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Yu ZZ, Peng CX, Liu J, Zhang B, Zhou XG, Zhang GJ. DomBpred: Protein Domain Boundary Prediction Based on Domain-Residue Clustering Using Inter-Residue Distance. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:912-922. [PMID: 35594218 DOI: 10.1109/tcbb.2022.3175905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Domain boundary prediction is one of the most important problems in the study of protein structure and function, especially for large proteins. At present, most domain boundary prediction methods have low accuracy and limitations in dealing with multi-domain proteins. In this study, we develop a sequence-based protein domain boundary prediction, named DomBpred. In DomBpred, the input sequence is first classified as either a single-domain protein or a multi-domain protein through a designed effective sequence metric based on a constructed single-domain sequence library. For the multi-domain protein, a domain-residue clustering algorithm inspired by Ising model is proposed to cluster the spatially close residues according inter-residue distance. The unclassified residues and the residues at the edge of the cluster are then tuned by the secondary structure to form potential cut points. Finally, a domain boundary scoring function is proposed to recursively evaluate the potential cut points to generate the domain boundary. DomBpred is tested on a large-scale test set of FUpred comprising 2549 proteins. Experimental results show that DomBpred better performs than the state-of-the-art methods in classifying whether protein sequences are composed by single or multiple domains, and the Matthew's correlation coefficient is 0.882. Moreover, on 849 multi-domain proteins, the domain boundary distance and normalised domain overlap scores of DomBpred are 0.523 and 0.824, respectively, which are 5.0% and 4.2% higher than those of the best comparison method, respectively. Comparison with other methods on the given test set shows that DomBpred outperforms most state-of-the-art sequence-based methods and even achieves better results than the top-level template-based method. The executable program is freely available at https://github.com/iobio-zjut/DomBpred and the online server at http://zhanglab-bioinf.com/DomBpred/.
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2
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Zhu K, Su H, Peng Z, Yang J. A unified approach to protein domain parsing with inter-residue distance matrix. Bioinformatics 2023; 39:7025502. [PMID: 36734597 PMCID: PMC9919455 DOI: 10.1093/bioinformatics/btad070] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 01/02/2023] [Accepted: 02/01/2023] [Indexed: 02/04/2023] Open
Abstract
MOTIVATION It is fundamental to cut multi-domain proteins into individual domains, for precise domain-based structural and functional studies. In the past, sequence-based and structure-based domain parsing was carried out independently with different methodologies. The recent progress in deep learning-based protein structure prediction provides the opportunity to unify sequence-based and structure-based domain parsing. RESULTS Based on the inter-residue distance matrix, which can be either derived from the input structure or predicted by trRosettaX, we can decode the domain boundaries under a unified framework. We name the proposed method UniDoc. The principle of UniDoc is based on the well-accepted physical concept of maximizing intra-domain interaction while minimizing inter-domain interaction. Comprehensive tests on five benchmark datasets indicate that UniDoc outperforms other state-of-the-art methods in terms of both accuracy and speed, for both sequence-based and structure-based domain parsing. The major contribution of UniDoc is providing a unified framework for structure-based and sequence-based domain parsing. We hope that UniDoc would be a convenient tool for protein domain analysis. AVAILABILITY AND IMPLEMENTATION https://yanglab.nankai.edu.cn/UniDoc/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Kun Zhu
- School of Mathematical Sciences, Nankai University, Tianjin 300071, China
| | - Hong Su
- School of Mathematical Sciences, Nankai University, Tianjin 300071, China
| | - Zhenling Peng
- Ministry of Education Frontiers Science Center for Nonlinear Expectations, Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao 266237, China
| | - Jianyi Yang
- Ministry of Education Frontiers Science Center for Nonlinear Expectations, Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao 266237, China
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3
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Mulnaes D, Golchin P, Koenig F, Gohlke H. TopDomain: Exhaustive Protein Domain Boundary Metaprediction Combining Multisource Information and Deep Learning. J Chem Theory Comput 2021; 17:4599-4613. [PMID: 34161735 DOI: 10.1021/acs.jctc.1c00129] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Protein domains are independent, functional, and stable structural units of proteins. Accurate protein domain boundary prediction plays an important role in understanding protein structure and evolution, as well as for protein structure prediction. Current domain boundary prediction methods differ in terms of boundary definition, methodology, and training databases resulting in disparate performance for different proteins. We developed TopDomain, an exhaustive metapredictor, that uses deep neural networks to combine multisource information from sequence- and homology-based features of over 50 primary predictors. For this purpose, we developed a new domain boundary data set termed the TopDomain data set, in which the true annotations are informed by SCOPe annotations, structural domain parsers, human inspection, and deep learning. We benchmark TopDomain against 2484 targets with 3354 boundaries from the TopDomain test set and achieve F1 scores of 78.4% and 73.8% for multidomain boundary prediction within ±20 residues and ±10 residues of the true boundary, respectively. When examined on targets from CASP11-13 competitions, TopDomain achieves F1 scores of 47.5% and 42.8% for multidomain proteins. TopDomain significantly outperforms 15 widely used, state-of-the-art ab initio and homology-based domain boundary predictors. Finally, we implemented TopDomainTMC, which accurately predicts whether domain parsing is necessary for the target protein.
