Prediction and analysis of canonical EF hand loop and qualitative estimation of Ca²⁺ binding affinity.

The diversity of functions carried out by EF hand-containing calcium-binding proteins is due to various interactions made by these proteins as well as the range of affinity levels for Ca²⁺ displayed by them. However, accurate methods are not available for prediction of binding affinities. Here, amin...

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Main Authors: Mohit Mazumder, Narendra Padhan, Alok Bhattacharya, Samudrala Gourinath
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2014-01-01
Series:PLoS ONE
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24760183/pdf/?tool=EBI
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spelling doaj-c66ac7c97e114588b575c33bd0ceddb12021-03-04T09:29:59ZengPublic Library of Science (PLoS)PLoS ONE1932-62032014-01-0194e9620210.1371/journal.pone.0096202Prediction and analysis of canonical EF hand loop and qualitative estimation of Ca²⁺ binding affinity.Mohit MazumderNarendra PadhanAlok BhattacharyaSamudrala GourinathThe diversity of functions carried out by EF hand-containing calcium-binding proteins is due to various interactions made by these proteins as well as the range of affinity levels for Ca²⁺ displayed by them. However, accurate methods are not available for prediction of binding affinities. Here, amino acid patterns of canonical EF hand sequences obtained from available crystal structures were used to develop a classifier that distinguishes Ca²⁺-binding loops and non Ca²⁺-binding regions with 100% accuracy. To investigate further, we performed a proteome-wide prediction for E. histolytica, and classified known EF-hand proteins. We compared our results with published methods on the E. histolytica proteome scan, and demonstrated our method to be more specific and accurate for predicting potential canonical Ca²⁺-binding loops. Furthermore, we annotated canonical EF-hand motifs and classified them based on their Ca²⁺-binding affinities using support vector machines. Using a novel method generated from position-specific scoring metrics and then tested against three different experimentally derived EF-hand-motif datasets, predictions of Ca²⁺-binding affinities were between 87 and 90% accurate. Our results show that the tool described here is capable of predicting Ca²⁺-binding affinity constants of EF-hand proteins.https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24760183/pdf/?tool=EBI
collection DOAJ
language English
format Article
sources DOAJ
author Mohit Mazumder
Narendra Padhan
Alok Bhattacharya
Samudrala Gourinath
spellingShingle Mohit Mazumder
Narendra Padhan
Alok Bhattacharya
Samudrala Gourinath
Prediction and analysis of canonical EF hand loop and qualitative estimation of Ca²⁺ binding affinity.
PLoS ONE
author_facet Mohit Mazumder
Narendra Padhan
Alok Bhattacharya
Samudrala Gourinath
author_sort Mohit Mazumder
title Prediction and analysis of canonical EF hand loop and qualitative estimation of Ca²⁺ binding affinity.
title_short Prediction and analysis of canonical EF hand loop and qualitative estimation of Ca²⁺ binding affinity.
title_full Prediction and analysis of canonical EF hand loop and qualitative estimation of Ca²⁺ binding affinity.
title_fullStr Prediction and analysis of canonical EF hand loop and qualitative estimation of Ca²⁺ binding affinity.
title_full_unstemmed Prediction and analysis of canonical EF hand loop and qualitative estimation of Ca²⁺ binding affinity.
title_sort prediction and analysis of canonical ef hand loop and qualitative estimation of ca²⁺ binding affinity.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2014-01-01
description The diversity of functions carried out by EF hand-containing calcium-binding proteins is due to various interactions made by these proteins as well as the range of affinity levels for Ca²⁺ displayed by them. However, accurate methods are not available for prediction of binding affinities. Here, amino acid patterns of canonical EF hand sequences obtained from available crystal structures were used to develop a classifier that distinguishes Ca²⁺-binding loops and non Ca²⁺-binding regions with 100% accuracy. To investigate further, we performed a proteome-wide prediction for E. histolytica, and classified known EF-hand proteins. We compared our results with published methods on the E. histolytica proteome scan, and demonstrated our method to be more specific and accurate for predicting potential canonical Ca²⁺-binding loops. Furthermore, we annotated canonical EF-hand motifs and classified them based on their Ca²⁺-binding affinities using support vector machines. Using a novel method generated from position-specific scoring metrics and then tested against three different experimentally derived EF-hand-motif datasets, predictions of Ca²⁺-binding affinities were between 87 and 90% accurate. Our results show that the tool described here is capable of predicting Ca²⁺-binding affinity constants of EF-hand proteins.
url https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24760183/pdf/?tool=EBI
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