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Beltrán JF, Herrera-Belén L, Parraguez-Contreras F, Farías JG, Machuca-Sepúlveda J, Short S. MultiToxPred 1.0: a novel comprehensive tool for predicting 27 classes of protein toxins using an ensemble machine learning approach. BMC Bioinformatics 2024; 25:148. [PMID: 38609877 PMCID: PMC11010298 DOI: 10.1186/s12859-024-05748-z] [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] [Received: 08/07/2023] [Accepted: 03/14/2024] [Indexed: 04/14/2024] Open
Abstract
Protein toxins are defense mechanisms and adaptations found in various organisms and microorganisms, and their use in scientific research as therapeutic candidates is gaining relevance due to their effectiveness and specificity against cellular targets. However, discovering these toxins is time-consuming and expensive. In silico tools, particularly those based on machine learning and deep learning, have emerged as valuable resources to address this challenge. Existing tools primarily focus on binary classification, determining whether a protein is a toxin or not, and occasionally identifying specific types of toxins. For the first time, we propose a novel approach capable of classifying protein toxins into 27 distinct categories based on their mode of action within cells. To accomplish this, we assessed multiple machine learning techniques and found that an ensemble model incorporating the Light Gradient Boosting Machine and Quadratic Discriminant Analysis algorithms exhibited the best performance. During the tenfold cross-validation on the training dataset, our model exhibited notable metrics: 0.840 accuracy, 0.827 F1 score, 0.836 precision, 0.840 sensitivity, and 0.989 AUC. In the testing stage, using an independent dataset, the model achieved 0.846 accuracy, 0.838 F1 score, 0.847 precision, 0.849 sensitivity, and 0.991 AUC. These results present a powerful next-generation tool called MultiToxPred 1.0, accessible through a web application. We believe that MultiToxPred 1.0 has the potential to become an indispensable resource for researchers, facilitating the efficient identification of protein toxins. By leveraging this tool, scientists can accelerate their search for these toxins and advance their understanding of their therapeutic potential.
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Affiliation(s)
- Jorge F Beltrán
- Department of Chemical Engineering, Faculty of Engineering and Science, Universidad de La Frontera, Ave. Francisco Salazar, 01145, Temuco, Chile.
| | - Lisandra Herrera-Belén
- Departamento de Ciencias Básicas, Facultad de Ciencias, Universidad Santo Tomas, Temuco, Chile
| | - Fernanda Parraguez-Contreras
- Department of Chemical Engineering, Faculty of Engineering and Science, Universidad de La Frontera, Ave. Francisco Salazar, 01145, Temuco, Chile
| | - Jorge G Farías
- Department of Chemical Engineering, Faculty of Engineering and Science, Universidad de La Frontera, Ave. Francisco Salazar, 01145, Temuco, Chile
| | - Jorge Machuca-Sepúlveda
- Department of Chemical Engineering, Faculty of Engineering and Science, Universidad de La Frontera, Ave. Francisco Salazar, 01145, Temuco, Chile
| | - Stefania Short
- Department of Chemical Engineering, Faculty of Engineering and Science, Universidad de La Frontera, Ave. Francisco Salazar, 01145, Temuco, Chile
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Romero JA, Putko P, Urbańczyk M, Kazimierczuk K, Zawadzka-Kazimierczuk A. Linear discriminant analysis reveals hidden patterns in NMR chemical shifts of intrinsically disordered proteins. PLoS Comput Biol 2022; 18:e1010258. [PMID: 36201530 PMCID: PMC9578625 DOI: 10.1371/journal.pcbi.1010258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 10/18/2022] [Accepted: 09/20/2022] [Indexed: 11/27/2022] Open
Abstract
NMR spectroscopy is key in the study of intrinsically disordered proteins (IDPs). Yet, even the first step in such an analysis-the assignment of observed resonances to particular nuclei-is often problematic due to low peak dispersion in the spectra of IDPs. We show that the assignment process can be aided by finding "hidden" chemical shift patterns specific to the amino acid residue types. We find such patterns in the training data from the Biological Magnetic Resonance Bank using linear discriminant analysis, and then use them to classify spin systems in an α-synuclein sample prepared by us. We describe two situations in which the procedure can greatly facilitate the analysis of NMR spectra. The first involves the mapping of spin systems chains onto the protein sequence, which is part of the assignment procedure-a prerequisite for any NMR-based protein analysis. In the second, the method supports assignment transfer between similar samples. We conducted experiments to demonstrate these cases, and both times the majority of spin systems could be unambiguously assigned to the correct residue types.