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Affiliation(s)
- Daniel Mulnaes
- Institut für Pharmazeutische und Medizinische Chemie, Heinrich-Heine-Universität Düsseldorf, Universitätsstr. 1, 40225 Düsseldorf, Germany
| | - Pegah Golchin
- Institut für Pharmazeutische und Medizinische Chemie, Heinrich-Heine-Universität Düsseldorf, Universitätsstr. 1, 40225 Düsseldorf, Germany
| | - Filip Koenig
- Institut für Pharmazeutische und Medizinische Chemie, Heinrich-Heine-Universität Düsseldorf, Universitätsstr. 1, 40225 Düsseldorf, Germany
| | - Holger Gohlke
- Institut für Pharmazeutische und Medizinische Chemie, Heinrich-Heine-Universität Düsseldorf, Universitätsstr. 1, 40225 Düsseldorf, Germany.,John von Neumann Institute for Computing (NIC), Jülich Supercomputing Centre (JSC), Institute of Biological Information Processing (IBI-7: Structural Biochemistry) & Institute of Bio- and Geosciences (IBG-4: Bioinformatics), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
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4
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Shi Q, Chen W, Huang S, Jin F, Dong Y, Wang Y, Xue Z. DNN-Dom: predicting protein domain boundary from sequence alone by deep neural network. Bioinformatics 2020; 35:5128-5136. [PMID: 31197306 DOI: 10.1093/bioinformatics/btz464] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Revised: 05/07/2019] [Accepted: 06/05/2019] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Accurate delineation of protein domain boundary plays an important role for protein engineering and structure prediction. Although machine-learning methods are widely used to predict domain boundary, these approaches often ignore long-range interactions among residues, which have been proven to improve the prediction performance. However, how to simultaneously model the local and global interactions to further improve domain boundary prediction is still a challenging problem. RESULTS This article employs a hybrid deep learning method that combines convolutional neural network and gate recurrent units' models for domain boundary prediction. It not only captures the local and non-local interactions, but also fuses these features for prediction. Additionally, we adopt balanced Random Forest for classification to deal with high imbalance of samples and high dimensions of deep features. Experimental results show that our proposed approach (DNN-Dom) outperforms existing machine-learning-based methods for boundary prediction. We expect that DNN-Dom can be useful for assisting protein structure and function prediction. AVAILABILITY AND IMPLEMENTATION The method is available as DNN-Dom Server at http://isyslab.info/DNN-Dom/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Qiang Shi
- School of Software Engineering and College of Life Science & Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Weiya Chen
- School of Software Engineering and College of Life Science & Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Siqi Huang
- School of Software Engineering and College of Life Science & Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Fanglin Jin
- School of Software Engineering and College of Life Science & Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Yinghao Dong
- School of Software Engineering and College of Life Science & Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Yan Wang
- School of Software Engineering and College of Life Science & Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Zhidong Xue
- School of Software Engineering and College of Life Science & Technology, Huazhong University of Science and Technology, Wuhan 430074, China
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5
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Hong SH, Joo K, Lee J. ConDo: protein domain boundary prediction using coevolutionary information. Bioinformatics 2020; 35:2411-2417. [PMID: 30500873 DOI: 10.1093/bioinformatics/bty973] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2018] [Revised: 11/15/2018] [Accepted: 11/29/2018] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Domain boundary prediction is one of the most important problems in the study of protein structure and function. Many sequence-based domain boundary prediction methods are either template-based or machine learning (ML) based. ML-based methods often perform poorly due to their use of only local (i.e. short-range) features. These conventional features such as sequence profiles, secondary structures and solvent accessibilities are typically restricted to be within 20 residues of the domain boundary candidate. RESULTS To address the performance of ML-based methods, we developed a new protein domain boundary prediction method (ConDo) that utilizes novel long-range features such as coevolutionary information in addition to the aforementioned local window features as inputs for ML. Toward this purpose, two types of coevolutionary information were extracted from multiple sequence alignment using direct coupling analysis: (i) partially aligned sequences, and (ii) correlated mutation information. Both the partially aligned sequence information and the modularity of residue-residue couplings possess long-range correlation information. AVAILABILITY AND IMPLEMENTATION https://github.com/gicsaw/ConDo.git. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | - Keehyoung Joo
