CavitySpace: A Database of Potential Ligand Binding Sites in the Human Proteome

Wang, Shiwei; Lin, Haoyu; Huang, Zhixian; He, Yihua; Deng, Xiaobing; Xu, Youjun; Pei, Jianfeng; Lai, Luhua

doi:10.3390/biom12070967

Cited by 26 publications

(16 citation statements)

References 38 publications

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“…Identifying binding cavities is essential for understanding the relationship between structure and function in proteins and an essential step for drug design [39,61,63,66]. However, over the last few years, the number of three-dimensional protein structures has increased considerably [27,43].…”

Section: Discussionmentioning

confidence: 99%

“…There are many tools oriented to protein structural characterization and cavities prediction [28,61], and the number of 3D structures data is growing in big steps [24]. Nevertheless, as we have shown CaviDB is not only a useful tool for obtaining protein cavities features and their dynamics but also provides an easy and accessible way of analyzing structural data.…”

Section: The Advantages Of Cavidb Over Existing Servicesmentioning

confidence: 99%

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CaviDB: a database of cavities and their features in the structural and conformational space of proteins

Rueda

Bulgarelli²,

Palopoli

et al. 2022

Preprint

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Proteins are the functional and evolutionary units of cells. On their surface, proteins are sculpted into numerous concavities and bulges, offering unique microenvironments for ligand binding or catalysis. The dynamics, size, and chemical features of these cavities are essential for the mechanistic understanding of protein function. Here we present CaviDB (https://www.cavidb.org), a novel database of cavities and their features in known protein structures, which integrates the results from commonly used software for cavities detection with protein features obtained from sequence, structure, and function analyses. Additionally, each protein in CaviDB is associated with its corresponding conformers which help to analyze conformational changes in cavities as well. We were able to characterize a total number of 16,533,339 cavities, 62,0431 of them predicted as druggable targets. CaviDB contains 276,432 different proteins, with information about all their conformers. It also offers the capability to compare cavities and their features from different conformational states of the protein. Furthermore, we have recently added the available models from the AlphaFold database versions 2 and 3, which allow further cavity explorations and comparisons. Each entry information is organized in sections, highlighting the general cavities descriptors, including the inter-cavities contacts, activated residues per cavity, the information about druggable cavities, and the global protein descriptors. The data retrieved by the user can be downloaded in a format that is easy to parse and integrate with custom pipelines for protein analysis. CaviDB aims to offer a comprehensive database for use not only in different aspects of drug design and discovery but also to better understand the basis of the protein structure-function relationship better. With its unique approach, CaviDB provides an essential resource for the wide community of bioinformaticians in particular and biologists in general.

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Section: Discussionmentioning

confidence: 99%

Section: The Advantages Of Cavidb Over Existing Servicesmentioning

confidence: 99%

CaviDB: a database of cavities and their features in the structural and conformational space of proteins

Rueda

Bulgarelli²,

Palopoli

et al. 2022

Preprint

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show abstract

“…Starting from really druggable protein–ligand complexes [ 152 ] is often advised in the case of medicinal chemistry applications [ 20 , 24 , 129 , 138 ]. Due to the increasing accuracy of deep learning methods [ 153 , 154 ] to predict protein structures with near-atomic resolution, the druggable pocketome is predicted to significantly expand in the next years [ 155 ]. Therefore, clear guidelines, as those recently proposed in ProSPECCTs [ 38 ], are welcome.…”

Section: Retrospective Evaluations and Datasetsmentioning

confidence: 99%

Estimating the Similarity between Protein Pockets

Eguida

Rognan

2022

IJMS

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With the exponential increase in publicly available protein structures, the comparison of protein binding sites naturally emerged as a scientific topic to explain observations or generate hypotheses for ligand design, notably to predict ligand selectivity for on- and off-targets, explain polypharmacology, and design target-focused libraries. The current review summarizes the state-of-the-art computational methods applied to pocket detection and comparison as well as structural druggability estimates. The major strengths and weaknesses of current pocket descriptors, alignment methods, and similarity search algorithms are presented. Lastly, an exhaustive survey of both retrospective and prospective applications in diverse medicinal chemistry scenarios illustrates the capability of the existing methods and the hurdle that still needs to be overcome for more accurate predictions.

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“…They are based on various approaches, e.g., similarity detection of template structures 4 or deep learning. 5,6 However, these tools focus on binding sites for small molecule ligands and do not allow prediction of other important binding sites for drug discovery, e.g., water and other ligand types.…”

Section: ■ Introductionmentioning

confidence: 99%

“…Recently, some web servers have been developed that allow prediction of binding sites for AlphaFold protein structures to detect cavities in these protein structures. They are based on various approaches, e.g., similarity detection of template structures or deep learning. , However, these tools focus on binding sites for small molecule ligands and do not allow prediction of other important binding sites for drug discovery, e.g., water and other ligand types.…”

Section: Introductionmentioning

confidence: 99%

ProBiS-Fold Approach for Annotation of Human Structures from the AlphaFold Database with No Corresponding Structure in the PDB to Discover New Druggable Binding Sites

Konc

Janežič

2022

J. Chem. Inf. Model.

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ProBiS (Protein Binding Sites), a local structure-based comparison algorithm, is used in the new ProBiS-Fold web server to annotate human structures from the AlphaFold database without a corresponding structure in the Protein Data Bank (PDB) to discover new druggable binding sites. The ProBiS algorithm is used to compare each query protein structure predicted by the AlphaFold approach with the protein structures from the PDB to identify similarities between known binding sites found in the PDB and yet unknown binding sites in the AlphaFold database. Ligands bound in these identified similar PDB sites are then transferred to each query protein from the AlphaFold database, and binding sites are identified as ligand clusters on an AlphaFold protein. Small molecule binding sites are assigned druggability scores based on the similarity of their ligands to known drugs, allowing them to be ranked according to their perceived and actual potential for drug development. ProBiS-Fold provides interactive and downloadable binding sites for the entire human structural proteome, including more than 3000 new druggable binding sites that have no corresponding structure in the PDB, taking into account AlphaFold's model quality, to enable protein structure−function relationship studies and pharmaceutical drug discovery research. The web server is freely accessible to academic users at http://probis-fold.insilab.org.

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CavitySpace: A Database of Potential Ligand Binding Sites in the Human Proteome

Cited by 26 publications

References 38 publications

CaviDB: a database of cavities and their features in the structural and conformational space of proteins

CaviDB: a database of cavities and their features in the structural and conformational space of proteins

Estimating the Similarity between Protein Pockets

ProBiS-Fold Approach for Annotation of Human Structures from the AlphaFold Database with No Corresponding Structure in the PDB to Discover New Druggable Binding Sites

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