1
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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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Erdős G, Dosztányi Z. AIUPred: combining energy estimation with deep learning for the enhanced prediction of protein disorder. Nucleic Acids Res 2024; 52:W176-W181. [PMID: 38747347 PMCID: PMC11223784 DOI: 10.1093/nar/gkae385] [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: 02/29/2024] [Revised: 04/19/2024] [Accepted: 05/07/2024] [Indexed: 07/06/2024] Open
Abstract
Intrinsically disordered proteins and protein regions (IDPs/IDRs) carry out important biological functions without relying on a single well-defined conformation. As these proteins are a challenge to study experimentally, computational methods play important roles in their characterization. One of the commonly used tools is the IUPred web server which provides prediction of disordered regions and their binding sites. IUPred is rooted in a simple biophysical model and uses a limited number of parameters largely derived on globular protein structures only. This enabled an incredibly fast and robust prediction method, however, its limitations have also become apparent in light of recent breakthrough methods using deep learning techniques. Here, we present AIUPred, a novel version of IUPred which incorporates deep learning techniques into the energy estimation framework. It achieves improved performance while keeping the robustness of the original method. Based on the evaluation of recent benchmark datasets, AIUPred scored amongst the top three single sequence based methods. With a new web server we offer fast and reliable visual analysis for users as well as options to analyze whole genomes in mere seconds with the downloadable package. AIUPred is available at https://aiupred.elte.hu.
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Affiliation(s)
- Gábor Erdős
- Department of Biochemistry, Eötvös Loránd University, Pázmány Péter stny 1/c, Budapest H-1117, Hungary
| | - Zsuzsanna Dosztányi
- Department of Biochemistry, Eötvös Loránd University, Pázmány Péter stny 1/c, Budapest H-1117, Hungary
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3
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Basu S, Zhao B, Biró B, Faraggi E, Gsponer J, Hu G, Kloczkowski A, Malhis N, Mirdita M, Söding J, Steinegger M, Wang D, Wang K, Xu D, Zhang J, Kurgan L. DescribePROT in 2023: more, higher-quality and experimental annotations and improved data download options. Nucleic Acids Res 2024; 52:D426-D433. [PMID: 37933852 PMCID: PMC10767971 DOI: 10.1093/nar/gkad985] [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: 09/14/2023] [Revised: 10/12/2023] [Accepted: 10/16/2023] [Indexed: 11/08/2023] Open
Abstract
The DescribePROT database of amino acid-level descriptors of protein structures and functions was substantially expanded since its release in 2020. This expansion includes substantial increase in the size, scope, and quality of the underlying data, the addition of experimental structural information, the inclusion of new data download options, and an upgraded graphical interface. DescribePROT currently covers 19 structural and functional descriptors for proteins in 273 reference proteomes generated by 11 accurate and complementary predictive tools. Users can search our resource in multiple ways, interact with the data using the graphical interface, and download data at various scales including individual proteins, entire proteomes, and whole database. The annotations in DescribePROT are useful for a broad spectrum of studies that include investigations of protein structure and function, development and validation of predictive tools, and to support efforts in understanding molecular underpinnings of diseases and development of therapeutics. DescribePROT can be freely accessed at http://biomine.cs.vcu.edu/servers/DESCRIBEPROT/.
