scholarly journals Image based effective feature generation for protein structural class and ligand binding prediction

2019 ◽  
Author(s):  
Nafees Sadique ◽  
Al Amin Neaz Ahmed ◽  
Md Tajul Islam ◽  
Md. Nawshad Pervage ◽  
Swakkhar Shatabda
Keyword(s):  
Ligand Binding ◽  
Local Binary Pattern ◽  
Tertiary Structure ◽  
Three Dimensional ◽  
Machine Learning Algorithms ◽  
Binding Prediction ◽  
Structural Class ◽  
Protein Structural Class ◽  
Uniform Local Binary Pattern ◽  
Pattern Histogram

Proteins are the building blocks of all cells in both human and all our living creatures of the world. Most of the work in the living organism is performed by Proteins. Proteins are polymers of amino acid monomers which are biomolecules or macromolecules. The tertiary structure of protein represents the three-dimensional shape of a protein. The functions, classification and binding sites are governed by protein’s tertiary structure. If two protein structures are alike then the two proteins can be of the same kind implying similar structural class and ligand binding properties. In this paper, we have used protein structure to generate effective features for applications in structural similarity to detect structural class and ligand binding. Firstly, we analyze the effectiveness of a group of image based features to predict the structural class of a protein. These features are derived from the image generated by the distance matrix of the tertiary structure of a given protein. They include local binary pattern histogram, Gabor filtered local binary pattern histogram, separate row multiplication matrix with uniform local binary pattern histogram, neighbour block subtraction matrix with uniform local binary pattern histogram and atom bond. The experiments were done on a standard benchmark dataset. We have demonstrated the effectiveness of these features over a large variety of supervised machine learning algorithms. Experiments suggest Random Forest is the best performing classifier on the selected dataset using the set of features. We believe the excellent performance of Hybrid LBP in terms of accuracy would motivate the researchers and practitioners to use it to identify protein structural class. To facilitate that, a classification model using Hybrid LBP is readily available for use at http://brl.uiu.ac.bd/PL/. Protein-Ligand binding is accountable for managing the tasks of biological receptors that helps to cure diseases and many more. So, binding prediction between protein and ligand is important for understanding a protein’s activity or to accelerate docking computations in virtual screening-based drug design. Protein-Ligand Binding Prediction requires three-dimensional tertiary structure of the target protein to be searched for ligand binding. In this paper, we’ve proposed a supervised learning algorithm for predicting Protein-Ligand Binding which is a Similarity-Based Clustering approach using the same set of features. Our algorithm works better than most popular and widely used machine learning algorithms

scholarly journals Image based effective feature generation for protein structural class and ligand binding prediction

2019 ◽  
Author(s):  
Nafees Sadique ◽  
Al Amin Neaz Ahmed ◽  
Md Tajul Islam ◽  
Md. Nawshad Pervage ◽  
Swakkhar Shatabda
Keyword(s):  
Ligand Binding ◽  
Local Binary Pattern ◽  
Tertiary Structure ◽  
Three Dimensional ◽  
Machine Learning Algorithms ◽  
Binding Prediction ◽  
Structural Class ◽  
Protein Structural Class ◽  
Uniform Local Binary Pattern ◽  
Pattern Histogram

Proteins are the building blocks of all cells in both human and all our living creatures of the world. Most of the work in the living organism is performed by Proteins. Proteins are polymers of amino acid monomers which are biomolecules or macromolecules. The tertiary structure of protein represents the three-dimensional shape of a protein. The functions, classification and binding sites are governed by protein’s tertiary structure. If two protein structures are alike then the two proteins can be of the same kind implying similar structural class and ligand binding properties. In this paper, we have used protein structure to generate effective features for applications in structural similarity to detect structural class and ligand binding. Firstly, we analyze the effectiveness of a group of image based features to predict the structural class of a protein. These features are derived from the image generated by the distance matrix of the tertiary structure of a given protein. They include local binary pattern histogram, Gabor filtered local binary pattern histogram, separate row multiplication matrix with uniform local binary pattern histogram, neighbour block subtraction matrix with uniform local binary pattern histogram and atom bond. The experiments were done on a standard benchmark dataset. We have demonstrated the effectiveness of these features over a large variety of supervised machine learning algorithms. Experiments suggest Random Forest is the best performing classifier on the selected dataset using the set of features. We believe the excellent performance of Hybrid LBP in terms of accuracy would motivate the researchers and practitioners to use it to identify protein structural class. To facilitate that, a classification model using Hybrid LBP is readily available for use at http://brl.uiu.ac.bd/PL/. Protein-Ligand binding is accountable for managing the tasks of biological receptors that helps to cure diseases and many more. So, binding prediction between protein and ligand is important for understanding a protein’s activity or to accelerate docking computations in virtual screening-based drug design. Protein-Ligand Binding Prediction requires three-dimensional tertiary structure of the target protein to be searched for ligand binding. In this paper, we’ve proposed a supervised learning algorithm for predicting Protein-Ligand Binding which is a Similarity-Based Clustering approach using the same set of features. Our algorithm works better than most popular and widely used machine learning algorithms

