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Protein Remote Homology Detection and Fold Recognition Based on Sequence-Order Frequency Matrix.
Liu, B, Chen, J, Guo, M, Wang, X
IEEE/ACM transactions on computational biology and bioinformatics. 2019;(1):292-300
Abstract
Protein remote homology detection and fold recognition are two critical tasks for the studies of protein structures and functions. Currently, the profile-based methods achieve the state-of-the-art performance in these fields. However, the widely used sequence profiles, like position-specific frequency matrix (PSFM) and position-specific scoring matrix (PSSM), ignore the sequence-order effects along protein sequence. In this study, we have proposed a novel profile, called sequence-order frequency matrix (SOFM), to extract the sequence-order information of neighboring residues from multiple sequence alignment (MSA). Combined with two profile feature extraction approaches, top-n-grams and the Smith-Waterman algorithm, the SOFMs are applied to protein remote homology detection and fold recognition, and two predictors called SOFM-Top and SOFM-SW are proposed. Experimental results show that SOFM contains more information content than other profiles, and these two predictors outperform other state-of-the-art methods. It is anticipated that SOFM will become a very useful profile in the studies of protein structures and functions.
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2.
ProtDet-CCH: Protein Remote Homology Detection by Combining Long Short-Term Memory and Ranking Methods.
Liu, B, Li, S
IEEE/ACM transactions on computational biology and bioinformatics. 2019;(4):1203-1210
Abstract
As one of the most challenging tasks in sequence analysis, protein remote homology detection has been extensively studied. Methods based on discriminative models and ranking approaches have achieved the state-of-the-art performance, and these two kinds of methods are complementary. In this study, three LSTM models have been applied to construct the predictors for protein remote homology detection, including ULSTM, BLSTM, and CNN-BLSTM. They are able to automatically extract the local and global sequence order information. Combined with PSSMs, the CNN-BLSTM achieved the best performance among the three LSTM-based models. We named this method as CNN-BLSTM-PSSM. Finally, a new method called ProtDet-CCH was proposed by combining CNN-BLSTM-PSSM and a ranking method HHblits. Tested on a widely used SCOP benchmark dataset, ProtDet-CCH achieved an ROC score of 0.998, and an ROC50 score of 0.982, significantly outperforming other existing state-of-the-art methods. Experimental results on two updated SCOPe independent datasets showed that ProtDet-CCH can achieve stable performance. Furthermore, our method can provide useful insights for studying the features and motifs of protein families and superfamilies. It is anticipated that ProtDet-CCH will become a very useful tool for protein remote homology detection.
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3.
PepQuery enables fast, accurate, and convenient proteomic validation of novel genomic alterations.
Wen, B, Wang, X, Zhang, B
Genome research. 2019;(3):485-493
Abstract
Massively parallel or second-generation sequencing-based genomic studies continuously identify new genomic alterations that may lead to novel protein sequences, which are attractive candidates for disease biomarkers and therapeutic targets after proteomic validation. Integrative proteogenomic methods have been developed to use mass spectrometry (MS)-based proteomics data for such validation. These methods replace the reference sequence database in proteomic database searching with a customized protein database that incorporates sample- or disease-specific sequences derived from DNA or RNA sequencing, thus enabling the identification of novel protein sequences. Although useful, this spectrum-centric approach requires a full evaluation of all possible spectrum-peptide pairs, which is time-consuming, error-prone, and difficult to apply. Here, we present PepQuery, a peptide-centric approach that focuses on only novel DNA or protein sequences of interest. PepQuery allows quick and easy proteomic validation of genomic alterations without customized database construction. We demonstrated the sensitivity and specificity of the approach in validating completely novel proteins, novel splice junctions, and single amino acid variants using simulations and experimental data. Notably, enabling unrestricted modification searching in PepQuery reduced false positives by up to 95%. We implemented PepQuery as both web-based and stand-alone applications. The web version provides direct access to more than half a billion MS/MS spectra from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and other cancer proteomic studies. The stand-alone version supports batch analysis and user-provided MS/MS data. PepQuery will increase the usage of proteogenomics beyond the proteomics community and will broaden the application of proteogenomics in personalized medicine.
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4.
NeuroPP: A Tool for the Prediction of Neuropeptide Precursors Based on Optimal Sequence Composition.
