Cargando…

AmylPepPred: Amyloidogenic Peptide Prediction tool

We present an efficient computational architecture designed using supervised machine learning model to predict amyloid fibril forming protein segments, named AmylPepPred. The proposed prediction model is based on bio-physio-chemical properties of primary sequences and auto-correlation function of th...

Descripción completa

Detalles Bibliográficos
Autores principales: Nair, Smitha Sunil Kumaran, Reddy, NV Subba, Hareesha, KS
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Biomedical Informatics 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3524944/
https://www.ncbi.nlm.nih.gov/pubmed/23275694
http://dx.doi.org/10.6026/97320630008994
_version_ 1782253375541215232
author Nair, Smitha Sunil Kumaran
Reddy, NV Subba
Hareesha, KS
author_facet Nair, Smitha Sunil Kumaran
Reddy, NV Subba
Hareesha, KS
author_sort Nair, Smitha Sunil Kumaran
collection PubMed
description We present an efficient computational architecture designed using supervised machine learning model to predict amyloid fibril forming protein segments, named AmylPepPred. The proposed prediction model is based on bio-physio-chemical properties of primary sequences and auto-correlation function of their amino acid indices. AmylPepPred provides a user friendly web interface for the researchers to easily observe the fibril forming and non-fibril forming hexmers in a given protein sequence. We expect that this stratagem will be highly encouraging in discovering fibril forming regions in proteins thereby benefit in finding therapeutic agents that specifically aim these sequences for the inhibition and cure of amyloid illnesses. AVAILABILITY: AmylPepPred is available freely for academic use at www.zoommicro.in/amylpeppred
format Online
Article
Text
id pubmed-3524944
institution National Center for Biotechnology Information
language English
publishDate 2012
publisher Biomedical Informatics
record_format MEDLINE/PubMed
spelling pubmed-35249442012-12-28 AmylPepPred: Amyloidogenic Peptide Prediction tool Nair, Smitha Sunil Kumaran Reddy, NV Subba Hareesha, KS Bioinformation Software We present an efficient computational architecture designed using supervised machine learning model to predict amyloid fibril forming protein segments, named AmylPepPred. The proposed prediction model is based on bio-physio-chemical properties of primary sequences and auto-correlation function of their amino acid indices. AmylPepPred provides a user friendly web interface for the researchers to easily observe the fibril forming and non-fibril forming hexmers in a given protein sequence. We expect that this stratagem will be highly encouraging in discovering fibril forming regions in proteins thereby benefit in finding therapeutic agents that specifically aim these sequences for the inhibition and cure of amyloid illnesses. AVAILABILITY: AmylPepPred is available freely for academic use at www.zoommicro.in/amylpeppred Biomedical Informatics 2012-10-13 /pmc/articles/PMC3524944/ /pubmed/23275694 http://dx.doi.org/10.6026/97320630008994 Text en © 2012 Biomedical Informatics This is an open-access article, which permits unrestricted use, distribution, and reproduction in any medium, for non-commercial purposes, provided the original author and source are credited.
spellingShingle Software
Nair, Smitha Sunil Kumaran
Reddy, NV Subba
Hareesha, KS
AmylPepPred: Amyloidogenic Peptide Prediction tool
title AmylPepPred: Amyloidogenic Peptide Prediction tool
title_full AmylPepPred: Amyloidogenic Peptide Prediction tool
title_fullStr AmylPepPred: Amyloidogenic Peptide Prediction tool
title_full_unstemmed AmylPepPred: Amyloidogenic Peptide Prediction tool
title_short AmylPepPred: Amyloidogenic Peptide Prediction tool
title_sort amylpeppred: amyloidogenic peptide prediction tool
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3524944/
https://www.ncbi.nlm.nih.gov/pubmed/23275694
http://dx.doi.org/10.6026/97320630008994
work_keys_str_mv AT nairsmithasunilkumaran amylpeppredamyloidogenicpeptidepredictiontool
AT reddynvsubba amylpeppredamyloidogenicpeptidepredictiontool
AT hareeshaks amylpeppredamyloidogenicpeptidepredictiontool