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Ruan K, Bai G, Fang Y, Li D, Li T, Liu X, Lu B, Lu Q, Songyang Z, Sun S, Wang Z, Zhang X, Zhou W, Zhang H. Biomolecular condensates and disease pathogenesis. SCIENCE CHINA. LIFE SCIENCES 2024; 67:1792-1832. [PMID: 39037698 DOI: 10.1007/s11427-024-2661-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2024] [Accepted: 06/21/2024] [Indexed: 07/23/2024]
Abstract
Biomolecular condensates or membraneless organelles (MLOs) formed by liquid-liquid phase separation (LLPS) divide intracellular spaces into discrete compartments for specific functions. Dysregulation of LLPS or aberrant phase transition that disturbs the formation or material states of MLOs is closely correlated with neurodegeneration, tumorigenesis, and many other pathological processes. Herein, we summarize the recent progress in development of methods to monitor phase separation and we discuss the biogenesis and function of MLOs formed through phase separation. We then present emerging proof-of-concept examples regarding the disruption of phase separation homeostasis in a diverse array of clinical conditions including neurodegenerative disorders, hearing loss, cancers, and immunological diseases. Finally, we describe the emerging discovery of chemical modulators of phase separation.
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Affiliation(s)
- Ke Ruan
- The First Affiliated Hospital & School of Life Sciences, Ministry of Education Key Laboratory for Membrane-less Organelles & Cellular Dynamics, Hefei National Research Center for Interdisciplinary Sciences at the Microscale, Center for Advanced Interdisciplinary Science and Biomedicine of IHM, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230027, China.
| | - Ge Bai
- Nanhu Brain-computer Interface Institute, Hangzhou, 311100, China.
- Department of Neurology of Second Affiliated Hospital and School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China.
| | - Yanshan Fang
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 201210, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Dan Li
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, 200030, China.
| | - Tingting Li
- Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, 100191, China.
| | - Xingguo Liu
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China.
- Joint School of Life Sciences, Guangzhou Medical University, Guangzhou, 510000, China.
| | - Boxun Lu
- Neurology Department at Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, School of Life Sciences, Fudan University, Shanghai, 200433, China.
| | - Qing Lu
- Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Bio-X Institutes, Shanghai Jiao Tong University, Shanghai, 200030, China.
| | - Zhou Songyang
- State Key Laboratory of Biocontrol, MOE Key Laboratory of Gene Function and Regulation and Guangzhou Key Laboratory of Healthy Aging Research, School of Life Sciences, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510275, China.
| | - Shuguo Sun
- Department of Human Anatomy, Histology and Embryology, School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
| | - Zheng Wang
- The Second Affiliated Hospital, School of Basic Medical Sciences, Institute of Biomedical Innovation, Jiangxi Medical College, Nanchang University, Nanchang, 330031, China.
| | - Xin Zhang
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, 310024, China.
| | - Wen Zhou
- Department of Immunology and Microbiology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, 518055, China.
| | - Hong Zhang
- National Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China.
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China.
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Holehouse AS, Kragelund BB. The molecular basis for cellular function of intrinsically disordered protein regions. Nat Rev Mol Cell Biol 2024; 25:187-211. [PMID: 37957331 DOI: 10.1038/s41580-023-00673-0] [Citation(s) in RCA: 55] [Impact Index Per Article: 55.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/26/2023] [Indexed: 11/15/2023]
Abstract
Intrinsically disordered protein regions exist in a collection of dynamic interconverting conformations that lack a stable 3D structure. These regions are structurally heterogeneous, ubiquitous and found across all kingdoms of life. Despite the absence of a defined 3D structure, disordered regions are essential for cellular processes ranging from transcriptional control and cell signalling to subcellular organization. Through their conformational malleability and adaptability, disordered regions extend the repertoire of macromolecular interactions and are readily tunable by their structural and chemical context, making them ideal responders to regulatory cues. Recent work has led to major advances in understanding the link between protein sequence and conformational behaviour in disordered regions, yet the link between sequence and molecular function is less well defined. Here we consider the biochemical and biophysical foundations that underlie how and why disordered regions can engage in productive cellular functions, provide examples of emerging concepts and discuss how protein disorder contributes to intracellular information processing and regulation of cellular function.
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Affiliation(s)
- Alex S Holehouse
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St Louis, MO, USA.
- Center for Biomolecular Condensates, Washington University in St Louis, St Louis, MO, USA.
| | - Birthe B Kragelund
- REPIN, Structural Biology and NMR Laboratory, Department of Biology, University of Copenhagen, Copenhagen, Denmark.
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3
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Paromov V, Uversky VN, Cooley A, Liburd LE, Mukherjee S, Na I, Dayhoff GW, Pratap S. The Proteomic Analysis of Cancer-Related Alterations in the Human Unfoldome. Int J Mol Sci 2024; 25:1552. [PMID: 38338831 PMCID: PMC10855131 DOI: 10.3390/ijms25031552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 01/11/2024] [Accepted: 01/11/2024] [Indexed: 02/12/2024] Open
Abstract
Many proteins lack stable 3D structures. These intrinsically disordered proteins (IDPs) or hybrid proteins containing ordered domains with intrinsically disordered protein regions (IDPRs) often carry out regulatory functions related to molecular recognition and signal transduction. IDPs/IDPRs constitute a substantial portion of the human proteome and are termed "the unfoldome". Herein, we probe the human breast cancer unfoldome and investigate relations between IDPs and key disease genes and pathways. We utilized bottom-up proteomics, MudPIT (Multidimensional Protein Identification Technology), to profile differentially expressed IDPs in human normal (MCF-10A) and breast cancer (BT-549) cell lines. Overall, we identified 2271 protein groups in the unfoldome of normal and cancer proteomes, with 148 IDPs found to be significantly differentially expressed in cancer cells. Further analysis produced annotations of 140 IDPs, which were then classified to GO (Gene Ontology) categories and pathways. In total, 65% (91 of 140) IDPs were related to various diseases, and 20% (28 of 140) mapped to cancer terms. A substantial portion of the differentially expressed IDPs contained disordered regions, confirmed by in silico characterization. Overall, our analyses suggest high levels of interactivity in the human cancer unfoldome and a prevalence of moderately and highly disordered proteins in the network.
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Affiliation(s)
- Victor Paromov
- Meharry Proteomics Core, RCMI Research Capacity Core, School of Medicine, Meharry Medical College, Nashville, TN 37208, USA;
| | - Vladimir N. Uversky
- Department of Molecular Medicine, USF Health Byrd Alzheimer’s Research Institute, Morsani College of Medicine, University of South Florida, Tampa, FL 33613, USA; (V.N.U.); (I.N.)
| | - Ayorinde Cooley
- Meharry Bioinformatics Core, Department of Microbiology, Immunology and Physiology, School of Medicine, Meharry Medical College, Nashville, TN 37208, USA;
| | - Lincoln E. Liburd
- Department of Biochemistry, Cancer Biology, Neuroscience & Pharmacology, School of Medicine, Meharry Medical College, Nashville, TN 37208, USA (S.M.)
| | - Shyamali Mukherjee
- Department of Biochemistry, Cancer Biology, Neuroscience & Pharmacology, School of Medicine, Meharry Medical College, Nashville, TN 37208, USA (S.M.)
| | - Insung Na
- Department of Molecular Medicine, USF Health Byrd Alzheimer’s Research Institute, Morsani College of Medicine, University of South Florida, Tampa, FL 33613, USA; (V.N.U.); (I.N.)
| | - Guy W. Dayhoff
- Department of Chemistry, College of Art and Sciences, University of South Florida, Tampa, FL 33613, USA;
| | - Siddharth Pratap
- Meharry Proteomics Core, RCMI Research Capacity Core, School of Medicine, Meharry Medical College, Nashville, TN 37208, USA;
- Meharry Bioinformatics Core, Department of Microbiology, Immunology and Physiology, School of Medicine, Meharry Medical College, Nashville, TN 37208, USA;
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Ohno S, Manabe N, Yamaguchi Y. Prediction of protein structure and AI. J Hum Genet 2024:10.1038/s10038-023-01215-4. [PMID: 38177398 DOI: 10.1038/s10038-023-01215-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 12/10/2023] [Indexed: 01/06/2024]
Abstract
AlphaFold, an artificial intelligence (AI)-based tool for predicting the 3D structure of proteins, is now widely recognized for its high accuracy and versatility in the folding of human proteins. AlphaFold is useful for understanding structure-function relationships from protein 3D structure models and can serve as a template or a reference for experimental structural analysis including X-ray crystallography, NMR and cryo-EM analysis. Its use is expanding among researchers, not only in structural biology but also in other research fields. Researchers are currently exploring the full potential of AlphaFold-generated protein models. Predicting disease severity caused by missense mutations is one such application. This article provides an overview of the 3D structural modeling of AlphaFold based on deep learning techniques and highlights the challenges in predicting the pathogenicity of missense mutations.
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Affiliation(s)
- Shiho Ohno
- Division of Structural Glycobiology, Institute of Molecular Biomembrane and Glycobiology, Tohoku Medical and Pharmaceutical University, 4-4-1 Komatsushima, Aoba-ku, Sendai, Miyagi, 981-8558, Japan
| | - Noriyoshi Manabe
- Division of Structural Glycobiology, Institute of Molecular Biomembrane and Glycobiology, Tohoku Medical and Pharmaceutical University, 4-4-1 Komatsushima, Aoba-ku, Sendai, Miyagi, 981-8558, Japan
| | - Yoshiki Yamaguchi
- Division of Structural Glycobiology, Institute of Molecular Biomembrane and Glycobiology, Tohoku Medical and Pharmaceutical University, 4-4-1 Komatsushima, Aoba-ku, Sendai, Miyagi, 981-8558, Japan.
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Tarantino A, Ciconte G, Ghiroldi A, Mastrocinque F, Micaglio E, Boccellino A, Negro G, Piccoli M, Cirillo F, Vicedomini G, Santinelli V, Anastasia L, Pappone C. Challenges in Brugada Syndrome Stratification: Investigating SCN5A Mutation Localization and Clinical Phenotypes. Int J Mol Sci 2023; 24:16658. [PMID: 38068978 PMCID: PMC10706434 DOI: 10.3390/ijms242316658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 11/15/2023] [Accepted: 11/19/2023] [Indexed: 12/18/2023] Open
Abstract
Brugada Syndrome (BrS) is a genetic heart condition linked to sudden cardiac death. Though the SCN5A gene is primarily associated with BrS, there is a lack of comprehensive studies exploring the connection between SCN5A mutation locations and the clinical presentations of the syndrome. This study aimed to address this gap and gain further understanding of the syndrome. The investigation classified 36 high-risk BrS patients based on SCN5A mutations within the transmembrane/structured (TD) and intra-domain loops (IDLs) lacking a 3D structure. We characterized the intrinsically disordered regions (IDRs) abundant in IDLs, using bioinformatics tools to predict IDRs and post-translational modifications (PTMs) in NaV1.5. Interestingly, it was found that current predictive tools often underestimate the impacts of mutations in IDLs and disordered regions. Moreover, patients with SCN5A mutations confined to IDL regions-previously deemed 'benign'-displayed clinical symptoms similar to those carrying 'damaging' variants. Our research illuminates the difficulty in stratifying patients based on SCN5A mutation locations, emphasizing the vital role of IDLs in the NaV1.5 channel's functioning and protein interactions. We advocate for caution when using predictive tools for mutation evaluation in these regions and call for the development of improved strategies in accurately assessing BrS risk.
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Affiliation(s)
- Adriana Tarantino
- Institute for Molecular and Translational Cardiology (IMTC), IRCCS Policlinico San Donato, Piazza Malan 2, San Donato Milanese, 20097 Milan, Italy; (A.T.); (M.P.); (F.C.)
- School of Medicine, University Vita-Salute San Raffaele, Via Olgettina 58, 20132 Milan, Italy;
| | - Giuseppe Ciconte
- School of Medicine, University Vita-Salute San Raffaele, Via Olgettina 58, 20132 Milan, Italy;
- Arrhythmology Department, IRCCS Policlinico San Donato, Piazza Malan 2, San Donato Milanese, 20097 Milan, Italy; (F.M.); (E.M.); (A.B.); (G.N.); (G.V.); (V.S.)
| | - Andrea Ghiroldi
- Institute for Molecular and Translational Cardiology (IMTC), IRCCS Policlinico San Donato, Piazza Malan 2, San Donato Milanese, 20097 Milan, Italy; (A.T.); (M.P.); (F.C.)
| | - Flavio Mastrocinque
- Arrhythmology Department, IRCCS Policlinico San Donato, Piazza Malan 2, San Donato Milanese, 20097 Milan, Italy; (F.M.); (E.M.); (A.B.); (G.N.); (G.V.); (V.S.)
| | - Emanuele Micaglio
- Arrhythmology Department, IRCCS Policlinico San Donato, Piazza Malan 2, San Donato Milanese, 20097 Milan, Italy; (F.M.); (E.M.); (A.B.); (G.N.); (G.V.); (V.S.)
| | - Antonio Boccellino
- Arrhythmology Department, IRCCS Policlinico San Donato, Piazza Malan 2, San Donato Milanese, 20097 Milan, Italy; (F.M.); (E.M.); (A.B.); (G.N.); (G.V.); (V.S.)
| | - Gabriele Negro
- Arrhythmology Department, IRCCS Policlinico San Donato, Piazza Malan 2, San Donato Milanese, 20097 Milan, Italy; (F.M.); (E.M.); (A.B.); (G.N.); (G.V.); (V.S.)
| | - Marco Piccoli
- Institute for Molecular and Translational Cardiology (IMTC), IRCCS Policlinico San Donato, Piazza Malan 2, San Donato Milanese, 20097 Milan, Italy; (A.T.); (M.P.); (F.C.)
| | - Federica Cirillo
- Institute for Molecular and Translational Cardiology (IMTC), IRCCS Policlinico San Donato, Piazza Malan 2, San Donato Milanese, 20097 Milan, Italy; (A.T.); (M.P.); (F.C.)
| | - Gabriele Vicedomini
- Arrhythmology Department, IRCCS Policlinico San Donato, Piazza Malan 2, San Donato Milanese, 20097 Milan, Italy; (F.M.); (E.M.); (A.B.); (G.N.); (G.V.); (V.S.)
| | - Vincenzo Santinelli
- Arrhythmology Department, IRCCS Policlinico San Donato, Piazza Malan 2, San Donato Milanese, 20097 Milan, Italy; (F.M.); (E.M.); (A.B.); (G.N.); (G.V.); (V.S.)
| | - Luigi Anastasia
- Institute for Molecular and Translational Cardiology (IMTC), IRCCS Policlinico San Donato, Piazza Malan 2, San Donato Milanese, 20097 Milan, Italy; (A.T.); (M.P.); (F.C.)
