of the features into smaller informative feature subsets. This reduced feature subset was then used to train/build three different individual models. In the process of combining the three distinct classifier models, we used an average voting approach to combine the results of these three different classifiers that we constructed to return the final probability prediction. The method could predict subcellular localizations in both single- and multilabel locations, based on the voting probability. Experimental results indicated that the proposed ensemble method could achieve correct classification with an overall accuracy of 84.58% for 11 compartments, on the basis of the testing dataset. Full article
(This article belongs to the Special Issue The Applications and Advancements of the Methods for Protein Subcellular Localization)
►▼ Show Figures

Figure 1

Review

Jump to: Research

18 pages, 1376 KiB  
Review
Bird Eye View of Protein Subcellular Localization Prediction
by Ravindra Kumar and Sandeep Kumar Dhanda
Life 2020, 10(12), 347; https://doi.org/10.3390/life10120347 - 14 Dec 2020
Cited by 12 | Viewed by 5416
Abstract
Proteins are made up of long chain of amino acids that perform a variety of functions in different organisms. The activity of the proteins is determined by the nucleotide sequence of their genes and by its 3D structure. In addition, it is essential [...] Read more.
Proteins are made up of long chain of amino acids that perform a variety of functions in different organisms. The activity of the proteins is determined by the nucleotide sequence of their genes and by its 3D structure. In addition, it is essential for proteins to be destined to their specific locations or compartments to perform their structure and functions. The challenge of computational prediction of subcellular localization of proteins is addressed in various in silico methods. In this review, we reviewed the progress in this field and offered a bird eye view consisting of a comprehensive listing of tools, types of input features explored, machine learning approaches employed, and evaluation matrices applied. We hope the review will be useful for the researchers working in the field of protein localization predictions. Full article
(This article belongs to the Special Issue The Applications and Advancements of the Methods for Protein Subcellular Localization)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop