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bioRxiv · 10.1101/2021.04.08.439084

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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Abstract

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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Sercu, T., Verkuil, R., Meier, J., Amos, B., Lin, Z., Chen, C., Liu, J., LeCun, Y., Rives, A.. 2021-04-11. Neural Potts Model. https://doi.org/10.1101/2021.04.08.439084

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