- Center for Advanced Computation, Korea Institute for Advanced Study, Korea
| | - Jooyoung Lee
- School of Computational Sciences.,Center for Advanced Computation, Korea Institute for Advanced Study, Korea
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6
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Wang Y, Wang J, Li R, Shi Q, Xue Z, Zhang Y. ThreaDomEx: a unified platform for predicting continuous and discontinuous protein domains by multiple-threading and segment assembly. Nucleic Acids Res 2019; 45:W400-W407. [PMID: 28498994 PMCID: PMC5793814 DOI: 10.1093/nar/gkx410] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2017] [Accepted: 04/28/2017] [Indexed: 12/21/2022] Open
Abstract
We develop a hierarchical pipeline, ThreaDomEx, for both continuous domain (CD) and discontinuous domain (DCD) structure predictions. Starting from a query sequence, ThreaDomEx first threads it through the PDB to identify multiple structure templates, where a profile of domain conservation score (DC-score) is derived for domain-segment assignment. To further detect DCDs that consist of separated segments along the sequence, a boundary-clustering algorithm is used to refine the DCD-linker locations. In case that the templates do not contain DCDs, a domain-segment assembly process, guided by symmetry comparison, is applied for further DCD detections. ThreaDomEx was tested a set of 1111 proteins and achieved a normalized domain overlap score of 89.3% compared to experimental data, which is significantly higher than other state-of-the-art methods. It also recalls 26.7% of DCDs with 72.7% precision on the proteins for which threading failed to detect any DCDs. The server provides facilities for users to interactively refine the domain models by adjusting DC-score threshold, deleting and adding domain linkers, and assembling domain segments, which are particularly helpful for the hard targets for which current methods have a low accuracy while human-expert knowledge and experimental insights can be used for refining models. ThreaDomEX server is available at http://zhanglab.ccmb.med.umich.edu/ThreaDomEx.
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Affiliation(s)
- Yan Wang
- Key Laboratory of Molecular Biophysics of the Ministry of Education, School of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China.,Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Jian Wang
- Key Laboratory of Molecular Biophysics of the Ministry of Education, School of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Ruiming Li
- School of Software, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Qiang Shi
- School of Software, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Zhidong Xue
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.,School of Software, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Yang Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.,Department of Biological Chemistry, University of Michigan, Ann Arbor, MI 48109, USA
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7
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Jiang Y, Wang D, Xu D. DeepDom: Predicting protein domain boundary from sequence alone using stacked bidirectional LSTM. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2019; 24:66-75. [PMID: 30864311 PMCID: PMC6417825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Protein domain boundary prediction is usually an early step to understand protein function and structure. Most of the current computational domain boundary prediction methods suffer from low accuracy and limitation in handling multi-domain types, or even cannot be applied on certain targets such as proteins with discontinuous domain. We developed an ab-initio protein domain predictor using a stacked bidirectional LSTM model in deep learning. Our model is trained by a large amount of protein sequences without using feature engineering such as sequence profiles. Hence, the predictions using our method is much faster than others, and the trained model can be applied to any type of target proteins without constraint. We evaluated DeepDom by a 10-fold cross validation and also by applying it on targets in different categories from CASP 8 and CASP 9. The comparison with other methods has shown that DeepDom outperforms most of the current ab-initio methods and even achieves better results than the top-level template-based method in certain cases. The code of DeepDom and the test data we used in CASP 8, 9 can be accessed through GitHub at https://github.com/yuexujiang/DeepDom.
