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

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 Grammatical-Restrained Hidden Conditional Random Field for CCD identification and refinement. A final neural network predicts the oligomerization state. When tested on a blind test set routinely adopted, CoCoNat obtains a performance superior to the current state-of-the-art both for residue-level and segment-level CCD. CoCoNat significantly outperforms the most recent state-of-the-art methods on register annotation and prediction of oligomerization states. Availability and implementation: CoCoNat web server is available at https://coconat.biocomp.unibo.it. Standalone version is available on GitHub at https://github.com/BolognaBiocomp/coconat.

Automated summary

This article introduces a novel method called CoCoNat for predicting coiled-coil helix boundaries, residue-level register annotation, and oligomerization state. The method combines two state-of-the-art protein language models and implements a deep learning procedure with a Grammatical-Restrained Hidden Conditional Random Field. CoCoNat outperforms current state-of-the-art methods in residue-level and segment-level CCD, as well as in register annotation and prediction of oligomerization states.