1
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Nesterov SV, Ilyinsky NS, Plokhikh KS, Manuylov VD, Chesnokov YM, Vasilov RG, Kuznetsova IM, Turoverov KK, Gordeliy VI, Fonin AV, Uversky VN. Order wrapped in chaos: On the roles of intrinsically disordered proteins and RNAs in the arrangement of the mitochondrial enzymatic machines. Int J Biol Macromol 2024; 267:131455. [PMID: 38588835 DOI: 10.1016/j.ijbiomac.2024.131455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Revised: 04/03/2024] [Accepted: 04/05/2024] [Indexed: 04/10/2024]
Abstract
The analysis of cryo-electron tomography images of human and rat mitochondria revealed that the mitochondrial matrix is at least as crowded as the cytosol. To mitigate the crowding effects, metabolite transport in the mitochondria primarily occurs through the intermembrane space, which is significantly less crowded. The scientific literature largely ignores how enzyme systems and metabolite transport are organized in the crowded environment of the mitochondrial matrix. Under crowded conditions, multivalent interactions carried out by disordered protein regions (IDRs), may become extremely important. We analyzed the human mitochondrial proteome to determine the presence and physiological significance of IDRs. Despite mitochondrial proteins being generally more ordered than cytosolic or overall proteome proteins, disordered regions plays a significant role in certain mitochondrial compartments and processes. Even in highly ordered enzyme systems, there are proteins with long IDRs. Some IDRs act as binding elements between highly ordered subunits, while the roles of others are not yet established. Mitochondrial systems, like their bacterial ancestors, rely less on IDRs and more on RNA for LLPS compartmentalization. More evolutionarily advanced subsystems that enable mitochondria-cell interactions contain more IDRs. The study highlights the crucial and often overlooked role played by IDRs and non-coding RNAs in mitochondrial organization.
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Affiliation(s)
- Semen V Nesterov
- National Research Center "Kurchatov Institute", Moscow 123182, Russia; Moscow Institute of Physics and Techonology, Dolgoprudny, Moscow Region 141701, Russia; Institute of Cytology, Russian Academy of Sciences, Saint Petersburg 194064, Russia.
| | - Nikolay S Ilyinsky
- Moscow Institute of Physics and Techonology, Dolgoprudny, Moscow Region 141701, Russia.
| | | | - Vladimir D Manuylov
- Moscow Institute of Physics and Techonology, Dolgoprudny, Moscow Region 141701, Russia
| | - Yuriy M Chesnokov
- National Research Center "Kurchatov Institute", Moscow 123182, Russia
| | - Raif G Vasilov
- National Research Center "Kurchatov Institute", Moscow 123182, Russia
| | - Irina M Kuznetsova
- Institute of Cytology, Russian Academy of Sciences, Saint Petersburg 194064, Russia
| | | | - Valentin I Gordeliy
- Institut de Biologie Structurale Jean-Pierre Ebel, Université Grenoble Alpes-Commissariat à l'Energie Atomique et aux Energies Alternatives-CNRS, 38027 Grenoble, France
| | - Alexander V Fonin
- Institute of Cytology, Russian Academy of Sciences, Saint Petersburg 194064, Russia
| | - Vladimir N Uversky
- Department of Molecular Medicine and USF Health Byrd Alzheimer's Research Institute, Morsani College of Medicine, University of South Florida, 12901 Bruce B. Downs Blvd., MDC07, Tampa, FL 33612, USA.
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2
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Salcedo-Tacuma D, Asad N, Howells G, Anderson R, Smith DM. Proteasome hyperactivation rewires the proteome enhancing stress resistance, proteostasis, lipid metabolism and ERAD in C. elegans. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.04.588128. [PMID: 38617285 PMCID: PMC11014606 DOI: 10.1101/2024.04.04.588128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/16/2024]
Abstract
Proteasome dysfunction is implicated in the pathogenesis of neurodegenerative diseases and age-related proteinopathies. Using a C. elegans model, we demonstrate that 20S proteasome hyperactivation, facilitated by 20S gate-opening, accelerates the targeting of intrinsically disordered proteins. This leads to increased protein synthesis, extensive rewiring of the proteome and transcriptome, enhanced oxidative stress defense, accelerated lipid metabolism, and peroxisome proliferation. It also promotes ER-associated degradation (ERAD) of aggregation-prone proteins, such as alpha-1 antitrypsin (ATZ) and various lipoproteins. Notably, our results reveal that 20S proteasome hyperactivation suggests a novel role in ERAD with broad implications for proteostasis-related disorders, simultaneously affecting lipid homeostasis and peroxisome proliferation. Furthermore, the enhanced cellular capacity to mitigate proteostasis challenges, alongside unanticipated acceleration of lipid metabolism is expected to contribute to the longevity phenotype of this mutant. Remarkably, the mechanism of longevity induced by 20S gate opening appears unique, independent of known longevity and stress-resistance pathways. These results support the therapeutic potential of 20S proteasome activation in mitigating proteostasis-related disorders broadly and provide new insights into the complex interplay between proteasome activity, cellular health, and aging.
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Affiliation(s)
- David Salcedo-Tacuma
- Department of Biochemistry and Molecular Medicine, West Virginia University School of Medicine, 4 Medical Center Dr., Morgantown, WV USA
| | - Nadeeem. Asad
- Department of Biochemistry and Molecular Medicine, West Virginia University School of Medicine, 4 Medical Center Dr., Morgantown, WV USA
| | - Giovanni Howells
- Department of Biochemistry and Molecular Medicine, West Virginia University School of Medicine, 4 Medical Center Dr., Morgantown, WV USA
| | - Raymond Anderson
- Department of Biochemistry and Molecular Medicine, West Virginia University School of Medicine, 4 Medical Center Dr., Morgantown, WV USA
| | - David M. Smith
- Department of Biochemistry and Molecular Medicine, West Virginia University School of Medicine, 4 Medical Center Dr., Morgantown, WV USA
- Department of Neuroscience, Rockefeller Neuroscience Institute, West Virginia University, Morgantown, West Virginia, USA
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3
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Pepelnjak M, Rogawski R, Arkind G, Leushkin Y, Fainer I, Ben-Nissan G, Picotti P, Sharon M. Systematic identification of 20S proteasome substrates. Mol Syst Biol 2024; 20:403-427. [PMID: 38287148 PMCID: PMC10987551 DOI: 10.1038/s44320-024-00015-y] [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: 09/13/2023] [Revised: 12/13/2023] [Accepted: 01/05/2024] [Indexed: 01/31/2024] Open
Abstract
For years, proteasomal degradation was predominantly attributed to the ubiquitin-26S proteasome pathway. However, it is now evident that the core 20S proteasome can independently target proteins for degradation. With approximately half of the cellular proteasomes comprising free 20S complexes, this degradation mechanism is not rare. Identifying 20S-specific substrates is challenging due to the dual-targeting of some proteins to either 20S or 26S proteasomes and the non-specificity of proteasome inhibitors. Consequently, knowledge of 20S proteasome substrates relies on limited hypothesis-driven studies. To comprehensively explore 20S proteasome substrates, we employed advanced mass spectrometry, along with biochemical and cellular analyses. This systematic approach revealed hundreds of 20S proteasome substrates, including proteins undergoing specific N- or C-terminal cleavage, possibly for regulation. Notably, these substrates were enriched in RNA- and DNA-binding proteins with intrinsically disordered regions, often found in the nucleus and stress granules. Under cellular stress, we observed reduced proteolytic activity in oxidized proteasomes, with oxidized protein substrates exhibiting higher structural disorder compared to unmodified proteins. Overall, our study illuminates the nature of 20S substrates, offering crucial insights into 20S proteasome biology.
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Affiliation(s)
- Monika Pepelnjak
- Institute of Molecular Systems Biology, Department of Biology, ETH Zurich, Zurich, Switzerland
| | - Rivkah Rogawski
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, 7610001, Israel
| | - Galina Arkind
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, 7610001, Israel
| | - Yegor Leushkin
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, 7610001, Israel
| | - Irit Fainer
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, 7610001, Israel
| | - Gili Ben-Nissan
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, 7610001, Israel
| | - Paola Picotti
- Institute of Molecular Systems Biology, Department of Biology, ETH Zurich, Zurich, Switzerland.
| | - Michal Sharon
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, 7610001, Israel.