- School of Medicine, University Vita-Salute San Raffaele, Via Olgettina 58, 20132 Milan, Italy;
| | - Carlo Pappone
- School of Medicine, University Vita-Salute San Raffaele, Via Olgettina 58, 20132 Milan, Italy;
- Arrhythmology Department, IRCCS Policlinico San Donato, Piazza Malan 2, San Donato Milanese, 20097 Milan, Italy; (F.M.); (E.M.); (A.B.); (G.N.); (G.V.); (V.S.)
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Ahmed R, Forman-Kay JD. Aberrant phase separation: linking IDR mutations to disease. Cell Res 2023; 33:583-584. [PMID: 37016021 PMCID: PMC10397348 DOI: 10.1038/s41422-023-00804-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/06/2023] Open
Affiliation(s)
- Rashik Ahmed
- Program in Molecular Medicine, The Hospital for Sick Children, Toronto, ON, Canada
- Department of Biochemistry, University of Toronto, Toronto, ON, Canada
| | - Julie D Forman-Kay
- Program in Molecular Medicine, The Hospital for Sick Children, Toronto, ON, Canada.
- Department of Biochemistry, University of Toronto, Toronto, ON, Canada.
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Johansson KE, Mashahreh B, Hartmann-Petersen R, Ravid T, Lindorff-Larsen K. Prediction of Quality-control Degradation Signals in Yeast Proteins. J Mol Biol 2023; 435:167915. [PMID: 36495918 DOI: 10.1016/j.jmb.2022.167915] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 11/26/2022] [Accepted: 12/01/2022] [Indexed: 12/12/2022]
Abstract
Effective proteome homeostasis is key to cellular and organismal survival, and cells therefore contain efficient quality control systems to monitor and remove potentially toxic misfolded proteins. Such general protein quality control to a large extent relies on the efficient and robust delivery of misfolded or unfolded proteins to the ubiquitin-proteasome system. This is achieved via recognition of so-called degradation motifs-degrons-that are assumed to become exposed as a result of protein misfolding. Despite their importance, the nature and sequence properties of quality-control degrons remain elusive. Here, we have used data from a yeast-based screen of 23,600 17-residue peptides to build a predictor of quality-control degrons. The resulting model, QCDPred (Quality Control Degron Prediction), achieves good accuracy using only the sequence composition of the peptides as input. Our analysis reveals that strong degrons are enriched in hydrophobic amino acids and depleted in negatively charged amino acids, in line with the expectation that they are buried in natively folded proteins. We applied QCDPred to the yeast proteome, enabling us to analyse more widely the potential effects of degrons. As an example, we show a correlation between cellular abundance and degron potential in disordered regions of proteins. Together with recent results on membrane proteins, our work suggest that the recognition of exposed hydrophobic residues is a key and generic mechanism for proteome homeostasis. QCDPred is freely available as open source code and via a web interface.
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Affiliation(s)
- Kristoffer E Johansson
- Linderstrøm-Lang Centre for Protein Science, Section for Biomolecular Sciences, Department of Biology, University for Copenhagen, Copenhagen, Denmark. https://twitter.com/kristofferenoee
| | - Bayan Mashahreh
- Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Rasmus Hartmann-Petersen
- Linderstrøm-Lang Centre for Protein Science, Section for Biomolecular Sciences, Department of Biology, University for Copenhagen, Copenhagen, Denmark. https://twitter.com/rasmushartmannp
| | - Tommer Ravid
- Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel.
| | - Kresten Lindorff-Larsen
- Linderstrøm-Lang Centre for Protein Science, Section for Biomolecular Sciences, Department of Biology, University for Copenhagen, Copenhagen, Denmark.
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Hasenahuer MA, Sanchis-Juan A, Laskowski RA, Baker JA, Stephenson JD, Orengo CA, Raymond FL, Thornton JM. Mapping the Constrained Coding Regions in the Human Genome to Their Corresponding Proteins. J Mol Biol 2023; 435:167892. [PMID: 36410474 PMCID: PMC9875310 DOI: 10.1016/j.jmb.2022.167892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Revised: 11/08/2022] [Accepted: 11/14/2022] [Indexed: 11/23/2022]
Abstract
Constrained Coding Regions (CCRs) in the human genome have been derived from DNA sequencing data of large cohorts of healthy control populations, available in the Genome Aggregation Database (gnomAD) [1]. They identify regions depleted of protein-changing variants and thus identify segments of the genome that have been constrained during human evolution. By mapping these DNA-defined regions from genomic coordinates onto the corresponding protein positions and combining this information with protein annotations, we have explored the distribution of CCRs and compared their co-occurrence with different protein functional features, previously annotated at the amino acid level in public databases. As expected, our results reveal that functional amino acids involved in interactions with DNA/RNA, protein-protein contacts and catalytic sites are the protein features most likely to be highly constrained for variation in the control population. More surprisingly, we also found that linear motifs, linear interacting peptides (LIPs), disorder-order transitions upon binding with other protein partners and liquid-liquid phase separating (LLPS) regions are also strongly associated with high constraint for variability. We also compared intra-species constraints in the human CCRs with inter-species conservation and functional residues to explore how such CCRs may contribute to the analysis of protein variants. As has been previously observed, CCRs are only weakly correlated with conservation, suggesting that intraspecies constraints complement interspecies conservation and can provide more information to interpret variant effects.
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Affiliation(s)
- Marcia A. Hasenahuer
- European Molecular Biology Laboratory – European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK,Cambridge Institute for Medical Research, University of Cambridge, Cambridge CB2 0XY, UK,Institute of Structural and Molecular Biology, University College London, London WC1E 6BT, UK,Corresponding author at: European Molecular Biology Laboratory – European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK. @MarHasenahuer
| | - Alba Sanchis-Juan
- Department of Haematology, NHS Blood and Transplant Centre, University of Cambridge, Cambridge CB2 0XY, UK,NIHR BioResource, Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Cambridge CB2 0QQ, UK
| | - Roman A. Laskowski
- European Molecular Biology Laboratory – European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - James A. Baker
- European Molecular Biology Laboratory – European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - James D. Stephenson
- European Molecular Biology Laboratory – European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Christine A. Orengo
- Institute of Structural and Molecular Biology, University College London, London WC1E 6BT, UK
| | - F. Lucy Raymond
- Cambridge Institute for Medical Research, University of Cambridge, Cambridge CB2 0XY, UK,NIHR BioResource, Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Cambridge CB2 0QQ, UK
| | - Janet M. Thornton
- European Molecular Biology Laboratory – European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
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Sun B, Kekenes-Huskey PM. Myofilament-associated proteins with intrinsic disorder (MAPIDs) and their resolution by computational modeling. Q Rev Biophys 2023; 56:e2. [PMID: 36628457 PMCID: PMC11070111 DOI: 10.1017/s003358352300001x] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
The cardiac sarcomere is a cellular structure in the heart that enables muscle cells to contract. Dozens of proteins belong to the cardiac sarcomere, which work in tandem to generate force and adapt to demands on cardiac output. Intriguingly, the majority of these proteins have significant intrinsic disorder that contributes to their functions, yet the biophysics of these intrinsically disordered regions (IDRs) have been characterized in limited detail. In this review, we first enumerate these myofilament-associated proteins with intrinsic disorder (MAPIDs) and recent biophysical studies to characterize their IDRs. We secondly summarize the biophysics governing IDR properties and the state-of-the-art in computational tools toward MAPID identification and characterization of their conformation ensembles. We conclude with an overview of future computational approaches toward broadening the understanding of intrinsic disorder in the cardiac sarcomere.
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Affiliation(s)
- Bin Sun
- Research Center for Pharmacoinformatics (The State-Province Key Laboratories of Biomedicine-Pharmaceutics of China), Department of Medicinal Chemistry and Natural Medicine Chemistry, College of Pharmacy, Harbin Medical University, Harbin 150081, China
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10
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Sun L, Liu XP, Yan X, Wu S, Tang X, Chen C, Li G, Hu H, Wang D, Li S. Identification of molecular subtypes based on liquid-liquid phase separation and cross-talk with immunological phenotype in bladder cancer. Front Immunol 2022; 13:1059568. [PMID: 36518754 PMCID: PMC9742536 DOI: 10.3389/fimmu.2022.1059568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Accepted: 11/14/2022] [Indexed: 11/30/2022] Open
Abstract
Background Mounting evidence has demonstrated that an imbalance in liquid-liquid phase separation (LLPS) can induce alteration in the spatiotemporal coordination of biomolecular condensates, which plays a role in carcinogenesis and cachexia. However, the role of LLPS in the occurrence and progression of bladder cancer (BLCA) remains to be elucidated. Identifying the role of LLPS in carcinogenesis may aid in cancer therapeutics. Methods A total of 1,351 BLCA samples from six cohorts were retrieved from publicly available databases like The Cancer Genome Atlas, Gene Expression Omnibus, and ArrayExpress. The samples were divided into three distinct clusters, and their multi-dimensional heterogeneities were explored. The LLPS patterns of all patients were determined based on the LLPS-related risk score (LLPSRS), and its multifaceted landscape was depicted and experimentally validated at the multi-omics level. Finally, a cytotoxicity-related and LLPSRS-based classifier was established to predict the patient's response to immune checkpoint blockade (ICB) treatment. Results Three LLPS-related subtypes were identified and validated. The differences in prognosis, tumor microenvironment (TME) features, cancer hallmarks, and certain signatures of the three LLPS-related subtypes were validated. LLPSRS was calculated, which could be used as a prognostic biomarker. A close correlation was observed between clinicopathological features, genomic variations, biological mechanisms, immune infiltration in TME, chemosensitivity, and LLPSRS. Furthermore, our classifier could effectively predict immunotherapy response in patients with BLCA. Conclusions Our study identified a novel categorization of BLCA patients based on LLPS. The LLPSRS could predict the prognosis of patients and aid in designing personalized medicine. Further, our binary classifier could effectively predict patients' sensitivity to immunotherapy.
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Affiliation(s)
- Le Sun
- Department of Urology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Xiao-Ping Liu
- Department of Biological Repositories, Cancer Precision Diagnosis and Treatment and Translational Medicine Hubei Engineering Research Center, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Xin Yan
- Department of Urology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Shaojie Wu
- Department of Urology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Xiaoyu Tang
- Department of Urology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Chen Chen
- Department of Biological Repositories, Cancer Precision Diagnosis and Treatment and Translational Medicine Hubei Engineering Research Center, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Gang Li
- Department of Biological Repositories, Cancer Precision Diagnosis and Treatment and Translational Medicine Hubei Engineering Research Center, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Hankun Hu
- Department of Pharmacy, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Du Wang
- The Institute of Technological Sciences, Wuhan University, Wuhan, China
| | - Sheng Li
- Department of Urology, Zhongnan Hospital of Wuhan University, Wuhan, China,Department of Biological Repositories, Cancer Precision Diagnosis and Treatment and Translational Medicine Hubei Engineering Research Center, Zhongnan Hospital of Wuhan University, Wuhan, China,*Correspondence: Sheng Li,
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11
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Bakker MJ, Mládek A, Semrád H, Zapletal V, Pavlíková Přecechtělová J. Improving IDP theoretical chemical shift accuracy and efficiency through a combined MD/ADMA/DFT and machine learning approach. Phys Chem Chem Phys 2022; 24:27678-27692. [PMID: 36373847 DOI: 10.1039/d2cp01638a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
This work extends the multi-scale computational scheme for the quantum mechanics (QM) calculations of Nuclear Magnetic Resonance (NMR) chemical shifts (CSs) in proteins that lack a well-defined 3D structure. The scheme couples the sampling of an intrinsically disordered protein (IDP) by classical molecular dynamics (MD) with protein fragmentation using the adjustable density matrix assembler (ADMA) and density functional theory (DFT) calculations. In contrast to our early investigation on IDPs (Pavlíková Přecechtělová et al., J. Chem. Theory Comput., 2019, 15, 5642-5658) and the state-of-the art NMR calculations for structured proteins, a partial re-optimization was implemented on the raw MD geometries in vibrational normal mode coordinates to enhance the accuracy of the MD/ADMA/DFT computational scheme. In addition, machine-learning based cluster analysis was performed on the scheme to explore its potential in producing protein structure ensembles (CLUSTER ensembles) that yield accurate CSs at a reduced computational cost. The performance of the cluster-based calculations is validated against results obtained with conventional structural ensembles consisting of MD snapshots extracted from the MD trajectory at regular time intervals (REGULAR ensembles). CS calculations performed with the refined MD/ADMA/DFT framework employed the 6-311++G(d,p) basis set that outperformed IGLO-III calculations with the same density functional approximation (B3LYP) and both explicit and implicit solvation. The partial geometry optimization did not universally improve the agreement of computed CSs with the experiment but substantially decreased errors associated with the ensemble averaging. A CLUSTER ensemble with 50 structures yielded ensemble averages close to those obtained with a REGULAR ensemble consisting of 500 MD frames. The cluster based calculations thus required only a fraction of the computational time.
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Affiliation(s)
- Michael J Bakker
- Faculty of Pharmacy in Hradec Králové, Charles University, Akademika Heyrovského 1203/8, 500 05 Hradec Králové, Czech Republic.
| | - Arnošt Mládek
- Faculty of Pharmacy in Hradec Králové, Charles University, Akademika Heyrovského 1203/8, 500 05 Hradec Králové, Czech Republic.
| | - Hugo Semrád
- Faculty of Pharmacy in Hradec Králové, Charles University, Akademika Heyrovského 1203/8, 500 05 Hradec Králové, Czech Republic. .,Department of Chemistry, Faculty of Science, Masaryk University, Kotlářská 267/2, 611 37 Brno, Czech Republic
| | - Vojtěch Zapletal
- Faculty of Pharmacy in Hradec Králové, Charles University, Akademika Heyrovského 1203/8, 500 05 Hradec Králové, Czech Republic.
| | - Jana Pavlíková Přecechtělová
- Faculty of Pharmacy in Hradec Králové, Charles University, Akademika Heyrovského 1203/8, 500 05 Hradec Králové, Czech Republic.
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12
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Cheng B, Yang X, Cheng S, Li C, Zhang H, Liu L, Meng P, Jia Y, Wen Y, Zhang F. A large-scale polygenic risk score analysis identified candidate proteins associated with anxiety, depression and neuroticism. Mol Brain 2022; 15:66. [PMID: 35870967 PMCID: PMC9308259 DOI: 10.1186/s13041-022-00954-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 07/09/2022] [Indexed: 11/10/2022] Open
Abstract
Psychiatric disorders and neuroticism are closely associated with central nervous system, whose proper functioning depends on efficient protein renewal. This study aims to systematically analyze the association between anxiety / depression / neuroticism and each of the 439 proteins. 47,536 pQTLs of 439 proteins in brain, plasma and cerebrospinal fluid (CSF) were collected from recent genome-wide association study. Polygenic risk scores (PRS) of the 439 proteins were then calculated using the UK Biobank cohort, including 120,729 subjects of neuroticism, 255,354 subjects of anxiety and 316,513 subjects of depression. Pearson correlation analyses were performed to evaluate the correlation between each protein and each of the mental traits by using calculated PRSs as the instrumental variables of protein. In general population, six correlations were identified in plasma and CSF such as plasma protease C1 inhibitor (C1-INH) with neuroticism score (r = - 0.011, P = 2.56 × 10- 9) in plasma, C1-INH with neuroticism score (r = -0.010, P = 3.09 × 10- 8) in CSF, and ERBB1 with self-reported depression (r = - 0.012, P = 4.65 × 10- 5) in CSF. C1-INH and ERBB1 may induce neuroticism and depression by affecting brain function and synaptic development. Gender subgroup analyses found that BST1 was correlated with neuroticism score in male CSF (r = - 0.011, P = 1.80 × 10- 5), while CNTN2 was correlated with depression score in female brain (r = - 0.013, P = 6.43 × 10- 4). BST1 and CNTN2 may be involved in nervous system metabolism and brain health. Six common candidate proteins were associated with all three traits (P < 0.05) and were confirmed in relevant proteomic studies, such as C1-INH in plasma, CNTN2 and MSP in the brain. Our results provide novel clues for revealing the roles of proteins in the development of anxiety, depression and neuroticism.
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Affiliation(s)
- Bolun Cheng
- Key Laboratory of Trace Elements and Endemic Diseases, Collaborative Innovation Center of Endemic Disease and Health Promotion for Silk Road Region, School of Public Health, Health Science Center, Xi'an Jiaotong University, 76 Yan Ta West Road, 710061, Xi'an, People's Republic of China.,Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an, People's Republic of China
| | - Xuena Yang
- Key Laboratory of Trace Elements and Endemic Diseases, Collaborative Innovation Center of Endemic Disease and Health Promotion for Silk Road Region, School of Public Health, Health Science Center, Xi'an Jiaotong University, 76 Yan Ta West Road, 710061, Xi'an, People's Republic of China.,Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an, People's Republic of China
| | - Shiqiang Cheng
- Key Laboratory of Trace Elements and Endemic Diseases, Collaborative Innovation Center of Endemic Disease and Health Promotion for Silk Road Region, School of Public Health, Health Science Center, Xi'an Jiaotong University, 76 Yan Ta West Road, 710061, Xi'an, People's Republic of China.,Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an, People's Republic of China
| | - Chun'e Li
- Key Laboratory of Trace Elements and Endemic Diseases, Collaborative Innovation Center of Endemic Disease and Health Promotion for Silk Road Region, School of Public Health, Health Science Center, Xi'an Jiaotong University, 76 Yan Ta West Road, 710061, Xi'an, People's Republic of China.,Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an, People's Republic of China
| | - Huijie Zhang
- Key Laboratory of Trace Elements and Endemic Diseases, Collaborative Innovation Center of Endemic Disease and Health Promotion for Silk Road Region, School of Public Health, Health Science Center, Xi'an Jiaotong University, 76 Yan Ta West Road, 710061, Xi'an, People's Republic of China.,Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an, People's Republic of China
| | - Li Liu
- Key Laboratory of Trace Elements and Endemic Diseases, Collaborative Innovation Center of Endemic Disease and Health Promotion for Silk Road Region, School of Public Health, Health Science Center, Xi'an Jiaotong University, 76 Yan Ta West Road, 710061, Xi'an, People's Republic of China.,Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an, People's Republic of China
| | - Peilin Meng
- Key Laboratory of Trace Elements and Endemic Diseases, Collaborative Innovation Center of Endemic Disease and Health Promotion for Silk Road Region, School of Public Health, Health Science Center, Xi'an Jiaotong University, 76 Yan Ta West Road, 710061, Xi'an, People's Republic of China.,Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an, People's Republic of China
| | - Yumeng Jia
- Key Laboratory of Trace Elements and Endemic Diseases, Collaborative Innovation Center of Endemic Disease and Health Promotion for Silk Road Region, School of Public Health, Health Science Center, Xi'an Jiaotong University, 76 Yan Ta West Road, 710061, Xi'an, People's Republic of China.,Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an, People's Republic of China
| | - Yan Wen
- Key Laboratory of Trace Elements and Endemic Diseases, Collaborative Innovation Center of Endemic Disease and Health Promotion for Silk Road Region, School of Public Health, Health Science Center, Xi'an Jiaotong University, 76 Yan Ta West Road, 710061, Xi'an, People's Republic of China.,Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an, People's Republic of China
| | - Feng Zhang
- Key Laboratory of Trace Elements and Endemic Diseases, Collaborative Innovation Center of Endemic Disease and Health Promotion for Silk Road Region, School of Public Health, Health Science Center, Xi'an Jiaotong University, 76 Yan Ta West Road, 710061, Xi'an, People's Republic of China. .,Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an, People's Republic of China.
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13
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Bhuyan AK. Negative Thermal Expansion and Disorder-to-Order Collapse of an Intrinsically Disordered Protein under Marginally Denaturing Conditions. J Phys Chem B 2022; 126:5055-5065. [PMID: 35786899 DOI: 10.1021/acs.jpcb.2c03386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Recent work with intrinsically disordered proteins (IDPs) has projected a myriad of their survival instincts based mainly on the total charge content, the abundance of polar residues, and the paucity of hydrophobic amino acids. This work uses a plant IDP AtPP16-1 (Arabidopsis thaliana phloem protein class 16-1), whose solution NMR structure was determined by us recently, to show legitimate negative thermal expansion (NTE) of the native state. The thermal expansion continues to be negative even as the tertiary structure is perturbed by ultralow levels of urea up to 0.4 M. The NTE of these subdenatured states is called apparent NTE because they are prone to undergo conformational changes with temperature. Hydrodynamic shrinkage of the NTE IDP is also observed by dynamic light scattering (DLS) and NMR-measured global rotational correlation time (τc). The protein with denatured tertiary structure but otherwise preserved native-state secondary structure collapses to a dynamically rigid state. The data are mainly based on thermal coefficients of chemical shift and nuclear relaxation measured by heteronuclear NMR. The hydrodynamic shrinkage and collapse under marginally varying solvent compositions that may arise from unstable tertiary structure and dynamic disorder of chain segments across the backbone could be a generic property of IDPs.
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Affiliation(s)
- Abani K Bhuyan
- School of Chemistry University of Hyderabad, Hyderabad 50046, India
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14
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Zou H, Pan T, Gao Y, Chen R, Li S, Guo J, Tian Z, Xu G, Xu J, Ma Y, Li Y. Pan-cancer assessment of mutational landscape in intrinsically disordered hotspots reveals potential driver genes. Nucleic Acids Res 2022; 50:e49. [PMID: 35061901 PMCID: PMC9122534 DOI: 10.1093/nar/gkac028] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 12/22/2021] [Accepted: 01/10/2022] [Indexed: 11/13/2022] Open
Abstract
Large-scale cancer genome sequencing has enabled the catalogs of somatic mutations; however, the mutational impact on intrinsically disordered protein regions (IDRs) has not been systematically investigated to date. Here, we comprehensively characterized the mutational landscapes of IDRs and found that IDRs have higher mutation frequencies across diverse cancers. We thus developed a computational method, ROI-Driver, to identify putative driver genes enriching IDR and domain hotspots in cancer. Numerous well-known cancer-related oncogenes or tumor suppressors that play important roles in cancer signaling regulation, development and immune response were identified at a higher resolution. In particular, the incorporation of IDR structures helps in the identification of novel potential driver genes that play central roles in human protein-protein interaction networks. Interestingly, we found that the putative driver genes with IDR hotspots were significantly enriched with predicted phase separation propensities, suggesting that IDR mutations disrupt phase separation in key cellular pathways. We also identified an appreciable number of clinically relevant genes enriching IDR mutational hotspots that exhibited differential expression patterns and are associated with cancer patient survival. In summary, combinations of mutational effects on IDRs significantly increase the sensitivity of driver detection and are likely to open new therapeutic avenues for various cancers.
