Nejvíce citovaný článek - PubMed ID 20556825
By merging advanced dimensionality reduction (DR) and clustering algorithm (CA) techniques, our study advances the sampling procedure for predicting NMR chemical shifts (CS) in intrinsically disordered proteins (IDPs), making a significant leap forward in the field of protein analysis/modeling. We enhance NMR CS sampling by generating clustered ensembles that accurately reflect the different properties and phenomena encapsulated by the IDP trajectories. This investigation critically assessed different rapid CS predictors, both neural network (e.g., Sparta+ and ShiftX2) and database-driven (ProCS-15), and highlighted the need for more advanced quantum calculations and the subsequent need for more tractable-sized conformational ensembles. Although neural network CS predictors outperformed ProCS-15 for all atoms, all tools showed poor agreement with HN CSs, and the neural network CS predictors were unable to capture the influence of phosphorylated residues, highly relevant for IDPs. This study also addressed the limitations of using direct clustering with collective variables, such as the widespread implementation of the GROMOS algorithm. Clustered ensembles (CEs) produced by this algorithm showed poor performance with chemical shifts compared to sequential ensembles (SEs) of similar size. Instead, we implement a multiscale DR and CA approach and explore the challenges and limitations of applying these algorithms to obtain more robust and tractable CEs. The novel feature of this investigation is the use of solvent-accessible surface area (SASA) as one of the fingerprints for DR alongside previously investigated α carbon distance/angles or ϕ/ψ dihedral angles. The ensembles produced with SASA tSNE DR produced CEs better aligned with the experimental CS of between 0.17 and 0.36 r2 (0.18-0.26 ppm) depending on the system and replicate. Furthermore, this technique produced CEs with better agreement than traditional SEs in 85.7% of all ensemble sizes. This study investigates the quality of ensembles produced based on different input features, comparing latent spaces produced by linear vs nonlinear DR techniques and a novel integrated silhouette score scanning protocol for tSNE DR.
Ameloblastin (Ambn) as an intrinsically disordered protein (IDP) stands for an important role in the formation of enamel-the hardest biomineralized tissue commonly formed in vertebrates. The human ameloblastin (AMBN) is expressed in two isoforms: full-length isoform I (AMBN ISO I) and isoform II (AMBN ISO II), which is about 15 amino acid residues shorter than AMBN ISO I. The significant feature of AMBN-its oligomerization ability-is enabled due to a specific sequence encoded by exon 5 present at the N-terminal part in both known isoforms. In this study, we characterized AMBN ISO I and AMBN ISO II by biochemical and biophysical methods to determine their common features and differences. We confirmed that both AMBN ISO I and AMBN ISO II form oligomers in in vitro conditions. Due to an important role of AMBN in biomineralization, we further addressed the calcium (Ca2+)-binding properties of AMBN ISO I and ISO II. The binding properties of AMBN to Ca2+ may explain the role of AMBN in biomineralization and more generally in Ca2+ homeostasis processes.
- Klíčová slova
- ameloblastin, biomineralization, calcium binding, intrinsically disordered protein (IDPs), oligomerization,
- MeSH
- biologické modely MeSH
- hydrodynamika MeSH
- lidé MeSH
- multimerizace proteinu MeSH
- protein - isoformy MeSH
- proteiny vázající vápník chemie metabolismus MeSH
- proteiny zubní skloviny chemie metabolismus MeSH
- spektrální analýza MeSH
- teplota MeSH
- vápník metabolismus MeSH
- vazba proteinů MeSH
- vnitřně neuspořádané proteiny metabolismus MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- Názvy látek
- AMBN protein, human MeSH Prohlížeč
- protein - isoformy MeSH
- proteiny vázající vápník MeSH
- proteiny zubní skloviny MeSH
- vápník MeSH
- vnitřně neuspořádané proteiny MeSH
