DomSVR: domain boundary prediction with support vector regression from sequence information alone

Zhang, Youzhi; Liu, Chunmei; Burge, Legand; Li, Jinyan; Mahmood, Mohammad F.; Southerland, William M.; Gloster, Clay; Wang, Bing

doi:10.1007/s00726-010-0506-6

Cited by 25 publications

(12 citation statements)

References 42 publications

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“…Improving the accuracy in predicting multidomain boundaries is a challenging task because the accuracy usually is considerably less than 40% [35]. We assessed the performances of DomHR on single-domain, two-domain, and multidomain sequences in the S1508 set (Table 3), and the results showed that our approach was a balanced predictor with an excellent ability to predict single-domain, two-domain and multidomain boundaries.…”

Section: Resultsmentioning

confidence: 99%

“…DomSVR [35] predicted domain boundaries using SVR starting from the protein sequence alone and using only profiles generated from an AAindex database [36]. DROP [4] developed an SVM to predict domain linkers using 25 optimal features selected from a set of 3000 features including PSSMs and over 2000 physicochemical properties via a random forest algorithm.…”

Section: Introductionmentioning

confidence: 99%

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DomHR: Accurately Identifying Domain Boundaries in Proteins Using a Hinge Region Strategy

Zhang

Song

et al. 2013

PLoS ONE

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MotivationThe precise prediction of protein domains, which are the structural, functional and evolutionary units of proteins, has been a research focus in recent years. Although many methods have been presented for predicting protein domains and boundaries, the accuracy of predictions could be improved.ResultsIn this study we present a novel approach, DomHR, which is an accurate predictor of protein domain boundaries based on a creative hinge region strategy. A hinge region was defined as a segment of amino acids that covers part of a domain region and a boundary region. We developed a strategy to construct profiles of domain-hinge-boundary (DHB) features generated by sequence-domain/hinge/boundary alignment against a database of known domain structures. The DHB features had three elements: normalized domain, hinge, and boundary probabilities. The DHB features were used as input to identify domain boundaries in a sequence. DomHR used a nonredundant dataset as the training set, the DHB and predicted shape string as features, and a conditional random field as the classification algorithm. In predicted hinge regions, a residue was determined to be a domain or a boundary according to a decision threshold. After decision thresholds were optimized, DomHR was evaluated by cross-validation, large-scale prediction, independent test and CASP (Critical Assessment of Techniques for Protein Structure Prediction) tests. All results confirmed that DomHR outperformed other well-established, publicly available domain boundary predictors for prediction accuracy.AvailabilityThe DomHR is available at http://cal.tongji.edu.cn/domain/.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

DomHR: Accurately Identifying Domain Boundaries in Proteins Using a Hinge Region Strategy

Zhang

Song

et al. 2013

PLoS ONE

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“…Machine learning techniques have played very important roles in various protein-related problems, such as B-factors prediction [ 26 ], domain identification [ 27 ], function annotation [ 28 ], membrane protein type prediction [ 29 ], and protein retrieval [ 30 ]. Here we propose to use the random forest model for the binding site prediction.…”

Section: Methodsmentioning

confidence: 99%

LigandRFs: random forest ensemble to identify ligand-binding residues from sequence information alone

2014

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BackgroundProtein-ligand binding is important for some proteins to perform their functions. Protein-ligand binding sites are the residues of proteins that physically bind to ligands. Despite of the recent advances in computational prediction for protein-ligand binding sites, the state-of-the-art methods search for similar, known structures of the query and predict the binding sites based on the solved structures. However, such structural information is not commonly available.ResultsIn this paper, we propose a sequence-based approach to identify protein-ligand binding residues. We propose a combination technique to reduce the effects of different sliding residue windows in the process of encoding input feature vectors. Moreover, due to the highly imbalanced samples between the ligand-binding sites and non ligand-binding sites, we construct several balanced data sets, for each of which a random forest (RF)-based classifier is trained. The ensemble of these RF classifiers forms a sequence-based protein-ligand binding site predictor.ConclusionsExperimental results on CASP9 and CASP8 data sets demonstrate that our method compares favorably with the state-of-the-art protein-ligand binding site prediction methods.

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“…All the regression tasks can be formulated as to seek an estimation function which can approximate the observations within an acceptable error range. In this study, least square support vector regression (LS-SVR), a version of SVR which can reduce the complexity of optimization processes, was adopted for the drift time prediction[33]. …”

Section: Methodsmentioning

confidence: 99%

Prediction of peptide drift time in ion mobility mass spectrometry from sequence-based features

Wang

Zhang

et al. 2013

BMC Bioinformatics

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BackgroundIon mobility-mass spectrometry (IMMS), an analytical technique which combines the features of ion mobility spectrometry (IMS) and mass spectrometry (MS), can rapidly separates ions on a millisecond time-scale. IMMS becomes a powerful tool to analyzing complex mixtures, especially for the analysis of peptides in proteomics. The high-throughput nature of this technique provides a challenge for the identification of peptides in complex biological samples. As an important parameter, peptide drift time can be used for enhancing downstream data analysis in IMMS-based proteomics.ResultsIn this paper, a model is presented based on least square support vectors regression (LS-SVR) method to predict peptide ion drift time in IMMS from the sequence-based features of peptide. Four descriptors were extracted from peptide sequence to represent peptide ions by a 34-component vector. The parameters of LS-SVR were selected by a grid searching strategy, and a 10-fold cross-validation approach was employed for the model training and testing. Our proposed method was tested on three datasets with different charge states. The high prediction performance achieve demonstrate the effectiveness and efficiency of the prediction model.ConclusionsOur proposed LS-SVR model can predict peptide drift time from sequence information in relative high prediction accuracy by a test on a dataset of 595 peptides. This work can enhance the confidence of protein identification by combining with current protein searching techniques.

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DomSVR: domain boundary prediction with support vector regression from sequence information alone

Cited by 25 publications

References 42 publications

DomHR: Accurately Identifying Domain Boundaries in Proteins Using a Hinge Region Strategy

DomHR: Accurately Identifying Domain Boundaries in Proteins Using a Hinge Region Strategy

LigandRFs: random forest ensemble to identify ligand-binding residues from sequence information alone

Prediction of peptide drift time in ion mobility mass spectrometry from sequence-based features

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