Reference-dependent alignment precision on Set2.6K.

<p>See <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003500#pcbi-1003500-t004" target="_blank">Table 4</a> for explanation.</p

Homology detection performance for mainly beta proteins at the fold level.

<p>Homology detection performance for mainly beta proteins at the fold level.</p

Model a multiple sequence alignment (left) by a Markov Random Fields (right).

<p>Model a multiple sequence alignment (left) by a Markov Random Fields (right).</p

The model quality, measured by TM-score, of our method and HHpred for the 36 CASP10 hard targets.

<p>One point represents two models generated by our method (x-axis) and HHpred (y-axis).</p

Reference-dependent alignment recall on Set2.6K.

<p>See <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003500#pcbi-1003500-t001" target="_blank">Table 1</a> for explanation.</p

Homology detection performance at the fold level.

<p>Homology detection performance at the fold level.</p

Contribution of edge alignment potential and mutual information, measured by alignment recall improvement on two benchmarks Set3.6K and Set2.6K.

<p>The structure alignments generated by DeepAlign are used as reference alignments.</p

Homology detection performance for mainly beta proteins at the superfamily level.

<p>Homology detection performance for mainly beta proteins at the superfamily level.</p

Homology detection performance at the superfamily level.

<p>Homology detection performance at the superfamily level.</p

Reference-dependent alignment recall on Set3.6K.

<p>Three structure alignment tools (TMalign, Matt and DeepAlign) are used to generate reference alignments. “4-offset” means that 4-position off the exact match is allowed. The bold indicates the best results.</p