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Affiliation(s)
- Sushmita Basu
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA
| | - Bi Zhao
- Genomics Program, College of Public Health, University of South Florida, Tampa, FL, USA
| | - Bálint Biró
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA
- Department of Animal Biotechnology, Hungarian University of Agriculture and Life Sciences, Gödöllő, Hungary
| | - Eshel Faraggi
- Physics Department, Indiana University, Indianapolis, IN, USA
| | - Jörg Gsponer
- Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada
| | - Gang Hu
- School of Statistics and Data Science, LPMC and KLMDASR, Nankai University, Tianjin, P.R. China
| | - Andrzej Kloczkowski
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, USA
| | - Nawar Malhis
- Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada
| | - Milot Mirdita
- School of Biological Sciences, Seoul National University, Seoul, Republic of Korea
| | - Johannes Söding
- Quantitative and Computational Biology, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
| | - Martin Steinegger
- School of Biological Sciences, Seoul National University, Seoul, Republic of Korea
- Institute of Molecular Biology & Genetics, Seoul National University, Seoul, Republic of Korea
- Artificial Intelligence Institute, Seoul National University, Seoul, South Korea
| | - Duolin Wang
- Department of Electrical Engineer and Computer Science, Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, USA
| | - Kui Wang
- School of Statistics and Data Science, LPMC and KLMDASR, Nankai University, Tianjin, P.R. China
| | - Dong Xu
- Department of Electrical Engineer and Computer Science, Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, USA
| | - Jian Zhang
- School of Computer and Information Technology, Xinyang Normal University, Xinyang, P.R. China
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA
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4
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Patel KN, Chavda D, Manna M. Molecular Docking of Intrinsically Disordered Proteins: Challenges and Strategies. Methods Mol Biol 2024; 2780:165-201. [PMID: 38987470 DOI: 10.1007/978-1-0716-3985-6_11] [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] [Indexed: 07/12/2024]
Abstract
Intrinsically disordered proteins (IDPs) are a novel class of proteins that have established a significant importance and attention within a very short period of time. These proteins are essentially characterized by their inherent structural disorder, encoded mainly by their amino acid sequences. The profound abundance of IDPs and intrinsically disordered regions (IDRs) in the biological world delineates their deep-rooted functionality. IDPs and IDRs convey such extensive functionality through their unique dynamic nature, which enables them to carry out huge number of multifaceted biomolecular interactions and make them "interaction hub" of the cellular systems. Additionally, with such widespread functions, their misfunctioning is also intimately associated with multiple diseases. Thus, understanding the dynamic heterogeneity of various IDPs along with their interactions with respective binding partners is an important field with immense potentials in biomolecular research. In this context, molecular docking-based computational approaches have proven to be remarkable in case of ordered proteins. Molecular docking methods essentially model the biomolecular interactions in both structural and energetic terms and use this information to characterize the putative interactions between the two participant molecules. However, direct applications of the conventional docking methods to study IDPs are largely limited by their structural heterogeneity and demands for unique IDP-centric strategies. Thus, in this chapter, we have presented an overview of current methodologies for successful docking operations involving IDPs and IDRs. These specialized methods majorly include the ensemble-based and fragment-based approaches with their own benefits and limitations. More recently, artificial intelligence and machine learning-assisted approaches are also used to significantly reduce the complexity and computational burden associated with various docking applications. Thus, this chapter aims to provide a comprehensive summary of major challenges and recent advancements of molecular docking approaches in the IDP field for their better utilization and greater applicability.Asp (D).
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Affiliation(s)
- Keyur N Patel
- Applied Phycology and Biotechnology Division, CSIR Central Salt and Marine Chemicals Research Institute, Bhavnagar, Gujarat, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh, India
| | - Dhruvil Chavda
- Applied Phycology and Biotechnology Division, CSIR Central Salt and Marine Chemicals Research Institute, Bhavnagar, Gujarat, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh, India
| | - Moutusi Manna
- Applied Phycology and Biotechnology Division, CSIR Central Salt and Marine Chemicals Research Institute, Bhavnagar, Gujarat, India.
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh, India.
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5
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Song J, Kurgan L. Availability of web servers significantly boosts citations rates of bioinformatics methods for protein function and disorder prediction. BIOINFORMATICS ADVANCES 2023; 3:vbad184. [PMID: 38146538 PMCID: PMC10749743 DOI: 10.1093/bioadv/vbad184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 12/08/2023] [Accepted: 12/15/2023] [Indexed: 12/27/2023]
Abstract
Motivation Development of bioinformatics methods is a long, complex and resource-hungry process. Hundreds of these tools were released. While some methods are highly cited and used, many suffer relatively low citation rates. We empirically analyze a large collection of recently released methods in three diverse protein function and disorder prediction areas to identify key factors that contribute to increased citations. Results We show that provision of a working web server significantly boosts citation rates. On average, methods with working web servers generate three times as many citations compared to tools that are available as only source code, have no code and no server, or are no longer available. This observation holds consistently across different research areas and publication years. We also find that differences in predictive performance are unlikely to impact citation rates. Overall, our empirical results suggest that a relatively low-cost investment into the provision and long-term support of web servers would substantially increase the impact of bioinformatics tools.