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4
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Kurgan L, Hu G, Wang K, Ghadermarzi S, Zhao B, Malhis N, Erdős G, Gsponer J, Uversky VN, Dosztányi Z. Tutorial: a guide for the selection of fast and accurate computational tools for the prediction of intrinsic disorder in proteins. Nat Protoc 2023; 18:3157-3172. [PMID: 37740110 DOI: 10.1038/s41596-023-00876-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 06/21/2023] [Indexed: 09/24/2023]
Abstract
Intrinsic disorder is instrumental for a wide range of protein functions, and its analysis, using computational predictions from primary structures, complements secondary and tertiary structure-based approaches. In this Tutorial, we provide an overview and comparison of 23 publicly available computational tools with complementary parameters useful for intrinsic disorder prediction, partly relying on results from the Critical Assessment of protein Intrinsic Disorder prediction experiment. We consider factors such as accuracy, runtime, availability and the need for functional insights. The selected tools are available as web servers and downloadable programs, offer state-of-the-art predictions and can be used in a high-throughput manner. We provide examples and instructions for the selected tools to illustrate practical aspects related to the submission, collection and interpretation of predictions, as well as the timing and their limitations. We highlight two predictors for intrinsically disordered proteins, flDPnn as accurate and fast and IUPred as very fast and moderately accurate, while suggesting ANCHOR2 and MoRFchibi as two of the best-performing predictors for intrinsically disordered region binding. We link these tools to additional resources, including databases of predictions and web servers that integrate multiple predictive methods. Altogether, this Tutorial provides a hands-on guide to comparatively evaluating multiple predictors, submitting and collecting their own predictions, and reading and interpreting results. It is suitable for experimentalists and computational biologists interested in accurately and conveniently identifying intrinsic disorder, facilitating the functional characterization of the rapidly growing collections of protein sequences.
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Affiliation(s)
- Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA.
| | - Gang Hu
- School of Statistics and Data Science, LPMC and KLMDASR, Nankai University, Tianjin, China
| | - Kui Wang
- School of Statistics and Data Science, LPMC and KLMDASR, Nankai University, Tianjin, China
| | - Sina Ghadermarzi
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA
| | - Bi Zhao
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA
| | - Nawar Malhis
- Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada
| | - Gábor Erdős
- MTA-ELTE Momentum Bioinformatics Research Group, Department of Biochemistry, Eötvös Loránd University, Budapest, Hungary
| | - Jörg Gsponer
- Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada.
| | - Vladimir N Uversky
- Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL, USA.
- Byrd Alzheimer's Center and Research Institute, Morsani College of Medicine, University of South Florida, Tampa, FL, USA.
| | - Zsuzsanna Dosztányi
- MTA-ELTE Momentum Bioinformatics Research Group, Department of Biochemistry, Eötvös Loránd University, Budapest, Hungary.
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5
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Manyilov VD, Ilyinsky NS, Nesterov SV, Saqr BMGA, Dayhoff GW, Zinovev EV, Matrenok SS, Fonin AV, Kuznetsova IM, Turoverov KK, Ivanovich V, Uversky VN. Chaotic aging: intrinsically disordered proteins in aging-related processes. Cell Mol Life Sci 2023; 80:269. [PMID: 37634152 PMCID: PMC11073068 DOI: 10.1007/s00018-023-04897-3] [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] [Received: 04/16/2023] [Revised: 07/03/2023] [Accepted: 07/24/2023] [Indexed: 08/29/2023]
Abstract
The development of aging is associated with the disruption of key cellular processes manifested as well-established hallmarks of aging. Intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) have no stable tertiary structure that provide them a power to be configurable hubs in signaling cascades and regulate many processes, potentially including those related to aging. There is a need to clarify the roles of IDPs/IDRs in aging. The dataset of 1702 aging-related proteins was collected from established aging databases and experimental studies. There is a noticeable presence of IDPs/IDRs, accounting for about 36% of the aging-related dataset, which is however less than the disorder content of the whole human proteome (about 40%). A Gene Ontology analysis of the used here aging proteome reveals an abundance of IDPs/IDRs in one-third of aging-associated processes, especially in genome regulation. Signaling pathways associated with aging also contain IDPs/IDRs on different hierarchical levels, revealing the importance of "structure-function continuum" in aging. Protein-protein interaction network analysis showed that IDPs present in different clusters associated with different aging hallmarks. Protein cluster with IDPs enrichment has simultaneously high liquid-liquid phase separation (LLPS) probability, "nuclear" localization and DNA-associated functions, related to aging hallmarks: genomic instability, telomere attrition, epigenetic alterations, and stem cells exhaustion. Intrinsic disorder, LLPS, and aggregation propensity should be considered as features that could be markers of pathogenic proteins. Overall, our analyses indicate that IDPs/IDRs play significant roles in aging-associated processes, particularly in the regulation of DNA functioning. IDP aggregation, which can lead to loss of function and toxicity, could be critically harmful to the cell. A structure-based analysis of aging and the identification of proteins that are particularly susceptible to disturbances can enhance our understanding of the molecular mechanisms of aging and open up new avenues for slowing it down.
