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/ .
- Publikační typ
- časopisecké články MeSH
- MeSH
- fibroblastový růstový faktor 2 * MeSH
- fibroblasty MeSH
- hojení ran MeSH
- lidé MeSH
- popálení * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- dopisy MeSH
The haloalkane dehalogenase DhaA from Rhodococcus rhodochrous NCIMB 13064 is a bacterial enzyme that shows catalytic activity for the hydrolytic degradation of the highly toxic industrial pollutant 1,2,3-trichloropropane (TCP). Mutagenesis focused on the access tunnels of DhaA produced protein variants with significantly improved activity towards TCP. Three mutants of DhaA named DhaA04 (C176Y), DhaA14 (I135F) and DhaA15 (C176Y + I135F) were constructed in order to study the functional relevance of the tunnels connecting the buried active site of the protein with the surrounding solvent. All three protein variants were crystallized using the sitting-drop vapour-diffusion technique. The crystals of DhaA04 belonged to the orthorhombic space group P2(1)2(1)2(1), while the crystals of DhaA14 and DhaA15 had triclinic symmetry in space group P1. The crystal structures of DhaA04, DhaA14 and DhaA15 with ligands present in the active site were solved and refined using diffraction data to 1.23, 0.95 and 1.22 A, resolution, respectively. Structural comparisons of the wild type and the three mutants suggest that the tunnels play a key role in the processes of ligand exchange between the buried active site and the surrounding solvent.
- MeSH
- hydrolasy chemie genetika MeSH
- izoenzymy chemie genetika MeSH
- krystalografie rentgenová MeSH
- ligandy MeSH
- molekulární modely MeSH
- mutace MeSH
- proteinové inženýrství MeSH
- Rhodococcus enzymologie MeSH
- terciární struktura proteinů MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
