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 FTSite: high accuracy detection of ligand binding sites on unbound protein structures

2011 ◽  
Vol 28 (2) ◽  
pp. 286-287 ◽  
Author(s):  
Chi-Ho Ngan ◽  
David R. Hall ◽  
Brandon Zerbe ◽  
Laurie E. Grove ◽  
Dima Kozakov ◽  
...  
Keyword(s):  
Ligand Binding ◽  
Binding Sites ◽  
Protein Structures ◽  
High Accuracy ◽  
Ligand Binding Sites

scholarly journals webPDBinder: a server for the identification of ligand binding sites on protein structures

2013 ◽  
Vol 41 (W1) ◽  
pp. W308-W313 ◽  
Author(s):  
Valerio Bianchi ◽  
Iolanda Mangone ◽  
Fabrizio Ferrè ◽  
Manuela Helmer-Citterich ◽  
Gabriele Ausiello
Keyword(s):  
Ligand Binding ◽  
Binding Sites ◽  
Protein Structures ◽  
Ligand Binding Sites

scholarly journals DeepPocket: Ligand Binding Site Detection and Segmentation using 3D Convolutional Neural Networks

2021 ◽  
Author(s):  
Rishal Aggarwal ◽  
Akash Gupta ◽  
Vineeth Chelur ◽  
C. V. Jawahar ◽  
U. Deva Priyakumar
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Ligand Binding ◽  
Binding Site ◽  
Convolutional Neural Networks ◽  
Binding Sites ◽  
Protein Structures ◽  
3D Structure ◽  
Ligand Binding Site ◽  
Structure Based Drug Design

<div> A structure-based drug design pipeline involves the development of potential drug molecules or ligands that form stable complexes with a given receptor at its binding site. A prerequisite to this is finding druggable and functionally relevant binding sites on the 3D structure of the protein. Although several methods for detecting binding sites have been developed beforehand, a majority of them surprisingly fail in the identification and ranking of binding sites accurately. The rapid adoption and success of deep learning algorithms in various sections of structural biology beckons the usage of such algorithms for accurate binding site detection. As a combination of geometry based software and deep learning, we report a novel framework, DeepPocket that utilises 3D convolutional neural networks for the rescoring of pockets identified by Fpocket and further segments these identified cavities on the protein surface. Apart from this, we also propose another dataset SC6K containing protein structures submitted in the Protein Data Bank (PDB) from January 2018 till February 2020 for ligand binding site (LBS) detection. DeepPocket's results on various binding site datasets and SC6K highlights its better performance over current state-of-the-art methods and good generalization ability over novel structures. </div><div><br></div>

scholarly journals Systematic domain-based aggregation of protein structures highlights DNA-, RNA- and other ligand-binding positions

2018 ◽  
Vol 47 (2) ◽  
pp. 582-593 ◽  
Author(s):  
Shilpa Nadimpalli Kobren ◽  
Mona Singh
Keyword(s):  
Ligand Binding ◽  
Small Molecules ◽  
Binding Sites ◽  
Protein Structures ◽  
Complex Structures ◽  
Disease Etiology ◽  
Systematic Assessment ◽  
Protein Ligand Interactions ◽  
Ligand Binding Sites ◽  
Ligand Interactions

Abstract Domains are fundamental subunits of proteins, and while they play major roles in facilitating protein–DNA, protein–RNA and other protein–ligand interactions, a systematic assessment of their various interaction modes is still lacking. A comprehensive resource identifying positions within domains that tend to interact with nucleic acids, small molecules and other ligands would expand our knowledge of domain functionality as well as aid in detecting ligand-binding sites within structurally uncharacterized proteins. Here, we introduce an approach to identify per-domain-position interaction ‘frequencies’ by aggregating protein co-complex structures by domain and ascertaining how often residues mapping to each domain position interact with ligands. We perform this domain-based analysis on ∼91000 co-complex structures, and infer positions involved in binding DNA, RNA, peptides, ions or small molecules across 4128 domains, which we refer to collectively as the InteracDome. Cross-validation testing reveals that ligand-binding positions for 2152 domains are highly consistent and can be used to identify residues facilitating interactions in ∼63–69% of human genes. Our resource of domain-inferred ligand-binding sites should be a great aid in understanding disease etiology: whereas these sites are enriched in Mendelian-associated and cancer somatic mutations, they are depleted in polymorphisms observed across healthy populations. The InteracDome is available at http://interacdome.princeton.edu.

scholarly journals GRaSP: a graph-based residue neighborhood strategy to predict binding sites

