NDLI: Proteasomal cleavage site prediction of protein antigen using BP neural network based on a new set of amino acid descriptor

Content Provider	Springer Nature Link
Author	Wang, Yuanqiang Lin, Yong Shu, Mao Wang, Rui Hu, Yong Lin, Zhihua
Copyright Year	2013
Abstract	The accurate identification of cytotoxic T lymphocyte epitopes is becoming increasingly important in peptide vaccine design. The ubiquitin–proteasome system plays a key role in processing and presenting major histocompatibility complex class I restricted epitopes by degrading the antigenic protein. To enhance the specificity and efficiency of epitope prediction and identification, the recognition mode between the ubiquitin–proteasome complex and the protein antigen must be considered. Hence, a model that accurately predicts proteasomal cleavage must be established. This study proposes a new set of parameters to characterize the cleavage window and uses a backpropagation neural network algorithm to build a model that accurately predicts proteasomal cleavage. The accuracy of the prediction model, which depends on the window sizes of the cleavage, reaches 95.454 % for the N-terminus and 95.011 % for the C-terminus. The results show that the identification of proteasomal cleavage sites depends on the sequence next to it and that the prediction performance of the C-terminus is better than that of the N-terminus on average. Thus, models based on the properties of amino acids can be highly reliable and reflect the structural features of interactions between proteasomes and peptide sequences.
Starting Page	3045
Ending Page	3052
Page Count	8
File Format	PDF
ISSN	16102940
Journal	Journal of Molecular Modeling
Volume Number	19
Issue Number	8
e-ISSN	09485023
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2013-04-13
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Backpropagation (BP) neural network Cleavage site Epitope Proteasome Computer Applications in Chemistry Molecular Medicine Computer Application in Life Sciences Characterization and Evaluation of Materials Theoretical and Computational Chemistry
Content Type	Text
Resource Type	Article
Subject	Organic Chemistry Physical and Theoretical Chemistry Computational Theory and Mathematics Catalysis Inorganic Chemistry Computer Science Applications

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

Predicting proteasomal cleavage sites: a comparison of available methods

Generation and evaluation of dimension-reduced amino acid parameter representations by artificial neural networks

A consistent force field parameter set for zwitterionic amino acid residues

Prediction of polyamide properties using quantum-chemical methods and BP artificial neural networks

Prediction of lower critical solution temperature of N-isopropylacrylamide–acrylic acid copolymer by an artificial neural network model

Fast learning optimized prediction methodology (FLOPRED) for protein secondary structure prediction

Aromaticity in cyanuric acid

Identification of SARS-CoV-2 Main Protease (Mpro) Cleavage Sites Using Two-Dimensional Electrophoresis and In Silico Cleavage Site Prediction.

Abrogation of CTL Epitope Processing by Single Amino Acid Substitution Flanking the C-Terminal Proteasome Cleavage Site

Proteasomal cleavage site prediction of protein antigen using BP neural network based on a new set of amino acid descriptor

Similar Documents

Predicting proteasomal cleavage sites: a comparison of available methods

Generation and evaluation of dimension-reduced amino acid parameter representations by artificial neural networks

A consistent force field parameter set for zwitterionic amino acid residues

Prediction of polyamide properties using quantum-chemical methods and BP artificial neural networks

Prediction of lower critical solution temperature of N-isopropylacrylamide–acrylic acid copolymer by an artificial neural network model

Fast learning optimized prediction methodology (FLOPRED) for protein secondary structure prediction

Aromaticity in cyanuric acid

Identification of SARS-CoV-2 Main Protease (Mpro) Cleavage Sites Using Two-Dimensional Electrophoresis and In Silico Cleavage Site Prediction.

Abrogation of CTL Epitope Processing by Single Amino Acid Substitution Flanking the C-Terminal Proteasome Cleavage Site

Proteasomal cleavage site prediction of protein antigen using BP neural network based on a new set of amino acid descriptor