Elucidation of Characteristic Structural Features of Ligand Binding Sites of Protein Kinases:  A Neural Network Approach

2006 ◽  
Vol 46 (5) ◽  
pp. 2158-2166 ◽  
Author(s):  
Tomoko Niwa
Keyword(s):  
Neural Network ◽  
Ligand Binding ◽  
Protein Kinases ◽  
Binding Sites ◽  
Structural Features ◽  
Network Approach ◽  
Neural Network Approach ◽  
Ligand 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.

Recognition of ion ligand binding sites based on amino acid features with the fusion of energy, physicochemical and structural features

2020 ◽  
Vol 26 ◽  
Author(s):  
Shan Wang ◽  
Xiuzhen Hu ◽  
Zhenxing Feng ◽  
Liu Liu ◽  
Kai Sun ◽  
...  
Keyword(s):  
Amino Acid ◽  
Ligand Binding ◽  
Binding Site ◽  
Binding Sites ◽  
Metal Ion ◽  
Structural Features ◽  
Support Vector ◽  
Sequence Information ◽  
Ligand Binding Site ◽  
Ligand Binding Sites

Background: Rational drug molecular design based on virtual screening requires the ligand binding site to be known. Recently, the recognition of ion ligand binding site has become an important research direction in pharmacology. Methods: In this work, we selected the binding residues of 4 acid radical ion ligands(NO2 - , CO3 2- , SO4 2- and PO4 3- ) and 10 metal ion ligands (Zn2+,Cu2+, Fe2+, Fe3+, Ca2+, Mg2+, Mn2+, Na+ , K+ and Co2+) as research objects. Based on the protein sequence information, we extracted amino acid features, energy, physicochemical and structure features. Then we incorporating the above features and input them into the MultilayerPerceptron (MLP) and support vector machine (SVM) algorithms. Results: In the independent test, the best accuracy was higher than 92.5%, which was better than the previous result on Conclusion: Finally, we set up a free web server for the prediction of protein-ion ligand binding sites (http://39.104.77.103:8081/lsb/HomePage/HomePage.html). This study is helpful for molecular drug design.

A fuzzy neural network approach for modeling the growth kinetics of FeB and Fe2B layers during the boronizing process

2018 ◽  
Vol 106 (6) ◽  
pp. 603 ◽  
Author(s):  
Bendaoud Mebarek ◽  
Mourad Keddam
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Fuzzy Neural Network ◽  
Treatment Time ◽  
Calculation Model ◽  
Average Error ◽  
Network Approach ◽  
Neural Network Approach ◽  
Fuzzy Neural ◽  
Kinetics Of

In this paper, we develop a boronizing process simulation model based on fuzzy neural network (FNN) approach for estimating the thickness of the FeB and Fe2B layers. The model represents a synthesis of two artificial intelligence techniques; the fuzzy logic and the neural network. Characteristics of the fuzzy neural network approach for the modelling of boronizing process are presented in this study. In order to validate the results of our calculation model, we have used the learning base of experimental data of the powder-pack boronizing of Fe-15Cr alloy in the temperature range from 800 to 1050 °C and for a treatment time ranging from 0.5 to 12 h. The obtained results show that it is possible to estimate the influence of different process parameters. Comparing the results obtained by the artificial neural network to experimental data, the average error generated from the fuzzy neural network was 3% for the FeB layer and 3.5% for the Fe2B layer. The results obtained from the fuzzy neural network approach are in agreement with the experimental data. Finally, the utilization of fuzzy neural network approach is well adapted for the boronizing kinetics of Fe-15Cr alloy.

A neural network approach to anomaly detection in spectra

1997 ◽  
Author(s):  
Daniel Benzing ◽  
Kevin Whitaker ◽  
Dedra Moore ◽  
Daniel Benzing ◽  
Kevin Whitaker ◽  
...  
Keyword(s):  
Neural Network ◽  
Anomaly Detection ◽  
Network Approach ◽  
Neural Network Approach
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