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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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2
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Kumar M, Michael S, Alvarado-Valverde J, Zeke A, Lazar T, Glavina J, Nagy-Kanta E, Donagh J, Kalman Z, Pascarelli S, Palopoli N, Dobson L, Suarez C, Van Roey K, Krystkowiak I, Griffin J, Nagpal A, Bhardwaj R, Diella F, Mészáros B, Dean K, Davey N, Pancsa R, Chemes L, Gibson T. ELM-the Eukaryotic Linear Motif resource-2024 update. Nucleic Acids Res 2024; 52:D442-D455. [PMID: 37962385 PMCID: PMC10767929 DOI: 10.1093/nar/gkad1058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 10/22/2023] [Accepted: 10/24/2023] [Indexed: 11/15/2023] Open
Abstract
Short Linear Motifs (SLiMs) are the smallest structural and functional components of modular eukaryotic proteins. They are also the most abundant, especially when considering post-translational modifications. As well as being found throughout the cell as part of regulatory processes, SLiMs are extensively mimicked by intracellular pathogens. At the heart of the Eukaryotic Linear Motif (ELM) Resource is a representative (not comprehensive) database. The ELM entries are created by a growing community of skilled annotators and provide an introduction to linear motif functionality for biomedical researchers. The 2024 ELM update includes 346 novel motif instances in areas ranging from innate immunity to both protein and RNA degradation systems. In total, 39 classes of newly annotated motifs have been added, and another 17 existing entries have been updated in the database. The 2024 ELM release now includes 356 motif classes incorporating 4283 individual motif instances manually curated from 4274 scientific publications and including >700 links to experimentally determined 3D structures. In a recent development, the InterPro protein module resource now also includes ELM data. ELM is available at: http://elm.eu.org.
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Affiliation(s)
- Manjeet Kumar
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg 69117, Germany
| | - Sushama Michael
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg 69117, Germany
| | - Jesús Alvarado-Valverde
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg 69117, Germany
- Collaboration for joint PhD degree between EMBL and Heidelberg University, Faculty of Biosciences, Germany
| | - András Zeke
- Institute of Enzymology, HUN-REN Research Centre for Natural Sciences, Budapest 1117, Hungary
| | - Tamas Lazar
- VIB-VUB Center for Structural Biology, Vlaams Instituut voor Biotechnologie, Pleinlaan 2, 1050 Brussels, Belgium
- Structural Biology Brussels, Department of Bioengineering, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
| | - Juliana Glavina
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín (UNSAM), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), CP 1650, Buenos Aires, Argentina
- Escuela de Bio y Nanotecnologías (EByN), Universidad Nacional de San Martín, Av. 25 de Mayo y Francia, CP1650 San Martín, Buenos Aires, Argentina
| | - Eszter Nagy-Kanta
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter u. 50/A, Budapest 1083, Hungary
| | - Juan Mac Donagh
- Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Bernal, Buenos Aires, Argentina
| | - Zsofia E Kalman
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg 69117, Germany
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter u. 50/A, Budapest 1083, Hungary
| | - Stefano Pascarelli
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Nicolas Palopoli
- Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Bernal, Buenos Aires, Argentina
| | - László Dobson
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg 69117, Germany
- Department of Bioinformatics, Semmelweis University, Tűzoltó u. 7, Budapest 1094, Hungary
| | - Carmen Florencia Suarez
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín (UNSAM), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), CP 1650, Buenos Aires, Argentina
- Escuela de Bio y Nanotecnologías (EByN), Universidad Nacional de San Martín, Av. 25 de Mayo y Francia, CP1650 San Martín, Buenos Aires, Argentina
| | - Kim Van Roey
- Health Services Research, Sciensano, Brussels, Belgium
| | - Izabella Krystkowiak
- Institute of Cancer Research, Chester Beatty Laboratories, 237 Fulham Rd, Chelsea, London SW3 6JB, UK
| | - Juan Esteban Griffin
- Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Bernal, Buenos Aires, Argentina
| | - Anurag Nagpal
- Department of Biological Sciences, BITS Pilani, K. K. Birla Goa campus, Zuarinagar, Goa 403726, India
| | - Rajesh Bhardwaj
- Inselspital, University of Bern, Freiburgstrasse 15, CH-3010 Bern, Switzerland
