Prediction and Analysis of Canonical EF Hand Loop and Qualitative Estimation of Ca2+ Binding Affinity

Abstract

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. The web server is freely available a