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Affiliation(s)
- Yuexu Jiang
- Department of Electrical Engineering and Computer Science, Bond Life Sciences Center, University of Missouri, Columbia, Missouri 65211, USA
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8
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Richa T, Ide S, Suzuki R, Ebina T, Kuroda Y. Fast H-DROP: A thirty times accelerated version of H-DROP for interactive SVM-based prediction of helical domain linkers. J Comput Aided Mol Des 2016; 31:237-244. [PMID: 28028736 DOI: 10.1007/s10822-016-9999-8] [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: 07/24/2016] [Accepted: 12/10/2016] [Indexed: 10/20/2022]
Abstract
Efficient and rapid prediction of domain regions from amino acid sequence information alone is often required for swift structural and functional characterization of large multi-domain proteins. Here we introduce Fast H-DROP, a thirty times accelerated version of our previously reported H-DROP (Helical Domain linker pRediction using OPtimal features), which is unique in specifically predicting helical domain linkers (boundaries). Fast H-DROP, analogously to H-DROP, uses optimum features selected from a set of 3000 ones by combining a random forest and a stepwise feature selection protocol. We reduced the computational time from 8.5 min per sequence in H-DROP to 14 s per sequence in Fast H-DROP on an 8 Xeon processor Linux server by using SWISS-PROT instead of Genbank non-redundant (nr) database for generating the PSSMs. The sensitivity and precision of Fast H-DROP assessed by cross-validation were 33.7 and 36.2%, which were merely ~2% lower than that of H-DROP. The reduced computational time of Fast H-DROP, without affecting prediction performances, makes it more interactive and user-friendly. Fast H-DROP and H-DROP are freely available from http://domserv.lab.tuat.ac.jp/ .
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Affiliation(s)
- Tambi Richa
- Department of Biotechnology and Life Science, Tokyo University of Agriculture and Technology, 12-24-16 Nakamachi, Koganei-shi, Tokyo, 184-8588, Japan
| | - Soichiro Ide
- Department of Biotechnology and Life Science, Tokyo University of Agriculture and Technology, 12-24-16 Nakamachi, Koganei-shi, Tokyo, 184-8588, Japan
| | - Ryosuke Suzuki
- Department of Biotechnology and Life Science, Tokyo University of Agriculture and Technology, 12-24-16 Nakamachi, Koganei-shi, Tokyo, 184-8588, Japan
| | - Teppei Ebina
- Department of Biotechnology and Life Science, Tokyo University of Agriculture and Technology, 12-24-16 Nakamachi, Koganei-shi, Tokyo, 184-8588, Japan.,Department of Physiology, Graduate school of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Yutaka Kuroda
- Department of Biotechnology and Life Science, Tokyo University of Agriculture and Technology, 12-24-16 Nakamachi, Koganei-shi, Tokyo, 184-8588, Japan.
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Chatterjee P, Basu S, Zubek J, Kundu M, Nasipuri M, Plewczynski D. PDP-CON: prediction of domain/linker residues in protein sequences using a consensus approach. J Mol Model 2016; 22:72. [PMID: 26969678 PMCID: PMC4788683 DOI: 10.1007/s00894-016-2933-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2015] [Accepted: 02/17/2016] [Indexed: 01/04/2023]
Abstract
The prediction of domain/linker residues in protein sequences is a crucial task in the functional classification of proteins, homology-based protein structure prediction, and high-throughput structural genomics. In this work, a novel consensus-based machine-learning technique was applied for residue-level prediction of the domain/linker annotations in protein sequences using ordered/disordered regions along protein chains and a set of physicochemical properties. Six different classifiers-decision tree, Gaussian naïve Bayes, linear discriminant analysis, support vector machine, random forest, and multilayer perceptron-were exhaustively explored for the residue-level prediction of domain/linker regions. The protein sequences from the curated CATH database were used for training and cross-validation experiments. Test results obtained by applying the developed PDP-CON tool to the mutually exclusive, independent proteins of the CASP-8, CASP-9, and CASP-10 databases are reported. An n-star quality consensus approach was used to combine the results yielded by different classifiers. The average PDP-CON accuracy and F-measure values for the CASP targets were found to be 0.86 and 0.91, respectively. The dataset, source code, and all supplementary materials for this work are available at https://cmaterju.org/cmaterbioinfo/ for noncommercial use.