2020 ◽  
Vol 36 (Supplement_2) ◽  
pp. i726-i734
Author(s):  
Charles A Santana ◽  
Sabrina de A Silveira ◽  
João P A Moraes ◽  
Sandro C Izidoro ◽  
Raquel C de Melo-Minardi ◽  
...  
Keyword(s):  
Ligand Binding ◽  
Binding Site ◽  
Binding Sites ◽  
Protein Function ◽  
Learning Strategy ◽  
State Of The Art ◽  
Supplementary Information ◽  
Major Step ◽  
Ligand Binding Sites ◽  
Residue Neighborhood

Abstract Motivation The discovery of protein–ligand-binding sites is a major step for elucidating protein function and for investigating new functional roles. Detecting protein–ligand-binding sites experimentally is time-consuming and expensive. Thus, a variety of in silico methods to detect and predict binding sites was proposed as they can be scalable, fast and present low cost. Results We proposed Graph-based Residue neighborhood Strategy to Predict binding sites (GRaSP), a novel residue centric and scalable method to predict ligand-binding site residues. It is based on a supervised learning strategy that models the residue environment as a graph at the atomic level. Results show that GRaSP made compatible or superior predictions when compared with methods described in the literature. GRaSP outperformed six other residue-centric methods, including the one considered as state-of-the-art. Also, our method achieved better results than the method from CAMEO independent assessment. GRaSP ranked second when compared with five state-of-the-art pocket-centric methods, which we consider a significant result, as it was not devised to predict pockets. Finally, our method proved scalable as it took 10–20 s on average to predict the binding site for a protein complex whereas the state-of-the-art residue-centric method takes 2–5 h on average. Availability and implementation The source code and datasets are available at https://github.com/charles-abreu/GRaSP. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals PUResNet: prediction of protein-ligand binding sites using deep residual neural network

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Jeevan Kandel ◽  
Hilal Tayara ◽  
Kil To Chong
Keyword(s):  
Protein Structure ◽  
Ligand Binding ◽  
Binding Sites ◽  
Protein Structures ◽  
Structural Similarity ◽  
Vital Role ◽  
Biological Functions ◽  
True Nature ◽  
Ligand Binding Sites ◽  
Deep Learning Model

Abstract Background Predicting protein-ligand binding sites is a fundamental step in understanding the functional characteristics of proteins, which plays a vital role in elucidating different biological functions and is a crucial step in drug discovery. A protein exhibits its true nature after binding to its interacting molecule known as a ligand that binds only in the favorable binding site of the protein structure. Different computational methods exploiting the features of proteins have been developed to identify the binding sites in the protein structure, but none seems to provide promising results, and therefore, further investigation is required. Results In this study, we present a deep learning model PUResNet and a novel data cleaning process based on structural similarity for predicting protein-ligand binding sites. From the whole scPDB (an annotated database of druggable binding sites extracted from the Protein DataBank) database, 5020 protein structures were selected to address this problem, which were used to train PUResNet. With this, we achieved better and justifiable performance than the existing methods while evaluating two independent sets using distance, volume and proportion metrics.

scholarly journals De novo protein fold families expand the designable ligand binding site space

2021 ◽  
Vol 17 (11) ◽  
pp. e1009620
Author(s):  
Xingjie Pan ◽  
Tanja Kortemme
Keyword(s):  
Ligand Binding ◽  
Binding Site ◽  
Binding Sites ◽  
De Novo ◽  
Protein Structures ◽  
Protein Fold ◽  
Protein Families ◽  
Existing Structures ◽  
Fold Family ◽  
Ligand Binding Sites

A major challenge in designing proteins de novo to bind user-defined ligands with high affinity is finding backbones structures into which a new binding site geometry can be engineered with high precision. Recent advances in methods to generate protein fold families de novo have expanded the space of accessible protein structures, but it is not clear to what extend de novo proteins with diverse geometries also expand the space of designable ligand binding functions. We constructed a library of 25,806 high-quality ligand binding sites and developed a fast protocol to place (“match”) these binding sites into both naturally occurring and de novo protein families with two fold topologies: Rossman and NTF2. Each matching step involves engineering new binding site residues into each protein “scaffold”, which is distinct from the problem of comparing already existing binding pockets. 5,896 and 7,475 binding sites could be matched to the Rossmann and NTF2 fold families, respectively. De novo designed Rossman and NTF2 protein families can support 1,791 and 678 binding sites that cannot be matched to naturally existing structures with the same topologies, respectively. While the number of protein residues in ligand binding sites is the major determinant of matching success, ligand size and primary sequence separation of binding site residues also play important roles. The number of matched binding sites are power law functions of the number of members in a fold family. Our results suggest that de novo sampling of geometric variations on diverse fold topologies can significantly expand the space of designable ligand binding sites for a wealth of possible new protein functions.

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