1 research outputs found
Predicting membrane protein contacts from non-membrane proteins by deep transfer learning
Computational prediction of membrane protein (MP) structures is very
challenging partially due to lack of sufficient solved structures for homology
modeling. Recently direct evolutionary coupling analysis (DCA) sheds some light
on protein contact prediction and accordingly, contact-assisted folding, but
DCA is effective only on some very large-sized families since it uses
information only in a single protein family. This paper presents a deep
transfer learning method that can significantly improve MP contact prediction
by learning contact patterns and complex sequence-contact relationship from
thousands of non-membrane proteins (non-MPs). Tested on 510 non-redundant MPs,
our deep model (learned from only non-MPs) has top L/10 long-range contact
prediction accuracy 0.69, better than our deep model trained by only MPs (0.63)
and much better than a representative DCA method CCMpred (0.47) and the CASP11
winner MetaPSICOV (0.55). The accuracy of our deep model can be further
improved to 0.72 when trained by a mix of non-MPs and MPs. When only contacts
in transmembrane regions are evaluated, our method has top L/10 long-range
accuracy 0.62, 0.57, and 0.53 when trained by a mix of non-MPs and MPs, by
non-MPs only, and by MPs only, respectively, still much better than MetaPSICOV
(0.45) and CCMpred (0.40). All these results suggest that sequence-structure
relationship learned by our deep model from non-MPs generalizes well to MP
contact prediction. Improved contact prediction also leads to better
contact-assisted folding. Using only top predicted contacts as restraints, our
deep learning method can fold 160 and 200 of 510 MPs with TMscore>0.6 when
trained by non-MPs only and by a mix of non-MPs and MPs, respectively, while
CCMpred and MetaPSICOV can do so for only 56 and 77 MPs, respectively. Our
contact-assisted folding also greatly outperforms homology modeling