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Affiliation(s)
- Vladimir D Manyilov
- Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, Institutskiy Pereulok, 9, Dolgoprudny, 141700, Russia
| | - Nikolay S Ilyinsky
- Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, Institutskiy Pereulok, 9, Dolgoprudny, 141700, Russia.
| | - Semen V Nesterov
- Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, Institutskiy Pereulok, 9, Dolgoprudny, 141700, Russia
- Institute of Cytology, Russian Academy of Sciences, Saint Petersburg, 194064, Russia
| | - Baraa M G A Saqr
- Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, Institutskiy Pereulok, 9, Dolgoprudny, 141700, Russia
| | - Guy W Dayhoff
- Department of Chemistry, University of South Florida, Tampa, FL, USA
| | - Egor V Zinovev
- Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, Institutskiy Pereulok, 9, Dolgoprudny, 141700, Russia
| | - Simon S Matrenok
- Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, Institutskiy Pereulok, 9, Dolgoprudny, 141700, Russia
| | - Alexander V Fonin
- Institute of Cytology, Russian Academy of Sciences, Saint Petersburg, 194064, Russia
| | - Irina M Kuznetsova
- Institute of Cytology, Russian Academy of Sciences, Saint Petersburg, 194064, Russia
| | | | - Valentin Ivanovich
- Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, Institutskiy Pereulok, 9, Dolgoprudny, 141700, Russia
| | - Vladimir N Uversky
- Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, Institutskiy Pereulok, 9, Dolgoprudny, 141700, Russia.
- Department of Molecular Medicine and USF Health Byrd Alzheimer's Research Institute, Morsani College of Medicine, University of South Florida, 12901 Bruce B. Downs Blvd., MDC07, Tampa, FL, 33612, USA.
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6
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Basu S, Gsponer J, Kurgan L. DEPICTER2: a comprehensive webserver for intrinsic disorder and disorder function prediction. Nucleic Acids Res 2023:7151337. [PMID: 37140058 DOI: 10.1093/nar/gkad330] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 04/12/2023] [Accepted: 04/18/2023] [Indexed: 05/05/2023] Open
Abstract
Intrinsic disorder in proteins is relatively abundant in nature and essential for a broad spectrum of cellular functions. While disorder can be accurately predicted from protein sequences, as it was empirically demonstrated in recent community-organized assessments, it is rather challenging to collect and compile a comprehensive prediction that covers multiple disorder functions. To this end, we introduce the DEPICTER2 (DisorderEd PredictIon CenTER) webserver that offers convenient access to a curated collection of fast and accurate disorder and disorder function predictors. This server includes a state-of-the-art disorder predictor, flDPnn, and five modern methods that cover all currently predictable disorder functions: disordered linkers and protein, peptide, DNA, RNA and lipid binding. DEPICTER2 allows selection of any combination of the six methods, batch predictions of up to 25 proteins per request and provides interactive visualization of the resulting predictions. The webserver is freely available at http://biomine.cs.vcu.edu/servers/DEPICTER2/.
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Affiliation(s)
- Sushmita Basu
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA
| | - Jörg Gsponer
- Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA
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7
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Zhang F, Li M, Zhang J, Kurgan L. HybridRNAbind: prediction of RNA interacting residues across structure-annotated and disorder-annotated proteins. Nucleic Acids Res 2023; 51:e25. [PMID: 36629262 PMCID: PMC10018345 DOI: 10.1093/nar/gkac1253] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 11/22/2022] [Accepted: 12/15/2022] [Indexed: 01/12/2023] Open
Abstract
The sequence-based predictors of RNA-binding residues (RBRs) are trained on either structure-annotated or disorder-annotated binding regions. A recent study of predictors of protein-binding residues shows that they are plagued by high levels of cross-predictions (protein binding residues are predicted as nucleic acid binding) and that structure-trained predictors perform poorly for the disorder-annotated regions and vice versa. Consequently, we analyze a representative set of the structure and disorder trained predictors of RBRs to comprehensively assess quality of their predictions. Our empirical analysis that relies on a new and low-similarity benchmark dataset reveals that the structure-trained predictors of RBRs perform well for the structure-annotated proteins while the disorder-trained predictors provide accurate results for the disorder-annotated proteins. However, these methods work only modestly well on the opposite types of annotations, motivating the need for new solutions. Using an empirical approach, we design HybridRNAbind meta-model that generates accurate predictions and low amounts of cross-predictions when tested on data that combines structure and disorder-annotated RBRs. We release this meta-model as a convenient webserver which is available at https://www.csuligroup.com/hybridRNAbind/.