| | - Francesca Diella
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg 69117, Germany
| | - Bálint Mészáros
- Department of Structural Biology and Center of Excellence for Data Driven Discovery, St Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN 38105, USA
| | - Kellie Dean
- School of Biochemistry and Cell Biology, 3.91 Western Gateway Building, University College Cork, Cork, Ireland
| | - Norman E Davey
- Institute of Cancer Research, Chester Beatty Laboratories, 237 Fulham Rd, Chelsea, London SW3 6JB, UK
| | - Rita Pancsa
- Institute of Enzymology, HUN-REN Research Centre for Natural Sciences, Budapest 1117, Hungary
| | - Lucía B Chemes
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín (UNSAM), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), CP 1650, Buenos Aires, Argentina
- Escuela de Bio y Nanotecnologías (EByN), Universidad Nacional de San Martín, Av. 25 de Mayo y Francia, CP1650 San Martín, Buenos Aires, Argentina
| | - Toby J Gibson
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg 69117, Germany
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Liu D, Li ZA, Li Y, Molloy DP, Huang C. The DYW domain of RARE1 plays an indispensable role in regulating accD-C794 RNA editing in Arabidopsis thaliana. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2023; 334:111751. [PMID: 37263527 DOI: 10.1016/j.plantsci.2023.111751] [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: 11/29/2022] [Revised: 05/27/2023] [Accepted: 05/29/2023] [Indexed: 06/03/2023]
Abstract
The Arabidopsis pentatricopeptide repeat (PPR) proteins, required for accD RNA editing 1 (RARE1) and early chloroplast biogenesis 2 (AtECB2), each contain a DYW domain deemed essential for cytosine deamination at the accD-C794 RNA editing site in chloroplasts. Complementation assays using the rare1 mutant investigate the correlation between these PPRs and their respective DYW domain functions in RNA editing of accD-C794. The results demonstrate that the coding sequence of AtECB2 cannot replace that of RARE1. Moreover, rare1 mutants complemented with DYW-deleted RARE1 failed to recover the RNA editing of accD-C794 even in the presence of the highly similar DYW domain of the AtECB2 protein. These findings indicate that RARE1 and AtECB2 possess divergent roles in RNA editing, with specificity for accD-C794 directly attributable to DYW domain within RARE1. Structural modeling data suggest this functioning pertains to a local α-helical motif that residues slightly N-terminal to the consensus glutamate and CXXCH motif in the DYW domain for cytidine deamination during C-to-U editing by RARE1 that is absent within AtECB2.
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Affiliation(s)
- Dan Liu
- College of Bioscience and Biotechnology, Hunan Agricultural University, Changsha 410128, China
| | - Zi-Ang Li
- College of Bioscience and Biotechnology, Hunan Agricultural University, Changsha 410128, China
| | - Yi Li
- College of Bioscience and Biotechnology, Hunan Agricultural University, Changsha 410128, China
| | - David P Molloy
- Department of Biochemistry and Molecular Biology, Basic Medical College, Chongqing Medical University, Chongqing 400016, China; Center for Molecular Medicine and Cancer Research, Basic Medical College, Chongqing Medical University, Chongqing 400016, China.
| | - Chao Huang
- College of Bioscience and Biotechnology, Hunan Agricultural University, Changsha 410128, China.
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Varadi M, Bordin N, Orengo C, Velankar S. The opportunities and challenges posed by the new generation of deep learning-based protein structure predictors. Curr Opin Struct Biol 2023; 79:102543. [PMID: 36807079 DOI: 10.1016/j.sbi.2023.102543] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 01/04/2023] [Accepted: 01/13/2023] [Indexed: 02/21/2023]
Abstract
The function of proteins can often be inferred from their three-dimensional structures. Experimental structural biologists spent decades studying these structures, but the accelerated pace of protein sequencing continuously increases the gaps between sequences and structures. The early 2020s saw the advent of a new generation of deep learning-based protein structure prediction tools that offer the potential to predict structures based on any number of protein sequences. In this review, we give an overview of the impact of this new generation of structure prediction tools, with examples of the impacted field in the life sciences. We discuss the novel opportunities and new scientific and technical challenges these tools present to the broader scientific community. Finally, we highlight some potential directions for the future of computational protein structure prediction.
