Dataset Information

Redefining the architecture of ferlin proteins: Insights into multi-domain protein structure and function.

ABSTRACT: Ferlins are complex, multi-domain proteins, involved in membrane trafficking, membrane repair, and exocytosis. The large size of ferlin proteins and the lack of consensus regarding domain boundaries have slowed progress in understanding molecular-level details of ferlin protein structure and function. However, in silico protein folding techniques have significantly enhanced our understanding of the complex ferlin family domain structure. We used RoseTTAFold to assemble full-length models for the six human ferlin proteins (dysferlin, myoferlin, otoferlin, Fer1L4, Fer1L5, and Fer1L6). Our full-length ferlin models were used to obtain objective domain boundaries, and these boundaries were supported by AlphaFold2 predictions. Despite the differences in amino acid sequence between the ferlin proteins, the domain ranges and distinct subdomains in the ferlin domains are remarkably consistent. Further, the RoseTTAFold/AlphaFold2 in silico boundary predictions allowed us to describe and characterize a previously unknown C2 domain, ubiquitous in all human ferlins, which we refer to as C2-FerA. At present, the ferlin domain-domain interactions implied by the full-length in silico models are predicted to have a low accuracy; however, the use of RoseTTAFold and AlphaFold2 as a domain finder has proven to be a powerful research tool for understanding ferlin structure.

SUBMITTER: Dominguez MJ

PROVIDER: S-EPMC9333456 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Peptide toxins isolated from animal venom secretions have proven to be useful pharmacological tools for probing the structure and function of a number of molecular receptors. Their molecular structures are stabilized by posttranslational formation of multiple disulfide bonds formed between sidechain thiols of cysteine residues, resulting in high thermal and chemical stability. Many of these peptides have been found to be potent modulators of ion channels, making them particularly influential in this field. Recently, several peptide toxins have been described that have an unusual tandem repeat organization, while also eliciting a unique pharmacological response toward ion channels. Most of these are two-domain peptide toxins from spider venoms, such as the double-knot toxin (DkTx), isolated from the Earth Tiger tarantula (Haplopelma schmidti). The unusual pharmacology of DkTx is its high avidity for its receptor (TRPV1), a property that has been attributed to a bivalent mode-of-action. DkTx has subsequently proven a powerful tool for elucidating the structural basis for the function of the TRPV1 channel. Interestingly, all tandem repeat peptides functionally characterized to date share this high avidity to their respective binding targets, suggesting they comprise an unrecognized structural class of peptides with unique structural features that result in a characteristic set of pharmacological properties. In this article, we explore the prevalence of this emerging class of peptides, which we have named Secreted, Cysteine-rich REpeat Peptides, or "SCREPs." To achieve this, we have employed data mining techniques to extract SCREP-like sequences from the UniProtKB database, yielding approximately sixty thousand candidates. These results indicate that SCREPs exist within a diverse range of species with greatly varying sizes and predicted fold types, and likely include peptides with novel structures and unique modes of action. We present our approach to mining this database for discovery of novel ion-channel modulators and discuss a number of "hits" as promising leads for further investigation. Our database of SCREPs thus constitutes a novel resource for biodiscovery and highlights the value of a data-driven approach to the identification of new bioactive pharmacological tools and therapeutic lead molecules.

Dataset Information

Redefining the architecture of ferlin proteins: Insights into multi-domain protein structure and function.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets