Large-scale annotation of biochemically relevant pockets and tunnels in cognate enzyme-ligand complexes

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00159816%3A_____%2F24%3A00081403" target="_blank" >RIV/00159816:_____/24:00081403 - isvavai.cz</a>

Alternative codes found

RIV/00216224:14310/24:00137540

Result on the web

<a href="https://pmc.ncbi.nlm.nih.gov/articles/PMC11481355/pdf/13321_2024_Article_907.pdf" target="_blank" >https://pmc.ncbi.nlm.nih.gov/articles/PMC11481355/pdf/13321_2024_Article_907.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1186/s13321-024-00907-z" target="_blank" >10.1186/s13321-024-00907-z</a>

Result language

angličtina

Original language name

Large-scale annotation of biochemically relevant pockets and tunnels in cognate enzyme-ligand complexes

Original language description

Tunnels in enzymes with buried active sites are key structural features allowing the entry of substrates and the release of products, thus contributing to the catalytic efficiency. Targeting the bottlenecks of protein tunnels is also a powerful protein engineering strategy. However, the identification of functional tunnels in multiple protein structures is a non-trivial task that can only be addressed computationally. We present a pipeline integrating automated structural analysis with an in-house machine-learning predictor for the annotation of protein pockets, followed by the calculation of the energetics of ligand transport via biochemically relevant tunnels. A thorough validation using eight distinct molecular systems revealed that CaverDock analysis of ligand un/binding is on par with time-consuming molecular dynamics simulations, but much faster. The optimized and validated pipeline was applied to annotate more than 17,000 cognate enzyme-ligand complexes. Analysis of ligand un/binding energetics indicates that the top priority tunnel has the most favourable energies in 75% of cases. Moreover, energy profiles of cognate ligands revealed that a simple geometry analysis can correctly identify tunnel bottlenecks only in 50% of cases. Our study provides essential information for the interpretation of results from tunnel calculation and energy profiling in mechanistic enzymology and protein engineering. We formulated several simple rules allowing identification of biochemically relevant tunnels based on the binding pockets, tunnel geometry, and ligand transport energy profiles.Scientific contributionsThe pipeline introduced in this work allows for the detailed analysis of a large set of protein-ligand complexes, focusing on transport pathways. We are introducing a novel predictor for determining the relevance of binding pockets for tunnel calculation. For the first time in the field, we present a high-throughput energetic analysis of ligand binding and unbinding, showing that approximate methods for these simulations can identify additional mutagenesis hotspots in enzymes compared to purely geometrical methods. The predictor is included in the supplementary material and can also be accessed at https://github.com/Faranehhad/Large-Scale-Pocket-Tunnel-Annotation.git. The tunnel data calculated in this study has been made publicly available as part of the ChannelsDB 2.0 database, accessible at https://channelsdb2.biodata.ceitec.cz/.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10608 - Biochemistry and molecular biology

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Journal of Cheminformatics

ISSN

1758-2946

e-ISSN

1758-2946

Volume of the periodical

16

Issue of the periodical within the volume

1

Country of publishing house

GB - UNITED KINGDOM

Number of pages

14

Pages from-to

114

UT code for WoS article

001332053400001

EID of the result in the Scopus database

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