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Affiliation(s)
- Mihaly Varadi
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Welcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.
| | - Nicola Bordin
- Institute of Structural and Molecular Biology, University College, London, London, WC1E 6BT, UK. https://twitter.com/nicolabordin
| | - Christine Orengo
- Institute of Structural and Molecular Biology, University College, London, London, WC1E 6BT, UK
| | - Sameer Velankar
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Welcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
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5
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Non-synonymous variation and protein structure of candidate genes associated with selection in farm and wild populations of turbot (Scophthalmus maximus). Sci Rep 2023; 13:3019. [PMID: 36810752 PMCID: PMC9944912 DOI: 10.1038/s41598-023-29826-z] [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: 11/02/2022] [Accepted: 02/10/2023] [Indexed: 02/24/2023] Open
Abstract
Non-synonymous variation (NSV) of protein coding genes represents raw material for selection to improve adaptation to the diverse environmental scenarios in wild and livestock populations. Many aquatic species face variations in temperature, salinity and biological factors throughout their distribution range that is reflected by the presence of allelic clines or local adaptation. The turbot (Scophthalmus maximus) is a flatfish of great commercial value with a flourishing aquaculture which has promoted the development of genomic resources. In this study, we developed the first atlas of NSVs in the turbot genome by resequencing 10 individuals from Northeast Atlantic Ocean. More than 50,000 NSVs where detected in the ~ 21,500 coding genes of the turbot genome, and we selected 18 NSVs to be genotyped using a single Mass ARRAY multiplex on 13 wild populations and three turbot farms. We detected signals of divergent selection on several genes related to growth, circadian rhythms, osmoregulation and oxygen binding in the different scenarios evaluated. Furthermore, we explored the impact of NSVs identified on the 3D structure and functional relationship of the correspondent proteins. In summary, our study provides a strategy to identify NSVs in species with consistently annotated and assembled genomes to ascertain their role in adaptation.
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6
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Raicu AM, Kadiyala D, Niblock M, Jain A, Yang Y, Bird KM, Bertholf K, Seenivasan A, Siddiq M, Arnosti DN. The Cynosure of CtBP: Evolution of a Bilaterian Transcriptional Corepressor. Mol Biol Evol 2023; 40:msad003. [PMID: 36625090 PMCID: PMC9907507 DOI: 10.1093/molbev/msad003] [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: 06/27/2022] [Revised: 12/16/2022] [Accepted: 01/03/2023] [Indexed: 01/11/2023] Open
Abstract
Evolution of sequence-specific transcription factors clearly drives lineage-specific innovations, but less is known about how changes in the central transcriptional machinery may contribute to evolutionary transformations. In particular, transcriptional regulators are rich in intrinsically disordered regions that appear to be magnets for evolutionary innovation. The C-terminal Binding Protein (CtBP) is a transcriptional corepressor derived from an ancestral lineage of alpha hydroxyacid dehydrogenases; it is found in mammals and invertebrates, and features a core NAD-binding domain as well as an unstructured C-terminus (CTD) of unknown function. CtBP can act on promoters and enhancers to repress transcription through chromatin-linked mechanisms. Our comparative phylogenetic study shows that CtBP is a bilaterian innovation whose CTD of about 100 residues is present in almost all orthologs. CtBP CTDs contain conserved blocks of residues and retain a predicted disordered property, despite having variations in the primary sequence. Interestingly, the structure of the C-terminus has undergone radical transformation independently in certain lineages including flatworms and nematodes. Also contributing to CTD diversity is the production of myriad alternative RNA splicing products, including the production of "short" tailless forms of CtBP in Drosophila. Additional diversity stems from multiple gene duplications in vertebrates, where up to five CtBP orthologs have been observed. Vertebrate lineages show fewer major modifications in the unstructured CTD, possibly because gene regulatory constraints of the vertebrate body plan place specific constraints on this domain. Our study highlights the rich regulatory potential of this previously unstudied domain of a central transcriptional regulator.