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Affiliation(s)
- Haozhe Zou
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, Hainan Provincial Key Laboratory for Human Reproductive Medicine and Genetic Research, International Technology Cooperation Base ‘China–Myanmar Joint Research Center for Prevention and Treatment of Regional Major Disease’ by the Ministry of Science and Technology of China, Hainan Provincial Clinical Research Center for Thalassemia, The First Affiliated Hospital of Hainan Medical University, College of Biomedical Information and Engineering, Hainan Medical University, Haikou 571199, China
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Tao Pan
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, Hainan Provincial Key Laboratory for Human Reproductive Medicine and Genetic Research, International Technology Cooperation Base ‘China–Myanmar Joint Research Center for Prevention and Treatment of Regional Major Disease’ by the Ministry of Science and Technology of China, Hainan Provincial Clinical Research Center for Thalassemia, The First Affiliated Hospital of Hainan Medical University, College of Biomedical Information and Engineering, Hainan Medical University, Haikou 571199, China
| | - Yueying Gao
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, Hainan Provincial Key Laboratory for Human Reproductive Medicine and Genetic Research, International Technology Cooperation Base ‘China–Myanmar Joint Research Center for Prevention and Treatment of Regional Major Disease’ by the Ministry of Science and Technology of China, Hainan Provincial Clinical Research Center for Thalassemia, The First Affiliated Hospital of Hainan Medical University, College of Biomedical Information and Engineering, Hainan Medical University, Haikou 571199, China
| | - Renwei Chen
- Hainan Women and Children’s Medical Center, Hainan Medical University, Haikou 571199, China
| | - Si Li
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, Hainan Provincial Key Laboratory for Human Reproductive Medicine and Genetic Research, International Technology Cooperation Base ‘China–Myanmar Joint Research Center for Prevention and Treatment of Regional Major Disease’ by the Ministry of Science and Technology of China, Hainan Provincial Clinical Research Center for Thalassemia, The First Affiliated Hospital of Hainan Medical University, College of Biomedical Information and Engineering, Hainan Medical University, Haikou 571199, China
| | - Jing Guo
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, Hainan Provincial Key Laboratory for Human Reproductive Medicine and Genetic Research, International Technology Cooperation Base ‘China–Myanmar Joint Research Center for Prevention and Treatment of Regional Major Disease’ by the Ministry of Science and Technology of China, Hainan Provincial Clinical Research Center for Thalassemia, The First Affiliated Hospital of Hainan Medical University, College of Biomedical Information and Engineering, Hainan Medical University, Haikou 571199, China
| | - Zhanyu Tian
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, Hainan Provincial Key Laboratory for Human Reproductive Medicine and Genetic Research, International Technology Cooperation Base ‘China–Myanmar Joint Research Center for Prevention and Treatment of Regional Major Disease’ by the Ministry of Science and Technology of China, Hainan Provincial Clinical Research Center for Thalassemia, The First Affiliated Hospital of Hainan Medical University, College of Biomedical Information and Engineering, Hainan Medical University, Haikou 571199, China
| | - Gang Xu
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, Hainan Provincial Key Laboratory for Human Reproductive Medicine and Genetic Research, International Technology Cooperation Base ‘China–Myanmar Joint Research Center for Prevention and Treatment of Regional Major Disease’ by the Ministry of Science and Technology of China, Hainan Provincial Clinical Research Center for Thalassemia, The First Affiliated Hospital of Hainan Medical University, College of Biomedical Information and Engineering, Hainan Medical University, Haikou 571199, China
| | - Juan Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Yanlin Ma
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, Hainan Provincial Key Laboratory for Human Reproductive Medicine and Genetic Research, International Technology Cooperation Base ‘China–Myanmar Joint Research Center for Prevention and Treatment of Regional Major Disease’ by the Ministry of Science and Technology of China, Hainan Provincial Clinical Research Center for Thalassemia, The First Affiliated Hospital of Hainan Medical University, College of Biomedical Information and Engineering, Hainan Medical University, Haikou 571199, China
| | - Yongsheng Li
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, Hainan Provincial Key Laboratory for Human Reproductive Medicine and Genetic Research, International Technology Cooperation Base ‘China–Myanmar Joint Research Center for Prevention and Treatment of Regional Major Disease’ by the Ministry of Science and Technology of China, Hainan Provincial Clinical Research Center for Thalassemia, The First Affiliated Hospital of Hainan Medical University, College of Biomedical Information and Engineering, Hainan Medical University, Haikou 571199, China
- Hainan Women and Children’s Medical Center, Hainan Medical University, Haikou 571199, China
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15
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Phase-Separated Subcellular Compartmentation and Related Human Diseases. Int J Mol Sci 2022; 23:ijms23105491. [PMID: 35628304 PMCID: PMC9141834 DOI: 10.3390/ijms23105491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 05/12/2022] [Accepted: 05/13/2022] [Indexed: 02/06/2023] Open
Abstract
In live cells, proteins and nucleic acids can associate together through multivalent interactions, and form relatively isolated phases that undertake designated biological functions and activities. In the past decade, liquid–liquid phase separation (LLPS) has gradually been recognized as a general mechanism for the intracellular organization of biomolecules. LLPS regulates the assembly and composition of dozens of membraneless organelles and condensates in cells. Due to the altered physiological conditions or genetic mutations, phase-separated condensates may undergo aberrant formation, maturation or gelation that contributes to the onset and progression of various diseases, including neurodegenerative disorders and cancers. In this review, we summarize the properties of different membraneless organelles and condensates, and discuss multiple phase separation-regulated biological processes. Based on the dysregulation and mutations of several key regulatory proteins and signaling pathways, we also exemplify how aberrantly regulated LLPS may contribute to human diseases.
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16
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Dindo M, Pascarelli S, Chiasserini D, Grottelli S, Costantini C, Uechi G, Giardina G, Laurino P, Cellini B. Structural dynamics shape the fitness window of alanine:glyoxylate aminotransferase. Protein Sci 2022; 31:e4303. [PMID: 35481644 PMCID: PMC8996469 DOI: 10.1002/pro.4303] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 03/02/2022] [Accepted: 03/17/2022] [Indexed: 01/24/2023]
Abstract
The conformational landscape of a protein is constantly expanded by genetic variations that have a minimal impact on the function(s) while causing subtle effects on protein structure. The wider the conformational space sampled by these variants, the higher the probabilities to adapt to changes in environmental conditions. However, the probability that a single mutation may result in a pathogenic phenotype also increases. Here we present a paradigmatic example of how protein evolution balances structural stability and dynamics to maximize protein adaptability and preserve protein fitness. We took advantage of known genetic variations of human alanine:glyoxylate aminotransferase (AGT1), which is present as a common major allelic form (AGT-Ma) and a minor polymorphic form (AGT-Mi) expressed in 20% of Caucasian population. By integrating crystallographic studies and molecular dynamics simulations, we show that AGT-Ma is endowed with structurally unstable (frustrated) regions, which become disordered in AGT-Mi. An in-depth biochemical characterization of variants from an anticonsensus library, encompassing the frustrated regions, correlates this plasticity to a fitness window defined by AGT-Ma and AGT-Mi. Finally, co-immunoprecipitation analysis suggests that structural frustration in AGT1 could favor additional functions related to protein-protein interactions. These results expand our understanding of protein structural evolution by establishing that naturally occurring genetic variations tip the balance between stability and frustration to maximize the ensemble of conformations falling within a well-defined fitness window, thus expanding the adaptability potential of the protein.
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Affiliation(s)
- Mirco Dindo
- Protein Engineering and Evolution UnitOkinawa Institute of Science and Technology Graduate UniversityOkinawaJapan
| | - Stefano Pascarelli
- Protein Engineering and Evolution UnitOkinawa Institute of Science and Technology Graduate UniversityOkinawaJapan
| | | | - Silvia Grottelli
- Department of Medicine and SurgeryUniversity of PerugiaPerugiaItaly
| | | | - Gen‐Ichiro Uechi
- Protein Engineering and Evolution UnitOkinawa Institute of Science and Technology Graduate UniversityOkinawaJapan
| | - Giorgio Giardina
- Department of Biochemical Sciences “A. Rossi Fanelli”Sapienza University of RomeRomeItaly
| | - Paola Laurino
- Protein Engineering and Evolution UnitOkinawa Institute of Science and Technology Graduate UniversityOkinawaJapan
| | - Barbara Cellini
- Department of Medicine and SurgeryUniversity of PerugiaPerugiaItaly
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17
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Goh GKM, Dunker AK, Foster JA, Uversky VN. Shell Disorder Models Detect That Omicron Has Harder Shells with Attenuation but Is Not a Descendant of the Wuhan-Hu-1 SARS-CoV-2. Biomolecules 2022; 12:631. [PMID: 35625559 PMCID: PMC9139003 DOI: 10.3390/biom12050631] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Revised: 04/17/2022] [Accepted: 04/20/2022] [Indexed: 02/01/2023] Open
Abstract
Before the SARS-CoV-2 Omicron variant emergence, shell disorder models (SDM) suggested that an attenuated precursor from pangolins may have entered humans in 2017 or earlier. This was based on a shell disorder analysis of SARS-CoV-1/2 and pangolin-Cov-2017. The SDM suggests that Omicron is attenuated with almost identical N (inner shell) disorder as pangolin-CoV-2017 (N-PID (percentage of intrinsic disorder): 44.8% vs. 44.9%-lower than other variants). The outer shell disorder (M-PID) of Omicron is lower than that of other variants and pangolin-CoV-2017 (5.4% vs. 5.9%). COVID-19-related CoVs have the lowest M-PIDs (hardest outer shell) among all CoVs. This is likely to be responsible for the higher contagiousness of SARS-CoV-2 and Omicron, since hard outer shell protects the virion from salivary/mucosal antimicrobial enzymes. Phylogenetic study using M reveals that Omicron branched off from an ancestor of the Wuhan-Hu-1 strain closely related to pangolin-CoVs. M, being evolutionarily conserved in COVID-19, is most ideal for COVID-19 phylogenetic study. Omicron may have been hiding among burrowing animals (e.g., pangolins) that provide optimal evolutionary environments for attenuation and increase shell hardness, which is essential for fecal-oral-respiratory transmission via buried feces. Incoming data support SDM e.g., the presence of fewer infectious particles in the lungs than in the bronchi upon infection.
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Affiliation(s)
| | - A. Keith Dunker
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA;
| | - James A. Foster
- Department of Biological Sciences, University of Idaho, Moscow, ID 83844, USA;
- Institute for Bioinformatics and Evolutionary Studies, University of Idaho, Moscow, ID 83844, USA
| | - Vladimir N. Uversky
- Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL 33612, USA;
- Institute for Biological Instrumentation, Russian Academy of Sciences, Pushchino, 142290 Moscow Region, Russia
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18
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Spielmann M, Kircher M. Computational and experimental methods for classifying variants of unknown clinical significance. Cold Spring Harb Mol Case Stud 2022; 8:mcs.a006196. [PMID: 35483875 PMCID: PMC9059783 DOI: 10.1101/mcs.a006196] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
The increase in sequencing capacity, reduction in costs, and national and international coordinated efforts have led to the widespread introduction of next-generation sequencing (NGS) technologies in patient care. More generally, human genetics and genomic medicine are gaining importance for more and more patients. Some communities are already discussing the prospect of sequencing each individual's genome at time of birth. Together with digital health records, this shall enable individualized treatments and preventive measures, so-called precision medicine. A central step in this process is the identification of disease causal mutations or variant combinations that make us more susceptible for diseases. Although various technological advances have improved the identification of genetic alterations, the interpretation and ranking of the identified variants remains a major challenge. Based on our knowledge of molecular processes or previously identified disease variants, we can identify potentially functional genetic variants and, using different lines of evidence, we are sometimes able to demonstrate their pathogenicity directly. However, the vast majority of variants are classified as variants of uncertain clinical significance (VUSs) with not enough experimental evidence to determine their pathogenicity. In these cases, computational methods may be used to improve the prioritization and an increasing toolbox of experimental methods is emerging that can be used to assay the molecular effects of VUSs. Here, we discuss how computational and experimental methods can be used to create catalogs of variant effects for a variety of molecular and cellular phenotypes. We discuss the prospects of integrating large-scale functional data with machine learning and clinical knowledge for the development of accurate pathogenicity predictions for clinical applications.
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Affiliation(s)
- Malte Spielmann
- Institute of Human Genetics, University of Lübeck, 23562 Lübeck, Germany;,Institute of Human Genetics, Christian-Albrechts-Universität, 24105 Kiel, Germany;,Human Molecular Genomics Group, Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany;,DZHK (German Centre for Cardiovascular Research), partner site Hamburg/Lübeck/Kiel, 23562 Lübeck, Germany
| | - Martin Kircher
- Institute of Human Genetics, University of Lübeck, 23562 Lübeck, Germany;,Berlin Institute of Health at Charité—Universitätsmedizin Berlin, 10117 Berlin, Germany;,DZHK (German Centre for Cardiovascular Research), partner site Berlin, 10115 Berlin, Germany
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19
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Chakrabarti P, Chakravarty D. Intrinsically disordered proteins/regions and insight into their biomolecular interactions. Biophys Chem 2022; 283:106769. [DOI: 10.1016/j.bpc.2022.106769] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 01/26/2022] [Accepted: 01/26/2022] [Indexed: 12/20/2022]
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20
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Karaca E, Prévost C, Sacquin-Mora S. Modeling the Dynamics of Protein-Protein Interfaces, How and Why? Molecules 2022; 27:1841. [PMID: 35335203 PMCID: PMC8950966 DOI: 10.3390/molecules27061841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Revised: 03/06/2022] [Accepted: 03/08/2022] [Indexed: 12/07/2022] Open
Abstract
Protein-protein assemblies act as a key component in numerous cellular processes. Their accurate modeling at the atomic level remains a challenge for structural biology. To address this challenge, several docking and a handful of deep learning methodologies focus on modeling protein-protein interfaces. Although the outcome of these methods has been assessed using static reference structures, more and more data point to the fact that the interaction stability and specificity is encoded in the dynamics of these interfaces. Therefore, this dynamics information must be taken into account when modeling and assessing protein interactions at the atomistic scale. Expanding on this, our review initially focuses on the recent computational strategies aiming at investigating protein-protein interfaces in a dynamic fashion using enhanced sampling, multi-scale modeling, and experimental data integration. Then, we discuss how interface dynamics report on the function of protein assemblies in globular complexes, in fuzzy complexes containing intrinsically disordered proteins, as well as in active complexes, where chemical reactions take place across the protein-protein interface.
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Affiliation(s)
- Ezgi Karaca
- Izmir Biomedicine and Genome Center, Izmir 35340, Turkey;
- Izmir International Biomedicine and Genome Institute, Dokuz Eylul University, Izmir 35340, Turkey
| | - Chantal Prévost
- CNRS, Laboratoire de Biochimie Théorique, UPR9080, Université de Paris, 13 rue Pierre et Marie Curie, 75005 Paris, France;
- Institut de Biologie Physico-Chimique, Fondation Edmond de Rothschild, PSL Research University, 75006 Paris, France
| | - Sophie Sacquin-Mora
- CNRS, Laboratoire de Biochimie Théorique, UPR9080, Université de Paris, 13 rue Pierre et Marie Curie, 75005 Paris, France;
- Institut de Biologie Physico-Chimique, Fondation Edmond de Rothschild, PSL Research University, 75006 Paris, France
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21
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Zheng J, Wu Z, Qiu Y, Wang X, Jiang X. An integrative multi-omics analysis based on liquid–liquid phase separation delineates distinct subtypes of lower-grade glioma and identifies a prognostic signature. J Transl Med 2022; 20:55. [PMID: 35093128 PMCID: PMC8800244 DOI: 10.1186/s12967-022-03266-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 01/17/2022] [Indexed: 12/11/2022] Open
Abstract
Abstract
Background
Emerging evidences have indicated that the aberrant liquid–liquid phase separation (LLPS) leads to the dysfunction of biomolecular condensates, thereby contributing to the tumorigenesis and progression. Nevertheless, it remains unclear whether or how the LLPS of specific molecules affects the prognosis and tumor immune microenvironment (TIME) of patients with lower-grade glioma (LGG).
