title: Neural Potts Model
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.
authors: Tom Sercu Robert Verkuil Joshua Meier Brandon Amos Zeming Lin Caroline Chen Jason Liu Yann LeCun Alexander Rives
hIndex: Yann LeCun 120