Prediction of Heme Binding Sites in Heme Proteins Using an Integrative Sequence Profile Coupling Evolutionary Information with Physicochemical Properties

2011 ◽  
Author(s):  
Yi Xiong ◽  
Wen Zhang ◽  
Tao Zeng ◽  
Juan Liu
Keyword(s):  
Physicochemical Properties ◽  
Binding Sites ◽  
Heme Proteins ◽  
Evolutionary Information ◽  
Heme Binding ◽  
Sequence Profile

scholarly journals A protein with multiple heme-binding sites from rabbit serum.

1982 ◽  
Vol 257 (7) ◽  
pp. 3925-3931 ◽  
Author(s):  
K Tsutsui ◽  
G C Mueller
Keyword(s):  
Binding Sites ◽  
Rabbit Serum ◽  
Heme Binding

scholarly journals SOHSite: incorporating evolutionary information and physicochemical properties to identify protein S-sulfenylation sites

BMC Genomics ◽  
2016 ◽  
Vol 17 (S1) ◽  
Author(s):  
Van-Minh Bui ◽  
Shun-Long Weng ◽  
Cheng-Tsung Lu ◽  
Tzu-Hao Chang ◽  
Julia Tzu-Ya Weng ◽  
...  
Keyword(s):  
Physicochemical Properties ◽  
Protein S ◽  
Evolutionary Information

scholarly journals Predicting RNA-binding sites of proteins using support vector machines and evolutionary information

2008 ◽  
Vol 9 (Suppl 12) ◽  
pp. S6 ◽  
Author(s):  
Cheng-Wei Cheng ◽  
Emily Su ◽  
Jenn-Kang Hwang ◽  
Ting-Yi Sung ◽  
Wen-Lian Hsu
Keyword(s):  
Support Vector Machines ◽  
Binding Sites ◽  
Rna Binding ◽  
Evolutionary Information ◽  
Support Vector ◽  
Vector Machines ◽  
Rna Binding Sites

scholarly journals BionoiNet: ligand-binding site classification with off-the-shelf deep neural network

2020 ◽  
Vol 36 (10) ◽  
pp. 3077-3083
Author(s):  
Wentao Shi ◽  
Jeffrey M Lemoine ◽  
Abd-El-Monsif A Shawky ◽  
Manali Singha ◽  
Limeng Pu ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Ligand Binding ◽  
Binding Sites ◽  
Protein Structures ◽  
Supplementary Information ◽  
Heme Binding ◽  
Unseen Data ◽  
Ligand Binding Sites ◽  
Binding Pockets

Abstract Motivation Fast and accurate classification of ligand-binding sites in proteins with respect to the class of binding molecules is invaluable not only to the automatic functional annotation of large datasets of protein structures but also to projects in protein evolution, protein engineering and drug development. Deep learning techniques, which have already been successfully applied to address challenging problems across various fields, are inherently suitable to classify ligand-binding pockets. Our goal is to demonstrate that off-the-shelf deep learning models can be employed with minimum development effort to recognize nucleotide- and heme-binding sites with a comparable accuracy to highly specialized, voxel-based methods. Results We developed BionoiNet, a new deep learning-based framework implementing a popular ResNet model for image classification. BionoiNet first transforms the molecular structures of ligand-binding sites to 2D Voronoi diagrams, which are then used as the input to a pretrained convolutional neural network classifier. The ResNet model generalizes well to unseen data achieving the accuracy of 85.6% for nucleotide- and 91.3% for heme-binding pockets. BionoiNet also computes significance scores of pocket atoms, called BionoiScores, to provide meaningful insights into their interactions with ligand molecules. BionoiNet is a lightweight alternative to computationally expensive 3D architectures. Availability and implementation BionoiNet is implemented in Python with the source code freely available at: https://github.com/CSBG-LSU/BionoiNet. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Bach1, a heme-dependent transcription factor, reveals presence of multiple heme binding sites with distinct coordination structure

IUBMB Life ◽  
2007 ◽  
Vol 59 (8) ◽  
pp. 542-551 ◽  
Author(s):  
Shusuke Hira ◽  
Takeshi Tomita ◽  
Toshitaka Matsui ◽  
Kazuhiko Igarashi ◽  
Masao Ikeda-Saito
Keyword(s):  
Transcription Factor ◽  
Binding Sites ◽  
Heme Binding ◽  
Coordination Structure

scholarly journals 2P098 Heme binding site in Rhodnius prolixus α-glucosidase promoting the heme crystallization(02. Heme proteins,Poster)

2013 ◽  
Vol 53 (supplement1-2) ◽  
pp. S175
Author(s):  
Shotaro Kaku ◽  
Keisuke Nakatani ◽  
Haruto Ishikawa ◽  
Yasuhisa Mizutani
Keyword(s):  
Binding Site ◽  
Heme Proteins ◽  
Rhodnius Prolixus ◽  
Heme Binding

Preferred heme binding sites of histidine-rich glycoprotein

Biochemistry ◽  
1985 ◽  
Vol 24 (21) ◽  
pp. 5919-5924 ◽  
Author(s):  
Mary Kappel Burch ◽  
William T. Morgan
Keyword(s):  
Binding Sites ◽  
Heme Binding
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