Methods
We integrated the transcriptome information of 3585 LLPS-related genes to comprehensively evaluate the LLPS patterns of 423 patients with LGG in The Cancer Genome Atlas (TCGA) cohort. Then, we systematically demonstrated the differences among four LLPS subtypes based on multi-omics analyses. In addition, we constructed the LLPS-related prognostic risk score (LPRS) for individualized integrative assessment.
Results
Based on the expression profiles of 85 scaffolds, 355 regulators, and 3145 clients in LGG, we identified four LLPS subtypes, namely LS1, LS2, LS3 and LS4.
We confirmed that there were significant differences in prognosis, clinicopathological features, cancer hallmarks, genomic alterations, TIME patterns and immunotherapeutic responses among four LLPS subtypes. In addition, a prognostic signature called LPRS was constructed for individualized integrative assessment. LPRS exhibited a robust predictive capacity for prognosis of LGG patients in multiple cohorts. Moreover, LPRS was found to be correlated with clinicopathological features, cancer hallmarks, genomic alterations and TIME patterns of LGG patients. The predictive power of LPRS in response to immune checkpoint inhibitor (ICI) therapy was also prominent.
Conclusions
This study provided a novel classification of LGG patients based on LLPS. The constructed LPRS might facilitate individualized prognosis prediction and better immunotherapy options for LGG patients.
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Hóbor F, Hegedüs Z, Ibarra AA, Petrovicz VL, Bartlett GJ, Sessions RB, Wilson AJ, Edwards TA. Understanding p300-transcription factor interactions using sequence variation and hybridization. RSC Chem Biol 2022; 3:592-603. [PMID: 35656479 PMCID: PMC9092470 DOI: 10.1039/d2cb00026a] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 04/10/2022] [Indexed: 11/21/2022] Open
Abstract
The hypoxic response is central to cell function and plays a significant role in the growth and survival of solid tumours. HIF-1 regulates the hypoxic response by activating over 100...
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Affiliation(s)
- Fruzsina Hóbor
- Astbury Centre for Structural Molecular Biology, University of Leeds, Woodhouse Lane Leeds LS2 9JT UK
- School of Molecular and Cellular Biology, University of Leeds Woodhouse Lane Leeds LS2 9JT UK
| | - Zsófia Hegedüs
- Department of Medical Chemistry, University of Szeged Dóm tér 8 H-6720 Szeged Hungary
| | - Amaurys Avila Ibarra
- School of Biochemistry, University of Bristol, Medical Sciences Building, University Walk Bristol BS8 1TD UK
- BrisSynBio, University of Bristol, Life Sciences Building Tyndall Avenue Bristol BS8 1TQ UK
| | - Vencel L Petrovicz
- Department of Medical Chemistry, University of Szeged Dóm tér 8 H-6720 Szeged Hungary
| | - Gail J Bartlett
- School of Biochemistry, University of Bristol, Medical Sciences Building, University Walk Bristol BS8 1TD UK
- BrisSynBio, University of Bristol, Life Sciences Building Tyndall Avenue Bristol BS8 1TQ UK
- School of Chemistry, University of Bristol Cantock's Close Bristol BS8 1TS UK
| | - Richard B Sessions
- School of Biochemistry, University of Bristol, Medical Sciences Building, University Walk Bristol BS8 1TD UK
- BrisSynBio, University of Bristol, Life Sciences Building Tyndall Avenue Bristol BS8 1TQ UK
| | - Andrew J Wilson
- Astbury Centre for Structural Molecular Biology, University of Leeds, Woodhouse Lane Leeds LS2 9JT UK
- School of Chemistry, University of Leeds Woodhouse Lane Leeds LS2 9JT UK
| | - Thomas A Edwards
- Astbury Centre for Structural Molecular Biology, University of Leeds, Woodhouse Lane Leeds LS2 9JT UK
- School of Molecular and Cellular Biology, University of Leeds Woodhouse Lane Leeds LS2 9JT UK
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23
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Seera S, Nagarajaram HA. Effect of Disease Causing Missense Mutations on Intrinsically Disordered Regions in Proteins. Protein Pept Lett 2021; 29:254-267. [PMID: 34825861 DOI: 10.2174/0929866528666211126161200] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 10/01/2021] [Accepted: 10/01/2021] [Indexed: 11/22/2022]
Abstract
BACKGROUND It is well known that disease-causing missense mutations (DCMMs) reduce the structural stability/integrity of the proteins with well-defined 3D structures, thereby impacting their molecular functions. However, it is not known in what way DCMMs affect the intrinsically disordered proteins (IDPs) that do not adopt well defined stable 3D structures. METHODS In order to investigate how DCMMs may impact intrinsically disordered regions (IDRs) in proteins, we undertook Molecular Dynamics (MD) based studies on three different examples of functionally important IDRs with known DCMMs. Our studies revealed that the functional impact of DCMMs is in reducing the conformational heterogeneity of IDRs, which is intrinsic and quintessential for their multi-faceted cellular roles. RESULTS These results are reinforced by energy landscapes of the wildtype and mutant IDRs where the former is characterized by many local minima separated by low barriers, whereas the latter are characterized by one global minimum and several local minima separated by high energy barriers. Our MD based studies also indicate that DCMMs stabilize very few structural possibilities of IDRs either by the newly formed interactions induced by the substituted side chains or by means of restricted or increased flexibilities of the backbone conformations at the mutation sites. CONCLUSION Furthermore, the structural possibilities stabilized by DCMMs do not support the native functional roles of the IDRs, thereby leading to disease conditions.
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Affiliation(s)
| | - Hampapathalu A Nagarajaram
- Laboratory of Computational Biology, Department of Systems and Computational Biology, School of Life Sciences, University of Hyderabad, Hyderabad, India
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24
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Gong X, Zhang Y, Chen J. Advanced Sampling Methods for Multiscale Simulation of Disordered Proteins and Dynamic Interactions. Biomolecules 2021; 11:1416. [PMID: 34680048 PMCID: PMC8533332 DOI: 10.3390/biom11101416] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 09/22/2021] [Accepted: 09/24/2021] [Indexed: 11/16/2022] Open
Abstract
Intrinsically disordered proteins (IDPs) are highly prevalent and play important roles in biology and human diseases. It is now also recognized that many IDPs remain dynamic even in specific complexes and functional assemblies. Computer simulations are essential for deriving a molecular description of the disordered protein ensembles and dynamic interactions for a mechanistic understanding of IDPs in biology, diseases, and therapeutics. Here, we provide an in-depth review of recent advances in the multi-scale simulation of disordered protein states, with a particular emphasis on the development and application of advanced sampling techniques for studying IDPs. These techniques are critical for adequate sampling of the manifold functionally relevant conformational spaces of IDPs. Together with dramatically improved protein force fields, these advanced simulation approaches have achieved substantial success and demonstrated significant promise towards the quantitative and predictive modeling of IDPs and their dynamic interactions. We will also discuss important challenges remaining in the atomistic simulation of larger systems and how various coarse-grained approaches may help to bridge the remaining gaps in the accessible time- and length-scales of IDP simulations.
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Affiliation(s)
- Xiping Gong
- Department of Chemistry, University of Massachusetts Amherst, Amherst, MA 01003, USA; (X.G.); (Y.Z.)
| | - Yumeng Zhang
- Department of Chemistry, University of Massachusetts Amherst, Amherst, MA 01003, USA; (X.G.); (Y.Z.)
| | - Jianhan Chen
- Department of Chemistry, University of Massachusetts Amherst, Amherst, MA 01003, USA; (X.G.); (Y.Z.)
- Department of Biochemistry and Molecular Biology, University of Massachusetts Amherst, Amherst, MA 01003, USA
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25
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Schrag LG, Liu X, Thevarajan I, Prakash O, Zolkiewski M, Chen J. Cancer-Associated Mutations Perturb the Disordered Ensemble and Interactions of the Intrinsically Disordered p53 Transactivation Domain. J Mol Biol 2021; 433:167048. [PMID: 33984364 PMCID: PMC8286338 DOI: 10.1016/j.jmb.2021.167048] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Revised: 04/30/2021] [Accepted: 05/04/2021] [Indexed: 01/08/2023]
Abstract
Intrinsically disordered proteins (IDPs) are key components of regulatory networks that control crucial aspects of cell decision making. The intrinsically disordered transactivation domain (TAD) of tumor suppressor p53 mediates its interactions with multiple regulatory pathways to control the p53 homeostasis during the cellular response to genotoxic stress. Many cancer-associated mutations have been discovered in p53-TAD, but their structural and functional consequences are poorly understood. Here, by combining atomistic simulations, NMR spectroscopy, and binding assays, we demonstrate that cancer-associated mutations can significantly perturb the balance of p53 interactions with key activation and degradation regulators. Importantly, the four mutations studied in this work do not all directly disrupt the known interaction interfaces. Instead, at least three of these mutations likely modulate the disordered state of p53-TAD to perturb its interactions with regulators. Specifically, NMR and simulation analysis together suggest that these mutations can modulate the level of conformational expansion as well as rigidity of the disordered state. Our work suggests that the disordered conformational ensemble of p53-TAD can serve as a central conduit in regulating the response to various cellular stimuli at the protein-protein interaction level. Understanding how the disordered state of IDPs may be modulated by regulatory signals and/or disease associated perturbations will be essential in the studies on the role of IDPs in biology and diseases.
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Affiliation(s)
- Lynn G Schrag
- Department of Biochemistry and Molecular Biophysics, Kansas State University, Manhattan, KS 66505, USA
| | - Xiaorong Liu
- Department of Chemistry, University of Massachusetts Amherst, Amherst, MA 01003, USA
| | - Indhujah Thevarajan
- Department of Biochemistry and Molecular Biophysics, Kansas State University, Manhattan, KS 66505, USA
| | - Om Prakash
- Department of Biochemistry and Molecular Biophysics, Kansas State University, Manhattan, KS 66505, USA.
| | - Michal Zolkiewski
- Department of Biochemistry and Molecular Biophysics, Kansas State University, Manhattan, KS 66505, USA.
| | - Jianhan Chen
- Department of Chemistry, University of Massachusetts Amherst, Amherst, MA 01003, USA; Department of Biochemistry and Molecular Biology, University of Massachusetts Amherst, Amherst, MA 01003, USA.
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26
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On the specificity of protein-protein interactions in the context of disorder. Biochem J 2021; 478:2035-2050. [PMID: 34101805 PMCID: PMC8203207 DOI: 10.1042/bcj20200828] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Revised: 05/14/2021] [Accepted: 05/17/2021] [Indexed: 02/07/2023]
Abstract
With the increased focus on intrinsically disordered proteins (IDPs) and their large interactomes, the question about their specificity — or more so on their multispecificity — arise. Here we recapitulate how specificity and multispecificity are quantified and address through examples if IDPs in this respect differ from globular proteins. The conclusion is that quantitatively, globular proteins and IDPs are similar when it comes to specificity. However, compared with globular proteins, IDPs have larger interactome sizes, a phenomenon that is further enabled by their flexibility, repetitive binding motifs and propensity to adapt to different binding partners. For IDPs, this adaptability, interactome size and a higher degree of multivalency opens for new interaction mechanisms such as facilitated exchange through trimer formation and ultra-sensitivity via threshold effects and ensemble redistribution. IDPs and their interactions, thus, do not compromise the definition of specificity. Instead, it is the sheer size of their interactomes that complicates its calculation. More importantly, it is this size that challenges how we conceptually envision, interpret and speak about their specificity.
