A study of a hierarchical structure of proteins and ligand binding sites of receptors using the TSR ‐based structure comparison method and development of a size‐filtering feature designed for comparing different sizes of protein structures

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.

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BSAlign: A RAPID GRAPH-BASED ALGORITHM FOR DETECTING LIGAND-BINDING SITES IN PROTEIN STRUCTURES

Genome Informatics 2008 ◽

10.1142/9781848163324_0006 ◽

2008 ◽

Cited By ~ 5

Author(s):

ZEYAR AUNG ◽

JOO CHUAN TONG

Keyword(s):

Ligand Binding ◽

Binding Sites ◽

Protein Structures ◽

Ligand Binding Sites

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

Nucleic Acids Research ◽

10.1093/nar/gkt457 ◽

2013 ◽

Vol 41 (W1) ◽

pp. W308-W313 ◽

Cited By ~ 3

Author(s):

Valerio Bianchi ◽

Iolanda Mangone ◽

Fabrizio Ferrè ◽

Manuela Helmer-Citterich ◽

Gabriele Ausiello

Keyword(s):

Ligand Binding ◽

Binding Sites ◽

Protein Structures ◽

Ligand Binding Sites

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

Nucleic Acids Research ◽

10.1093/nar/gky1224 ◽

2018 ◽

Vol 47 (2) ◽

pp. 582-593 ◽

Cited By ~ 5

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.

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

Journal of Cheminformatics ◽

10.1186/s13321-021-00547-7 ◽

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.

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

PLoS Computational Biology ◽

10.1371/journal.pcbi.1009620 ◽

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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EasyMIFs and SiteHound: a toolkit for the identification of ligand-binding sites in protein structures

Bioinformatics ◽

10.1093/bioinformatics/btp562 ◽

2009 ◽

Vol 25 (23) ◽

pp. 3185-3186 ◽

Cited By ~ 48

Author(s):

Dario Ghersi ◽

Roberto Sanchez

Keyword(s):

Ligand Binding ◽

Binding Sites ◽

Protein Structures ◽

Ligand Binding Sites

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

10.1101/2021.01.13.426598 ◽

2021 ◽

Author(s):

Xingjie Pan ◽

Tanja Kortemme

Keyword(s):

Ligand Binding ◽

Binding Site ◽

Binding Sites ◽

Method Development ◽

De Novo ◽

Protein Structures ◽

Protein Fold ◽

Ligand Binding Site ◽

Protein Families ◽

Ligand Binding Sites

AbstractA major challenge in designing proteins de novo to bind user-defined ligands with high specificity and affinity is finding backbones structures that can accommodate a desired binding site geometry 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. 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.Author summaryDe novo design of proteins that can bind to novel and highly diverse user-defined small molecule ligands could have broad biomedical and synthetic biology applications. Because ligand binding site geometries need to be accommodated by protein backbone scaffolds at high accuracy, the diversity of scaffolds is a major limitation for designing new ligand binding functions. Advances in computational protein structure design methods have significantly increased the number of accessible stable scaffold structures. Understanding how many new ligand binding sites can be accommodated by the de novo scaffolds is important for designing novel ligand binding proteins. To answer this question, we constructed a large library of ligand binding sites from the Protein Data Bank (PDB). We tested the number of ligand binding sites that can be accommodated by de novo scaffolds and naturally existing scaffolds with same fold topologies. The results showed that de novo scaffolds significantly expanded the ligand binding space of their respective fold topologies. We also identified factors that affect difficulties of binding site accommodation, as well as the relationship between the number of scaffolds and the accessible ligand binding site space. We believe our findings will benefit future method development and applications of ligand binding protein design.

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