Computational Screening of Phase-separating Proteins
Phase separation is an important mechanism that mediates the compartmentalization of proteins in cells. Proteins that can undergo phase separation in cells share certain typical sequence features, like intrinsically disordered regions (IDRs) and multiple modular domains. Sequence-based analysis tool...
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Published in | Genomics, proteomics & bioinformatics Vol. 19; no. 1; pp. 13 - 24 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
Published |
Elsevier B.V
01.02.2021
Department of Biomedical Informatics,School of Basic Medical Sciences,Peking University Health Science Center,Beijing 100191,China%Department of Biomedical Informatics,School of Basic Medical Sciences,Peking University Health Science Center,Beijing 100191,China Institute of Systems Biomedicine,School of Basic Medical Sciences,Peking University Health Science Center,Beijing 100191,China%MOE Key Laboratory of Bioinformatics,Bioinformatics Division and Center for Synthetic&Systems Biology,BNRist,School of Medicine,Tsinghua University,Beijing 100084,China Elsevier |
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Abstract | Phase separation is an important mechanism that mediates the compartmentalization of proteins in cells. Proteins that can undergo phase separation in cells share certain typical sequence features, like intrinsically disordered regions (IDRs) and multiple modular domains. Sequence-based analysis tools are commonly used in the screening of these proteins. However, current phase separation predictors are mostly designed for IDR-containing proteins, thus inevitably overlook the phase-separating proteins with relatively low IDR content. Features other than amino acid sequence could provide crucial information for identifying possible phase-separating proteins: protein–protein interaction (PPI) networks show multivalent interactions that underlie phase separation process; post-translational modifications (PTMs) are crucial in the regulation of phase separation behavior; spherical structures revealed in immunofluorescence (IF)images indicate condensed droplets formed by phase-separating proteins, distinguishing these proteins from non-phase-separating proteins. Here, we summarize the sequence-based tools for predicting phase-separating proteins and highlight the importance of incorporating PPIs, PTMs, and IF images into phase separation prediction in future studies. |
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AbstractList | Phase separation is an important mechanism that mediates the compartmentalization of proteins in cells. Proteins that can undergo phase separation in cells share certain typical sequence features, like intrinsically disordered regions (IDRs) and multiple modular domains. Sequence-based analysis tools are commonly used in the screening of these proteins. However, current phase separation predictors are mostly designed for IDR-containing proteins, thus inevitably overlook the phase-separating proteins with relatively low IDR content. Features other than amino acid sequence could provide crucial information for identifying possible phase-separating proteins: protein–protein interaction (PPI) networks show multivalent interactions that underlie phase separation process; post-translational modifications (PTMs) are crucial in the regulation of phase separation behavior; spherical structures revealed in immunofluorescence (IF)images indicate condensed droplets formed by phase-separating proteins, distinguishing these proteins from non-phase-separating proteins. Here, we summarize the sequence-based tools for predicting phase-separating proteins and highlight the importance of incorporating PPIs, PTMs, and IF images into phase separation prediction in future studies. Phase separation is an important mechanism that mediates the compartmentalization of proteins in cells. Proteins that can undergo phase separation in cells share certain typical sequence features, like intrinsically disordered regions (IDRs) and multiple modular domains. Sequence-based analysis tools are commonly used in the screening of these proteins. However, current phase separation predictors are mostly designed for IDR-containing proteins, thus inevitably overlook the phase-separating proteins with relatively low IDR content. Features other than amino acid sequence could provide crucial information for identifying possible phase-separating proteins:pro-tein–protein interaction (PPI) networks show multivalent interactions that underlie phase separation process; post-translational modifications (PTMs) are crucial in the regulation of phase separation behavior;spherical structures revealed in immunofluorescence (IF) images indicate condensed dro-plets formed by phase-separating proteins, distinguishing these proteins from non-phase-separating