Neural Potts Model
Sercu, T.; Verkuil, R.; Meier, J.; Amos, B.; Lin, Z.; Chen, C.; Liu, J.; LeCun, Y.; Rives, A.
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AO_SCPLOWBSTRACTC_SCPLOWWe 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.
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