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Affiliation(s)
- Javier A. Romero
- Centre of New Technologies, University of Warsaw, Warsaw, Poland
| | - Paulina Putko
- Centre of New Technologies, University of Warsaw, Warsaw, Poland
| | - Mateusz Urbańczyk
- Institute of Physical Chemistry, Polish Academy of Sciences, Warsaw, Poland
| | | | - Anna Zawadzka-Kazimierczuk
- Biological and Chemical Research Centre, Faculty of Chemistry, University of Warsaw, Warsaw, Poland
- * E-mail: (KK); (AZK)
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Rudnev VR, Kulikova LI, Nikolsky KS, Malsagova KA, Kopylov AT, Kaysheva AL. Current Approaches in Supersecondary Structures Investigation. Int J Mol Sci 2021; 22:11879. [PMID: 34769310 PMCID: PMC8584461 DOI: 10.3390/ijms222111879] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 10/27/2021] [Accepted: 10/29/2021] [Indexed: 11/16/2022] Open
Abstract
Proteins expressed during the cell cycle determine cell function, topology, and responses to environmental influences. The development and improvement of experimental methods in the field of structural biology provide valuable information about the structure and functions of individual proteins. This work is devoted to the study of supersecondary structures of proteins and determination of their structural motifs, description of experimental methods for their detection, databases, and repositories for storage, as well as methods of molecular dynamics research. The interest in the study of supersecondary structures in proteins is due to their autonomous stability outside the protein globule, which makes it possible to study folding processes, conformational changes in protein isoforms, and aberrant proteins with high productivity.
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Affiliation(s)
- Vladimir R. Rudnev
- Biobanking Group, Branch of Institute of Biomedical Chemistry “Scientific and Education Center”, 109028 Moscow, Russia; (V.R.R.); (L.I.K.); (K.S.N.); (A.T.K.); (A.L.K.)
- Institute of Theoretical and Experimental Biophysics, Russian Academy of Sciences, 142290 Pushchino, Russia
| | - Liudmila I. Kulikova
- Biobanking Group, Branch of Institute of Biomedical Chemistry “Scientific and Education Center”, 109028 Moscow, Russia; (V.R.R.); (L.I.K.); (K.S.N.); (A.T.K.); (A.L.K.)
- Institute of Theoretical and Experimental Biophysics, Russian Academy of Sciences, 142290 Pushchino, Russia
- Institute of Mathematical Problems of Biology RAS—The Branch of Keldysh Institute of Applied Mathematics of Russian Academy of Sciences, 142290 Pushchino, Russia
| | - Kirill S. Nikolsky
- Biobanking Group, Branch of Institute of Biomedical Chemistry “Scientific and Education Center”, 109028 Moscow, Russia; (V.R.R.); (L.I.K.); (K.S.N.); (A.T.K.); (A.L.K.)
| | - Kristina A. Malsagova
- Biobanking Group, Branch of Institute of Biomedical Chemistry “Scientific and Education Center”, 109028 Moscow, Russia; (V.R.R.); (L.I.K.); (K.S.N.); (A.T.K.); (A.L.K.)
| | - Arthur T. Kopylov
- Biobanking Group, Branch of Institute of Biomedical Chemistry “Scientific and Education Center”, 109028 Moscow, Russia; (V.R.R.); (L.I.K.); (K.S.N.); (A.T.K.); (A.L.K.)
| | - Anna L. Kaysheva
- Biobanking Group, Branch of Institute of Biomedical Chemistry “Scientific and Education Center”, 109028 Moscow, Russia; (V.R.R.); (L.I.K.); (K.S.N.); (A.T.K.); (A.L.K.)