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27
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Zhou JB, Xiong Y, An K, Ye ZQ, Wu YD. IDRMutPred: predicting disease-associated germline nonsynonymous single nucleotide variants (nsSNVs) in intrinsically disordered regions. Bioinformatics 2021; 36:4977-4983. [PMID: 32756939 PMCID: PMC7755418 DOI: 10.1093/bioinformatics/btaa618] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Revised: 06/28/2020] [Accepted: 07/01/2020] [Indexed: 01/09/2023] Open
Abstract
Motivation Despite of the lack of folded structure, intrinsically disordered regions (IDRs) of proteins play versatile roles in various biological processes, and many nonsynonymous single nucleotide variants (nsSNVs) in IDRs are associated with human diseases. The continuous accumulation of nsSNVs resulted from the wide application of NGS has driven the development of disease-association prediction methods for decades. However, their performance on nsSNVs in IDRs remains inferior, possibly due to the domination of nsSNVs from structured regions in training data. Therefore, it is highly demanding to build a disease-association predictor specifically for nsSNVs in IDRs with better performance. Results We present IDRMutPred, a machine learning-based tool specifically for predicting disease-associated germline nsSNVs in IDRs. Based on 17 selected optimal features that are extracted from sequence alignments, protein annotations, hydrophobicity indices and disorder scores, IDRMutPred was trained using three ensemble learning algorithms on the training dataset containing only IDR nsSNVs. The evaluation on the two testing datasets shows that all the three prediction models outperform 17 other popular general predictors significantly, achieving the ACC between 0.856 and 0.868 and MCC between 0.713 and 0.737. IDRMutPred will prioritize disease-associated IDR germline nsSNVs more reliably than general predictors. Availability and implementation The software is freely available at http://www.wdspdb.com/IDRMutPred. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jing-Bo Zhou
- Lab of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China
| | - Yao Xiong
- Lab of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China
| | - Ke An
- Lab of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China
| | - Zhi-Qiang Ye
- Lab of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China.,Shenzhen Bay Laboratory, Shenzhen 518055, China
| | - Yun-Dong Wu
- Lab of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China.,Shenzhen Bay Laboratory, Shenzhen 518055, China.,College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
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28
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Zarin T, Strome B, Peng G, Pritišanac I, Forman-Kay JD, Moses AM. Identifying molecular features that are associated with biological function of intrinsically disordered protein regions. eLife 2021; 10:e60220. [PMID: 33616531 PMCID: PMC7932695 DOI: 10.7554/elife.60220] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Accepted: 02/22/2021] [Indexed: 12/17/2022] Open
Abstract
In previous work, we showed that intrinsically disordered regions (IDRs) of proteins contain sequence-distributed molecular features that are conserved over evolution, despite little sequence similarity that can be detected in alignments (Zarin et al., 2019). Here, we aim to use these molecular features to predict specific biological functions for individual IDRs and identify the molecular features within them that are associated with these functions. We find that the predictable functions are diverse. Examining the associated molecular features, we note some that are consistent with previous reports and identify others that were previously unknown. We experimentally confirm that elevated isoelectric point and hydrophobicity, features that are positively associated with mitochondrial localization, are necessary for mitochondrial targeting function. Remarkably, increasing isoelectric point in a synthetic IDR restores weak mitochondrial targeting. We believe feature analysis represents a new systematic approach to understand how biological functions of IDRs are specified by their protein sequences.
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Affiliation(s)
- Taraneh Zarin
- Department of Cell and Systems Biology, University of TorontoTorontoCanada
| | - Bob Strome
- Department of Cell and Systems Biology, University of TorontoTorontoCanada
| | - Gang Peng
- Department of Cell and Systems Biology, University of TorontoTorontoCanada
| | - Iva Pritišanac
- Department of Cell and Systems Biology, University of TorontoTorontoCanada
- Program in Molecular Medicine, Hospital for Sick ChildrenTorontoCanada
| | - Julie D Forman-Kay
- Program in Molecular Medicine, Hospital for Sick ChildrenTorontoCanada
- Department of Biochemistry, University of TorontoTorontoCanada
| | - Alan M Moses
- Department of Cell and Systems Biology, University of TorontoTorontoCanada
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29
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A disease-causing mutation K240E disrupts ferroportin trafficking by SUMO (ferroportin SUMOylation). Biochem Biophys Rep 2021; 25:100873. [PMID: 33490642 PMCID: PMC7809393 DOI: 10.1016/j.bbrep.2020.100873] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Revised: 10/20/2020] [Accepted: 12/10/2020] [Indexed: 01/01/2023] Open
Abstract
Ferroportin (Fpn/IREG1/MTP1) is the only known transporter mediating iron efflux from epithelial cells and macrophages, and thus regulates how much iron is released into the circulation. Consequently, Fpn mutations are associated with haemochromatosis. Fpn itself is post-translationally regulated by hepcidin (Hepc) which induces its redistribution and degradation in a ubiquitin-dependent process. Together, the two proteins appear to be the nexus for iron homeostasis. Here we show that a rare gain-of-function mutation (K240E) that is associated with iron overload, impedes Fpn binding and subcellular trafficking by the small ubiquitin-like modifier (SUMO). Whereas wild-type Fpn is ensconced within vesicular bodies, the FpnK240E mutant appeared diffused within the cell when co-expressed with SUMO. Furthermore, compared with wild type Fpn, the sumoylation-defective mutant was constitutively-active, resulting in a lower intracellular labile iron pool than the former. These findings suggest that SUMO may regulate iron homeostasis by controlling Fpn trafficking. Ferroportin (Fpn) regulates iron efflux. A disease causing mutation (K240E) in a patient causes iron-overload. Fpn K240 is a SUMO conjugation site important for Fpn trafficking to endosomes by SUMO. The Fpn mutant K240E cannot be trafficked properly by SUMO and is a gain-of-function mutant that is constitutively active. FpnK240E effluxes more iron from intracellular stores than wild type Fpn.
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30
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Tsang B, Pritišanac I, Scherer SW, Moses AM, Forman-Kay JD. Phase Separation as a Missing Mechanism for Interpretation of Disease Mutations. Cell 2020; 183:1742-1756. [DOI: 10.1016/j.cell.2020.11.050] [Citation(s) in RCA: 69] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Revised: 11/04/2020] [Accepted: 11/25/2020] [Indexed: 02/08/2023]
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31
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Carmi G, Tagore S, Gorohovski A, Sivan A, Raviv-Shay D, Frenkel-Morgenstern M. Design principles of gene evolution for niche adaptation through changes in protein-protein interaction networks. Sci Rep 2020; 10:15628. [PMID: 32973219 PMCID: PMC7519090 DOI: 10.1038/s41598-020-71976-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Accepted: 08/24/2020] [Indexed: 12/15/2022] Open
Abstract
In contrast to fossorial and above-ground organisms, subterranean species have adapted to the extreme stresses of living underground. We analyzed the predicted protein–protein interactions (PPIs) of all gene products, including those of stress-response genes, among nine subterranean, ten fossorial, and 13 aboveground species. We considered 10,314 unique orthologous protein families and constructed 5,879,879 PPIs in all organisms using ChiPPI. We found strong association between PPI network modulation and adaptation to specific habitats, noting that mutations in genes and changes in protein sequences were not linked directly with niche adaptation in the organisms sampled. Thus, orthologous hypoxia, heat-shock, and circadian clock proteins were found to cluster according to habitat, based on PPIs rather than on sequence similarities. Curiously, "ordered" domains were preserved in aboveground species, while "disordered" domains were conserved in subterranean organisms, and confirmed for proteins in DistProt database. Furthermore, proteins with disordered regions were found to adopt significantly less optimal codon usage in subterranean species than in fossorial and above-ground species. These findings reveal design principles of protein networks by means of alterations in protein domains, thus providing insight into deep mechanisms of evolutionary adaptation, generally, and particularly of species to underground living and other confined habitats.
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Affiliation(s)
- Gon Carmi
- The Azrieli Faculty of Medicine, Bar-Ilan University, 8 Henrietta Szold St, 13195, Safed, Israel
| | - Somnath Tagore
- The Azrieli Faculty of Medicine, Bar-Ilan University, 8 Henrietta Szold St, 13195, Safed, Israel.,Department of Systems Biology, Columbia University Medical Center, Herbert Irving Cancer Research Center, New York, USA
| | - Alessandro Gorohovski
- The Azrieli Faculty of Medicine, Bar-Ilan University, 8 Henrietta Szold St, 13195, Safed, Israel
| | - Aviad Sivan
- The Azrieli Faculty of Medicine, Bar-Ilan University, 8 Henrietta Szold St, 13195, Safed, Israel
| | - Dorith Raviv-Shay
- The Azrieli Faculty of Medicine, Bar-Ilan University, 8 Henrietta Szold St, 13195, Safed, Israel
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32
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Hiraoka M, Ishikawa A, Matsuzawa F, Aikawa SI, Sakurai A. A variant in the RP1L1 gene in a family with occult macular dystrophy in a predicted intrinsically disordered region. Ophthalmic Genet 2020; 41:599-605. [PMID: 32940107 DOI: 10.1080/13816810.2020.1821383] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
SIGNIFICANCE The responsible genetic variants for occult macular dystrophy (OMD) were found at the predicted intrinsically disordered region (IDR) of the RP1L1 gene. PURPOSE We examined the phenotypes and genotypes of family members from OMD. In addition, the genetic characteristics of the RP1L1 gene in OMD were investigated. METHODS Whole-exome sequencing was applied on two affected family members, and Sanger sequencing was performed on three members. The structural property of RP1L1 and pathogenic variants was analyzed using predictor of natural disordered regions (PONDR). RESULTS Two affected members showed moderate visual impairment and relative central scotoma. The spectral domain optical coherence tomography (SD-OCT) images showed an absence of the interdigitation zone (IZ) and ellipsoid zone (EZ) in one case, and an obscure EZ line in the other case. A RP1L1 variant (c.3593 C > T, p.Ser1198Phe) was identified in two affected members but not in the unaffected member. The PONDR analysis showed that the region from p.1189 to p.1248 could be predicted to be an IDR in the RP1L1 molecule. And the p. Ser1198Phe variant showed significant reduction of PONDR score. CONCLUSIONS Although, the major pathogenic variant of OMD is p.Arg45Trp, multiple reports indicate that the region between p.1194 and p.1201 is another hot spot of OMD. The PONDR analysis predicted that the RP1L1 molecule is one of the intrinsically disordered proteins. It is speculated that the region around p.1200 is essential for the normal function of the RP1L1 molecule, and the missense variants of that area cause the development of OMD.
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Affiliation(s)
- Miki Hiraoka
- Department of Ophthalmology, Health Sciences University of Hokkaido , Sapporo, Hokkaido, Japan
| | - Aki Ishikawa
- Department of Medical Genetics and Genomics, Sapporo Medical University , Sapporo, Hokkaido Japan
| | | | | | - Akihiro Sakurai
- Department of Medical Genetics and Genomics, Sapporo Medical University , Sapporo, Hokkaido Japan
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33
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Chen J, Liu X, Chen J. Targeting Intrinsically Disordered Proteins through Dynamic Interactions. Biomolecules 2020; 10:E743. [PMID: 32403216 PMCID: PMC7277182 DOI: 10.3390/biom10050743] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Revised: 05/04/2020] [Accepted: 05/09/2020] [Indexed: 12/18/2022] Open
Abstract
Intrinsically disordered proteins (IDPs) are over-represented in major disease pathways and have attracted significant interest in understanding if and how they may be targeted using small molecules for therapeutic purposes. While most existing studies have focused on extending the traditional structure-centric drug design strategies and emphasized exploring pre-existing structure features of IDPs for specific binding, several examples have also emerged to suggest that small molecules could achieve specificity in binding IDPs and affect their function through dynamic and transient interactions. These dynamic interactions can modulate the disordered conformational ensemble and often lead to modest compaction to shield functionally important interaction sites. Much work remains to be done on further elucidation of the molecular basis of the dynamic small molecule-IDP interaction and determining how it can be exploited for targeting IDPs in practice. These efforts will rely critically on an integrated experimental and computational framework for disordered protein ensemble characterization. In particular, exciting advances have been made in recent years in enhanced sampling techniques, Graphic Processing Unit (GPU)-computing, and protein force field optimization, which have now allowed rigorous physics-based atomistic simulations to generate reliable structure ensembles for nontrivial IDPs of modest sizes. Such de novo atomistic simulations will play crucial roles in exploring the exciting opportunity of targeting IDPs through dynamic interactions.
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Affiliation(s)
- Jianlin Chen
- Department of Hematology, Taizhou Central Hospital (Taizhou University Hospital), Taizhou 318000, Zhejiang, China;
| | - Xiaorong Liu
- Department of Chemistry, University of Massachusetts, Amherst, MA 01003, USA;
| | - Jianhan Chen
- Department of Chemistry, University of Massachusetts, Amherst, MA 01003, USA;
- Department of Biochemistry and Molecular Biology, University of Massachusetts, Amherst, MA 01003, USA
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34
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Liu X, Chen J. Modulation of p53 Transactivation Domain Conformations by Ligand Binding and Cancer-Associated Mutations. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2020; 25:195-206. [PMID: 31797597 PMCID: PMC6934143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Intrinsically disordered proteins (IDPs) are important functional proteins, and their deregulation are linked to numerous human diseases including cancers. Understanding how disease-associated mutations or drug molecules can perturb the sequence-disordered ensemble-function-disease relationship of IDPs remains challenging, because it requires detailed characterization of the heterogeneous structural ensembles of IDPs. In this work, we combine the latest atomistic force field a99SB-disp, enhanced sampling technique replica exchange with solute tempering, and GPU-accelerated molecular dynamics simulations to investigate how four cancer-associated mutations, K24N, N29K/N30D, D49Y, and W53G, and binding of an anti-cancer molecule, epigallocatechin gallate (EGCG), modulate the disordered ensemble of the transactivation domain (TAD) of tumor suppressor p53. Through extensive sampling, in excess of 1.0 μs per replica, well-converged structural ensembles of wild-type and mutant p53-TAD as well as WT p53-TAD in the presence of EGCG were generated. The results reveal that mutants could induce local structural changes and affect secondary structural properties. Interestingly, both EGCG binding and N29K/N30D could also induce long-range structural reorganizations and lead to more compact structures that could shield key binding sites of p53-TAD regulators. Further analysis reveals that the effects of EGCG binding are mainly achieved through nonspecific interactions. These observations are generally consistent with on-going NMR studies and binding assays. Our studies suggest that induced conformational collapse of IDPs may be a general mechanism for shielding functional sites, thus inhibiting recognition of their targets. The current study also demonstrates that atomistic simulations provide a viable approach for studying the sequence-disordered ensemble-function-disease relationships of IDPs and developing new drug design strategies targeting regulatory IDPs.
