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Basu S, Kurgan L. Taxonomy-specific assessment of intrinsic disorder predictions at residue and region levels in higher eukaryotes, protists, archaea, bacteria and viruses. Comput Struct Biotechnol J 2024; 23:1968-1977. [PMID: 38765610 PMCID: PMC11098722 DOI: 10.1016/j.csbj.2024.04.059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 04/23/2024] [Accepted: 04/24/2024] [Indexed: 05/22/2024] Open
Abstract
Intrinsic disorder predictors were evaluated in several studies including the two large CAID experiments. However, these studies are biased towards eukaryotic proteins and focus primarily on the residue-level predictions. We provide first-of-its-kind assessment that comprehensively covers the taxonomy and evaluates predictions at the residue and disordered region levels. We curate a benchmark dataset that uniformly covers eukaryotic, archaeal, bacterial, and viral proteins. We find that predictive performance differs substantially across taxonomy, where viruses are predicted most accurately, followed by protists and higher eukaryotes, while bacterial and archaeal proteins suffer lower levels of accuracy. These trends are consistent across predictors. We also find that current tools, except for flDPnn, struggle with reproducing native distributions of the numbers and sizes of the disordered regions. Moreover, analysis of two variants of disorder predictions derived from the AlphaFold2 predicted structures reveals that they produce accurate residue-level propensities for archaea, bacteria and protists. However, they underperform for higher eukaryotes and generally struggle to accurately identify disordered regions. Our results motivate development of new predictors that target bacteria and archaea and which produce accurate results at both residue and region levels. We also stress the need to include the region-level assessments in future assessments.
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Affiliation(s)
- Sushmita Basu
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA
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2
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Peña-Guerrero J, Fernández-Rubio C, García-Sosa AT, Nguewa PA. BRCT Domains: Structure, Functions, and Implications in Disease-New Therapeutic Targets for Innovative Drug Discovery against Infections. Pharmaceutics 2023; 15:1839. [PMID: 37514027 PMCID: PMC10386641 DOI: 10.3390/pharmaceutics15071839] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 06/12/2023] [Accepted: 06/22/2023] [Indexed: 07/30/2023] Open
Abstract
The search for new therapeutic targets and their implications in drug development remains an emerging scientific topic. BRCT-bearing proteins are found in Archaea, Bacteria, Eukarya, and viruses. They are traditionally involved in DNA repair, recombination, and cell cycle control. To carry out these functions, BRCT domains are able to interact with DNA and proteins. Moreover, such domains are also implicated in several pathogenic processes and malignancies including breast, ovarian, and lung cancer. Although these domains exhibit moderately conserved folding, their sequences show very low conservation. Interestingly, sequence variations among species are considered positive traits in the search for suitable therapeutic targets, since non-specific drug interactions might be reduced. These main characteristics of BRCT, as well as its critical implications in key biological processes in the cell, have prompted the study of these domains as therapeutic targets. This review explores the possible roles of BRCT domains as therapeutic targets for drug discovery. We describe their common structural features and relevant interactions and pathways, as well as their implications in pathologic processes. Drugs commonly used to target these domains are also presented. Finally, based on their structures, we describe new drug design possibilities using modern and innovative techniques.