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Agrawal S, Sisodia DS, Nagwani NK. Augmented sequence features and subcellular localization for functional characterization of unknown protein sequences. Med Biol Eng Comput 2021; 59:2297-2310. [PMID: 34545514 DOI: 10.1007/s11517-021-02436-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2020] [Accepted: 08/29/2021] [Indexed: 11/24/2022]
Abstract
Advances in high-throughput techniques lead to evolving a large number of unknown protein sequences (UPS). Functional characterization of UPS is significant for the investigation of disease symptoms and drug repositioning. Protein subcellular localization is imperative for the functional characterization of protein sequences. Diverse techniques are used on protein sequences for feature extraction. However, many times a single feature extraction technique leads to poor prediction performance. In this paper, two feature augmentations are described through sequence induced, physicochemical, and evolutionary information of the amino acid residues. While augmented features preserve the sequence-order-information and protein-residue-properties. Two bacterial protein datasets Gram-Positive (G +) and Gram-Negative (G-) are utilized for the experimental work. After performing essential preprocessing on protein datasets, two sets of feature vectors are obtained. These feature vectors are used separately to train the different individual and ensembles such as decision tree (C 4.5), k-nearest neighbor (k-NN), multi-layer perceptron (MLP), Naïve Bayes (NB), support vector machine (SVM), AdaBoost, gradient boosting machine (GBM), and random forest (RF) with fivefold cross-validation. Prediction results of the model demonstrate that overall accuracy reported by C4.5 is highest 99.57% on G + and 97.47% on G- datasets with known protein sequences. Similarly, for the UPS overall accuracy of G + is 85.17% with SVM and 82.45% with G- dataset using MLP.
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Affiliation(s)
- Saurabh Agrawal
- Department of Computer Science & Engineering, National Institute of Technology Raipur, GE Road, Raipur, Chhattisgarh, 492010, India.
| | - Dilip Singh Sisodia
- Department of Computer Science & Engineering, National Institute of Technology Raipur, GE Road, Raipur, Chhattisgarh, 492010, India
| | - Naresh Kumar Nagwani
- Department of Computer Science & Engineering, National Institute of Technology Raipur, GE Road, Raipur, Chhattisgarh, 492010, India
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Yonge F, Weixia X. Identification of Mitochondrial Proteins of Malaria Parasite Adding the New Parameter. LETT ORG CHEM 2019. [DOI: 10.2174/1570178615666180608100348] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Malaria has been one of the serious infectious diseases caused by Plasmodium falciparum (P. falciparum). Mitochondrial proteins of P. falciparum are regarded as effective drug targets against malaria. Thus, it is necessary to accurately identify mitochondrial proteins of malaria parasite. Many algorithms have been proposed for the prediction of mitochondrial proteins of malaria parasite and yielded the better results. However, the parameters used by these methods were primarily based on amino acid sequences. In this study, we added a novel parameter for predicting mitochondrial proteins of malaria parasite based on protein secondary structure. Firstly, we extracted three feature parameters, namely, three kinds of protein secondary structures compositions (3PSS), 20 amino acid compositions (20AAC) and 400 dipeptide compositions (400DC), and used the analysis of variance (ANOVA) to screen 400 dipeptides. Secondly, we adopted these features to predict mitochondrial proteins of malaria parasite by using support vector machine (SVM). Finally, we found that 1) adding the feature of protein secondary structure (3PSS) can indeed improve the prediction accuracy. This result demonstrated that the parameter of protein secondary structure is a valid feature in the prediction of mitochondrial proteins of malaria parasite; 2) feature combination can improve the prediction’s results; feature selection can reduce the dimension and simplify the calculation. We achieved the sensitivity (Sn) of 98.16%, the specificity (Sp) of 97.64% and overall accuracy (Acc) of 97.88% with 0.957 of Mathew’s correlation coefficient (MCC) by using 3PSS+ 20AAC+ 34DC as a feature in 15-fold cross-validation. This result is compared with that of the similar work in the same dataset, showing the superiority of our work.
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Affiliation(s)
- Feng Yonge
- College of Science, Inner Mongolia Agriculture University, Hohhot 010018, China
| | - Xie Weixia
- College of Science, Inner Mongolia Agriculture University, Hohhot 010018, China
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MacCarthy E, Perry D, Kc DB. Advances in Protein Super-Secondary Structure Prediction and Application to Protein Structure Prediction. Methods Mol Biol 2019; 1958:15-45. [PMID: 30945212 DOI: 10.1007/978-1-4939-9161-7_2] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Due to the advancement in various sequencing technologies, the gap between the number of protein sequences and the number of experimental protein structures is ever increasing. Community-wide initiatives like CASP have resulted in considerable efforts in the development of computational methods to accurately model protein structures from sequences. Sequence-based prediction of super-secondary structure has direct application in protein structure prediction, and there have been significant efforts in the prediction of super-secondary structure in the last decade. In this chapter, we first introduce the protein structure prediction problem and highlight some of the important progress in the field of protein structure prediction. Next, we discuss recent methods for the prediction of super-secondary structures. Finally, we discuss applications of super-secondary structure prediction in structure prediction/analysis of proteins. We also discuss prediction of protein structures that are composed of simple super-secondary structure repeats and protein structures that are composed of complex super-secondary structure repeats. Finally, we also discuss the recent trends in the field.
