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:10.1101/2022.01.25.477691

Cited by 3 publications

(1 citation statement)

References 29 publications

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“…Databases, such as HKPocket and KLIFS, are a comprehensive resource for kinase pocket structural information and kinase-ligand interactions [118] , [119] . Similarly, there are several online resources for the exploration of the pocketome, including PocketDB which contains predicted pockets from PDB-derived structures and CavitySpace which contains potential pockets in AlphaFold-predicted protein structures [120] , [121] . Additionally, a druggable pocket is not always the orthosteric site of a protein but allosteric sites have the potential to bind drug-like molecules that regulate the protein’s activity [122] .…”

Section: Assessing the Druggability Of Cancer Targetsmentioning

confidence: 99%

Data-driven analysis and druggability assessment methods to accelerate the identification of novel cancer targets

Beis¹,

Serafeim²,

Papasotiriou³

2023

Computational and Structural Biotechnology Journal

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Section: Assessing the Druggability Of Cancer Targetsmentioning

confidence: 99%

Data-driven analysis and druggability assessment methods to accelerate the identification of novel cancer targets

Beis¹,

Serafeim²,

Papasotiriou³

2023

Computational and Structural Biotechnology Journal

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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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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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CavitySpace: A database of potential ligand binding sites in the human proteome

Cited by 3 publications

References 29 publications

Data-driven analysis and druggability assessment methods to accelerate the identification of novel cancer targets

Data-driven analysis and druggability assessment methods to accelerate the identification of novel cancer targets

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

Estimating the Similarity between Protein Pockets

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