Focused conformational sampling in proteins

Bacci, Marco; Langini, Cassiano; Vymětal, Jiří; Caflisch, Amedeo; Vitalis, Andreas

doi:10.1063/1.4996879

Cited by 11 publications

(7 citation statements)

References 90 publications

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“…While this limitation cannot be solved systematically, it can be mitigated by generating series of structures in silico, e.g., by producing homology structures and generating conformational ensembles from sampling methods. [81][82][83] Crystal contacts, artifacts and protein chain repeats can produce spurious non-productive interchain cavities ( Figure 9A).…”

Section: Resultsmentioning

confidence: 99%

Automatic Cavity Identification and Decomposition into Subpockets with CAVIAR

Marchand¹,

Pirard²,

Ertl³

et al. 2020

Preprint

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<div>Motivation: The detection of small molecules binding sites in proteins is central to structure based</div><div>drug design. Many tools were developed in the last 40 years, but only few of them are available</div><div>today, open-source, and suitable for the analysis of large databases or for the integration in</div><div>automatic workflows. In addition, no software can characterize subpockets solely with the</div><div>information of the protein structure, a pivotal concept in fragment-based drug design.</div><div>Results: CAVIAR is a new open source tool for protein cavity identification and rationalization.</div><div>Protein pockets are automatically detected based on the protein structure. It comprises a subcavity</div><div>segmentation algorithm that decomposes binding sites into subpockets without requiring the</div><div>presence of a ligand. The defined subpockets mimick the empirical definitions of subpockets in</div><div>medicinal chemistry projects. A tool like CAVIAR may be valuable to support chemical biology,</div><div>medicinal chemistry and ligand identification efforts. Our analysis of the entire PDB and the</div><div>PDBBind confirms that liganded cavities tend to be bigger, more hydrophobic and more complex</div><div>than apo cavities. Moreover, in line with the paradigm of fragment-based drug design, the binding</div><div>affinity scales relatively well with the number of subcavities filled by the ligand. Compounds</div><div>binding to more than three of the subcavities identified by CAVIAR are mostly in the nanomolar</div><div>or better range of affinities to their target.</div><div>Availability and implementation: Installation notes, user manual and support for CAVIAR are</div><div>available at https://jr-marchand.github.io/caviar/. The CAVIAR GUI and CAVIAR command line</div><div>tool are available on GitHub at https://github.com/jr-marchand/caviar and the package is hosted</div><div>on Anaconda cloud at https://anaconda.org/jr-marchand/caviar under a MIT license. The GitHub</div><div>repository also hosts the validation datasets.</div>

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Section: Resultsmentioning

confidence: 99%

Automatic Cavity Identification and Decomposition into Subpockets with CAVIAR

Marchand¹,

Pirard²,

Ertl³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…While this limitation cannot be solved systematically, it can be mitigated by generating series of structures in silico, e.g., by generating conformational ensembles from sampling methods [81][82][83].…”

Section: Limitations Of the Methodsmentioning

confidence: 99%

CAVIAR: a method for automatic cavity detection, description and decomposition into subcavities

Marchand

Pirard

Ertl

et al. 2021

J Comput Aided Mol Des

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The accurate description of protein binding sites is essential to the determination of similarity and the application of machine learning methods to relate the binding sites to observed functions. This work describes CAVIAR, a new open source tool for generating descriptors for binding sites, using protein structures in PDB and mmCIF format as well as trajectory frames from molecular dynamics simulations as input. The applicability of CAVIAR descriptors is showcased by computing machine learning predictions of binding site ligandability. The method can also automatically assign subcavities, even in the absence of a bound ligand.The defined subpockets mimic the empirical definitions used in medicinal chemistry projects. It is shown that the experimental binding affinity scales relatively well with the number of subcavities filled by the ligand, with compounds binding to more than three subcavities having nanomolar or better affinities to the target. The CAVIAR descriptors and methods can be used in any machine learning-based investigations of problems involving binding sites, from protein engineering to hit identification. The full software code is available on GitHub and a conda package is hosted on Anaconda cloud. File list (2) download file view on ChemRxiv Marchandetal_MainText.pdf (2.03 MiB) download file view on ChemRxiv Marchandetal_SI.pdf (513.53 KiB)

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“…If a flexible cryptic pocket of interest is not present in the structure given as input to CAVIAR, it will not detect it. While this limitation cannot be solved systematically, it can be mitigated by generating series of structures in silico, e.g., by generating conformational ensembles from sampling methods [81][82][83].…”

Section: Limitations Of the Methodsmentioning

confidence: 99%

CAVIAR: a method for automatic cavity detection, description and decomposition into subcavities

Marchand

Pirard²,

Ertl³

et al. 2021

Preprint

View full text Add to dashboard Cite

<p></p><p>The accurate description of protein binding sites is essential to the determination of similarity and the application of machine learning methods to relate the binding sites to observed functions. This work describes CAVIAR, a new open source tool for generating descriptors for binding sites, using protein structures in PDB and mmCIF format as well as trajectory frames from molecular dynamics simulations as input. The applicability of CAVIAR descriptors is showcased by computing machine learning predictions of binding site ligandability. The method can also automatically assign subcavities, even in the absence of a bound ligand. The defined subpockets mimic the empirical definitions used in medicinal chemistry projects. It is shown that the experimental binding affinity scales relatively well with the number of subcavities filled by the ligand, with compounds binding to more than three subcavities having nanomolar or better affinities to the target. The CAVIAR descriptors and methods can be used in any machine learning-based investigations of problems involving binding sites, from protein engineering to hit identification. The full software code is available on GitHub and a conda package is hosted on Anaconda cloud.</p><p></p>

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Focused conformational sampling in proteins

Cited by 11 publications

References 90 publications

Automatic Cavity Identification and Decomposition into Subpockets with CAVIAR

Automatic Cavity Identification and Decomposition into Subpockets with CAVIAR

CAVIAR: a method for automatic cavity detection, description and decomposition into subcavities

CAVIAR: a method for automatic cavity detection, description and decomposition into subcavities

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