Abstract
We propose the Neural Potts Model objective as an amortized optimization problem. The objective enables training a single model with shared parameters to explicitly model energy landscapes across multiple protein families. Given a protein sequence as input, the model is trained to predict a pairwise coupling matrix for a Potts model energy function describing the local evolutionary landscape of the sequence. Couplings can be predicted for novel sequences. A controlled ablation experiment assessing unsupervised contact prediction on sets of related protein families finds a gain from amortization for low-depth multiple sequence alignments; the result is then confirmed on a database with broad coverage of protein sequences.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
tsercubda{at}fb.com,rverkuilbda{at}fb.com,jmeierbda{at}fb.com
zl2799{at}nyu.edu
carolinechen{at}fb.com
jasonliu{at}fb.com
yann,arives{at}cs.nyu.edu
↵† Work performed while at Facebook AI Research