NDLI: Proposing a highly accurate protein structural class predictor using segmentation-based features

Content Provider	Springer Nature : BioMed Central
Author	Dehzangi, Abdollah Paliwal, Kuldip Lyons, James Sharma, Alok Sattar, Abdul
Abstract	Background Prediction of the structural classes of proteins can provide important information about their functionalities as well as their major tertiary structures. It is also considered as an important step towards protein structure prediction problem. Despite all the efforts have been made so far, finding a fast and accurate computational approach to solve protein structural class prediction problem still remains a challenging problem in bioinformatics and computational biology. Results In this study we propose segmented distribution and segmented auto covariance feature extraction methods to capture local and global discriminatory information from evolutionary profiles and predicted secondary structure of the proteins. By applying SVM to our extracted features, for the first time we enhance the protein structural class prediction accuracy to over 90% and 85% for two popular low-homology benchmarks that have been widely used in the literature. We report 92.2% and 86.3% prediction accuracies for 25PDB and 1189 benchmarks which are respectively up to 7.9% and 2.8% better than previously reported results for these two benchmarks. Conclusion By proposing segmented distribution and segmented auto covariance feature extraction methods to capture local and global discriminatory information from evolutionary profiles and predicted secondary structure of the proteins, we are able to enhance the protein structural class prediction performance significantly.
Related Links	https://bmcgenomics.biomedcentral.com/counter/pdf/10.1186/1471-2164-15-S1-S2.pdf
Ending Page	13
Page Count	13
Starting Page	1
File Format	HTM / HTML
ISSN	14712164
DOI	10.1186/1471-2164-15-S1-S2
Journal	BMC Genomics
Issue Number	1
Volume Number	15
Language	English
Publisher	BioMed Central
Publisher Date	2014-01-24
Access Restriction	Open
Subject Keyword	Life Sciences Microarrays Proteomics Animal Genetics and Genomics Microbial Genetics and Genomics Plant Genetics and Genomics Protein structural class prediction problem Structural features Evolutionary features Segmented auto covariance Segmented distribution Support Vector Machine (SVM)
Content Type	Text
Resource Type	Article
Subject	Biotechnology Genetics
Journal Impact Factor	3.5/2023
5-Year Journal Impact Factor	4.1/2023

Sl.	Authority	Responsibilities	Communication Details
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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Proposing a highly accurate protein structural class predictor using segmentation-based features.

A segmentation-based method to extract structural and evolutionary features for protein fold recognition

A Segmentation-Based Method to Extract Structural and Evolutionary Features for Protein Fold Recognition

Proposing a highly accurate protein structural class predictor using segmentation-based features

ProTstab – predictor for cellular protein stability

SVM-based prediction of linear B-cell epitopes using Bayes Feature Extraction

Prediction of reversible disulfide based on features from local structural signatures

The prediction of the porcine pre-microRNAs in genome-wide based on support vector machine (SVM) and homology searching

Prediction and classification of ncRNAs using structural information

Proposing a highly accurate protein structural class predictor using segmentation-based features

Similar Documents

Proposing a highly accurate protein structural class predictor using segmentation-based features.

A segmentation-based method to extract structural and evolutionary features for protein fold recognition

A Segmentation-Based Method to Extract Structural and Evolutionary Features for Protein Fold Recognition

Proposing a highly accurate protein structural class predictor using segmentation-based features

ProTstab – predictor for cellular protein stability

SVM-based prediction of linear B-cell epitopes using Bayes Feature Extraction

Prediction of reversible disulfide based on features from local structural signatures

The prediction of the porcine pre-microRNAs in genome-wide based on support vector machine (SVM) and homology searching

Prediction and classification of ncRNAs using structural information

Proposing a highly accurate protein structural class predictor using segmentation-based features