PubMed Health⌕ Search

PubMed · 15111064

Preformed structural elements feature in partner recognition by intrinsically unstructured proteins.

Abstract

Intrinsically unstructured proteins (IUPs) are devoid of extensive structural order but often display signs of local and limited residual structure. To explain their effective functioning, we reasoned that such residual structure can be crucial in their interactions with their structured partner(s) in a way that preformed structural elements presage their final conformational state. To check this assumption, a database of 24 IUPs with known 3D structures in the bound state has been assembled and the distribution of secondary structure elements and backbone torsion angles have been analysed. The high proportion of residues in coil conformation and with phi, psi angles in the disallowed regions of the Ramachandran map compared to the reference set of globular proteins shows that IUPs are not fully ordered even in their bound form. To probe the effect of partner proteins on IUP folding, inherent conformational preferences of IUP sequences have been assessed by secondary structure predictions using the GOR, ALB and PROF algorithms. The accuracy of predicting secondary structure elements of IUPs is similar to that of their partner proteins and is significantly higher than the corresponding values for random sequences. We propose that strong conformational preferences mark regions in IUPs (mostly helices), which correspond to their final structural state, while regions with weak conformational preferences represent flexible linkers between them. In our interpretation, preformed elements could serve as initial contact points, the binding of which facilitates the reeling of the flexible regions onto the template. This finding implies that IUPs draw a functional advantage from preformed structural elements, as they enable their facile, kinetically and energetically less demanding, interaction with their physiological partner.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Monika Fuxreiter, István Simon, Peter Friedrich, Peter Tompa. 2004-05-14. Preformed structural elements feature in partner recognition by intrinsically unstructured proteins.. https://doi.org/10.1016/j.jmb.2004.03.017

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

ProteoParc: A Reference Protein Database Builder for Ancient and Nonmodel Organisms.

Over the past few years, the increasing interest in analyzing the proteome of extinct and nonmodel organisms has generated a new field of research expanding the scope of proteomics. The lack of curated databases and/or molecular data from these organisms forces researchers to manually search in different public repositories for related protein sequences, either for MS/MS peptide identification or ZooMS marker annotation. This can lead to format incongruences and hinder reproducibility between studies. To address this issue, we introduce ProteoParc, a user-friendly software that builds reference databases by systematically downloading and processing protein sequences from the most widely used public repositories. The pipeline's output is a nonredundant protein database, formatted in a way to be interpreted by typical peptide identification software. Moreover, the user can adjust the database dimension and composition by applying different criteria to include only a certain number of genes or species. Thus, ProteoParc is an easy and fast, custom-made bioinformatic tool useful for future paleoproteomics analysis in ancient samples related to understudied organisms.

Databases, Protein↗

Assessing Metal Ion Assignment Accuracy in Protein Data Bank Models via Elemental Spectroscopy.

Accurate representation of metal ions in macromolecular structures is critical for chemical interpretation, computational modeling, and machine-learning methods that rely on Protein Data Bank (PDB) entries. However, the elemental identity of metals modeled in crystallographic structures is often inferred indirectly and rarely validated experimentally. Here, we combine Particle Induced X-ray Emission (PIXE) and X-ray Fluorescence Spectroscopy (XRFS) to determine the elemental composition of protein samples used to generate 70 deposited metalloprotein crystal structures. By analyzing the original protein material employed for crystallization, but before the addition of crystallization buffer solutions, we assess whether the modeled metal ions in deposited structures are consistent with experimentally detectable elemental content. We find that in a majority of cases, the metals modeled in the corresponding PDB entries are inconsistent with the metals present in the protein samples before crystallization, or that additional metals are present but not represented in the structural models. Spectroscopic results were integrated with automated crystallographic validation metrics, including real-space Z-difference (RSZD) analysis and systematic rerefinement, to evaluate atomic-number mismatch at metal sites. PIXE and XRFS show strong agreement for dominant elemental signals and provide complementary, scalable approaches for identifying suspect metal assignments. This work does not address physiological or functional metalation but instead highlights a widespread data integrity issue in deposited macromolecular structures, PDB-wide. These results establish an experimentally corroborated link between elemental identity and crystallographic validation metrics, enabling the large-scale detection of chemically inconsistent annotations in structural databases used for computational modeling and machine learning.

Databases, Protein↗

BAV-LLPS: a database of bacterial, archaea, and virus liquid-liquid phase separation proteins.

MOTIVATION: Liquid-liquid phase separation (LLPS) is a key process underlying the formation of biomolecular condensates, such as membrane-less organelles, that compartmentalize biochemical processes inside the cells. While LLPS has been extensively studied in eukaryotes, its role in bacteria, archaea, and viruses remains far less characterized. Recent studies in bacteria have revealed that LLPS-driven condensates play critical roles in RNA processing, stress response, and pathogenicity. Similarly, many viruses exploit LLPS to facilitate crucial steps in their infection cycles, including viral entry, genome replication, assembly, and host immune evasion. RESULTS: In this work, we introduce a hand-curated database of LLPS proteins from bacteria, archaea, and viruses (BAV-LLPS Database). This resource, extended through sequence similarity searches, comprises over 5000 proteins and integrates diverse data including biological annotations, sequence features, predicted disordered regions, LLPS per site probability, and AlphaFold2-based structural models. Additionally, our web server enables users to explore both the curated and homologous derived datasets, providing a platform to uncover evolutionary relationships and intrinsic and differential properties of LLPS proteins across various taxonomic groups. This work seeks to deepen our understanding of LLPS mechanisms beyond eukaryotic organisms, emphasizing their significance across diverse life forms. It also aims to foster the development of specialized predictive tools that will facilitate the exploration and characterization of LLPS processes in a wide array of living organisms, thereby contributing to advancements in both fundamental biological research and applied biomedical sciences. AVAILABILITY AND IMPLEMENTATION: BAV-LLPS DB is freely accessible at https://bav-llps-db.bioinformatica.org/. The data can be retrieved from the website. The source code of the database can be downloaded from https://bav-llps-db.bioinformatica.org/download.

Databases, Protein↗