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\begin{document}

\title{Multi-head attention-based U-Nets for predicting protein domain
boundaries using 1D sequence features and 2D distance maps}



\author[1]{Sajid Mahmud}%
\author[1]{Zhiye Guo}%
\author[1]{Farhan Quadir}%
\author[1]{Jian Liu}%
\author[1]{Jianlin Cheng}%
\affil[1]{University of Missouri}%


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  \date{June 16, 2024}


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\begin{abstract}
The information about the domain architecture of proteins is useful for
studying protein structure and function. However, accurate prediction of
protein domain boundaries (i.e., sequence regions separating two
domains) from sequence remains a significant challenge. In this work, we
develop a deep learning method based on multi-head U-Nets (called
DistDom) to predict protein domain boundaries utilizing 1D sequence
features and predicted 2D inter-residue distance map as input. The 1D
features contain the evolutionary and physicochemical information of
protein sequences, whereas the 2D distance map includes the structural
information of proteins that was rarely used in domain boundary
prediction before. The 1D and 2D features are processed by the 1D and 2D
U-Nets respectively to generate hidden features. The hidden features are
then used by the multi-head attention to predict the probability of each
residue of a protein being in a domain boundary, leveraging both local
and global information in the features. The residue-level domain
boundary predictions can be used to classify proteins as single-domain
or multi-domain proteins. It classifies the CASP14 single-domain and
multi-domain targets at the accuracy of 72.7\%, 8.02\% more accurate
than the state-of-the-art method. Tested on the CASP14 multi-domain
protein targets with expert annotated domain boundaries, the average
per-target F1 measure score of the domain boundary prediction by DistDom
is 0.241, 18.72\% higher than the state-of-the-art method.%
\end{abstract}%



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\textbf{Hosted file}

\verb`DistDom_v5.docx`  available at \url{https://authorea.com/users/477406/articles/566029-multi-head-attention-based-u-nets-for-predicting-protein-domain-boundaries-using-1d-sequence-features-and-2d-distance-maps}\selectlanguage{english}
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