3D U-Net: A voxel-based method in binding site prediction of protein structure

2021 ◽  
Vol 19 (02) ◽  
pp. 2150006
Author(s):  
Fatemeh Nazem ◽  
Fahimeh Ghasemi ◽  
Afshin Fassihi ◽  
Alireza Mehri Dehnavi
Keyword(s):  
Drug Design ◽  
Binding Site ◽  
Binding Sites ◽  
Semantic Segmentation ◽  
Structure Based Drug Design ◽  
Site Prediction ◽  
Binding Site Prediction ◽  
Novel Proteins ◽  
Proposed Model ◽  
Deep Learning Model

Binding site prediction for new proteins is important in structure-based drug design. The identified binding sites may be helpful in the development of treatments for new viral outbreaks in the world when there is no information available about their pockets with COVID-19 being a case in point. Identification of the pockets using computational methods, as an alternative method, has recently attracted much interest. In this study, the binding site prediction is viewed as a semantic segmentation problem. An improved 3D version of the U-Net model based on the dice loss function is utilized to predict the binding sites accurately. The performance of the proposed model on the independent test datasets and SARS-COV-2 shows the segmentation model could predict the binding sites with a more accurate shape than the recently published deep learning model, i.e. DeepSite. Therefore, the model may help predict the binding sites of proteins and could be used in drug design for novel proteins.

scholarly journals PDBspheres - a method for finding 3D similarities in local regions in proteins

2022 ◽  
Author(s):  
Adam Zemla ◽  
Jonathan E. Allen ◽  
Dan Kirshner ◽  
Felice C. Lightstone
Keyword(s):  
Ligand Binding ◽  
Binding Site ◽  
Binding Sites ◽  
Protein Structures ◽  
Data Bank ◽  
Structure Alignment ◽  
Alignment Algorithm ◽  
Site Prediction ◽  
Binding Site Prediction ◽  
Structure Similarity

We present a structure-based method for finding and evaluating structural similarities in protein regions relevant to ligand binding. PDBspheres comprises an exhaustive library of protein structure regions (spheres) adjacent to complexed ligands derived from the Protein Data Bank (PDB), along with methods to find and evaluate structural matches between a protein of interest and spheres in the library. Currently, PDBspheres library contains more than 2 million spheres, organized to facilitate searches by sequence and/or structure similarity of protein-ligand binding sites or interfaces between interacting molecules. PDBspheres uses the LGA structure alignment algorithm as the main engine for detecting structure similarities between the protein of interest and library spheres. An all-atom structure similarity metric ensures that sidechain placement is taken into account in the PDBspheres primary assessment of confidence in structural matches. In this paper, we (1) describe the PDBspheres method, (2) demonstrate how PDBspheres can be used to detect and characterize binding sites in protein structures, (3) compare PDBspheres use for binding site prediction with seven other binding site prediction methods using a curated dataset of 2,528 ligand-bound and ligand-free crystal structures, and (4) use PDBspheres to cluster pockets and assess structural similarities among protein binding sites of the 4,876 structures in the refined set of PDBbind 2019 dataset. The PDBspheres library is made publicly available for download at https://proteinmodel.org/AS2TS/PDBspheres

scholarly journals PrankWeb: a web server for ligand binding site prediction and visualization

2019 ◽  
Vol 47 (W1) ◽  
pp. W345-W349 ◽  
Author(s):  
Lukas Jendele ◽  
Radoslav Krivak ◽  
Petr Skoda ◽  
Marian Novotny ◽  
David Hoksza
Keyword(s):  
Ligand Binding ◽  
Binding Site ◽  
Binding Sites ◽  
Web Application ◽  
Ligand Binding Site ◽  
Site Prediction ◽  
Binding Site Prediction ◽  
Server Side ◽  
The Web ◽  
Ligand Binding Site Prediction

AbstractPrankWeb is an online resource providing an interface to P2Rank, a state-of-the-art method for ligand binding site prediction. P2Rank is a template-free machine learning method based on the prediction of local chemical neighborhood ligandability centered on points placed on a solvent-accessible protein surface. Points with a high ligandability score are then clustered to form the resulting ligand binding sites. In addition, PrankWeb provides a web interface enabling users to easily carry out the prediction and visually inspect the predicted binding sites via an integrated sequence-structure view. Moreover, PrankWeb can determine sequence conservation for the input molecule and use this in both the prediction and result visualization steps. Alongside its online visualization options, PrankWeb also offers the possibility of exporting the results as a PyMOL script for offline visualization. The web frontend communicates with the server side via a REST API. In high-throughput scenarios, therefore, users can utilize the server API directly, bypassing the need for a web-based frontend or installation of the P2Rank application. PrankWeb is available at http://prankweb.cz/, while the web application source code and the P2Rank method can be accessed at https://github.com/jendelel/PrankWebApp and https://github.com/rdk/p2rank, respectively.

scholarly journals PrankWeb: web server for ligand binding-site prediction and visualization

