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Yu K, Liu Z, Cheng H, Li S, Zhang Q, Liu J, Ju HQ, Zuo Z, Zhao Q, Kang S, Liu ZX. dSCOPE: a software to detect sequences critical for liquid-liquid phase separation. Brief Bioinform 2023; 24:6927233. [PMID: 36528388 DOI: 10.1093/bib/bbac550] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 10/26/2022] [Accepted: 11/12/2022] [Indexed: 12/23/2022] Open
Abstract
Membrane-based cells are the fundamental structural and functional units of organisms, while evidences demonstrate that liquid-liquid phase separation (LLPS) is associated with the formation of membraneless organelles, such as P-bodies, nucleoli and stress granules. Many studies have been undertaken to explore the functions of protein phase separation (PS), but these studies lacked an effective tool to identify the sequence segments that critical for LLPS. In this study, we presented a novel software called dSCOPE (http://dscope.omicsbio.info) to predict the PS-driving regions. To develop the predictor, we curated experimentally identified sequence segments that can drive LLPS from published literature. Then sliding sequence window based physiological, biochemical, structural and coding features were integrated by random forest algorithm to perform prediction. Through rigorous evaluation, dSCOPE was demonstrated to achieve satisfactory performance. Furthermore, large-scale analysis of human proteome based on dSCOPE showed that the predicted PS-driving regions enriched various protein post-translational modifications and cancer mutations, and the proteins which contain predicted PS-driving regions enriched critical cellular signaling pathways. Taken together, dSCOPE precisely predicted the protein sequence segments critical for LLPS, with various helpful information visualized in the webserver to facilitate LLPS-related research.
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Affiliation(s)
- Kai Yu
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Zekun Liu
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Haoyang Cheng
- Department of Computer Science, The University of Hong Kong, Hong Kong 999077, China
| | - Shihua Li
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Qingfeng Zhang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Jia Liu
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Huai-Qiang Ju
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Zhixiang Zuo
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Qi Zhao
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Shiyang Kang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Ze-Xian Liu
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
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Wang L, Zhang J, Wang D, Song C. Membrane contact probability: An essential and predictive character for the structural and functional studies of membrane proteins. PLoS Comput Biol 2022; 18:e1009972. [PMID: 35353812 PMCID: PMC9000120 DOI: 10.1371/journal.pcbi.1009972] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 04/11/2022] [Accepted: 02/25/2022] [Indexed: 11/20/2022] Open
Abstract
One of the unique traits of membrane proteins is that a significant fraction of their hydrophobic amino acids is exposed to the hydrophobic core of lipid bilayers rather than being embedded in the protein interior, which is often not explicitly considered in the protein structure and function predictions. Here, we propose a characteristic and predictive quantity, the membrane contact probability (MCP), to describe the likelihood of the amino acids of a given sequence being in direct contact with the acyl chains of lipid molecules. We show that MCP is complementary to solvent accessibility in characterizing the outer surface of membrane proteins, and it can be predicted for any given sequence with a machine learning-based method by utilizing a training dataset extracted from MemProtMD, a database generated from molecular dynamics simulations for the membrane proteins with a known structure. As the first of many potential applications, we demonstrate that MCP can be used to systematically improve the prediction precision of the protein contact maps and structures. The distribution of residues on protein surfaces is largely determined by the surrounding environment. For soluble proteins, most of the residues on the outer surface are hydrophilic, and people use the quantity “solvent accessibility” to describe and predict these surface residues. In contrast, for membrane proteins that are embedded in a lipid bilayer, many of their surface residues are hydrophobic and membrane-contacting, but there is yet a widely-accepted quantity for the description or prediction of this characteristic property. Here, we propose a new quantity termed “membrane contact probability (MCP)”, which can be used to describe and predict the membrane-contacting surface residues of proteins. We also propose a machine learning-based method to predict MCP from protein sequences, utilizing the dataset generated by physics-based computer simulations. We demonstrate that a quantity such as MCP is helpful for protein structure prediction, and we believe that it will find broad applications in the structure and function studies of membrane proteins.
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Affiliation(s)
- Lei Wang
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary studies, Peking University, Beijing, China
| | - Jiangguo Zhang
- School of Life Sciences, Peking University, Beijing, China
| | - Dali Wang
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary studies, Peking University, Beijing, China
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Chen Song
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary studies, Peking University, Beijing, China
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- * E-mail:
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Mulnaes D, Schott-Verdugo S, Koenig F, Gohlke H. TopProperty: Robust Metaprediction of Transmembrane and Globular Protein Features Using Deep Neural Networks. J Chem Theory Comput 2021; 17:7281-7289. [PMID: 34663069 DOI: 10.1021/acs.jctc.1c00685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Transmembrane proteins (TMPs) are critical components of cellular life. However, due to experimental challenges, the number of experimentally resolved TMP structures is severely underrepresented in databases compared to their cellular abundance. Prediction of (per-residue) features such as transmembrane topology, membrane exposure, secondary structure, and solvent accessibility can be a useful starting point for experimental design or protein structure prediction but often requires different computational tools for different features or types of proteins. We present TopProperty, a metapredictor that predicts all of these features for TMPs or globular proteins. TopProperty is trained on datasets without bias toward a high number of sequence homologs, and the predictions are significantly better than the evaluated state-of-the-art primary predictors on all quality metrics. TopProperty eliminates the need for protein type- or feature-tailored tools, specifically for TMPs. TopProperty is freely available as a web server and standalone at https://cpclab.uni-duesseldorf.de/topsuite/.
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Affiliation(s)
- Daniel Mulnaes
- Institut für Pharmazeutische und Medizinische Chemie, Heinrich-Heine-Universität Düsseldorf, Düsseldorf 40225, Germany
| | - Stephan Schott-Verdugo
- John von Neumann Institute for Computing (NIC), Jülich Supercomputing Centre (JSC), Institute of Biological Information Processing (IBI-7: Structural Bioinformatics), and Institute of Bio- and Geosciences (IBG-4: Bioinformatics), Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Str., Jülich 52425, Germany
| | - Filip Koenig
- Institut für Pharmazeutische und Medizinische Chemie, Heinrich-Heine-Universität Düsseldorf, Düsseldorf 40225, Germany
| | - Holger Gohlke
- Institut für Pharmazeutische und Medizinische Chemie, Heinrich-Heine-Universität Düsseldorf, Düsseldorf 40225, Germany.,John von Neumann Institute for Computing (NIC), Jülich Supercomputing Centre (JSC), Institute of Biological Information Processing (IBI-7: Structural Bioinformatics), and Institute of Bio- and Geosciences (IBG-4: Bioinformatics), Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Str., Jülich 52425, Germany
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