RT Journal Article
SR Electronic
T1 Neural Potts Model
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.04.08.439084
DO 10.1101/2021.04.08.439084
A1 Tom Sercu
A1 Robert Verkuil
A1 Joshua Meier
A1 Brandon Amos
A1 Zeming Lin
A1 Caroline Chen
A1 Jason Liu
A1 Yann LeCun
A1 Alexander Rives
YR 2021
UL http://biorxiv.org/content/early/2021/04/11/2021.04.08.439084.abstract
AB 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 StatementThe authors have declared no competing interest.