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Wu Z, Basu S, Wu X, Kurgan L. qNABpredict: Quick, accurate, and taxonomy-aware sequence-based prediction of content of nucleic acid binding amino acids. Protein Sci 2023; 32:e4544. [PMID: 36519304 PMCID: PMC9798252 DOI: 10.1002/pro.4544] [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: 09/16/2022] [Revised: 12/07/2022] [Accepted: 12/08/2022] [Indexed: 12/23/2022]
Abstract
Protein sequence-based predictors of nucleic acid (NA)-binding include methods that predict NA-binding proteins and NA-binding residues. The residue-level tools produce more details but suffer high computational cost since they must predict every amino acid in the input sequence and rely on multiple sequence alignments. We propose an alternative approach that predicts content (fraction) of the NA-binding residues, offering more information than the protein-level prediction and much shorter runtime than the residue-level tools. Our first-of-its-kind content predictor, qNABpredict, relies on a small, rationally designed and fast-to-compute feature set that represents relevant characteristics extracted from the input sequence and a well-parametrized support vector regression model. We provide two versions of qNABpredict, a taxonomy-agnostic model that can be used for proteins of unknown taxonomic origin and more accurate taxonomy-aware models that are tailored to specific taxonomic kingdoms: archaea, bacteria, eukaryota, and viruses. Empirical tests on a low-similarity test dataset show that qNABpredict is 100 times faster and generates statistically more accurate content predictions when compared to the content extracted from results produced by the residue-level predictors. We also show that qNABpredict's content predictions can be used to improve results generated by the residue-level predictors. We release qNABpredict as a convenient webserver and source code at http://biomine.cs.vcu.edu/servers/qNABpredict/. This new tool should be particularly useful to predict details of protein-NA interactions for large protein families and proteomes.
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Affiliation(s)
- Zhonghua Wu
- School of Mathematical Sciences and LPMCNankai UniversityTianjinChina
| | - Sushmita Basu
- Department of Computer ScienceVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Xuantai Wu
- School of Mathematical Sciences and LPMCNankai UniversityTianjinChina
| | - Lukasz Kurgan
- Department of Computer ScienceVirginia Commonwealth UniversityRichmondVirginiaUSA
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2
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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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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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Singh J, Litfin T, Paliwal K, Singh J, Hanumanthappa AK, Zhou Y. SPOT-1D-Single: Improving the Single-Sequence-Based Prediction of Protein Secondary Structure, Backbone Angles, Solvent Accessibility and Half-Sphere Exposures using a Large Training Set and Ensembled Deep Learning. Bioinformatics 2021; 37:3464-3472. [PMID: 33983382 DOI: 10.1093/bioinformatics/btab316] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Revised: 04/06/2021] [Accepted: 04/26/2021] [Indexed: 02/01/2023] Open
Abstract
MOTIVATION Knowing protein secondary and other one-dimensional structural properties are essential for accurate protein structure and function prediction. As a result, many methods have been developed for predicting these one-dimensional structural properties. However, most methods relied on evolutionary information that may not exist for many proteins due to a lack of sequence homologs. Moreover, it is computationally intensive for obtaining evolutionary information as the library of protein sequences continues to expand exponentially. Here we developed a new single-sequence method called SPOT-1D-Single based on a large training dataset of 39120 proteins deposited prior to 2016 and an ensemble of hybrid Long-Short-Term-Memory bidirectional neural network and convolutional neural network. RESULTS We showed that SPOT-1D-Single consistently improves over SPIDER3-Single and ProteinUnet for secondary structure, solvent accessibility, contact number, and backbone angles prediction for all seven independent test sets (TEST2018, SPOT-2016, SPOT-2016-HQ, SPOT-2018, SPOT-2018-HQ, CASP12, and CASP13 free-modeling targets). For example, the predicted three-state secondary structure's accuracy ranges from 72.12-74.28% by SPOT-1D-Single, compared to 69.1-72.6% by SPIDER3-Single and 70.6-73% by ProteinUnet. SPOT-1D-Single also predicts SS3 and SS8 with 6.24% and 6.98% better accuracy than SPOT-1D on SPOT-2018 proteins with no homologs (Neff=1), respectively. The new method's improvement over existing techniques is due to a larger training set combined with ensembled learning. AVAILABILITY Standalone-version of SPOT-1D-Single is available at https://github.com/jas-preet/SPOT-1D-Single. Direct prediction can also be made at https://sparks-lab.org/server/spot-1d-single. The datasets used in this research can also be downloaded from GitHub.