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Affiliation(s)
- Fuhao Zhang
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Min Li
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Jian Zhang
- School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA
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8
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Patil A. Enrichment patterns of intrinsic disorder in proteins. Biophys Rev 2022; 14:1487-1493. [PMID: 36659984 PMCID: PMC9842814 DOI: 10.1007/s12551-022-01016-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 11/07/2022] [Indexed: 11/21/2022] Open
Abstract
Intrinsically disordered regions in proteins have been shown to be important in protein function. However, not all proteins contain the same amount of intrinsic disorder. The variation in the levels of intrinsic disorder in different types of proteins has been extensively studied over the last two decades. It is now known that the levels of intrinsic disorder vary in proteins across organisms, functions, diseases, and cellular locations. This review consolidates the known trends in the abundance of intrinsic disorder identified in groups of proteins across varying conditions and functions. It also presents new data towards the understanding of intrinsic disorder in cell type-specific proteins. Supplementary Information The online version contains supplementary material available at 10.1007/s12551-022-01016-7.
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Affiliation(s)
- Ashwini Patil
- Combinatics Inc., 2-2-6 Sugano, Ichikawa-Shi, Chiba, 272-0824 Japan
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9
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Compositional Bias of Intrinsically Disordered Proteins and Regions and Their Predictions. Biomolecules 2022; 12:biom12070888. [PMID: 35883444 PMCID: PMC9313023 DOI: 10.3390/biom12070888] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 06/10/2022] [Accepted: 06/10/2022] [Indexed: 11/17/2022] Open
Abstract
Intrinsically disordered regions (IDRs) carry out many cellular functions and vary in length and placement in protein sequences. This diversity leads to variations in the underlying compositional biases, which were demonstrated for the short vs. long IDRs. We analyze compositional biases across four classes of disorder: fully disordered proteins; short IDRs; long IDRs; and binding IDRs. We identify three distinct biases: for the fully disordered proteins, the short IDRs and the long and binding IDRs combined. We also investigate compositional bias for putative disorder produced by leading disorder predictors and find that it is similar to the bias of the native disorder. Interestingly, the accuracy of disorder predictions across different methods is correlated with the correctness of the compositional bias of their predictions highlighting the importance of the compositional bias. The predictive quality is relatively low for the disorder classes with compositional bias that is the most different from the “generic” disorder bias, while being much higher for the classes with the most similar bias. We discover that different predictors perform best across different classes of disorder. This suggests that no single predictor is universally best and motivates the development of new architectures that combine models that target specific disorder classes.
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10
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Chen Z, Liu X, Zhao P, Li C, Wang Y, Li F, Akutsu T, Bain C, Gasser RB, Li J, Yang Z, Gao X, Kurgan L, Song J. iFeatureOmega: an integrative platform for engineering, visualization and analysis of features from molecular sequences, structural and ligand data sets. Nucleic Acids Res 2022; 50:W434-W447. [PMID: 35524557 PMCID: PMC9252729 DOI: 10.1093/nar/gkac351] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 04/22/2022] [Accepted: 04/25/2022] [Indexed: 01/07/2023] Open
Abstract
The rapid accumulation of molecular data motivates development of innovative approaches to computationally characterize sequences, structures and functions of biological and chemical molecules in an efficient, accessible and accurate manner. Notwithstanding several computational tools that characterize protein or nucleic acids data, there are no one-stop computational toolkits that comprehensively characterize a wide range of biomolecules. We address this vital need by developing a holistic platform that generates features from sequence and structural data for a diverse collection of molecule types. Our freely available and easy-to-use iFeatureOmega platform generates, analyzes and visualizes 189 representations for biological sequences, structures and ligands. To the best of our knowledge, iFeatureOmega provides the largest scope when directly compared to the current solutions, in terms of the number of feature extraction and analysis approaches and coverage of different molecules. We release three versions of iFeatureOmega including a webserver, command line interface and graphical interface to satisfy needs of experienced bioinformaticians and less computer-savvy biologists and biochemists. With the assistance of iFeatureOmega, users can encode their molecular data into representations that facilitate construction of predictive models and analytical studies. We highlight benefits of iFeatureOmega based on three research applications, demonstrating how it can be used to accelerate and streamline research in bioinformatics, computational biology, and cheminformatics areas. The iFeatureOmega webserver is freely available at http://ifeatureomega.erc.monash.edu and the standalone versions can be downloaded from https://github.com/Superzchen/iFeatureOmega-GUI/ and https://github.com/Superzchen/iFeatureOmega-CLI/.
