Detecting Remote Evolutionary Relationships among Proteins by Large-Scale Semantic Embedding
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 sear...
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| Published in | PLoS computational biology Vol. 7; no. 1; p. e1001047 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
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United States
Public Library of Science
01.01.2011
Public Library of Science (PLoS) |
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| Abstract | 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. |
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| AbstractList | 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. 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. Searching a protein or DNA sequence database to find sequences that are evolutionarily related to a query is one of the foundational problems in computational biology. These database searches rely on pairwise comparisons of sequence similarity between the query and targets, but despite years of method refinements, pairwise comparisons still often fail to detect more distantly related targets. In this study, we adapt recent work from natural language processing to exploit the global structure of the data space in this detection problem. In particular, we borrow the idea of a semantic embedding, where by training on a large text data set, one learns an embedding of words into a low-dimensional semantic space such that words embedded close to each other are likely to be semantically related. We present the ProtEmbed algorithm, which learns an embedding of protein sequences into a semantic space where evolutionarily-related proteins are embedded in close proximity. The flexible training algorithm allows additional pieces of evidence, such as 3D structural information, to be incorporated in the learning process and enables ProtEmbed to achieve state-of-the-art performance for the task of detecting targets that have remote evolutionary relationships to the query. 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.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. 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. 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. Searching a protein or DNA sequence database to find sequences that are evolutionarily related to a query is one of the foundational problems in computational biology. These database searches rely on pairwise comparisons of sequence similarity between the query and targets, but despite years of method refinements, pairwise comparisons still often fail to detect more distantly related targets. In this study, we adapt recent work from natural language processing to exploit the global structure of the data space in this detection problem. In particular, we borrow the idea of a semantic embedding, where by training on a large text data set, one learns an embedding of words into a low-dimensional semantic space such that words embedded close to each other are likely to be semantically related. We present the ProtEmbed algorithm, which learns an embedding of protein sequences into a semantic space where evolutionarily-related proteins are embedded in close proximity. The flexible training algorithm allows additional pieces of evidence, such as 3D structural information, to be incorporated in the learning process and enables ProtEmbed to achieve state-of-the-art performance for the task of detecting targets that have remote evolutionary relationships to the query. |
| Audience | Academic |
| Author | Noble, William Stafford Leslie, Christina Melvin, Iain Weston, Jason |
| AuthorAffiliation | Stanford University, United States of America 1 NEC Laboratories America, Princeton, New Jersey, United States of America 4 Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America 3 Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America 2 Google, New York, New York, United States of America |
| AuthorAffiliation_xml | – name: 1 NEC Laboratories America, Princeton, New Jersey, United States of America – name: 2 Google, New York, New York, United States of America – name: Stanford University, United States of America – name: 3 Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America – name: 4 Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America |
| Author_xml | – sequence: 1 givenname: Iain surname: Melvin fullname: Melvin, Iain – sequence: 2 givenname: Jason surname: Weston fullname: Weston, Jason – sequence: 3 givenname: William Stafford surname: Noble fullname: Noble, William Stafford – sequence: 4 givenname: Christina surname: Leslie fullname: Leslie, Christina |
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| Cites_doi | 10.1093/bioinformatics/btq034 10.1093/bioinformatics/btm358 10.1093/bioinformatics/btn567 10.1073/pnas.0308067101 10.1016/S0022-2836(05)80134-2 10.1093/bioinformatics/btp452 10.1016/S0022-2836(05)80360-2 10.1111/1467-9868.00346 10.1111/j.2517-6161.1995.tb02031.x 10.1093/nar/25.17.3389 10.1093/nar/gki096 10.1093/nar/gki408 10.1110/ps.0215902 10.1110/ps.9.2.232 10.1016/0022-2836(81)90087-5 10.1093/nar/28.1.254 |
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| Copyright | COPYRIGHT 2011 Public Library of Science Melvin et al. 2011 2011 Melvin et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Melvin I, Weston J, Noble WS, Leslie C (2011) Detecting Remote Evolutionary Relationships among Proteins by Large-Scale Semantic Embedding. PLoS Comput Biol 7(1): e1001047. doi:10.1371/journal.pcbi.1001047 |
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| Keywords | Biological Evolution Proteins Algorithms Sequence Analysis, DNA |
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| Snippet | Virtually every molecular biologist has searched a protein or DNA sequence database to find sequences that are evolutionarily related to a given query.... Virtually every molecular biologist has searched a protein or DNA sequence database to find sequences that are evolutionarily related to a given query.... |
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| StartPage | e1001047 |
| SubjectTerms | Algorithms Bioinformatics Biological Evolution Computational Biology/Protein Homology Detection DNA Markov processes Methods Neighborhoods Physiological aspects Proteins Proteins - chemistry Proteins - genetics Semantics Sequence Analysis, DNA Studies |
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| Title | Detecting Remote Evolutionary Relationships among Proteins by Large-Scale Semantic Embedding |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/21298082 https://www.proquest.com/docview/1872839943 https://www.proquest.com/docview/850560910 https://pubmed.ncbi.nlm.nih.gov/PMC3029239 https://doaj.org/article/2a277e17bdbd4dd5add8156cd8a2073e http://dx.doi.org/10.1371/journal.pcbi.1001047 |
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