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\begin{document}

\title{Improving protein tertiary structure prediction by deep learning and
distance prediction in CASP14}



\author[1]{Jian Liu}%
\author[2]{Tianqi Wu}%
\author[1]{Zhiye Guo}%
\author[3]{Jie Hou}%
\author[1]{Jianlin Cheng}%
\affil[1]{University of Missouri}%
\affil[2]{University of Missouri - Columbia}%
\affil[3]{Saint Louis University}%


\vspace{-1em}



  
  \date{May 26, 2024}


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\begin{abstract}
Substantial progresses in protein structure prediction have been made by
utilizing deep-learning and residue-residue distance prediction since
CASP13. Inspired by the advances, we improve our CASP14 MULTICOM protein
structure prediction system in three main aspects: (1) a new deep
learning based protein inter-residue distance predictor (DeepDist) to
improve template-free (ab initio) tertiary structure prediction, (2) an
enhanced template-based tertiary structure prediction method, and (3)
distance-based model quality assessment methods empowered by deep
learning. In the 2020 CASP14 experiment, MULTICOM predictor was ranked
7th out of 146 predictors in protein tertiary structure prediction and
ranked 3rd out of 136 predictors in inter-domain structure predic-tion.
The results of MULTICOM demonstrate that the template-free modeling
based on deep learning and residue-residue distance prediction can
predict the correct topology for almost all template-based modeling
targets and a majority of hard targets (template-free targets or targets
whose templates cannot be recognized), which is a significant
improvement over the CASP13 MULTICOM predictor. The performance of
template-free tertiary structure prediction largely depends on the
accuracy of distance pre-dictions that is closely related to the quality
of multiple sequence alignments. The structural model quality assessment
works reasonably well on targets for which a sufficient number of good
models can be predicted, but may perform poorly when only a few good
models are predicted for a hard target and the distribution of model
quality scores is highly skewed.%
\end{abstract}%



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