scholarly journals Image-based effective feature generation for protein structural class and ligand binding prediction

2020 ◽  
Vol 6 ◽  
pp. e253
Author(s):  
Nafees Sadique ◽  
Al Amin Neaz Ahmed ◽  
Md Tajul Islam ◽  
Md. Nawshad Pervage ◽  
Swakkhar Shatabda
Keyword(s):  
Machine Learning ◽  
Ligand Binding ◽  
Tertiary Structure ◽  
Learning Algorithms ◽  
Three Dimensional ◽  
Machine Learning Algorithms ◽  
Binding Prediction ◽  
Structural Class ◽  
Protein Structural Class ◽  
Atom Bond

Proteins are the building blocks of all cells in both human and all living creatures of the world. Most of the work in the living organism is performed by proteins. Proteins are polymers of amino acid monomers which are biomolecules or macromolecules. The tertiary structure of protein represents the three-dimensional shape of a protein. The functions, classification and binding sites are governed by the protein’s tertiary structure. If two protein structures are alike, then the two proteins can be of the same kind implying similar structural class and ligand binding properties. In this paper, we have used the protein tertiary structure to generate effective features for applications in structural similarity to detect structural class and ligand binding. Firstly, we have analyzed the effectiveness of a group of image-based features to predict the structural class of a protein. These features are derived from the image generated by the distance matrix of the tertiary structure of a given protein. They include local binary pattern (LBP) histogram, Gabor filtered LBP histogram, separate row multiplication matrix with uniform LBP histogram, neighbor block subtraction matrix with uniform LBP histogram and atom bond. Separate row multiplication matrix and neighbor block subtraction matrix filters, as well as atom bond, are our novels. The experiments were done on a standard benchmark dataset. We have demonstrated the effectiveness of these features over a large variety of supervised machine learning algorithms. Experiments suggest support vector machines is the best performing classifier on the selected dataset using the set of features. We believe the excellent performance of Hybrid LBP in terms of accuracy would motivate the researchers and practitioners to use it to identify protein structural class. To facilitate that, a classification model using Hybrid LBP is readily available for use at http://brl.uiu.ac.bd/PL/. Protein-ligand binding is accountable for managing the tasks of biological receptors that help to cure diseases and many more. Therefore, binding prediction between protein and ligand is important for understanding a protein’s activity or to accelerate docking computations in virtual screening-based drug design. Protein-ligand binding prediction requires three-dimensional tertiary structure of the target protein to be searched for ligand binding. In this paper, we have proposed a supervised learning algorithm for predicting protein-ligand binding, which is a similarity-based clustering approach using the same set of features. Our algorithm works better than the most popular and widely used machine learning algorithms.

Prediction of Protein Structural Classes: Features Extraction to Classification Algorithm

2021 ◽  
Vol 18 ◽  
Author(s):  
Xiaoqing Liu ◽  
Zhenyu Yang ◽  
Yaoxin Wang ◽  
Qi Dai
Keyword(s):  
Tertiary Structure ◽  
Protein Sequencing ◽  
Conformational Space ◽  
Folding Rate ◽  
Class Prediction ◽  
Structural Class ◽  
Protein Structural Class ◽  
Dna Binding Sites ◽  
Structural Classes ◽  
Protein Structure Data

