\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[english]{babel}



\usepackage{float}








\begin{document}

\title{Estimating protein complex model accuracy based on ultrafast shape
recognition and deep learning in CASP15}



\author[1]{Gui-Jun Zhang}%
\author[1]{Jun Liu}%
\author[1]{Dong Liu}%
\author[1]{* Guangxing}%
\affil[1]{Zhejiang University of Technology College of Information Engineering}%


\vspace{-1em}



  
  \date{May 25, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
This article reports and analyzes the results of protein complex model
accuracy estimation by our methods (DeepUMQA3 and GraphGPSM) in the 15
\textsuperscript{th} Critical Assessment of techniques for protein
Structure Prediction (CASP15). The new deep learning-based multimeric
complex model accuracy estimation methods are proposed based on the
ensemble of three level features coupling with deep residual/graph
neural networks. For the input multimeric complex model, we describe it
from three levels: overall complex features, intra-monomer features, and
inter-monomer features. We designed an overall ultrafast shape
recognition (USR) to characterize the relationship between local
residues and the overall complex topology, and an inter-monomer USR to
characterize the relationship between the residues of one monomer and
the topology of other monomers. On the 39 complex targets of CASP15,
DeepUMQA3 (Group name: GuijunLab-RocketX) ranked first in the assessment
of interface residue accuracy. The Pearson correlation coefficient (PCC)
between the interface residues lDDT predicted by DeepUMQA3 and the real
lDDT is 0.570, and DeepUMQA3 achieved the highest PCC on 29 out of 39
targets. GraphGPSM (Group name: GuijunLab-PAthreader) had a TM-score
PCC\textgreater{}0.9 on 14 targets, showing a good ability to estimate
the overall fold accuracy.%
\end{abstract}%



\sloppy


\textbf{Hosted file}

\verb`manuscript.docx`  available at \url{https://authorea.com/users/607131/articles/635898-estimating-protein-complex-model-accuracy-based-on-ultrafast-shape-recognition-and-deep-learning-in-casp15}

\selectlanguage{english}
\FloatBarrier
\end{document}

