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Virtually every molecular biologist has searched a protein or DNA sequence database to find sequences that are evolutionarily related to a given query. Pairwise sequence comparison methods—i.e., measures of similarity between query and target sequences—provide the engine for sequence database search and have been the subject of 30 years of computational research. For the difficult problem of detecting remote evolutionary relationships between protein sequences, the most successful pairwise comparison methods involve building  local  models (e.g., profile hidden Markov models) of protein sequences. However, recent work in massive data domains like web search and natural language processing demonstrate the advantage of exploiting the  global  structure of the data space. Motivated by this work, we present a large-scale algorithm called P rot E mbed , which learns an embedding of protein sequences into a low-dimensional “semantic space.” Evolutionarily related proteins are embedded in close proximity, and additional pieces of evidence, such as 3D structural similarity or class labels, can be incorporated into the learning process. We find that P rot E mbed  achieves superior accuracy to widely used pairwise sequence methods like PSI-BLAST and HHSearch for remote homology detection; it also outperforms our previous R ank P rop  algorithm, which incorporates global structure in the form of a protein similarity network. Finally, the P rot E mbed  embedding space can be visualized, both at the global level and local to a given query, yielding intuition about the structure of protein sequence space.

Detecting Remote Evolutionary Relationships among Proteins by Large-Scale Semantic Embedding