NDLI: A novel approach for protein subcellular location prediction using amino acid exposure

Content Provider	Springer Nature : BioMed Central
Author	Mer, Arvind Singh Andrade-Navarro, Miguel A
Abstract	Background Proteins perform their functions in associated cellular locations. Therefore, the study of protein function can be facilitated by predictions of protein location. Protein location can be predicted either from the sequence of a protein alone by identification of targeting peptide sequences and motifs, or by homology to proteins of known location. A third approach, which is complementary, exploits the differences in amino acid composition of proteins associated to different cellular locations, and can be useful if motif and homology information are missing. Here we expand this approach taking into account amino acid composition at different levels of amino acid exposure. Results Our method has two stages. For stage one, we trained multiple Support Vector Machines (SVMs) to score eukaryotic protein sequences for membership to each of three categories: nuclear, cytoplasmic and extracellular, plus extra category nucleocytoplasmic, accounting for the fact that a large number of proteins shuttles between those two locations. In stage two we use an artificial neural network (ANN) to propose a category from the scores given to the four locations in stage one. The method reaches an accuracy of 68% when using as input 3D-derived values of amino acid exposure. Calibration of the method using predicted values of amino acid exposure allows classifying proteins without 3D-information with an accuracy of 62% and discerning proteins in different locations even if they shared high levels of identity. Conclusions In this study we explored the relationship between residue exposure and protein subcellular location. We developed a new algorithm for subcellular location prediction that uses residue exposure signatures. Our algorithm uses a novel approach to address the multiclass classification problem. The algorithm is implemented as web server 'NYCE’ and can be accessed at http://cbdm.mdc-berlin.de/~amer/nyce .
Related Links	https://bmcbioinformatics.biomedcentral.com/counter/pdf/10.1186/1471-2105-14-342.pdf
Ending Page	13
Page Count	13
Starting Page	1
File Format	HTM / HTML
ISSN	14712105
DOI	10.1186/1471-2105-14-342
Journal	BMC Bioinformatics
Issue Number	1
Volume Number	14
Language	English
Publisher	BioMed Central
Publisher Date	2013-11-28
Access Restriction	Open
Subject Keyword	Bioinformatics Microarrays Computational Biology Computer Appl. in Life Sciences Algorithms Support Vector Machine Artificial Neural Network Amino Acid Composition Support Vector Machine Model Protein Pair Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Molecular Biology Biochemistry Computer Science Applications Applied Mathematics Structural Biology
Journal Impact Factor	2.9/2023
5-Year Journal Impact Factor	3.6/2023

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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