Kang, J, Fang, Y, Yao, P, Li, N, Tang, Q, Huang, J
Interdisciplinary sciences, computational life sciences. 2019;(1):108-114
Abstract
Neuropeptides (NPs) are short secreted peptides produced mainly in the nervous system and digestive system. They activate signaling cascades to control a wide range of biological functions, such as metabolism, sensation, and behavior. NPs are typically produced from a larger NP precursor (NPP) which includes a signal peptide sequence, one or more NP sequences, and other sequences. With the drastic growth of unknown protein sequences generated in the post-genomic age, it is highly desired to develop computational methods for identifying NPP rapidly and efficiently. In this article, we developed a predictor for NPPs based on optimized sequence composition of single amino acid, dipeptide, and tripeptide. Evaluated with independent data set, the predictor showed excellent performance that achieved an accuracy of 88.65% with AUC of 0.95. The corresponding web server was developed, which is freely available at http://i.uestc.edu.cn/neuropeptide/neuropp/home.html . It can help relevant researchers to screen candidate NP precursor, shorten experimental cycle, and reduce costs.
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5.
Predicting subcellular localization of multi-label proteins by incorporating the sequence features into Chou's PseAAC.
Javed, F, Hayat, M
Genomics. 2019;(6):1325-1332
Abstract
The emergence of numerous genome projects has made the experimental classification of the protein localization almost impossible due to the exponential increase in the number of protein samples. However, most of the applications are merely developed for single-plex and completely ignored the presence of one protein at two or more locations in a cell. In this regard, few attempts were carried out to target Multi-label protein localizations; consequently, undesirable accuracies are achieved. This paper presents a novel approach, in which a discrete feature extraction method is fused with physicochemical properties of amino acids by using Chou's general form of Pseudo Amino Acid Composition. The technique is tested on two benchmark datasets namely: Gpos-mploc and Virus-mPLoc. The empirical results demonstrated that the proposed method yields better results via two examined classifiers i.e. ML-KNN and Rank-SVM. It is established that the proposed model has improved values in all performance measures considered for the comparison.
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6.
pLoc-mEuk: Predict subcellular localization of multi-label eukaryotic proteins by extracting the key GO information into general PseAAC.
Cheng, X, Xiao, X, Chou, KC
Genomics. 2018;(1):50-58
Abstract
Many efforts have been made in predicting the subcellular localization of eukaryotic proteins, but most of the existing methods have the following two limitations: (1) their coverage scope is less than ten locations and hence many organelles in an eukaryotic cell cannot be covered, and (2) they can only be used to deal with single-label systems in which each of the constituent proteins has one and only one location. Actually, proteins with multiple locations are particularly interesting since they may have some exceptional functions very important for in-depth understanding the biological process in a cell and for selecting drug target as well. Although several predictors (such as "Euk-mPLoc", "Euk-PLoc 2.0" and "iLoc-Euk") can cover up to 22 different location sites, and they also have the function to treat multi-labeled proteins, further efforts are needed to improve their prediction quality, particularly in enhancing the absolute true rate and in reducing the absolute false rate. Here we propose a new predictor called "pLoc-mEuk" by extracting the key GO (Gene Ontology) information into the general PseAAC (Pseudo Amino Acid Composition). Rigorous cross-validations on a high-quality and stringent benchmark dataset have indicated that the proposed pLoc-mEuk predictor is remarkably superior to iLoc-Euk, the best of the aforementioned three predictors. To maximize the convenience of most experimental scientists, a user-friendly web-server for the new predictor has been established at http://www.jci-bioinfo.cn/pLoc-mEuk/, by which users can easily get their desired results without the need to go through the complicated mathematics involved.
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7.
In silico identification of rescue sites by double force scanning.
Tiberti, M, Pandini, A, Fraternali, F, Fornili, A
Bioinformatics (Oxford, England). 2018;(2):207-214
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Abstract
MOTIVATION A deleterious amino acid change in a protein can be compensated by a second-site rescue mutation. These compensatory mechanisms can be mimicked by drugs. In particular, the location of rescue mutations can be used to identify protein regions that can be targeted by small molecules to reactivate a damaged mutant. RESULTS We present the first general computational method to detect rescue sites. By mimicking the effect of mutations through the application of forces, the double force scanning (DFS) method identifies the second-site residues that make the protein structure most resilient to the effect of pathogenic mutations. We tested DFS predictions against two datasets containing experimentally validated and putative evolutionary-related rescue sites. A remarkably good agreement was found between predictions and experimental data. Indeed, almost half of the rescue sites in p53 was correctly predicted by DFS, with 65% of remaining sites in contact with DFS predictions. Similar results were found for other proteins in the evolutionary dataset. AVAILABILITY AND IMPLEMENTATION The DFS code is available under GPL at https://fornililab.github.io/dfs/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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8.
