scholarly journals Fragmented blind docking: a novel protein–ligand binding prediction protocol

2021 ◽  
pp. 1-10
Author(s):  
Gianvito Grassoa ◽  
Arianna Di Gregorio ◽  
Bojan Mavkov ◽  
Dario Piga ◽  
Giuseppe Falvo D’Urso Labate ◽  
...  
Keyword(s):  
Ligand Binding ◽  
Binding Prediction ◽  
Novel Protein

scholarly journals DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity

2021 ◽  
Vol 15 ◽  
pp. 117793222110303
Author(s):  
Asad Ahmed ◽  
Bhavika Mam ◽  
Ramanathan Sowdhamini
Keyword(s):  
Deep Learning ◽  
Ligand Binding ◽  
Binding Affinity ◽  
Chemical Space ◽  
Biological Significance ◽  
Protein Crystal ◽  
Ligand Docking ◽  
Complex Data ◽  
Binding Prediction ◽  
Data Set

Protein-ligand binding prediction has extensive biological significance. Binding affinity helps in understanding the degree of protein-ligand interactions and is a useful measure in drug design. Protein-ligand docking using virtual screening and molecular dynamic simulations are required to predict the binding affinity of a ligand to its cognate receptor. Performing such analyses to cover the entire chemical space of small molecules requires intense computational power. Recent developments using deep learning have enabled us to make sense of massive amounts of complex data sets where the ability of the model to “learn” intrinsic patterns in a complex plane of data is the strength of the approach. Here, we have incorporated convolutional neural networks to find spatial relationships among data to help us predict affinity of binding of proteins in whole superfamilies toward a diverse set of ligands without the need of a docked pose or complex as user input. The models were trained and validated using a stringent methodology for feature extraction. Our model performs better in comparison to some existing methods used widely and is suitable for predictions on high-resolution protein crystal (⩽2.5 Å) and nonpeptide ligand as individual inputs. Our approach to network construction and training on protein-ligand data set prepared in-house has yielded significant insights. We have also tested DEELIG on few COVID-19 main protease-inhibitor complexes relevant to the current public health scenario. DEELIG-based predictions can be incorporated in existing databases including RSCB PDB, PDBMoad, and PDBbind in filling missing binding affinity data for protein-ligand complexes.

Improving protein–ligand binding prediction by considering the bridging water molecules in Autodock

2019 ◽  
Vol 18 (05) ◽  
pp. 1950027 ◽  
Author(s):  
Qiangna Lu ◽  
Lian-Wen Qi ◽  
Jinfeng Liu
Keyword(s):  
Ligand Binding ◽  
Considerable Improvement ◽  
Ligand Docking ◽  
Water Molecules ◽  
Binding Modes ◽  
Binding Prediction ◽  
Docking Simulations ◽  
Protein Ligand Interactions ◽  
Ligand Interactions ◽  
Docking Program

Water plays a significant role in determining the protein–ligand binding modes, especially when water molecules are involved in mediating protein–ligand interactions, and these important water molecules are receiving more and more attention in recent years. Considering the effects of water molecules has gradually become a routine process for accurate description of the protein–ligand interactions. As a free docking program, Autodock has been most widely used in predicting the protein–ligand binding modes. However, whether the inclusion of water molecules in Autodock would improve its docking performance has not been systematically investigated. Here, we incorporate important bridging water molecules into Autodock program, and systematically investigate the effectiveness of these water molecules in protein–ligand docking. This approach was evaluated using 18 structurally diverse protein–ligand complexes, in which several water molecules bridge the protein–ligand interactions. Different treatment of water molecules were tested by using the fixed and rotatable water molecules, and a considerable improvement in successful docking simulations was found when including these water molecules. This study illustrates the necessity of inclusion of water molecules in Autodock docking, and emphasizes the importance of a proper treatment of water molecules in protein–ligand binding predictions.

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.

scholarly journals PickPocket : Pocket binding prediction for specific ligands family using neural networks.

2020 ◽  
Author(s):  
Benjamin Thomas VIART ◽  
Claudio Lorenzi ◽  
María Moriel-Carretero ◽  
Sofia Kossida
Keyword(s):  
Neural Networks ◽  
Fatty Acid ◽  
Ligand Binding ◽  
Binding Sites ◽  
Structural Data ◽  
Binding Pocket ◽  
Fatty Acid Binding Proteins ◽  
Binding Prediction ◽  
Fatty Acid Binding ◽  
Binding Pockets

Most of the protein biological functions occur through contacts with other proteins or ligands. The residues that constitute the contact surface of a ligand-binding pocket are usually located far away within its sequence. Therefore, the identification of such motifs is more challenging than the linear protein domains. To discover new binding sites, we developed a tool called PickPocket that focuses on a small set of user-defined ligands and uses neural networks to train a ligand-binding prediction model. We tested PickPocket on fatty acid-like ligands due to their structural similarities and their under-representation in the ligand-pocket binding literature. Our results show that for fatty acid-like molecules, pocket descriptors and secondary structures are enough to obtain predictions with accuracy >90% using a dataset of 1740 manually curated ligand-binding pockets. The trained model could also successfully predict the ligand-binding pockets using unseen structural data of two recently reported fatty acid-binding proteins. We think that the PickPocket tool can help to discover new protein functions by investigating the binding sites of specific ligand families. The source code and all datasets contained in this work are freely available at https://github.com/benjaminviart/PickPocket .

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

Sign in / Sign up

Export Citation Format

Share Document