  The functions of proteins and RNAs are determined by a myriad of interactions
between their constituent residues, but most quantitative models of how
molecular phenotype depends on genotype must approximate this by simple
additive effects. While recent models have relaxed this constraint to also
account for pairwise interactions, these approaches do not provide a tractable
path towards modeling higher-order dependencies. Here, we show how latent
variable models with nonlinear dependencies can be applied to capture
beyond-pairwise constraints in biomolecules. We present a new probabilistic
model for sequence families, DeepSequence, that can predict the effects of
mutations across a variety of deep mutational scanning experiments
significantly better than site independent or pairwise models that are based on
the same evolutionary data. The model, learned in an unsupervised manner solely
from sequence information, is grounded with biologically motivated priors,
reveals latent organization of sequence families, and can be used to
extrapolate to new parts of sequence space