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Affiliation(s)
- Jiangning Song
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Clayton, VIC 3800, Australia
- Monash Data Futures Institute, Monash University, Clayton, VIC 3800, Australia
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, United States
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6
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Yang J, Cheng WX, Wu G, Sheng S, Zhang P. Prediction of folding patterns for intrinsic disordered protein. Sci Rep 2023; 13:20343. [PMID: 37990040 PMCID: PMC10663623 DOI: 10.1038/s41598-023-45969-5] [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: 09/05/2023] [Accepted: 10/26/2023] [Indexed: 11/23/2023] Open
Abstract
The conformation flexibility of natural protein causes both complexity and difficulty to understand the relationship between structure and function. The prediction of intrinsically disordered protein primarily is focusing on to disclose the regions with structural flexibility involving relevant biological functions and various diseases. The order of amino acids in protein sequence determines possible conformations, folding flexibility and biological function. Although many methods provided the information of intrinsically disordered protein (IDP), but the results are mainly limited to determine the locations of regions without knowledge of possible folding conformations. Here, the developed protein folding fingerprint adopted the protein folding variation matrix (PFVM) to reveal all possible folding patterns for the intrinsically disordered protein along its sequence. The PFVM integrally exhibited the intrinsically disordered protein with disordering regions, degree of disorder as well as folding pattern. The advantage of PFVM will not only provide rich information for IDP, but also may promote the study of protein folding problem.
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Affiliation(s)
- Jiaan Yang
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, Guangdong, China.
- Micro Biotech, Ltd., Shanghai, 200123, China.
| | - Wen-Xiang Cheng
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, Guangdong, China
| | - Gang Wu
- School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Sitong Sheng
- HYK High-throughput Biotechnology Institute, Shenzhen, 518057, Guangdong, China
| | - Peng Zhang
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, Guangdong, China
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7
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Basu S, Hegedűs T, Kurgan L. CoMemMoRFPred: Sequence-based Prediction of MemMoRFs by Combining Predictors of Intrinsic Disorder, MoRFs and Disordered Lipid-binding Regions. J Mol Biol 2023; 435:168272. [PMID: 37709009 DOI: 10.1016/j.jmb.2023.168272] [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: 07/05/2023] [Revised: 09/01/2023] [Accepted: 09/07/2023] [Indexed: 09/16/2023]
Abstract
Molecular recognition features (MoRFs) are a commonly occurring type of intrinsically disordered regions (IDRs) that undergo disorder-to-order transition upon binding to partner molecules. We focus on recently characterized and functionally important membrane-binding MoRFs (MemMoRFs). Motivated by the lack of computational tools that predict MemMoRFs, we use a dataset of experimentally annotated MemMoRFs to conceptualize, design, evaluate and release an accurate sequence-based predictor. We rely on state-of-the-art tools that predict residues that possess key characteristics of MemMoRFs, such as intrinsic disorder, disorder-to-order transition and lipid-binding. We identify and combine results from three tools that include flDPnn for the disorder prediction, DisoLipPred for the prediction of disordered lipid-binding regions, and MoRFCHiBiLight for the prediction of disorder-to-order transitioning protein binding regions. Our empirical analysis demonstrates that combining results produced by these three methods generates accurate predictions of MemMoRFs. We also show that use of a smoothing operator produces predictions that closely mimic the number and sizes of the native MemMoRF regions. The resulting CoMemMoRFPred method is available as an easy-to-use webserver at http://biomine.cs.vcu.edu/servers/CoMemMoRFPred. This tool will aid future studies of MemMoRFs in the context of exploring their abundance, cellular functions, and roles in pathologic phenomena.