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Affiliation(s)
- Elijah MacCarthy
- Department of Computational Science and Engineering, North Carolina A&T State University, Greensboro, NC, USA
| | - Derrick Perry
- Department of Computational Science and Engineering, North Carolina A&T State University, Greensboro, NC, USA
| | - Dukka B Kc
- Department of Computational Science and Engineering, North Carolina A&T State University, Greensboro, NC, USA.
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Fang C, Shang Y, Xu D. Improving Protein Gamma-Turn Prediction Using Inception Capsule Networks. Sci Rep 2018; 8:15741. [PMID: 30356073 PMCID: PMC6200818 DOI: 10.1038/s41598-018-34114-2] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2018] [Accepted: 10/06/2018] [Indexed: 01/17/2023] Open
Abstract
Protein gamma-turn prediction is useful in protein function studies and experimental design. Several methods for gamma-turn prediction have been developed, but the results were unsatisfactory with Matthew correlation coefficients (MCC) around 0.2–0.4. Hence, it is worthwhile exploring new methods for the prediction. A cutting-edge deep neural network, named Capsule Network (CapsuleNet), provides a new opportunity for gamma-turn prediction. Even when the number of input samples is relatively small, the capsules from CapsuleNet are effective to extract high-level features for classification tasks. Here, we propose a deep inception capsule network for gamma-turn prediction. Its performance on the gamma-turn benchmark GT320 achieved an MCC of 0.45, which significantly outperformed the previous best method with an MCC of 0.38. This is the first gamma-turn prediction method utilizing deep neural networks. Also, to our knowledge, it is the first published bioinformatics application utilizing capsule network, which will provide a useful example for the community. Executable and source code can be download at http://dslsrv8.cs.missouri.edu/~cf797/MUFoldGammaTurn/download.html.
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Affiliation(s)
- Chao Fang
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, 65211, USA
| | - Yi Shang
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, 65211, USA.
| | - Dong Xu
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, 65211, USA. .,Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, Missouri, 65211, USA.
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Tahir M, Hayat M, Khan SA. iNuc-ext-PseTNC: an efficient ensemble model for identification of nucleosome positioning by extending the concept of Chou's PseAAC to pseudo-tri-nucleotide composition. Mol Genet Genomics 2018; 294:199-210. [PMID: 30291426 DOI: 10.1007/s00438-018-1498-2] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2018] [Accepted: 09/28/2018] [Indexed: 10/28/2022]
Abstract
Nucleosome is a central element of eukaryotic chromatin, which composes of histone proteins and DNA molecules. It performs vital roles in many eukaryotic intra-nuclear processes, for instance, chromatin structure and transcriptional regulation formation. Identification of nucleosome positioning via wet lab is difficult; so, the attention is diverted towards the accurate intelligent automated prediction. In this regard, a novel intelligent automated model "iNuc-ext-PseTNC" is developed to identify the nucleosome positioning in genomes accurately. In this predictor, the sequences of DNA are mathematically represented by two different discrete feature extraction techniques, namely pseudo-tri-nucleotide composition (PseTNC) and pseudo-di-nucleotide composition. Several contemporary machine learning algorithms were examined. Further, the predictions of individual classifiers were integrated through an evolutionary genetic algorithm. The success rates of the ensemble model are higher than individual classifiers. After analyzing the prediction results, it is noticed that iNuc-ext-PseTNC model has achieved better performance in combination with PseTNC feature space, which are 94.3%, 93.14%, and 88.60% of accuracies using six-fold cross-validation test for the three benchmark datasets S1, S2, and S3, respectively. The achieved outcomes exposed that the results of iNuc-ext-PseTNC model are prominent compared to the existing methods so far notifiable in the literature. It is ascertained that the proposed model might be more fruitful and a practical tool for rudimentary academia and research.