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Affiliation(s)
- Zhen Chen
- Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, Zhengzhou 450046, China.,Center for Crop Genome Engineering, Henan Agricultural University, Zhengzhou 450046, China
| | - Xuhan Liu
- Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Einsteinweg 55, Leiden 2333 CC, The Netherlands
| | - Pei Zhao
- State Key Laboratory of Cotton Biology, Institute of Cotton Research of Chinese Academy of Agricultural Sciences (CAAS), Anyang 455000, China
| | - Chen Li
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia
| | - Yanan Wang
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia
| | - Fuyi Li
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia
| | - Tatsuya Akutsu
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto 611-0011, Japan
| | - Chris Bain
- Monash Data Future Institutes, Monash University, Melbourne, Victoria 3800, Australia
| | - Robin B Gasser
- Department of Veterinary Biosciences, Melbourne Veterinary School, The University of Melbourne, Parkville, Victoria 3010, Australia
| | - Junzhou Li
- Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, Zhengzhou 450046, China
| | - Zuoren Yang
- State Key Laboratory of Cotton Biology, Institute of Cotton Research of Chinese Academy of Agricultural Sciences (CAAS), Anyang 455000, China
| | - Xin Gao
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA
| | - Jiangning Song
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia.,Monash Data Future Institutes, Monash University, Melbourne, Victoria 3800, Australia
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11
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Kulkarni P, Bhattacharya S, Achuthan S, Behal A, Jolly MK, Kotnala S, Mohanty A, Rangarajan G, Salgia R, Uversky V. Intrinsically Disordered Proteins: Critical Components of the Wetware. Chem Rev 2022; 122:6614-6633. [PMID: 35170314 PMCID: PMC9250291 DOI: 10.1021/acs.chemrev.1c00848] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Despite the wealth of knowledge gained about intrinsically disordered proteins (IDPs) since their discovery, there are several aspects that remain unexplored and, hence, poorly understood. A living cell is a complex adaptive system that can be described as a wetware─a metaphor used to describe the cell as a computer comprising both hardware and software and attuned to logic gates─capable of "making" decisions. In this focused Review, we discuss how IDPs, as critical components of the wetware, influence cell-fate decisions by wiring protein interaction networks to keep them minimally frustrated. Because IDPs lie between order and chaos, we explore the possibility that they can be modeled as attractors. Further, we discuss how the conformational dynamics of IDPs manifests itself as conformational noise, which can potentially amplify transcriptional noise to stochastically switch cellular phenotypes. Finally, we explore the potential role of IDPs in prebiotic evolution, in forming proteinaceous membrane-less organelles, in the origin of multicellularity, and in protein conformation-based transgenerational inheritance of acquired characteristics. Together, these ideas provide a new conceptual framework to discern how IDPs may perform critical biological functions despite their lack of structure.
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Affiliation(s)
- Prakash Kulkarni
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA, USA
- Address for correspondence: Prakash Kulkarni, Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA 91010, , Vladimir N. Uversky, Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, Florida 33612,
| | - Supriyo Bhattacharya
- Integrative Genomics Core, City of Hope National Medical Center, Duarte, CA, USA
| | - Srisairam Achuthan
- Division of Research Informatics, Center for Informatics, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Amita Behal
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA, USA
| | - Mohit Kumar Jolly
- Center for BioSystems Science and Engineering, Indian Institute of Science, Bangalore 560012, India
| | - Sourabh Kotnala
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA, USA
| | - Atish Mohanty
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA, USA
| | - Govindan Rangarajan
- Department of Mathematics, Indian Institute of Science, Bangalore 560012, India
- Center for Neuroscience, Indian Institute of Science, Bangalore 560012, India
| | - Ravi Salgia
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA, USA
| | - Vladimir Uversky
- Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL, USA
- Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, Institutskiy pereulok, 9, Dolgoprudny, Moscow region 141700, Russia
- Address for correspondence: Prakash Kulkarni, Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA 91010, , Vladimir N. Uversky, Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, Florida 33612,
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12
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Wang G, Xue MQ, Shen HB, Xu YY. Learning protein subcellular localization multi-view patterns from heterogeneous data of imaging, sequence and networks. Brief Bioinform 2022; 23:6499983. [PMID: 35018423 DOI: 10.1093/bib/bbab539] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 11/03/2021] [Accepted: 11/20/2021] [Indexed: 11/13/2022] Open
Abstract
Location proteomics seeks to provide automated high-resolution descriptions of protein location patterns within cells. Many efforts have been undertaken in location proteomics over the past decades, thereby producing plenty of automated predictors for protein subcellular localization. However, most of these predictors are trained solely from high-throughput microscopic images or protein amino acid sequences alone. Unifying heterogeneous protein data sources has yet to be exploited. In this paper, we present a pipeline called sequence, image, network-based protein subcellular locator (SIN-Locator) that constructs a multi-view description of proteins by integrating multiple data types including images of protein expression in cells or tissues, amino acid sequences and protein-protein interaction networks, to classify the patterns of protein subcellular locations. Proteins were encoded by both handcrafted features and deep learning features, and multiple combining methods were implemented. Our experimental results indicated that optimal integrations can considerately enhance the classification accuracy, and the utility of SIN-Locator has been demonstrated through applying to new released proteins in the human protein atlas. Furthermore, we also investigate the contribution of different data sources and influence of partial absence of data. This work is anticipated to provide clues for reconciliation and combination of multi-source data for protein location analysis.