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Affiliation(s)
- Sushmita Basu
- Department of Computer Science, Virginia Commonwealth University, USA
| | - Tamás Hegedűs
- Department of Biophysics and Radiation Biology, Semmelweis University, Budapest, Hungary; ELKH-SE Biophysical Virology Research Group, Eötvös Loránd Research Network, Budapest, Hungary
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, USA.
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8
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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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9
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Zhao B, Ghadermarzi S, Kurgan L. Comparative evaluation of AlphaFold2 and disorder predictors for prediction of intrinsic disorder, disorder content and fully disordered proteins. Comput Struct Biotechnol J 2023; 21:3248-3258. [PMID: 38213902 PMCID: PMC10782001 DOI: 10.1016/j.csbj.2023.06.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 05/31/2023] [Accepted: 06/01/2023] [Indexed: 01/13/2024] Open
Abstract
We expand studies of AlphaFold2 (AF2) in the context of intrinsic disorder prediction by comparing it against a broad selection of 20 accurate, popular and recently released disorder predictors. We use 25% larger benchmark dataset with 646 proteins and cover protein-level predictions of disorder content and fully disordered proteins. AF2-based disorder predictions secure a relatively high Area Under receiver operating characteristic Curve (AUC) of 0.77 and are statistically outperformed by several modern disorder predictors that secure AUCs around 0.8 with median runtime of about 20 s compared to 1200 s for AF2. Moreover, AF2 provides modestly accurate predictions of fully disordered proteins (F1 = 0.59 vs. 0.91 for the best disorder predictor) and disorder content (mean absolute error of 0.21 vs. 0.15). AF2 also generates statistically more accurate disorder predictions for about 20% of proteins that have relatively short sequences and a few disordered regions that tend to be located at the sequence termini, and which are absent of disordered protein-binding regions. Interestingly, AF2 and the most accurate disorder predictors rely on deep neural networks, suggesting that these models are useful for protein structure and disorder predictions.
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Affiliation(s)
- Bi Zhao
- Genomics program, College of Public Health, University of South Florida, Tampa, FL, United States
| | - Sina Ghadermarzi
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, United States
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, United States
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10
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Abstract
There are over 100 computational predictors of intrinsic disorder. These methods predict amino acid-level propensities for disorder directly from protein sequences. The propensities can be used to annotate putative disordered residues and regions. This unit provides a practical and holistic introduction to the sequence-based intrinsic disorder prediction. We define intrinsic disorder, explain the format of computational prediction of disorder, and identify and describe several accurate predictors. We also introduce recently released databases of intrinsic disorder predictions and use an illustrative example to provide insights into how predictions should be interpreted and combined. Lastly, we summarize key experimental methods that can be used to validate computational predictions. © 2023 Wiley Periodicals LLC.
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Affiliation(s)
- Vladimir N Uversky
- Department of Molecular Medicine and USF Health Byrd Alzheimer's Research Institute, Morsani College of Medicine, University of South Florida, Tampa, Florida
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, Virginia
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11
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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: 5] [Impact Index Per Article: 5.0] [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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12
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Computational prediction of disordered binding regions. Comput Struct Biotechnol J 2023; 21:1487-1497. [PMID: 36851914 PMCID: PMC9957716 DOI: 10.1016/j.csbj.2023.02.018] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 02/08/2023] [Accepted: 02/08/2023] [Indexed: 02/12/2023] Open
Abstract
One of the key features of intrinsically disordered regions (IDRs) is their ability to interact with a broad range of partner molecules. Multiple types of interacting IDRs were identified including molecular recognition fragments (MoRFs), short linear sequence motifs (SLiMs), and protein-, nucleic acids- and lipid-binding regions. Prediction of binding IDRs in protein sequences is gaining momentum in recent years. We survey 38 predictors of binding IDRs that target interactions with a diverse set of partners, such as peptides, proteins, RNA, DNA and lipids. We offer a historical perspective and highlight key events that fueled efforts to develop these methods. These tools rely on a diverse range of predictive architectures that include scoring functions, regular expressions, traditional and deep machine learning and meta-models. Recent efforts focus on the development of deep neural network-based architectures and extending coverage to RNA, DNA and lipid-binding IDRs. We analyze availability of these methods and show that providing implementations and webservers results in much higher rates of citations/use. We also make several recommendations to take advantage of modern deep network architectures, develop tools that bundle predictions of multiple and different types of binding IDRs, and work on algorithms that model structures of the resulting complexes.