: The fast growing of protein sequencing and protein structure data has promoted the development of the protein structural class prediction. Several prediction methods have been proposed to study protein folding rate, DNA binding sites, as well as reducing the search of conformational space and realizing the prediction of tertiary structure. This paper introduces the current approaches of protein structural class prediction and emphasize their steps from information extraction to classification algorithms.

scholarly journals Application of Improved Three-Dimensional Kernel Approach to Prediction of Protein Structural Class

2013 ◽  
Vol 2013 ◽  
pp. 1-8
Author(s):  
Xu Liu ◽  
Yuchao Zhang ◽  
Hua Yang ◽  
Lisheng Wang ◽  
Shuaibing Liu
Keyword(s):  
Support Vector Machines ◽  
Three Dimensional ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Structural Class ◽  
Protein Structural Class ◽  
Complementary Role ◽  
Vector Machines ◽  
Kernel Approach ◽  
Leave One Out

Kernel methods, such as kernel PCA, kernel PLS, and support vector machines, are widely known machine learning techniques in biology, medicine, chemistry, and material science. Based on nonlinear mapping and Coulomb function, two 3D kernel approaches were improved and applied to predictions of the four protein tertiary structural classes of domains (all-α, all-β,α/β, andα + β) and five membrane protein types with satisfactory results. In a benchmark test, the performances of improved 3D kernel approach were compared with those of neural networks, support vector machines, and ensemble algorithm. Demonstration through leave-one-out cross-validation on working datasets constructed by investigators indicated that new kernel approaches outperformed other predictors. It has not escaped our notice that 3D kernel approaches may hold a high potential for improving the quality in predicting the other protein features as well. Or at the very least, it will play a complementary role to many of the existing algorithms in this regard.

Protein Structural Class Prediction viak-Separated Bigrams Using Position Specific Scoring Matrix

2014 ◽  
Vol 18 (4) ◽  
pp. 474-479 ◽  
Author(s):  
Harsh Saini ◽  
◽  
Gaurav Raicar ◽  
Alok Sharma ◽  
Sunil Lal ◽  
...  
Keyword(s):  
Amino Acids ◽  
Feature Extraction ◽  
Tertiary Structure ◽  
Position Specific Scoring Matrix ◽  
Class Prediction ◽  
Structural Class ◽  
Protein Structural Class ◽  
Protein Functions ◽  
Scoring Matrix ◽  
Feature Extraction Technique

Protein structural class prediction (SCP) is as important task in identifying protein tertiary structure and protein functions. In this study, we propose a feature extraction technique to predict secondary structures. The technique utilizes bigram (of adjacent andk-separated amino acids) information derived from Position Specific Scoring Matrix (PSSM). The technique has shown promising results when evaluated on benchmarked Ding and Dubchak dataset.

scholarly journals Recent Advances in the Prediction of Protein Structural Classes: Feature Descriptors and Machine Learning Algorithms

Crystals ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 324
Author(s):  
Lin Zhu ◽  
Mehdi D. Davari ◽  
Wenjin Li
Keyword(s):  
Machine Learning ◽  
Protein Structure ◽  
Protein Function ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Class Prediction ◽  
Structural Class ◽  
Protein Structural Class ◽  
Feature Descriptors ◽  
New Feature

In the postgenomic age, rapid growth in the number of sequence-known proteins has been accompanied by much slower growth in the number of structure-known proteins (as a result of experimental limitations), and a widening gap between the two is evident. Because protein function is linked to protein structure, successful prediction of protein structure is of significant importance in protein function identification. Foreknowledge of protein structural class can help improve protein structure prediction with significant medical and pharmaceutical implications. Thus, a fast, suitable, reliable, and reasonable computational method for protein structural class prediction has become pivotal in bioinformatics. Here, we review recent efforts in protein structural class prediction from protein sequence, with particular attention paid to new feature descriptors, which extract information from protein sequence, and the use of machine learning algorithms in both feature selection and the construction of new classification models. These new feature descriptors include amino acid composition, sequence order, physicochemical properties, multiprofile Bayes, and secondary structure-based features. Machine learning methods, such as artificial neural networks (ANNs), support vector machine (SVM), K-nearest neighbor (KNN), random forest, deep learning, and examples of their application are discussed in detail. We also present our view on possible future directions, challenges, and opportunities for the applications of machine learning algorithms for prediction of protein structural classes.

Sign in / Sign up

Export Citation Format

Share Document