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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Biomedical Informatics
2012
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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 |
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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 |
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