Detecting Remote Evolutionary Relationships among Proteins by Large-Scale Semantic Embedding
By Iain Melvin, Jason Aaron Edward Weston, William Stafford Noble, Christina S. Leslie. 2011
33 citations recorded by the source corpus.
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 ProtEmbed, 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 ProtEmbed achieves superior accuracy to widely used pairwise sequence methods like PSI-BLAST and HHSearch for remote homology detection; it also outperforms our previous RankProp algorithm, which incorporates global structure in the form of a protein similarity network. Finally, the ProtEmbed 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.
Subfields: Molecular Biology
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In Molecular Biology
- Michael Remmert
- Bin Liu
- Junjie Chen
- Johannes Söding
- Bonnie Berger
- Jinbo Xu
- Xiaolong Wang
- Mingyue Guo
Citations in the corpus
- Learning protein sequence embeddings using information from structure (2019, background): “[13] also learn fixed sized semantic embeddings by projecting alignment scores into a low dimensional vector to recapitulate rankings given by existing alignment tools and shared superfamily membership.”
- Deep Semantic Protein Representation for Annotation, Discovery, and Engineering (2018, background): “Previous work has shown that remote homology searches can be accelerated when operating in a compressed embedding space learned from known sequence similarities 25–28 .”
- Protein sequence comparison and fold recognition: progress and good-practice benchmarking. (2011, methodology): “Two recent algorithms, RANKPROP[51,52] and ProtEmbed [53 ], are independent of any fold definitions.”
- A comprehensive review and comparison of different computational methods for protein remote homology detection (2018, methodology): “Therefore, they are evaluated on two widely used benchmark data sets, SCOP-D [2, 18] for discriminative methods (Table 2), and SCOP-O [24, 98] for the other computational methods (Table 3).”