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Affiliation(s)
- Jaspreet Singh
- Signal Processing Laboratory, School of Engineering and Built Environment, Griffith University, Brisbane, QLD 4111, Australia
| | - Thomas Litfin
- School of Information and Communication Technology, Griffith University, Parklands Dr. Southport, QLD 4222, Australia
| | - Kuldip Paliwal
- Signal Processing Laboratory, School of Engineering and Built Environment, Griffith University, Brisbane, QLD 4111, Australia
| | - Jaswinder Singh
- Signal Processing Laboratory, School of Engineering and Built Environment, Griffith University, Brisbane, QLD 4111, Australia
| | - Anil Kumar Hanumanthappa
- Signal Processing Laboratory, School of Engineering and Built Environment, Griffith University, Brisbane, QLD 4111, Australia
| | - Yaoqi Zhou
- School of Information and Communication Technology, Griffith University, Parklands Dr. Southport, QLD 4222, Australia.,Institute for Glycomics, Griffith University, Parklands Dr. Southport, QLD 4222, Australia.,Institue for Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518055, China
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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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Zhao B, Katuwawala A, Oldfield CJ, Dunker AK, Faraggi E, Gsponer J, Kloczkowski A, Malhis N, Mirdita M, Obradovic Z, Söding J, Steinegger M, Zhou Y, Kurgan L. DescribePROT: database of amino acid-level protein structure and function predictions. Nucleic Acids Res 2021; 49:D298-D308. [PMID: 33119734 PMCID: PMC7778963 DOI: 10.1093/nar/gkaa931] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 09/11/2020] [Accepted: 10/05/2020] [Indexed: 12/30/2022] Open
Abstract
We present DescribePROT, the database of predicted amino acid-level descriptors of structure and function of proteins. DescribePROT delivers a comprehensive collection of 13 complementary descriptors predicted using 10 popular and accurate algorithms for 83 complete proteomes that cover key model organisms. The current version includes 7.8 billion predictions for close to 600 million amino acids in 1.4 million proteins. The descriptors encompass sequence conservation, position specific scoring matrix, secondary structure, solvent accessibility, intrinsic disorder, disordered linkers, signal peptides, MoRFs and interactions with proteins, DNA and RNAs. Users can search DescribePROT by the amino acid sequence and the UniProt accession number and entry name. The pre-computed results are made available instantaneously. The predictions can be accesses via an interactive graphical interface that allows simultaneous analysis of multiple descriptors and can be also downloaded in structured formats at the protein, proteome and whole database scale. The putative annotations included by DescriPROT are useful for a broad range of studies, including: investigations of protein function, applied projects focusing on therapeutics and diseases, and in the development of predictors for other protein sequence descriptors. Future releases will expand the coverage of DescribePROT. DescribePROT can be accessed at http://biomine.cs.vcu.edu/servers/DESCRIBEPROT/.
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Affiliation(s)
- Bi Zhao
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA
| | - Akila Katuwawala
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA
| | | | - A Keith Dunker
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Eshel Faraggi
- Battelle Center for Mathematical Medicine at the Nationwide Children's Hospital, and Department of Pediatrics, The Ohio State University, Columbus, OH, USA
| | - Jörg Gsponer
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada
| | - Andrzej Kloczkowski
- Battelle Center for Mathematical Medicine at the Nationwide Children's Hospital, and Department of Pediatrics, The Ohio State University, Columbus, OH, USA
| | - Nawar Malhis
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada
| | - Milot Mirdita
- Quantitative and Computational Biology, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
| | - Zoran Obradovic
- Department of Computer and Information Sciences, Temple University, Philadelphia, PA, USA
| | - Johannes Söding
- Quantitative and Computational Biology, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
| | - Martin Steinegger
- School of Biological Sciences and Institute of Molecular Biology & Genetics, Seoul National University, Seoul, Republic of Korea
| | - Yaoqi Zhou
- Institute for Glycomics, Griffith University, Gold Coast, Queensland, Australia
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA
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Katuwawala A, Oldfield CJ, Kurgan L. DISOselect: Disorder predictor selection at the protein level. Protein Sci 2020; 29:184-200. [PMID: 31642118 PMCID: PMC6933862 DOI: 10.1002/pro.3756] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Revised: 10/16/2019] [Accepted: 10/17/2019] [Indexed: 12/27/2022]
Abstract
The intense interest in the intrinsically disordered proteins in the life science community, together with the remarkable advancements in predictive technologies, have given rise to the development of a large number of computational predictors of intrinsic disorder from protein sequence. While the growing number of predictors is a positive trend, we have observed a considerable difference in predictive quality among predictors for individual proteins. Furthermore, variable predictor performance is often inconsistent between predictors for different proteins, and the predictor that shows the best predictive performance depends on the unique properties of each protein sequence. We propose a computational approach, DISOselect, to estimate the predictive performance of 12 selected predictors for individual proteins based on their unique sequence-derived properties. This estimation informs the users about the expected predictive quality for a selected disorder predictor and can be used to recommend methods that are likely to provide the best quality predictions. Our solution does not depend on the results of any disorder predictor; the estimations are made based solely on the protein sequence. Our solution significantly improves predictive performance, as judged with a test set of 1,000 proteins, when compared to other alternatives. We have empirically shown that by using the recommended methods the overall predictive performance for a given set of proteins can be improved by a statistically significant margin. DISOselect is freely available for non-commercial users through the webserver at http://biomine.cs.vcu.edu/servers/DISOselect/.
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Affiliation(s)
- Akila Katuwawala
- Department of Computer ScienceVirginia Commonwealth UniversityRichmondVirginia
| | | | - Lukasz Kurgan
- Department of Computer ScienceVirginia Commonwealth UniversityRichmondVirginia
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