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Affiliation(s)
- Piyali Chatterjee
- Department of Computer Science and Engineering, Netaji Subhash Engineering College, Garia, Kolkata, 700152, India
| | - Subhadip Basu
- Department of Computer Science and Engineering, Jadavpur University, Kolkata, 700032, India.
| | - Julian Zubek
- Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland.,Center of New Technologies, University of Warsaw, Banacha 2c, 02-097, Warsaw, Poland
| | - Mahantapas Kundu
- Department of Computer Science and Engineering, Jadavpur University, Kolkata, 700032, India
| | - Mita Nasipuri
- Department of Computer Science and Engineering, Jadavpur University, Kolkata, 700032, India
| | - Dariusz Plewczynski
- Center of New Technologies, University of Warsaw, Banacha 2c, 02-097, Warsaw, Poland. .,Faculty of Pharmacy, Medical University of Warsaw, Warsaw, Poland.
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10
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Xue Z, Jang R, Govindarajoo B, Huang Y, Wang Y. Extending Protein Domain Boundary Predictors to Detect Discontinuous Domains. PLoS One 2015; 10:e0141541. [PMID: 26502173 PMCID: PMC4621036 DOI: 10.1371/journal.pone.0141541] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2015] [Accepted: 10/10/2015] [Indexed: 11/18/2022] Open
Abstract
A variety of protein domain predictors were developed to predict protein domain boundaries in recent years, but most of them cannot predict discontinuous domains. Considering nearly 40% of multidomain proteins contain one or more discontinuous domains, we have developed DomEx to enable domain boundary predictors to detect discontinuous domains by assembling the continuous domain segments. Discontinuous domains are predicted by matching the sequence profile of concatenated continuous domain segments with the profiles from a single-domain library derived from SCOP and CATH, and Pfam. Then the matches are filtered by similarity to library templates, a symmetric index score and a profile-profile alignment score. DomEx recalled 32.3% discontinuous domains with 86.5% precision when tested on 97 non-homologous protein chains containing 58 continuous and 99 discontinuous domains, in which the predicted domain segments are within ±20 residues of the boundary definitions in CATH 3.5. Compared with our recently developed predictor, ThreaDom, which is the state-of-the-art tool to detect discontinuous-domains, DomEx recalled 26.7% discontinuous domains with 72.7% precision in a benchmark with 29 discontinuous-domain chains, where ThreaDom failed to predict any discontinuous domains. Furthermore, combined with ThreaDom, the method ranked number one among 10 predictors. The source code and datasets are available at https://github.com/xuezhidong/DomEx.
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Affiliation(s)
- Zhidong Xue
- School of Software Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
- * E-mail: (ZX); (YW)
| | - Richard Jang
- School of Software Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, United States of America
| | - Brandon Govindarajoo
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, United States of America
| | - Yichu Huang
- School of Software Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
| | - Yan Wang
- School of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
- * E-mail: (ZX); (YW)
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11
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Jing R, Sun J, Wang Y, Li M. Domain position prediction based on sequence information by using fuzzy mean operator. Proteins 2015; 83:1462-9. [PMID: 26009844 DOI: 10.1002/prot.24833] [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: 02/13/2015] [Revised: 04/23/2015] [Accepted: 05/17/2015] [Indexed: 11/09/2022]
Abstract
The prediction of protein domain region is an advantageous process on the study of protein structure and function. In this study, we proposed a new method, which is composed of fuzzy mean operator and region division, to predict the particular positions of domains in a target protein based on its sequence. The whole sequence is aligned and scored by using fuzzy mean operator, and the final determination of domain region position is realized by region division. A published benchmark is used for the comparison with previous researches. In addition, we generate two extra datasets to examine the stability of this method. Finally, the prediction accuracy of independent test dataset achieved by our method was up to 84.13%. We wish that this method could be useful for related researches.