2019 ◽  
Author(s):  
Lukas Jendele ◽  
Radoslav Krivak ◽  
Petr Skoda ◽  
Marian Novotny ◽  
David Hoksza
Keyword(s):  
Ligand Binding ◽  
Binding Site ◽  
Binding Sites ◽  
Web Application ◽  
Ligand Binding Site ◽  
Site Prediction ◽  
Binding Site Prediction ◽  
Link Type ◽  
The Web ◽  
Ligand Binding Site Prediction

ABSTRACTPrankWeb is an online resource providing an interface to P2Rank, a state-of-the-art ligand binding site prediction method. P2Rank is a template-free machine learning method which is based on the prediction of ligandability of local chemical neighborhoods centered on points placed on a solvent accessible surface of a protein. Points with high ligandability score are then clustered to form the resulting ligand binding sites. On top of that, PrankWeb then provides a web interface enabling users to easily carry out the prediction and visually inspect the predicted binding sites via an integrated sequence-structure view. Moreover, PrankWeb can determine sequence conservation for the input molecule and use it in both the prediction and results visualization steps. Alongside its online visualization options, PrankWeb also offers the possibility to export the results as a PyMOL script for offline visualization. The web frontend communicates with the serer side via a REST API. Therefore, in high-throughput scenarios users can utilize the server API directly, bypassing the need for a webbased front end or installation of the P2Rank application. PrankWeb is available at http://prankweb.cz/. The source code of the web application and the P2Rank method can be accessed at https://github.com/jendelel/PrankWebApp and https://github.com/rdk/p2rank, respectively.

Binding Site Extraction by Similar Subgraphs Mining from Protein Molecular Surfaces and Its Application to Protein Classification

2014 ◽  
Vol 23 (03) ◽  
pp. 1460007 ◽  
Author(s):  
Natsumi Kurumatani ◽  
Hiroyuki Monji ◽  
Takenao Ohkawa
Keyword(s):  
Binding Site ◽  
Binding Sites ◽  
Graph Mining ◽  
Surface Structures ◽  
Protein Classification ◽  
Molecular Surfaces ◽  
Site Prediction ◽  
Binding Site Prediction ◽  
Molecular Compounds ◽  
Ligand Group

Most proteins express their functions by binding with other proteins or molecular compounds (ligands). Since the characteristics of the local portion involved in binding (binding site) often determine the function of the protein, clarifying the location of the binding site of the protein helps analyze the function of proteins. Binding sites that bind to similar ligands often have common surface structures (surface motifs). Extracting the surface motifs among several proteins with similar functions improves binding site prediction. We propose a method that predicts binding sites by extracting the surface motifs that are frequently observed in only a specific set of proteins that bind to the same ligand (group). Since most binding sites have concave structures (pockets), the pockets are compared and common structures are searched for to extract the surface motifs by applying similar graph mining to the pocket data, which are represented as graphs. Common binding sites across several groups can be predicted in such a way to integrate more than one group. We also proposed a method of protein classification, in which the surface motifs extracted using the above method are evaluated on the assumption that a protein belongs to each one of the groups.

A Perspective on Water Site Prediction Methods for Structure Based Drug Design

2017 ◽  
Vol 17 (23) ◽  
Author(s):  
Alan P. Graves ◽  
Ian D. Wall ◽  
Colin M. Edge ◽  
James M. Woolven ◽  
Guanglei Cui ◽  
...  
Keyword(s):  
Drug Design ◽  
Prediction Methods ◽  
Structure Based Drug Design ◽  
Site Prediction

Cavity/Binding Site Prediction Approaches and Their Applications

2020 ◽  
pp. 49-69
Author(s):  
Himanshu Avashthi ◽  
Ambuj Srivastava ◽  
Dev Bukhsh Singh
Keyword(s):  
Binding Site ◽  
Site Prediction ◽  
Binding Site Prediction

scholarly journals Visual Saliency Prediction Based on Deep Learning

Information ◽  
2019 ◽  
Vol 10 (8) ◽  
pp. 257 ◽  
Author(s):  
Bashir Ghariba ◽  
Mohamed S. Shehata ◽  
Peter McGuire
Keyword(s):  
Deep Learning ◽  
Saliency Detection ◽  
Visual Saliency ◽  
Semantic Segmentation ◽  
Input Image ◽  
Human Eye ◽  
Proposed Model ◽  
Global Accuracy ◽  
Visual Saliency Detection ◽  
Deep Learning Model

Human eye movement is one of the most important functions for understanding our surroundings. When a human eye processes a scene, it quickly focuses on dominant parts of the scene, commonly known as a visual saliency detection or visual attention prediction. Recently, neural networks have been used to predict visual saliency. This paper proposes a deep learning encoder-decoder architecture, based on a transfer learning technique, to predict visual saliency. In the proposed model, visual features are extracted through convolutional layers from raw images to predict visual saliency. In addition, the proposed model uses the VGG-16 network for semantic segmentation, which uses a pixel classification layer to predict the categorical label for every pixel in an input image. The proposed model is applied to several datasets, including TORONTO, MIT300, MIT1003, and DUT-OMRON, to illustrate its efficiency. The results of the proposed model are quantitatively and qualitatively compared to classic and state-of-the-art deep learning models. Using the proposed deep learning model, a global accuracy of up to 96.22% is achieved for the prediction of visual saliency.

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