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Affiliation(s)
- José Peña-Guerrero
- ISTUN Institute of Tropical Health, Department of Microbiology and Parasitology, University of Navarra, IdiSNA (Navarra Institute for Health Research), E-31008 Pamplona, Navarra, Spain
| | - Celia Fernández-Rubio
- ISTUN Institute of Tropical Health, Department of Microbiology and Parasitology, University of Navarra, IdiSNA (Navarra Institute for Health Research), E-31008 Pamplona, Navarra, Spain
| | - Alfonso T García-Sosa
- Chair of Molecular Technology, Institute of Chemistry, University of Tartu, Ravila 14a, 50411 Tartu, Estonia
| | - Paul A Nguewa
- ISTUN Institute of Tropical Health, Department of Microbiology and Parasitology, University of Navarra, IdiSNA (Navarra Institute for Health Research), E-31008 Pamplona, Navarra, Spain
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3
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Sharma A, Krishna S, Sowdhamini R. Bioinformatics Analysis of Mutations Sheds Light on the Evolution of Dengue NS1 Protein With Implications in the Identification of Potential Functional and Druggable Sites. Mol Biol Evol 2023; 40:7043264. [PMID: 36795614 PMCID: PMC9989740 DOI: 10.1093/molbev/msad033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 12/26/2022] [Accepted: 01/30/2023] [Indexed: 02/17/2023] Open
Abstract
Non-structural protein (NS1) is a 350 amino acid long conserved protein in the dengue virus. Conservation of NS1 is expected due to its importance in dengue pathogenesis. The protein is known to exist in dimeric and hexameric states. The dimeric state is involved in its interaction with host proteins and viral replication, and the hexameric state is involved in viral invasion. In this work, we performed extensive structure and sequence analysis of NS1 protein, and uncovered the role of NS1 quaternary states in its evolution. A three-dimensional modeling of unresolved loop regions in NS1 structure is performed. "Conserved" and "Variable" regions within NS1 protein were identified from sequences obtained from patient samples and the role of compensatory mutations in selecting destabilizing mutations were identified. Molecular dynamics (MD) simulations were performed to extensively study the effect of a few mutations on NS1 structure stability and compensatory mutations. Virtual saturation mutagenesis, predicting the effect of every individual amino acid substitution on NS1 stability sequentially, revealed virtual-conserved and variable sites. The increase in number of observed and virtual-conserved regions across NS1 quaternary states suggest the role of higher order structure formation in its evolutionary conservation. Our sequence and structure analysis could enable in identifying possible protein-protein interfaces and druggable sites. Virtual screening of nearly 10,000 small molecules, including FDA-approved drugs, permitted us to recognize six drug-like molecules targeting the dimeric sites. These molecules could be promising due to their stable interactions with NS1 throughout the simulation.
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Affiliation(s)
- Abhishek Sharma
- National Centre for Biological Science, TIFR, Bangalore, India
| | - Sudhir Krishna
- National Centre for Biological Science, TIFR, Bangalore, India.,Department of School of Interdisciplinary Life Sciences, Indian Institute of Technology Goa, Farmagudi, Pond-403401, Goa, India
| | - Ramanathan Sowdhamini
- National Centre for Biological Science, TIFR, Bangalore, India.,Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India.,Computational Biology, Institute of Bioinformatics and Applied Biotechnology, Bangalore, India
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Iraji MS, Tanha J, Habibinejad M. Druggable protein prediction using a multi-canal deep convolutional neural network based on autocovariance method. Comput Biol Med 2022; 151:106276. [PMID: 36410099 DOI: 10.1016/j.compbiomed.2022.106276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 10/18/2022] [Accepted: 10/30/2022] [Indexed: 11/09/2022]
Abstract
Drug targets must be identified and positioned correctly to research and manufacture new drugs. In this study, rather than using traditional methods for drug expansion, the drug target is determined using machine learning. Machine learning has generated significant interest and desire in recent years and extensive research due to its low cost and speed of operation. As a result, it is critical to develop an intelligent classification system for drug proteins. This study proposes two distinct models for the prediction of druggable protein classes based on the deep learning method. The translation of drug-protein sequences is based on six physicochemical properties of amino acids. Following the application of the autocovariance method, converted sequences are used as fixed-length input vectors in deep stacked sparse auto-encoders (DSSAEs) network. The coded protein sequences are also considered and utilized as a six-channel input vector for the deep convolutional neural network model. The experimental results contributing to the deep convolution model are more efficient than previous studies for classifying druggable proteins. The proposed approach achieved a sensitivity of 96.92%, a specificity of 99.51%, and an accuracy of 98.29%.
