CoCoNat: a novel method based on deep learning for coiled-coil prediction.

ABSTRACT:

Motivation

Coiled-coil domains (CCD) are widespread in all organisms and perform several crucial functions. Given their relevance, the computational detection of CCD is very important for protein functional annotation. State-of-the-art prediction methods include the precise identification of CCD boundaries, the annotation of the typical heptad repeat pattern along the coiled-coil helices as well as the prediction of the oligomerization state.

Results

In this article, we describe CoCoNat, a novel method for predicting coiled-coil helix boundaries, residue-level register annotation, and oligomerization state. Our method encodes sequences with the combination of two state-of-the-art protein language models and implements a three-step deep learning procedure concatenated with a

SUBMITTER: Madeo G 

PROVIDER: S-EPMC10425188 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature