2-AIN-505, 2-AIN-251: Seminár z bioinformatiky (1) a (3)
Zima 2017

Sheng Wang, Siqi Sun, Zhen Li, Renyu Zhang, Jinbo Xu. Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model. PLoS Comput Biol, 13(1):e1005324. 2017.

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MOTIVATION: Protein contacts contain key information for the understanding of
protein structure and function and thus, contact prediction from sequence is an
important problem. Recently exciting progress has been made on this problem, but 
the predicted contacts for proteins without many sequence homologs is still of
low quality and not very useful for de novo structure prediction. METHOD: This
paper presents a new deep learning method that predicts contacts by integrating
both evolutionary coupling (EC) and sequence conservation information through an 
ultra-deep neural network formed by two deep residual neural networks. The first 
residual network conducts a series of 1-dimensional convolutional transformation 
of sequential features; the second residual network conducts a series of
2-dimensional convolutional transformation of pairwise information including
output of the first residual network, EC information and pairwise potential. By
using very deep residual networks, we can accurately model contact occurrence
patterns and complex sequence-structure relationship and thus, obtain
higher-quality contact prediction regardless of how many sequence homologs are
available for proteins in question. RESULTS: Our method greatly outperforms
existing methods and leads to much more accurate contact-assisted folding. Tested
on 105 CASP11 targets, 76 past CAMEO hard targets, and 398 membrane proteins, the
average top L long-range prediction accuracy obtained by our method, one
representative EC method CCMpred and the CASP11 winner MetaPSICOV is 0.47, 0.21
and 0.30, respectively; the average top L/10 long-range accuracy of our method,
CCMpred and MetaPSICOV is 0.77, 0.47 and 0.59, respectively. Ab initio folding
using our predicted contacts as restraints but without any force fields can yield
correct folds (i.e., TMscore>0.6) for 203 of the 579 test proteins, while that
using MetaPSICOV- and CCMpred-predicted contacts can do so for only 79 and 62 of 
them, respectively. Our contact-assisted models also have much better quality
than template-based models especially for membrane proteins. The 3D models built 
from our contact prediction have TMscore>0.5 for 208 of the 398 membrane
proteins, while those from homology modeling have TMscore>0.5 for only 10 of
them. Further, even if trained mostly by soluble proteins, our deep learning
method works very well on membrane proteins. In the recent blind CAMEO benchmark,
our fully-automated web server implementing this method successfully folded 6
targets with a new fold and only 0.3L-2.3L effective sequence homologs, including
one beta protein of 182 residues, one alpha+beta protein of 125 residues, one
alpha protein of 140 residues, one alpha protein of 217 residues, one alpha/beta 
of 260 residues and one alpha protein of 462 residues. Our method also achieved
the highest F1 score on free-modeling targets in the latest CASP (Critical
Assessment of Structure Prediction), although it was not fully implemented back
then. AVAILABILITY: http://raptorx.uchicago.edu/ContactMap/.