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Affiliation(s)
- Mohammad Saber Iraji
- Department of Computer Engineering and Information Technology, Payame Noor University, Tehran, Iran; Department of Computer Engineering, University of Tabriz, Tabriz, Iran.
| | - Jafar Tanha
- Department of Computer Engineering, University of Tabriz, Tabriz, Iran
| | - Mahboobeh Habibinejad
- Department of Computer Engineering and Information Technology, Payame Noor University, Tehran, Iran
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Data-driven analysis and druggability assessment methods to accelerate the identification of novel cancer targets. Comput Struct Biotechnol J 2022; 21:46-57. [PMID: 36514341 PMCID: PMC9732000 DOI: 10.1016/j.csbj.2022.11.042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 11/21/2022] [Accepted: 11/21/2022] [Indexed: 11/27/2022] Open
Abstract
Over the past few decades, drug discovery has greatly improved the outcomes for patients, but several challenges continue to hinder the rapid development of novel drugs. Addressing unmet clinical needs requires the pursuit of drug targets that have a higher likelihood to lead to the development of successful drugs. Here we describe a bioinformatic approach for identifying novel cancer drug targets by performing statistical analysis to ascertain quantitative changes in expression levels between protein-coding genes, as well as co-expression networks to classify these genes into groups. Subsequently, we provide an overview of druggability assessment methodologies to prioritize and select the best targets to pursue.
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Wang L, Wong L, Chen ZH, Hu J, Sun XF, Li Y, You ZH. MSPEDTI: Prediction of Drug-Target Interactions via Molecular Structure with Protein Evolutionary Information. BIOLOGY 2022; 11:740. [PMID: 35625468 PMCID: PMC9138588 DOI: 10.3390/biology11050740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 05/03/2022] [Accepted: 05/04/2022] [Indexed: 11/25/2022]
Abstract
The key to new drug discovery and development is first and foremost the search for molecular targets of drugs, thus advancing drug discovery and drug repositioning. However, traditional drug-target interactions (DTIs) is a costly, lengthy, high-risk, and low-success-rate system project. Therefore, more and more pharmaceutical companies are trying to use computational technologies to screen existing drug molecules and mine new drugs, leading to accelerating new drug development. In the current study, we designed a deep learning computational model MSPEDTI based on Molecular Structure and Protein Evolutionary to predict the potential DTIs. The model first fuses protein evolutionary information and drug structure information, then a deep learning convolutional neural network (CNN) to mine its hidden features, and finally accurately predicts the associated DTIs by extreme learning machine (ELM). In cross-validation experiments, MSPEDTI achieved 94.19%, 90.95%, 87.95%, and 86.11% prediction accuracy in the gold-standard datasets enzymes, ion channels, G-protein-coupled receptors (GPCRs), and nuclear receptors, respectively. MSPEDTI showed its competitive ability in ablation experiments and comparison with previous excellent methods. Additionally, 7 of 10 potential DTIs predicted by MSPEDTI were substantiated by the classical database. These excellent outcomes demonstrate the ability of MSPEDTI to provide reliable drug candidate targets and strongly facilitate the development of drug repositioning and drug development.
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Affiliation(s)
- Lei Wang
- Big Data and Intelligent Computing Research Center, Guangxi Academy of Sciences, Nanning 530007, China;
- College of Information Science and Engineering, Zaozhuang University, Zaozhuang 277160, China; (J.H.); (X.-F.S.)
| | - Leon Wong
- Big Data and Intelligent Computing Research Center, Guangxi Academy of Sciences, Nanning 530007, China;
| | - Zhan-Heng Chen
- Computer Science and Technology, Tongji University, Shanghai 200092, China;
| | - Jing Hu
- College of Information Science and Engineering, Zaozhuang University, Zaozhuang 277160, China; (J.H.); (X.-F.S.)
| | - Xiao-Fei Sun
- College of Information Science and Engineering, Zaozhuang University, Zaozhuang 277160, China; (J.H.); (X.-F.S.)