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Affiliation(s)
- Ana-Maria Raicu
- Cell and Molecular Biology Program, Michigan State University, East Lansing, Michigan
| | - Dhruva Kadiyala
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan
| | - Madeline Niblock
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan
| | | | - Yahui Yang
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan
| | - Kalynn M Bird
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan
| | - Kayla Bertholf
- Biochemistry and Molecular Biology Program, College of Wooster
| | - Akshay Seenivasan
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan
| | - Mohammad Siddiq
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, Michigan
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, Michigan
| | - David N Arnosti
- Cell and Molecular Biology Program, Michigan State University, East Lansing, Michigan
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan
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7
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Ten Plastomes of Crassula (Crassulaceae) and Phylogenetic Implications. BIOLOGY 2022; 11:biology11121779. [PMID: 36552287 PMCID: PMC9775174 DOI: 10.3390/biology11121779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 12/05/2022] [Accepted: 12/05/2022] [Indexed: 12/12/2022]
Abstract
The genus Crassula is the second-largest genus in the family Crassulaceae, with about 200 species. As an acknowledged super-barcode, plastomes have been extensively utilized for plant evolutionary studies. Here, we first report 10 new plastomes of Crassula. We further focused on the structural characterizations, codon usage, aversion patterns, and evolutionary rates of plastomes. The IR junction patterns-IRb had 110 bp expansion to rps19-were conservative among Crassula species. Interestingly, we found the codon usage patterns of matK gene in Crassula species are unique among Crassulaceae species with elevated ENC values. Furthermore, subgenus Crassula species have specific GC-biases in the matK gene. In addition, the codon aversion motifs from matK, pafI, and rpl22 contained phylogenetic implications within Crassula. The evolutionary rates analyses indicated all plastid genes of Crassulaceae were under the purifying selection. Among plastid genes, ycf1 and ycf2 were the most rapidly evolving genes, whereas psaC was the most conserved gene. Additionally, our phylogenetic analyses strongly supported that Crassula is sister to all other Crassulaceae species. Our findings will be useful for further evolutionary studies within the Crassula and Crassulaceae.
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Ahmed SS, Rifat ZT, Lohia R, Campbell AJ, Dunker AK, Rahman MS, Iqbal S. Characterization of intrinsically disordered regions in proteins informed by human genetic diversity. PLoS Comput Biol 2022; 18:e1009911. [PMID: 35275927 PMCID: PMC8942211 DOI: 10.1371/journal.pcbi.1009911] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 03/23/2022] [Accepted: 02/10/2022] [Indexed: 01/21/2023] Open
Abstract
All proteomes contain both proteins and polypeptide segments that don’t form a defined three-dimensional structure yet are biologically active—called intrinsically disordered proteins and regions (IDPs and IDRs). Most of these IDPs/IDRs lack useful functional annotation limiting our understanding of their importance for organism fitness. Here we characterized IDRs using protein sequence annotations of functional sites and regions available in the UniProt knowledgebase (“UniProt features”: active site, ligand-binding pocket, regions mediating protein-protein interactions, etc.). By measuring the statistical enrichment of twenty-five UniProt features in 981 IDRs of 561 human proteins, we identified eight features that are commonly located in IDRs. We then collected the genetic variant data from the general population and patient-based databases and evaluated the prevalence of population and pathogenic variations in IDPs/IDRs. We observed that some IDRs tolerate 2 to 12-times more single amino acid-substituting missense mutations than synonymous changes in the general population. However, we also found that 37% of all germline pathogenic mutations are located in disordered regions of 96 proteins. Based on the observed-to-expected frequency of mutations, we categorized 34 IDRs in 20 proteins (DDX3X, KIT, RB1, etc.) as intolerant to mutation. Finally, using statistical analysis and a machine learning approach, we demonstrate that mutation-intolerant IDRs carry a distinct signature of functional features. Our study presents a novel approach to assign functional importance to IDRs by leveraging the wealth of available genetic data, which will aid in a deeper understating of the role of IDRs in biological processes and disease mechanisms.
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Affiliation(s)
- Shehab S. Ahmed
- Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, ECE Building, West Palashi, Dhaka-1205, Bangladesh
| | - Zaara T. Rifat
- Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, ECE Building, West Palashi, Dhaka-1205, Bangladesh
| | - Ruchi Lohia
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America
| | - Arthur J. Campbell
- Center for the Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - A. Keith Dunker
- Center for Computational Biology and Bioinformatics, Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana, United States of America
| | - M. Sohel Rahman
- Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, ECE Building, West Palashi, Dhaka-1205, Bangladesh
- * E-mail: (MSR); (SI)
| | - Sumaiya Iqbal
- Center for the Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- * E-mail: (MSR); (SI)
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