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Affiliation(s)
- Ge Wang
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China.,Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510515, China
| | - Min-Qi Xue
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China.,Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510515, China
| | - Hong-Bin Shen
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200240, China.,School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Ying-Ying Xu
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China.,Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510515, China
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13
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Abstract
INTRODUCTION Intrinsic disorder prediction field develops, assesses, and deploys computational predictors of disorder in protein sequences and constructs and disseminates databases of these predictions. Over 40 years of research resulted in the release of numerous resources. AREAS COVERED We identify and briefly summarize the most comprehensive to date collection of over 100 disorder predictors. We focus on their predictive models, availability and predictive performance. We categorize and study them from a historical point of view to highlight informative trends. EXPERT OPINION We find a consistent trend of improvements in predictive quality as newer and more advanced predictors are developed. The original focus on machine learning methods has shifted to meta-predictors in early 2010s, followed by a recent transition to deep learning. The use of deep learners will continue in foreseeable future given recent and convincing success of these methods. Moreover, a broad range of resources that facilitate convenient collection of accurate disorder predictions is available to users. They include web servers and standalone programs for disorder prediction, servers that combine prediction of disorder and disorder functions, and large databases of pre-computed predictions. We also point to the need to address the shortage of accurate methods that predict disordered binding regions.
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Affiliation(s)
- Bi Zhao
- Department of Computer Science, Virginia Commonwealth University, Richmond, Virginia, USA
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, Virginia, USA
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14
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Zhao B, Katuwawala A, Oldfield CJ, Hu G, Wu Z, Uversky VN, Kurgan L. Intrinsic Disorder in Human RNA-Binding Proteins. J Mol Biol 2021; 433:167229. [PMID: 34487791 DOI: 10.1016/j.jmb.2021.167229] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 08/30/2021] [Accepted: 08/31/2021] [Indexed: 12/24/2022]
Abstract
Although RNA-binding proteins (RBPs) are known to be enriched in intrinsic disorder, no previous analysis focused on RBPs interacting with specific RNA types. We fill this gap with a comprehensive analysis of the putative disorder in RBPs binding to six common RNA types: messenger RNA (mRNA), transfer RNA (tRNA), small nuclear RNA (snRNA), non-coding RNA (ncRNA), ribosomal RNA (rRNA), and internal ribosome RNA (irRNA). We also analyze the amount of putative intrinsic disorder in the RNA-binding domains (RBDs) and non-RNA-binding-domain regions (non-RBD regions). Consistent with previous studies, we show that in comparison with human proteome, RBPs are significantly enriched in disorder. However, closer examination finds significant enrichment in predicted disorder for the mRNA-, rRNA- and snRNA-binding proteins, while the proteins that interact with ncRNA and irRNA are not enriched in disorder, and the tRNA-binding proteins are significantly depleted in disorder. We show a consistent pattern of significant disorder enrichment in the non-RBD regions coupled with low levels of disorder in RBDs, which suggests that disorder is relatively rarely utilized in the RNA-binding regions. Our analysis of the non-RBD regions suggests that disorder harbors posttranslational modification sites and is involved in the putative interactions with DNA. Importantly, we utilize experimental data from DisProt and independent data from Pfam to validate the above observations that rely on the disorder predictions. This study provides new insights into the distribution of disorder across proteins that bind different RNA types and the functional role of disorder in the regions where it is enriched.