| | - Yang Li
- School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230601, China;
| | - Zhu-Hong You
- Big Data and Intelligent Computing Research Center, Guangxi Academy of Sciences, Nanning 530007, China;
- School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China
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Kurgan L. Resources for computational prediction of intrinsic disorder in proteins. Methods 2022; 204:132-141. [DOI: 10.1016/j.ymeth.2022.03.018] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 03/25/2022] [Accepted: 03/29/2022] [Indexed: 12/26/2022] Open
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Yu L, Xue L, Liu F, Li Y, Jing R, Luo J. The applications of deep learning algorithms on in silico druggable proteins identification. J Adv Res 2022; 41:219-231. [PMID: 36328750 PMCID: PMC9637576 DOI: 10.1016/j.jare.2022.01.009] [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: 08/26/2021] [Revised: 12/21/2021] [Accepted: 01/18/2022] [Indexed: 11/20/2022] Open
Abstract
We developed the first deep learning-based druggable protein classifier for fast and accurate identification of potential druggable proteins. Experimental results on a standard dataset demonstrate that the prediction performance of deep learning model is comparable to those of existing methods. We visualized the representations of druggable proteins learned by deep learning models, which helps us understand how they work. Our analysis reconfirms that the attention mechanism is especially useful for explaining deep learning models.
Introduction The top priority in drug development is to identify novel and effective drug targets. In vitro assays are frequently used for this purpose; however, traditional experimental approaches are insufficient for large-scale exploration of novel drug targets, as they are expensive, time-consuming and laborious. Therefore, computational methods have emerged in recent decades as an alternative to aid experimental drug discovery studies by developing sophisticated predictive models to estimate unknown drugs/compounds and their targets. The recent success of deep learning (DL) techniques in machine learning and artificial intelligence has further attracted a great deal of attention in the biomedicine field, including computational drug discovery. Objectives This study focuses on the practical applications of deep learning algorithms for predicting druggable proteins and proposes a powerful predictor for fast and accurate identification of potential drug targets. Methods Using a gold-standard dataset, we explored several typical protein features and different deep learning algorithms and evaluated their performance in a comprehensive way. We provide an overview of the entire experimental process, including protein features and descriptors, neural network architectures, libraries and toolkits for deep learning modelling, performance evaluation metrics, model interpretation and visualization. Results Experimental results show that the hybrid model (architecture: CNN-RNN (BiLSTM) + DNN; feature: dictionary encoding + DC_TC_CTD) performed better than the other models on the benchmark dataset. This hybrid model was able to achieve 90.0% accuracy and 0.800 MCC on the test dataset and 84.8% and 0.703 on a nonredundant independent test dataset, which is comparable to those of existing methods. Conclusion We developed the first deep learning-based classifier for fast and accurate identification of potential druggable proteins. We hope that this study will be helpful for future researchers who would like to use deep learning techniques to develop relevant predictive models.
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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: 23] [Impact Index Per Article: 7.7] [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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Zavala-Barrera C, Del-Río-Robles JE, García-Jiménez I, Egusquiza-Alvarez CA, Hernández-Maldonado JP, Vázquez-Prado J, Reyes-Cruz G. The calcium sensing receptor (CaSR) promotes Rab27B expression and activity to control secretion in breast cancer cells. BIOCHIMICA ET BIOPHYSICA ACTA-MOLECULAR CELL RESEARCH 2021; 1868:119026. [PMID: 33845096 DOI: 10.1016/j.bbamcr.2021.119026] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Revised: 03/23/2021] [Accepted: 03/25/2021] [Indexed: 12/11/2022]
Abstract
Chemotactic and angiogenic factors secreted within the tumor microenvironment eventually facilitate the metastatic dissemination of cancer cells. Calcium-sensing receptor (CaSR) activates secretory pathways in breast cancer cells via a mechanism driven by vesicular trafficking of this receptor. However, it remains to be elucidated how endosomal proteins in secretory vesicles are controlled by CaSR. In the present study, we demonstrate that CaSR promotes expression of Rab27B and activates this secretory small GTPase via PI3K, PKA, mTOR and MADD, a guanine nucleotide exchange factor, also known as DENN/Rab3GEP. Active Rab27B leads secretion of various cytokines and chemokines, including IL-6, IL-1β, IL-8, IP-10 and RANTES. Expression of Rab27B is stimulated by CaSR in MDA-MB-231 and MCF-7 breast epithelial cancer cells, but not in non-cancerous MCF-10A cells. This regulatory mechanism also occurs in HeLa and PC3 cells. Our findings provide insightful information regarding how CaSR activates a Rab27B-dependent mechanism to control secretion of factors known to intervene in paracrine communication circuits within the tumor microenvironment.