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Affiliation(s)
- Runyu Jing
- Chemical Information Center (CIC), College of Chemistry, Sichuan University, Chengdu, 610064, China
| | - Jing Sun
- Chemical Information Center (CIC), College of Chemistry, Sichuan University, Chengdu, 610064, China
| | - Yuelong Wang
- Chemical Information Center (CIC), College of Chemistry, Sichuan University, Chengdu, 610064, China
| | - Menglong Li
- Chemical Information Center (CIC), College of Chemistry, Sichuan University, Chengdu, 610064, China
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12
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PDP-RF: Protein Domain Boundary Prediction Using Random Forest Classifier. LECTURE NOTES IN COMPUTER SCIENCE 2015. [DOI: 10.1007/978-3-319-19941-2_42] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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13
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Shatnawi M, Zaki N, Yoo PD. Protein inter-domain linker prediction using Random Forest and amino acid physiochemical properties. BMC Bioinformatics 2014; 15 Suppl 16:S8. [PMID: 25521329 PMCID: PMC4290662 DOI: 10.1186/1471-2105-15-s16-s8] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Protein chains are generally long and consist of multiple domains. Domains are distinct structural units of a protein that can evolve and function independently. The accurate prediction of protein domain linkers and boundaries is often regarded as the initial step of protein tertiary structure and function predictions. Such information not only enhances protein-targeted drug development but also reduces the experimental cost of protein analysis by allowing researchers to work on a set of smaller and independent units. In this study, we propose a novel and accurate domain-linker prediction approach based on protein primary structure information only. We utilize a nature-inspired machine-learning model called Random Forest along with a novel domain-linker profile that contains physiochemical and domain-linker information of amino acid sequences. RESULTS The proposed approach was tested on two well-known benchmark protein datasets and achieved 68% sensitivity and 99% precision, which is better than any existing protein domain-linker predictor. Without applying any data balancing technique such as class weighting and data re-sampling, the proposed approach is able to accurately classify inter-domain linkers from highly imbalanced datasets. CONCLUSION Our experimental results prove that the proposed approach is useful for domain-linker identification in highly imbalanced single- and multi-domain proteins.
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14
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Xue Z, Xu D, Wang Y, Zhang Y. ThreaDom: extracting protein domain boundary information from multiple threading alignments. Bioinformatics 2013; 29:i247-56. [PMID: 23812990 PMCID: PMC3694664 DOI: 10.1093/bioinformatics/btt209] [Citation(s) in RCA: 59] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Motivation: Protein domains are subunits that can fold and evolve independently. Identification of domain boundary locations is often the first step in protein folding and function annotations. Most of the current methods deduce domain boundaries by sequence-based analysis, which has low accuracy. There is no efficient method for predicting discontinuous domains that consist of segments from separated sequence regions. As template-based methods are most efficient for protein 3D structure modeling, combining multiple threading alignment information should increase the accuracy and reliability of computational domain predictions. Result: We developed a new protein domain predictor, ThreaDom, which deduces domain boundary locations based on multiple threading alignments. The core of the method development is the derivation of a domain conservation score that combines information from template domain structures and terminal and internal alignment gaps. Tested on 630 non-redundant sequences, without using homologous templates, ThreaDom generates correct single- and multi-domain classifications in 81% of cases, where 78% have the domain linker assigned within ±20 residues. In a second test on 486 proteins with discontinuous domains, ThreaDom achieves an average precision 84% and recall 65% in domain boundary prediction. Finally, ThreaDom was examined on 56 targets from CASP8 and had a domain overlap rate 73, 87 and 85% with the target for Free Modeling, Hard multiple-domain and discontinuous domain proteins, respectively, which are significantly higher than most domain predictors in the CASP8. Similar results were achieved on the targets from the most recently CASP9 and CASP10 experiments. Availability:http://zhanglab.ccmb.med.umich.edu/ThreaDom/. Contact:zhng@umich.edu Supplementary information:Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Zhidong Xue
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
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15
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PPM-Dom: A novel method for domain position prediction. Comput Biol Chem 2013; 47:8-15. [DOI: 10.1016/j.compbiolchem.2013.06.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2013] [Revised: 06/05/2013] [Accepted: 06/05/2013] [Indexed: 02/05/2023]
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16