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Affiliation(s)
- Xiaorong Liu
- Department of Chemistry, University of Massachusetts Amherst, Amherst, MA 01003, USA
| | - Jianhan Chen
- Department of Chemistry, University of Massachusetts Amherst, Amherst, MA 01003, USA,Department of Biochemistry and Molecular Biology, University of Massachusetts Amherst, Amherst, MA 01003, USA,
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35
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Pagel KA, Antaki D, Lian A, Mort M, Cooper DN, Sebat J, Iakoucheva LM, Mooney SD, Radivojac P. Pathogenicity and functional impact of non-frameshifting insertion/deletion variation in the human genome. PLoS Comput Biol 2019; 15:e1007112. [PMID: 31199787 PMCID: PMC6594643 DOI: 10.1371/journal.pcbi.1007112] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2018] [Revised: 06/26/2019] [Accepted: 05/17/2019] [Indexed: 11/19/2022] Open
Abstract
Differentiation between phenotypically neutral and disease-causing genetic variation remains an open and relevant problem. Among different types of variation, non-frameshifting insertions and deletions (indels) represent an understudied group with widespread phenotypic consequences. To address this challenge, we present a machine learning method, MutPred-Indel, that predicts pathogenicity and identifies types of functional residues impacted by non-frameshifting insertion/deletion variation. The model shows good predictive performance as well as the ability to identify impacted structural and functional residues including secondary structure, intrinsic disorder, metal and macromolecular binding, post-translational modifications, allosteric sites, and catalytic residues. We identify structural and functional mechanisms impacted preferentially by germline variation from the Human Gene Mutation Database, recurrent somatic variation from COSMIC in the context of different cancers, as well as de novo variants from families with autism spectrum disorder. Further, the distributions of pathogenicity prediction scores generated by MutPred-Indel are shown to differentiate highly recurrent from non-recurrent somatic variation. Collectively, we present a framework to facilitate the interrogation of both pathogenicity and the functional effects of non-frameshifting insertion/deletion variants. The MutPred-Indel webserver is available at http://mutpred.mutdb.org/.
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Affiliation(s)
- Kymberleigh A. Pagel
- School of Informatics, Computing, and Engineering, Indiana University, Bloomington, Indiana, United States of America
| | - Danny Antaki
- Department of Psychiatry, University of California San Diego, La Jolla, California, United States of America
| | - AoJie Lian
- Department of Psychiatry, University of California San Diego, La Jolla, California, United States of America
- Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, China
| | - Matthew Mort
- Institute of Medical Genetics, Cardiff University, Cardiff, United Kingdom
| | - David N. Cooper
- Institute of Medical Genetics, Cardiff University, Cardiff, United Kingdom
| | - Jonathan Sebat
- Department of Psychiatry, University of California San Diego, La Jolla, California, United States of America
| | - Lilia M. Iakoucheva
- Department of Psychiatry, University of California San Diego, La Jolla, California, United States of America
| | - Sean D. Mooney
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington, United States of America
| | - Predrag Radivojac
- School of Informatics, Computing, and Engineering, Indiana University, Bloomington, Indiana, United States of America
- Khoury College of Computer Sciences, Northeastern University, Boston, Massachusetts, United States of America
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36
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Li Y, Zhang Y, Li X, Yi S, Xu J. Gain-of-Function Mutations: An Emerging Advantage for Cancer Biology. Trends Biochem Sci 2019; 44:659-674. [PMID: 31047772 DOI: 10.1016/j.tibs.2019.03.009] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Revised: 03/21/2019] [Accepted: 03/26/2019] [Indexed: 02/08/2023]
Abstract
Advances in next-generation sequencing have identified thousands of genomic variants that perturb the normal functions of proteins, further contributing to diverse phenotypic consequences in cancer. Elucidating the functional pathways altered by loss-of-function (LOF) or gain-of-function (GOF) mutations will be crucial for prioritizing cancer-causing variants and their resultant therapeutic liabilities. In this review, we highlight the fundamental function of GOF mutations and discuss the potential mechanistic effects in the context of signaling networks. We also summarize advances in experimental and computational resources, which will dramatically help with studies on the functional and phenotypic consequences of mutations. Together, systematic investigations of the function of GOF mutations will provide an important missing piece for cancer biology and precision therapy.
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Affiliation(s)
- Yongsheng Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China; Department of Oncology, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA
| | - Yunpeng Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Xia Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China; College of Bioinformatics, Hainan Medical University, Haikou 570100, China.
| | - Song Yi
- Department of Oncology, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA; Department of Biomedical Engineering, Cockrell School of Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
| | - Juan Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
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37
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Peng Y, Alexov E, Basu S. Structural Perspective on Revealing and Altering Molecular Functions of Genetic Variants Linked with Diseases. Int J Mol Sci 2019; 20:ijms20030548. [PMID: 30696058 PMCID: PMC6386852 DOI: 10.3390/ijms20030548] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2018] [Revised: 01/25/2019] [Accepted: 01/26/2019] [Indexed: 12/25/2022] Open
Abstract
Structural information of biological macromolecules is crucial and necessary to deliver predictions about the effects of mutations-whether polymorphic or deleterious (i.e., disease causing), wherein, thermodynamic parameters, namely, folding and binding free energies potentially serve as effective biomarkers. It may be emphasized that the effect of a mutation depends on various factors, including the type of protein (globular, membrane or intrinsically disordered protein) and the structural context in which it occurs. Such information may positively aid drug-design. Furthermore, due to the intrinsic plasticity of proteins, even mutations involving radical change of the structural and physico⁻chemical properties of the amino acids (native vs. mutant) can still have minimal effects on protein thermodynamics. However, if a mutation causes significant perturbation by either folding or binding free energies, it is quite likely to be deleterious. Mitigating such effects is a promising alternative to the traditional approaches of designing inhibitors. This can be done by structure-based in silico screening of small molecules for which binding to the dysfunctional protein restores its wild type thermodynamics. In this review we emphasize the effects of mutations on two important biophysical properties, stability and binding affinity, and how structures can be used for structure-based drug design to mitigate the effects of disease-causing variants on the above biophysical properties.
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Affiliation(s)
- Yunhui Peng
- Department of Physics and Astronomy, Clemson University, Clemson, SC 29634, USA.
| | - Emil Alexov
- Department of Physics and Astronomy, Clemson University, Clemson, SC 29634, USA.
| | - Sankar Basu
- Department of Physics and Astronomy, Clemson University, Clemson, SC 29634, USA.
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38
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Dobson L, Mészáros B, Tusnády GE. Structural Principles Governing Disease-Causing Germline Mutations. J Mol Biol 2018; 430:4955-4970. [DOI: 10.1016/j.jmb.2018.10.005] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2018] [Accepted: 10/11/2018] [Indexed: 01/03/2023]
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39
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Kosciolek T, Buchan DWA, Jones DT. Predictions of Backbone Dynamics in Intrinsically Disordered Proteins Using De Novo Fragment-Based Protein Structure Predictions. Sci Rep 2017; 7:6999. [PMID: 28765603 PMCID: PMC5539115 DOI: 10.1038/s41598-017-07156-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2017] [Accepted: 06/23/2017] [Indexed: 11/08/2022] Open
Abstract
Intrinsically disordaered proteins (IDPs) are a prevalent phenomenon with over 30% of human proteins estimated to have long disordered regions. Computational methods are widely used to study IDPs, however, nearly all treat disorder in a binary fashion, not accounting for the structural heterogeneity present in disordered regions. Here, we present a new de novo method, FRAGFOLD-IDP, which addresses this problem. Using 200 protein structural ensembles derived from NMR, we show that FRAGFOLD-IDP achieves superior results compared to methods which can predict related data (NMR order parameter, or crystallographic B-factor). FRAGFOLD-IDP produces very good predictions for 33.5% of cases and helps to get a better insight into the dynamics of the disordered ensembles. The results also show it is not necessary to predict the correct fold of the protein to reliably predict per-residue fluctuations. It implies that disorder is a local property and it does not depend on the fold. Our results are orthogonal to DynaMine, the only other method significantly better than the naïve prediction. We therefore combine these two using a neural network. FRAGFOLD-IDP enables better insight into backbone dynamics in IDPs and opens exciting possibilities for the design of disordered ensembles, disorder-to-order transitions, or design for protein dynamics.
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Affiliation(s)
- Tomasz Kosciolek
- Bioinformatics Group, Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, United Kingdom
- Department of Pediatrics, University of California San Diego, La Jolla, CA, 92093, USA
| | - Daniel W A Buchan
- Bioinformatics Group, Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, United Kingdom
| | - David T Jones
- Bioinformatics Group, Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, United Kingdom.
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40
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The contribution of intrinsically disordered regions to protein function, cellular complexity, and human disease. Biochem Soc Trans 2017; 44:1185-1200. [PMID: 27911701 PMCID: PMC5095923 DOI: 10.1042/bst20160172] [Citation(s) in RCA: 263] [Impact Index Per Article: 37.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2016] [Revised: 07/20/2016] [Accepted: 07/22/2016] [Indexed: 12/23/2022]
Abstract
In the 1960s, Christian Anfinsen postulated that the unique three-dimensional structure of a protein is determined by its amino acid sequence. This work laid the foundation for the sequence–structure–function paradigm, which states that the sequence of a protein determines its structure, and structure determines function. However, a class of polypeptide segments called intrinsically disordered regions does not conform to this postulate. In this review, I will first describe established and emerging ideas about how disordered regions contribute to protein function. I will then discuss molecular principles by which regulatory mechanisms, such as alternative splicing and asymmetric localization of transcripts that encode disordered regions, can increase the functional versatility of proteins. Finally, I will discuss how disordered regions contribute to human disease and the emergence of cellular complexity during organismal evolution.
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41
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Mohanasundaram KA, Grover MP, Crowley TM, Goscinski A, Wouters MA. Mapping genotype-phenotype associations of nsSNPs in coiled-coil oligomerization domains of the human proteome. Hum Mutat 2017; 38:1378-1393. [PMID: 28489284 DOI: 10.1002/humu.23252] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2016] [Revised: 04/13/2017] [Accepted: 05/05/2017] [Indexed: 11/11/2022]
Abstract
We assessed the impact of disease mutations (DMs) versus polymorphisms (PYs) in coiled-coil (CC) domains in UniProt by modeling the structural and functional impact of variants in silico with the CC prediction program Multicoil. The structural impact of variants was evaluated with respect to three main metrics: the oligomerization score-to determine whether the variant is stabilizing or destabilizing-the oligomerization state, and the register-specific score. The functional impact was queried indirectly in several ways. First, we examined marginally stable CCs that were either stabilized or destabilized by the variant. Second, we looked for variants that altered the register of the wild-type CC near wild-type irregularities of likely functional importance, such as skips and stammers. Third, we searched for variants that altered the oligomerization state of the CC. DMs tended to be more destabilizing than PYs; but interestingly, PYs were more frequently associated with predicted changes in the oligomerization state. The functional impact was also queried by testing the association of CC variants with multiple phenotypes, that is, pleiotropy. Mutations in CC regions of proteins cause 155 different phenotypes and are more frequently associated with pleiotropy than proteins in general. Importantly, the CC region itself often encodes the pleiotropy.
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Affiliation(s)
| | - Mani P Grover
- School of Medicine, Deakin University, Geelong, Victoria, Australia
| | - Tamsyn M Crowley
- School of Medicine, Deakin University, Geelong, Victoria, Australia.,Australian Animal Health Laboratory, CSIRO Biosecurity Flagship, Geelong, Victoria, Australia
| | - Andrzej Goscinski
- School of Information Technology, Faculty of Science Engineering and Built Environment, Deakin University, Geelong, Victoria, Australia
| | - Merridee A Wouters
- School of Medicine, Deakin University, Geelong, Victoria, Australia.,Olivia Newton-John Cancer Research Institute, Heidelberg, Victoria, Australia.,School of Cancer Medicine, La Trobe University, Bundoora, Victoria, Australia
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42
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Kumar D, Sharma N, Giri R. Therapeutic Interventions of Cancers Using Intrinsically Disordered Proteins as Drug Targets: c-Myc as Model System. Cancer Inform 2017; 16:1176935117699408. [PMID: 28469390 PMCID: PMC5392011 DOI: 10.1177/1176935117699408] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2016] [Accepted: 02/20/2017] [Indexed: 12/29/2022] Open
Abstract
The concept of protein intrinsic disorder has taken the driving seat to understand regulatory proteins in general. Reports suggest that in mammals nearly 75% of signalling proteins contain long disordered regions with greater than 30 amino acid residues. Therefore, intrinsically disordered proteins (IDPs) have been implicated in several human diseases and should be considered as potential novel drug targets. Moreover, intrinsic disorder provides a huge multifunctional capability to hub proteins such as c-Myc and p53. c-Myc is the hot spot for understanding and developing therapeutics against cancers and cancer stem cells. Our past understanding is mainly based on in vitro and in vivo experiments conducted using c-Myc as whole protein. Using the reductionist approach, c-Myc oncoprotein has been divided into structured and disordered domains. A wealth of data is available dealing with the structured perspectives of c-Myc, but understanding c-Myc in terms of disordered domains has just begun. Disorderness provides enormous flexibility to proteins in general for binding to numerous partners. Here, we have reviewed the current progress on understanding c-Myc using the emerging concept of IDPs.