Recognition of Protein Pupylation Sites by Adopting Resampling Approach.
Li, T, Chen, Y, Li, T, Jia, C
Molecules (Basel, Switzerland). 2018;(12)
Abstract
With the in-depth study of posttranslational modification sites, protein ubiquitination has become the key problem to study the molecular mechanism of posttranslational modification. Pupylation is a widely used process in which a prokaryotic ubiquitin-like protein (Pup) is attached to a substrate through a series of biochemical reactions. However, the experimental methods of identifying pupylation sites is often time-consuming and laborious. This study aims to propose an improved approach for predicting pupylation sites. Firstly, the Pearson correlation coefficient was used to reflect the correlation among different amino acid pairs calculated by the frequency of each amino acid. Then according to a descending ranked order, the multiple types of features were filtered separately by values of Pearson correlation coefficient. Thirdly, to get a qualified balanced dataset, the K-means principal component analysis (KPCA) oversampling technique was employed to synthesize new positive samples and Fuzzy undersampling method was employed to reduce the number of negative samples. Finally, the performance of our method was verified by means of jackknife and a 10-fold cross-validation test. The average results of 10-fold cross-validation showed that the sensitivity (Sn) was 90.53%, specificity (Sp) was 99.8%, accuracy (Acc) was 95.09%, and Matthews Correlation Coefficient (MCC) was 0.91. Moreover, an independent test dataset was used to further measure its performance, and the prediction results achieved the Acc of 83.75%, MCC of 0.49, which was superior to previous predictors. The better performance and stability of our proposed method showed it is an effective way to predict pupylation sites.
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9.
Sequence fingerprints distinguish erroneous from correct predictions of intrinsically disordered protein regions.
Saravanan, KM, Dunker, AK, Krishnaswamy, S
Journal of biomolecular structure & dynamics. 2018;(16):4338-4351
Abstract
More than 60 prediction methods for intrinsically disordered proteins (IDPs) have been developed over the years, many of which are accessible on the World Wide Web. Nearly, all of these predictors give balanced accuracies in the ~65%-~80% range. Since predictors are not perfect, further studies are required to uncover the role of amino acid residues in native IDP as compared to predicted IDP regions. In the present work, we make use of sequences of 100% predicted IDP regions, false positive disorder predictions, and experimentally determined IDP regions to distinguish the characteristics of native versus predicted IDP regions. A higher occurrence of asparagine is observed in sequences of native IDP regions but not in sequences of false positive predictions of IDP regions. The occurrences of certain combinations of amino acids at the pentapeptide level provide a distinguishing feature in the IDPs with respect to globular proteins. The distinguishing features presented in this paper provide insights into the sequence fingerprints of amino acid residues in experimentally determined as compared to predicted IDP regions. These observations and additional work along these lines should enable the development of improvements in the accuracy of disorder prediction algorithm.
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10.
Where differences resemble: sequence-feature analysis in curated databases of intrinsically disordered proteins.
Necci, M, Piovesan, D, Tosatto, SCE
Database : the journal of biological databases and curation. 2018
Abstract
Intrinsic disorder (ID) in proteins is involved in crucial interactions in the living cell. As the importance of ID is increasingly recognized, so are detailed analyses aimed at its identification and characterization. An open question remains the existence of ID `flavors' representing different sub-phenomena. Several databases collect manually curated examples of experimentally validated ID, focusing on apparently different aspects of this phenomenon. The recent update of MobiDB presented the opportunity to carry out an in-depth comparison of the content of these validated ID collections, namely DIBS, DisProt, IDEAL, MFIB, FuzDB, ELM and UniProt. In order to assess what is specific to different ID flavors, we analyzed relevant sequence-based features, such as amino acid composition, length, taxa and gene ontology terms, highlighting differences and similarities among datasets. Despite that, the majority of the considered features are not statistically different across databases, with the exception of ELM. FuzDB also shares half of its entries with DisProt. In general, different ID databases describe similar phenomena. DisProt, which is the largest database, better represents the entire spectrum of different disorder flavors and the corresponding sequence diversity.