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Abrusán G, Zhang Y, Szilágyi A. Structure prediction and analysis of DNA transposon and LINE retrotransposon proteins. J Biol Chem 2013; 288:16127-38. [PMID: 23530042 PMCID: PMC3668768 DOI: 10.1074/jbc.m113.451500] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2013] [Revised: 03/21/2013] [Indexed: 01/15/2023] Open
Abstract
Despite the considerable amount of research on transposable elements, no large-scale structural analyses of the TE proteome have been performed so far. We predicted the structures of hundreds of proteins from a representative set of DNA and LINE transposable elements and used the obtained structural data to provide the first general structural characterization of TE proteins and to estimate the frequency of TE domestication and horizontal transfer events. We show that 1) ORF1 and Gag proteins of retrotransposons contain high amounts of structural disorder; thus, despite their very low conservation, the presence of disordered regions and probably their chaperone function is conserved. 2) The distribution of SCOP classes in DNA transposons and LINEs indicates that the proteins of DNA transposons are more ancient, containing folds that already existed when the first cellular organisms appeared. 3) DNA transposon proteins have lower contact order than randomly selected reference proteins, indicating rapid folding, most likely to avoid protein aggregation. 4) Structure-based searches for TE homologs indicate that the overall frequency of TE domestication events is low, whereas we found a relatively high number of cases where horizontal transfer, frequently involving parasites, is the most likely explanation for the observed homology.
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Affiliation(s)
- György Abrusán
- Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre of the Hungarian Academy of Sciences, 6701 Szeged, Hungary.
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17
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Li BQ, Hu LL, Chen L, Feng KY, Cai YD, Chou KC. Prediction of protein domain with mRMR feature selection and analysis. PLoS One 2012; 7:e39308. [PMID: 22720092 PMCID: PMC3376124 DOI: 10.1371/journal.pone.0039308] [Citation(s) in RCA: 78] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2011] [Accepted: 05/17/2012] [Indexed: 11/30/2022] Open
Abstract
The domains are the structural and functional units of proteins. With the avalanche of protein sequences generated in the postgenomic age, it is highly desired to develop effective methods for predicting the protein domains according to the sequences information alone, so as to facilitate the structure prediction of proteins and speed up their functional annotation. However, although many efforts have been made in this regard, prediction of protein domains from the sequence information still remains a challenging and elusive problem. Here, a new method was developed by combing the techniques of RF (random forest), mRMR (maximum relevance minimum redundancy), and IFS (incremental feature selection), as well as by incorporating the features of physicochemical and biochemical properties, sequence conservation, residual disorder, secondary structure, and solvent accessibility. The overall success rate achieved by the new method on an independent dataset was around 73%, which was about 28–40% higher than those by the existing method on the same benchmark dataset. Furthermore, it was revealed by an in-depth analysis that the features of evolution, codon diversity, electrostatic charge, and disorder played more important roles than the others in predicting protein domains, quite consistent with experimental observations. It is anticipated that the new method may become a high-throughput tool in annotating protein domains, or may, at the very least, play a complementary role to the existing domain prediction methods, and that the findings about the key features with high impacts to the domain prediction might provide useful insights or clues for further experimental investigations in this area. Finally, it has not escaped our notice that the current approach can also be utilized to study protein signal peptides, B-cell epitopes, HIV protease cleavage sites, among many other important topics in protein science and biomedicine.
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Affiliation(s)
- Bi-Qing Li
- Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
- Shanghai Center for Bioinformation Technology, Shanghai, China
| | - Le-Le Hu
- Institute of Systems Biology, Shanghai University, Shanghai, China
| | - Lei Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai, China
| | - Kai-Yan Feng
- Shanghai Center for Bioinformation Technology, Shanghai, China
| | - Yu-Dong Cai
- Institute of Systems Biology, Shanghai University, Shanghai, China
- Gordon Life Science Institute, San Diego, California, United States of America
- * E-mail: (YDC) (YC); (KCC) (KC)
| | - Kuo-Chen Chou
- Gordon Life Science Institute, San Diego, California, United States of America
- * E-mail: (YDC) (YC); (KCC) (KC)
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18
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Bondugula R, Wallqvist A, Lee MS. Can computationally designed protein sequences improve secondary structure prediction? Protein Eng Des Sel 2011; 24:455-61. [DOI: 10.1093/protein/gzr003] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
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