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Affiliation(s)
- Cesar Zavala-Barrera
- Departments of Cell Biology, Centro de Investigación y Estudios Avanzados del IPN (Cinvestav-IPN), Mexico City, Mexico
| | - Jorge Eduardo Del-Río-Robles
- Departments of Cell Biology, Centro de Investigación y Estudios Avanzados del IPN (Cinvestav-IPN), Mexico City, Mexico
| | - Irving García-Jiménez
- Departments of Cell Biology, Centro de Investigación y Estudios Avanzados del IPN (Cinvestav-IPN), Mexico City, Mexico
| | | | | | - José Vázquez-Prado
- Departments of Pharmacology, Centro de Investigación y Estudios Avanzados del IPN (Cinvestav-IPN), Mexico City, Mexico
| | - Guadalupe Reyes-Cruz
- Departments of Cell Biology, Centro de Investigación y Estudios Avanzados del IPN (Cinvestav-IPN), Mexico City, Mexico.
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Zhao B, Katuwawala A, Uversky VN, Kurgan L. IDPology of the living cell: intrinsic disorder in the subcellular compartments of the human cell. Cell Mol Life Sci 2021; 78:2371-2385. [PMID: 32997198 PMCID: PMC11071772 DOI: 10.1007/s00018-020-03654-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Revised: 09/09/2020] [Accepted: 09/22/2020] [Indexed: 12/11/2022]
Abstract
Intrinsic disorder can be found in all proteomes of all kingdoms of life and in viruses, being particularly prevalent in the eukaryotes. We conduct a comprehensive analysis of the intrinsic disorder in the human proteins while mapping them into 24 compartments of the human cell. In agreement with previous studies, we show that human proteins are significantly enriched in disorder relative to a generic protein set that represents the protein universe. In fact, the fraction of proteins with long disordered regions and the average protein-level disorder content in the human proteome are about 3 times higher than in the protein universe. Furthermore, levels of intrinsic disorder in the majority of human subcellular compartments significantly exceed the average disorder content in the protein universe. Relative to the overall amount of disorder in the human proteome, proteins localized in the nucleus and cytoskeleton have significantly increased amounts of disorder, measured by both high disorder content and presence of multiple long intrinsically disordered regions. We empirically demonstrate that, on average, human proteins are assigned to 2.3 subcellular compartments, with proteins localized to few subcellular compartments being more disordered than the proteins that are localized to many compartments. Functionally, the disordered proteins localized in the most disorder-enriched subcellular compartments are primarily responsible for interactions with nucleic acids and protein partners. This is the first-time disorder is comprehensively mapped into the human cell. Our observations add a missing piece to the puzzle of functional disorder and its organization inside the cell.
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Affiliation(s)
- Bi Zhao
- Department of Computer Science, Virginia Commonwealth University, 401 West Main Street, Room E4225, Richmond, VA, 23284, USA
| | - Akila Katuwawala
- Department of Computer Science, Virginia Commonwealth University, 401 West Main Street, Room E4225, Richmond, VA, 23284, USA
| | - Vladimir N Uversky
- Department of Molecular Medicine, 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.
- Laboratory of New Methods in Biology, Institute for Biological Instrumentation of the Russian Academy of Sciences, Federal Research Center "Pushchino Scientific Center for Biological Research of the Russian Academy of Sciences", Pushchino, Russia.
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, 401 West Main Street, Room E4225, Richmond, VA, 23284, USA.
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