proteins. Here, we summarize the sequence-based tools for predicting phase-separating proteins and highlight the importance of incorporating PPIs, PTMs, and IF images into phase separation prediction in future studies. Phase separation is an important mechanism that mediates the compartmentalization of proteins in cells. Proteins that can undergo phase separation in cells share certain typical sequence features, like intrinsically disordered regions (IDRs) and multiple modular domains. Sequence-based analysis tools are commonly used in the screening of these proteins. However, current phase separation predictors are mostly designed for IDR-containing proteins, thus inevitably overlook the phase-separating proteins with relatively low IDR content. Features other than amino acid sequence could provide crucial information for identifying possible phase-separating proteins: protein–protein interaction (PPI) networks show multivalent interactions that underlie phase separation process; post-translational modifications (PTMs) are crucial in the regulation of phase separation behavior; spherical structures revealed in immunofluorescence (IF) images indicate condensed droplets formed by phase-separating proteins, distinguishing these proteins from non-phase-separating proteins. Here, we summarize the sequence-based tools for predicting phase-separating proteins and highlight the importance of incorporating PPIs, PTMs, and IF images into phase separation prediction in future studies. |
Author | Chen, Taoyu Li, Tingting Chen, Zhaoming Yu, Chunyu Shen, Boyan Shi, Minglei |
AuthorAffiliation | Department of Biomedical Informatics,School of Basic Medical Sciences,Peking University Health Science Center,Beijing 100191,China%Department of Biomedical Informatics,School of Basic Medical Sciences,Peking University Health Science Center,Beijing 100191,China;Institute of Systems Biomedicine,School of Basic Medical Sciences,Peking University Health Science Center,Beijing 100191,China%MOE Key Laboratory of Bioinformatics,Bioinformatics Division and Center for Synthetic&Systems Biology,BNRist,School of Medicine,Tsinghua University,Beijing 100084,China |
AuthorAffiliation_xml | – name: Department of Biomedical Informatics,School of Basic Medical Sciences,Peking University Health Science Center,Beijing 100191,China%Department of Biomedical Informatics,School of Basic Medical Sciences,Peking University Health Science Center,Beijing 100191,China;Institute of Systems Biomedicine,School of Basic Medical Sciences,Peking University Health Science Center,Beijing 100191,China%MOE Key Laboratory of Bioinformatics,Bioinformatics Division and Center for Synthetic&Systems Biology,BNRist,School of Medicine,Tsinghua University,Beijing 100084,China |
Author_xml | – sequence: 1 givenname: Boyan orcidid: 0000-0002-1574-1958 surname: Shen fullname: Shen, Boyan organization: Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing 100191, China – sequence: 2 givenname: Zhaoming orcidid: 0000-0002-6810-5445 surname: Chen fullname: Chen, Zhaoming organization: Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing 100191, China – sequence: 3 givenname: Chunyu orcidid: 0000-0002-3519-1332 surname: Yu fullname: Yu, Chunyu organization: Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing 100191, China – sequence: 4 givenname: Taoyu orcidid: 0000-0002-8966-1264 surname: Chen fullname: Chen, Taoyu organization: Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing 100191, China – sequence: 5 givenname: Minglei orcidid: 0000-0002-1108-4233 surname: Shi fullname: Shi, Minglei organization: MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist, School of Medicine, Tsinghua University, Beijing 100084, China – sequence: 6 givenname: Tingting orcidid: 0000-0003-4266-0317 surname: Li fullname: Li, Tingting email: litt@hsc.pku.edu.cn organization: Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing 100191, China |
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Keywords | Immunofluorescence image Protein post-translational modification Phase separation Protein–protein interaction Prediction |
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Snippet | Phase separation is an important mechanism that mediates the compartmentalization of proteins in cells. Proteins that can undergo phase separation in cells... Phase separation is an important mechanism that mediates the compartmentalization of proteins in cells. Proteins that can undergo phase separation in cells... |
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SubjectTerms | amino acid sequences bioinformatics fluorescent antibody technique genomics Immunofluorescence image Phase separation Prediction Protein post-translational modification protein-protein interactions Protein–protein interaction proteomics Review separation |
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