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Affiliation(s)
- Muhammad Tahir
- Department of Computer Science, Abdul Wali Khan University Mardan, Mardan, KP, Pakistan
| | - Maqsood Hayat
- Department of Computer Science, Abdul Wali Khan University Mardan, Mardan, KP, Pakistan.
| | - Sher Afzal Khan
- Department of Computer Science, Abdul Wali Khan University Mardan, Mardan, KP, Pakistan
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Tahir M, Hayat M, Kabir M. Sequence based predictor for discrimination of enhancer and their types by applying general form of Chou's trinucleotide composition. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 146:69-75. [PMID: 28688491 DOI: 10.1016/j.cmpb.2017.05.008] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/04/2016] [Revised: 05/05/2017] [Accepted: 05/19/2017] [Indexed: 06/07/2023]
Abstract
BACKGROUND AND OBJECTIVES Enhancers are pivotal DNA elements, which are widely used in eukaryotes for activation of transcription genes. On the basis of enhancer strength, they are further classified into two groups; strong enhancers and weak enhancers. Due to high availability of huge amount of DNA sequences, it is needed to develop fast, reliable and robust intelligent computational method, which not only identify enhancers but also determines their strength. Considerable progress has been achieved in this regard; however, timely and precisely identification of enhancers is still a challenging task. METHODS Two-level intelligent computational model for identification of enhancers and their subgroups is proposed. Two different feature extraction techniques including di-nucleotide composition and tri-nucleotide composition were adopted for extraction of numerical descriptors. Four classification methods including probabilistic neural network, support vector machine, k-nearest neighbor and random forest were utilized for classification. RESULTS The proposed method yielded 77.25% of accuracy for dataset S1 contains enhancers and non-enhancers, whereas 64.70% of accuracy for dataset S2 comprises of strong enhancer and weak enhancer sequences using jackknife cross-validation test. CONCLUSION The predictive results validated that the proposed method is better than that of existing approaches so far reported in the literature. It is thus highly observed that the developed method will be useful and expedient for basic research and academia.
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Affiliation(s)
- Muhammad Tahir
- Department of Computer Science, Abdul Wali Khan University Mardan, KP Pakistan
| | - Maqsood Hayat
- Department of Computer Science, Abdul Wali Khan University Mardan, KP Pakistan.
| | - Muhammad Kabir
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China
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Li D, Hu X, Liu X, Feng Z, Ding C. Using feature optimization-based support vector machine method to recognize the β-hairpin motifs in enzymes. Saudi J Biol Sci 2016; 24:1361-1369. [PMID: 28855832 PMCID: PMC5562482 DOI: 10.1016/j.sjbs.2016.11.014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2016] [Revised: 11/16/2016] [Accepted: 11/17/2016] [Indexed: 11/28/2022] Open
Abstract
β-Hairpins in enzyme, a kind of special protein with catalytic functions, contain many binding sites which are essential for the functions of enzyme. With the increasing number of observed enzyme protein sequences, it is of especial importance to use bioinformatics techniques to quickly and accurately identify the β-hairpin in enzyme protein for further advanced annotation of structure and function of enzyme. In this work, the proposed method was trained and tested on a non-redundant enzyme β-hairpin database containing 2818 β-hairpins and 1098 non-β-hairpins. With 5-fold cross-validation on the training dataset, the overall accuracy of 90.08% and Matthew’s correlation coefficient (Mcc) of 0.74 were obtained, while on the independent test dataset, the overall accuracy of 88.93% and Mcc of 0.76 were achieved. Furthermore, the method was validated on 845 β-hairpins with ligand binding sites. With 5-fold cross-validation on the training dataset and independent test on the test dataset, the overall accuracies were 85.82% (Mcc of 0.71) and 84.78% (Mcc of 0.70), respectively. With an integration of mRMR feature selection and SVM algorithm, a reasonable high accuracy was achieved, indicating the method to be an effective tool for the further studies of β-hairpins in enzymes structure. Additionally, as a novelty for function prediction of enzymes, β-hairpins with ligand binding sites were predicted. Based on this work, a web server was constructed to predict β-hairpin motifs in enzymes (http://202.207.29.251:8080/).
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Affiliation(s)
- Dongmei Li
- College of Sciences, Inner Mongolia University of Technology, Hohhot 010051, China
| | - Xiuzhen Hu
- College of Sciences, Inner Mongolia University of Technology, Hohhot 010051, China
| | - Xingxing Liu
- College of Sciences, Inner Mongolia University of Technology, Hohhot 010051, China
| | - Zhenxing Feng
- College of Sciences, Inner Mongolia University of Technology, Hohhot 010051, China
| | - Changjiang Ding
- College of Sciences, Inner Mongolia University of Technology, Hohhot 010051, China
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