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13
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Protein Function Analysis through Machine Learning. Biomolecules 2022; 12:biom12091246. [PMID: 36139085 PMCID: PMC9496392 DOI: 10.3390/biom12091246] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Revised: 08/22/2022] [Accepted: 08/31/2022] [Indexed: 11/16/2022] Open
Abstract
Machine learning (ML) has been an important arsenal in computational biology used to elucidate protein function for decades. With the recent burgeoning of novel ML methods and applications, new ML approaches have been incorporated into many areas of computational biology dealing with protein function. We examine how ML has been integrated into a wide range of computational models to improve prediction accuracy and gain a better understanding of protein function. The applications discussed are protein structure prediction, protein engineering using sequence modifications to achieve stability and druggability characteristics, molecular docking in terms of protein–ligand binding, including allosteric effects, protein–protein interactions and protein-centric drug discovery. To quantify the mechanisms underlying protein function, a holistic approach that takes structure, flexibility, stability, and dynamics into account is required, as these aspects become inseparable through their interdependence. Another key component of protein function is conformational dynamics, which often manifest as protein kinetics. Computational methods that use ML to generate representative conformational ensembles and quantify differences in conformational ensembles important for function are included in this review. Future opportunities are highlighted for each of these topics.
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14
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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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15
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Biró B, Zhao B, Kurgan L. Complementarity of the residue-level protein function and structure predictions in human proteins. Comput Struct Biotechnol J 2022; 20:2223-2234. [PMID: 35615015 PMCID: PMC9118482 DOI: 10.1016/j.csbj.2022.05.003] [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: 02/21/2022] [Revised: 05/02/2022] [Accepted: 05/02/2022] [Indexed: 11/24/2022] Open
Abstract
Sequence-based predictors of the residue-level protein function and structure cover a broad spectrum of characteristics including intrinsic disorder, secondary structure, solvent accessibility and binding to nucleic acids. They were catalogued and evaluated in numerous surveys and assessments. However, methods focusing on a given characteristic are studied separately from predictors of other characteristics, while they are typically used on the same proteins. We fill this void by studying complementarity of a representative collection of methods that target different predictions using a large, taxonomically consistent, and low similarity dataset of human proteins. First, we bridge the gap between the communities that develop structure-trained vs. disorder-trained predictors of binding residues. Motivated by a recent study of the protein-binding residue predictions, we empirically find that combining the structure-trained and disorder-trained predictors of the DNA-binding and RNA-binding residues leads to substantial improvements in predictive quality. Second, we investigate whether diverse predictors generate results that accurately reproduce relations between secondary structure, solvent accessibility, interaction sites, and intrinsic disorder that are present in the experimental data. Our empirical analysis concludes that predictions accurately reflect all combinations of these relations. Altogether, this study provides unique insights that support combining results produced by diverse residue-level predictors of protein function and structure.
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Affiliation(s)
- Bálint Biró
- Institute of Genetics and Biotechnology, Hungarian University of Agriculture and Life Sciences, Gödöllő, Hungary
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, United States
| | - Bi Zhao
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, United States
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, United States
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Chakrabarti P, Chakravarty D. Intrinsically disordered proteins/regions and insight into their biomolecular interactions. Biophys Chem 2022; 283:106769. [DOI: 10.1016/j.bpc.2022.106769] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 01/26/2022] [Accepted: 01/26/2022] [Indexed: 12/20/2022]
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Zhao B, Kurgan L. Deep Learning in Prediction of Intrinsic Disorder in Proteins. Comput Struct Biotechnol J 2022; 20:1286-1294. [PMID: 35356546 PMCID: PMC8927795 DOI: 10.1016/j.csbj.2022.03.003] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 03/04/2022] [Accepted: 03/04/2022] [Indexed: 12/12/2022] Open
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