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Affiliation(s)
- Bi Zhao
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA
| | - Akila Katuwawala
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA
| | - Christopher J Oldfield
- Department of Microbiology and Immunology, Virginia Commonwealth University, Richmond, VA 23284, USA
| | - Gang Hu
- School of Statistics and Data Science, LPMC and KLMDASR, Nankai University, Tianjin 300071, China
| | - Zhonghua Wu
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, China
| | - Vladimir N Uversky
- Department of Molecular Medicine and USF Health Byrd Alzheimer's Research Institute, Morsani College of Medicine, University of South Florida, Tampa, FL 33612, USA
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA.
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15
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Hu G, Katuwawala A, Wang K, Wu Z, Ghadermarzi S, Gao J, Kurgan L. flDPnn: Accurate intrinsic disorder prediction with putative propensities of disorder functions. Nat Commun 2021; 12:4438. [PMID: 34290238 PMCID: PMC8295265 DOI: 10.1038/s41467-021-24773-7] [Citation(s) in RCA: 124] [Impact Index Per Article: 41.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 07/06/2021] [Indexed: 01/05/2023] Open
Abstract
Identification of intrinsic disorder in proteins relies in large part on computational predictors, which demands that their accuracy should be high. Since intrinsic disorder carries out a broad range of cellular functions, it is desirable to couple the disorder and disorder function predictions. We report a computational tool, flDPnn, that provides accurate, fast and comprehensive disorder and disorder function predictions from protein sequences. The recent Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiment and results on other test datasets demonstrate that flDPnn offers accurate predictions of disorder, fully disordered proteins and four common disorder functions. These predictions are substantially better than the results of the existing disorder predictors and methods that predict functions of disorder. Ablation tests reveal that the high predictive performance stems from innovative ways used in flDPnn to derive sequence profiles and encode inputs. flDPnn's webserver is available at http://biomine.cs.vcu.edu/servers/flDPnn/.
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Affiliation(s)
- Gang Hu
- School of Statistics and Data Science, LPMC and KLMDASR, Nankai University, Tianjin, China
| | - Akila Katuwawala
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA
| | - Kui Wang
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, China
| | - Zhonghua Wu
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, China
| | - Sina Ghadermarzi
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA
| | - Jianzhao Gao
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, China
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA.
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16
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Katuwawala A, Ghadermarzi S, Hu G, Wu Z, Kurgan L. QUARTERplus: Accurate disorder predictions integrated with interpretable residue-level quality assessment scores. Comput Struct Biotechnol J 2021; 19:2597-2606. [PMID: 34025946 PMCID: PMC8122155 DOI: 10.1016/j.csbj.2021.04.066] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 04/24/2021] [Accepted: 04/24/2021] [Indexed: 12/13/2022] Open
Abstract
A recent advance in the disorder prediction field is the development of the quality assessment (QA) scores. QA scores complement the propensities produced by the disorder predictors by identifying regions where these predictions are more likely to be correct. We develop, empirically test and release a new QA tool, QUARTERplus, that addresses several key drawbacks of the current QA method, QUARTER. QUARTERplus is the first solution that utilizes QA scores and the associated input disorder predictions to produce very accurate disorder predictions with the help of a modern deep learning meta-model. The deep neural network utilizes the QA scores to identify and fix the regions where the original/input disorder predictions are poor. More importantly, the accurate QUATERplus's predictions are accompanied by easy to interpret residue-level QA scores that reliably quantify their residue-level predictive quality. We provide these interpretable QA scores for QUARTERplus and 10 other popular disorder predictors. Empirical tests on a large and independent (low similarity) test dataset show that QUARTERplus predictions secure AUC = 0.93 and are statistically more accurate than the results of twelve state-of-the-art disorder predictors. We also demonstrate that the new QA scores produced by QUARTERplus are highly correlated with the actual predictive quality and that they can be effectively used to identify regions of correct disorder predictions. This feature empowers the users to easily identify which parts of the predictions generated by the modern disorder predictors are more trustworthy. QUARTERplus is available as a convenient webserver at http://biomine.cs.vcu.edu/servers/QUARTERplus/.
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Affiliation(s)
- Akila Katuwawala
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA
| | - Sina Ghadermarzi
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA
| | - Gang Hu
- School of Statistics and Data Science, LPMC and KLMDASR, Nankai University, Tianjin 300071, China
| | - Zhonghua Wu
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, China
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA
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