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Affiliation(s)
- Deepak Kumar
- School of Basic Sciences, Indian Institute of Technology Mandi, Mandi, India
| | - Nitin Sharma
- School of Basic Sciences, Indian Institute of Technology Mandi, Mandi, India
| | - Rajanish Giri
- School of Basic Sciences, Indian Institute of Technology Mandi, Mandi, India
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43
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Kumar V, Sami N, Kashav T, Islam A, Ahmad F, Hassan MI. Protein aggregation and neurodegenerative diseases: From theory to therapy. Eur J Med Chem 2016; 124:1105-1120. [DOI: 10.1016/j.ejmech.2016.07.054] [Citation(s) in RCA: 97] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2015] [Revised: 07/20/2016] [Accepted: 07/22/2016] [Indexed: 12/23/2022]
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44
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Tovo-Rodrigues L, Recamonde-Mendoza M, Paixão-Côrtes VR, Bruxel EM, Schuch JB, Friedrich DC, Rohde LA, Hutz MH. The role of protein intrinsic disorder in major psychiatric disorders. Am J Med Genet B Neuropsychiatr Genet 2016; 171:848-60. [PMID: 27184105 DOI: 10.1002/ajmg.b.32455] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/03/2015] [Accepted: 04/22/2016] [Indexed: 01/26/2023]
Abstract
Although new candidate genes for Autism Spectrum Disorder (ASD), Schizophrenia (SCZ), Attention-Deficit/Hyperactivity Disorder (ADHD), and Bipolar Disorder (BD) emerged from genome-wide association studies (GWAS), their underlying molecular mechanisms remain poorly understood. Evidences of the involvement of intrinsically disordered proteins in diseases have grown in the last decade. These proteins lack tridimensional structure under physiological conditions and are involved in important cellular functions such as signaling, recognition and regulation. The aim of the present study was to identify the role and abundance of intrinsically disordered proteins in a set of psychiatric diseases and to test whether diseases are different regarding protein intrinsic disorder. Our hypothesis is that differences across psychiatric illnesses phenotypes and symptoms may arise from differences in intrinsic protein disorder content and properties of each group. A bioinformatics prediction of intrinsic disorder was performed in proteins retrieved based on top findings from GWAS, Copy Number Variation and candidate gene investigations for each disease. This approach revealed that about 80% of studied proteins presented long stretches of disorder. This amount was significantly higher than that observed in general eukaryotic proteins, and those involved in cardiovascular diseases. These results suggest that proteins with intrinsic disorder are a common feature of neurodevelopment and synaptic transmission processes which are potentially involved in the etiology of psychiatric diseases. Moreover, we identified differences between ADHD and ASD when the binary prediction of structure and putative binding sites were compared. These differences may be related to variation in symptom complexity between both diseases. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Luciana Tovo-Rodrigues
- Department of Genetics, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil.,Postgraduate Program in Epidemiology, Federal University of Pelotas, Pelotas, RS, Brazil
| | - Mariana Recamonde-Mendoza
- Experimental and Molecular Cardiovascular Laboratory, Hospital de Clinicas de Porto Alegre, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil
| | | | - Estela M Bruxel
- Department of Genetics, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Jaqueline B Schuch
- Department of Genetics, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Deise C Friedrich
- Department of Genetics, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Luis A Rohde
- Child and Adolescent Psychiatric Division, Hospital de Clinicas de Porto Alegre, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil.,National Institute of Developmental Psychiatry for Children and Adolescents (INCT-CNPq), Brazil
| | - Mara H Hutz
- Department of Genetics, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil
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45
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Singh S, Colonna G, Di Bernardo G, Bergantino F, Cammarota M, Castello G, Costantini S. The gene expression profiling of hepatocellular carcinoma by a network analysis approach shows a dominance of intrinsically disordered proteins (IDPs) between hub nodes. MOLECULAR BIOSYSTEMS 2016; 11:2933-45. [PMID: 26267014 DOI: 10.1039/c5mb00434a] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
We have analyzed the transcriptomic data from patients with hepatocellular carcinoma (HCC) after viral HCV infection at the various stages of the disease by means of a networking analysis using the publicly available E-MTAB-950 dataset. The data was compared with those obtained in our group from HepG2 cells, a cancer cell line that lacks the viral infection. By sequential pruning of data, and also taking into account the data from cells of healthy patients as blanks, we were able to obtain a distribution of hub genes for the various stages that characterize the disease and finally, we isolated a metabolic sub-net specific to HCC alone. The general picture is that the basic organization to energetically and metabolically sustain the cells in both the normal and diseased conditions is the same, but a complex cluster of sub-networks controlled by hub genes drives the HCC progression with high metabolic flexibility and plasticity. In particular, we have extracted a sub-net of genes strictly correlated to other hub genes of the network from HepG2 cells, but specific for the HCC and mainly devoted to: (i) control at chromatin levels of cell division; (ii) control of ergastoplasmatic stress through protein degradation and misfolding; (iii) control of the immune response also through an increase of mature T-cells in the thymus. This sub-net is characterized by 26 hub genes coding for intrinsically disordered proteins with a high ability to interact with numerous molecular partners. Moreover, we have also noted that periphery molecules, that is, with one or very few interactions (e.g., cytokines or post-translational enzymes), which do not have a central role in the clusters that make up the global metabolic network, essentially have roles as information transporters. The results evidence a strong presence of intrinsically disordered proteins with key roles as hubs in the sub-networks that characterize the various stages of the disease, conferring a structural plasticity to the net nodes but an inherent functional versatility to the whole metabolic net. Thus, our present article provides a novel way of targeting the intrinsic disorder in HCC networks to dampen the cancer effects and provides new insight into the potential mechanisms of HCC. Taken together, the present findings suggest novel targets to design strategies for drug design and may support a rational intervention in the pharmacotherapy of HCC and other associated diseases.
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Affiliation(s)
- Sakshi Singh
- Dottorato in Biologia Computazionale, Dipartimento di Biochimica, Biofisica e Patologia generale, Seconda Università degli Studi di Napoli, Napoli, Italy
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46
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Rost B, Radivojac P, Bromberg Y. Protein function in precision medicine: deep understanding with machine learning. FEBS Lett 2016; 590:2327-41. [PMID: 27423136 PMCID: PMC5937700 DOI: 10.1002/1873-3468.12307] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2016] [Revised: 07/12/2016] [Accepted: 07/12/2016] [Indexed: 12/21/2022]
Abstract
Precision medicine and personalized health efforts propose leveraging complex molecular, medical and family history, along with other types of personal data toward better life. We argue that this ambitious objective will require advanced and specialized machine learning solutions. Simply skimming some low-hanging results off the data wealth might have limited potential. Instead, we need to better understand all parts of the system to define medically relevant causes and effects: how do particular sequence variants affect particular proteins and pathways? How do these effects, in turn, cause the health or disease-related phenotype? Toward this end, deeper understanding will not simply diffuse from deeper machine learning, but from more explicit focus on understanding protein function, context-specific protein interaction networks, and impact of variation on both.
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Affiliation(s)
- Burkhard Rost
- Department of Informatics and Bioinformatics, Institute for Advanced Studies, Technical University of Munich, Garching, Germany
| | - Predrag Radivojac
- School of Informatics and Computing, Indiana University, Bloomington, IN, USA
| | - Yana Bromberg
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, NJ, USA
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47
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Simulation of coupled folding and binding of an intrinsically disordered protein in explicit solvent with metadynamics. J Mol Graph Model 2016; 68:114-127. [PMID: 27423742 DOI: 10.1016/j.jmgm.2016.06.015] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2015] [Revised: 06/22/2016] [Accepted: 06/29/2016] [Indexed: 11/22/2022]
Abstract
The C-terminal domain of measles virus nucleoprotein is an intrinsically disordered protein that could bind to the X domain (XD) of phosphoprotein P to exert its physiological function. Experiments reveal that the minimal binding unit is a 21-residue α-helical molecular recognition element (α-MoRE-MeV), which adopts a fully helical conformation upon binding to XD. Due to currently limited computing power, direct simulation of this coupled folding and binding process with atomic force field in explicit solvent cannot be achieved. In this work, two advanced sampling methods, metadynamics and parallel tempering, are combined to characterize the free energy surface of this process and investigate the underlying mechanism. Starting from an unbound and partially folded state of α-MoRE-MeV, multiple folding and binding events are observed during the simulation and the energy landscape was well estimated. The results demonstrate that the isolated α-MoRE-MeV resembles a molten globule and rapidly interconverts between random coil and multiple partially helical states in solution. The coupled folding and binding process occurs through the induced fit mechanism, with the residual helical conformations providing the initial binding sites. Upon binding, α-MoRE-MeV can easily fold into helical conformation without obvious energy barriers. Two mechanisms, namely, the system tending to adopt the structure in which the free energy of isolated α-MoRE-MeV is the minimum, and the binding energy of α-MoRE-MeV to its partner protein XD tending to the minimum, jointly dominate the coupled folding and binding process. With the advanced sampling approach, more IDP systems could be simulated and common mechanisms concerning the coupled folding and binding process could be investigated in the future.
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48
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Niroula A, Vihinen M. Variation Interpretation Predictors: Principles, Types, Performance, and Choice. Hum Mutat 2016; 37:579-97. [DOI: 10.1002/humu.22987] [Citation(s) in RCA: 90] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2015] [Accepted: 03/07/2016] [Indexed: 12/18/2022]
Affiliation(s)
- Abhishek Niroula
- Department of Experimental Medical Science; Lund University; BMC B13 Lund SE-22184 Sweden
| | - Mauno Vihinen
- Department of Experimental Medical Science; Lund University; BMC B13 Lund SE-22184 Sweden
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49
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Schuch JB, Paixão-Côrtes VR, Friedrich DC, Tovo-Rodrigues L. The contribution of protein intrinsic disorder to understand the role of genetic variants uncovered by autism spectrum disorders exome studies. Am J Med Genet B Neuropsychiatr Genet 2016; 171B:479-91. [PMID: 26892727 DOI: 10.1002/ajmg.b.32431] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2015] [Accepted: 02/04/2016] [Indexed: 01/09/2023]
Abstract
Several autism spectrum disorders (ASD) exome studies suggest that coding single nucleotide variants (SNVs) play an important role on ASD etiology. Usually, the pathogenic effect of missense mutations is estimated through predictors that lose accuracy for those SNVs placed in intrinsically disordered regions of protein. Here, we used bioinformatics tools to investigate the effect of mutations described in ASD published exome studies (549 mutations) in protein disorder, considering post-translational modification, PEST and Molecular Recognition Features (MoRFs) motifs. Schizophrenia and type 2 diabetes (T2D) datasets were created for comparison purposes. The frequency of mutations predicted as disordered was comparable among the three datasets (38.1% in ASD, 35.7% in schizophrenia, 46.4% in T2D). However, the frequency of SNVs predicted to lead a gain or loss of functional sites or change intrinsic disorder tendencies was higher in ASD and schizophrenia than T2D (46.9%, 36.4%, and 23.1%, respectively). The results obtained by SIFT and PolyPhen-2 indicated that 38.9% and 34.4% of the mutations predicted, respectively, as tolerated and benign showed functional alterations in disorder properties. Given the frequency of mutations placed in IDRs and their functional impact, this study suggests that alterations in intrinsic disorder properties might play a role in ASD and schizophrenia etiologies. They should be taken into consideration when researching the pathogenicity of mutations in neurodevelopmental and psychiatric diseases. Finally, mutations with functional alterations in disorder properties must be potential targets for in vitro and in vivo functional studies.
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Affiliation(s)
- Jaqueline Bohrer Schuch
- Department of Genetics, Federal University of Rio Grande do Sul (UFRGS), Porto Alegre, Rio Grande do Sul, Brazil
| | | | - Deise C Friedrich
- Department of Cellular and Molecular Biology, Pontifical Catholic University of Rio Grande do Sul (PUCRS), Porto Alegre, Rio Grande do Sul, Brazil
| | - Luciana Tovo-Rodrigues
- Postgraduate Program in Epidemiology, Federal University of Pelotas (UFPel), Pelotas, Rio Grande do Sul, Brazil
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50
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Kannan S, Lane DP, Verma CS. Long range recognition and selection in IDPs: the interactions of the C-terminus of p53. Sci Rep 2016; 6:23750. [PMID: 27030593 PMCID: PMC4814905 DOI: 10.1038/srep23750] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2015] [Accepted: 03/15/2016] [Indexed: 11/09/2022] Open
Abstract
The C-terminal domain of p53 is an extensively studied IDP, interacting with different partners through multiple distinct conformations. To explore the interplay between preformed structural elements and intrinsic fluctuations in its folding and binding we combine extensive atomistic equilibrium and non-equilibrium simulations. We find that the free peptide segment rapidly interconverts between ordered and disordered states with significant populations of the conformations that are seen in the complexed states. The underlying global folding-binding landscape points to a synergistic mechanism in which recognition is dictated via long range electrostatic recognition which results in the formation of reactive structures as far away as 10 Å, and binding proceeds with the steering of selected conformations followed by induced folding at the target surface or within a close range.
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Affiliation(s)
| | - David P Lane
- p53 Laboratory (A*STAR), 8A Biomedical Grove, #06-04/05, Neuros/Immunos, Singapore 138648
| | - Chandra S Verma
- Bioinformatics Institute (A*STAR), 30 Biopolis Street, #07-01 Matrix, Singapore 138671.,School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, Singapore 637551.,Department of Biological Sciences, National University of Singapore, 14 Science Drive 4, Singapore 117543
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