We then develop a method of learning generative models of amino acid composition of evolutionarily related protein families (GREMLIN) that captures dependencies between sequential and long-range pairs of positions in the protein. GREMLIN is vastly more accurate than existing statistical models based on Hidden Markov Models; by effectively utilizing a distributed map-reduce framework, it also presents a scalable alternative to these extant approaches. 
