IBPred: A sequence-based predictor for identifying ion binding protein in phage

Yuan, Shi-Shi; Gao, Dong; Xie, Xue-Qin; Ma, Cai-Yi; Su, Wei; Zhang, Zhao-Yue; Zheng, Yan; Ding, Hui

doi:10.1016/j.csbj.2022.08.053

Cited by 13 publications

(7 citation statements)

References 77 publications

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“…Afterward, five performance evaluation parameters are used: accuracy (Acc), sensitivity (Sn), specificity (Sp), Mathew's correlation coefficient (MCC), and F-measure. 60,61 These parameters are calculated as follows: (15) (16) (17) (18) (19) in which ( 20) (21) where TN, TP, FN, and FP are the numbers of true negatives, true positives, false negatives, and false positives, respectively. TP denotes the number of antifreeze proteins.…”

Section: Validation Methodsmentioning

confidence: 99%

“…Additionally, DDE also considered the global motifs, which are key characteristics of proteins . Due to the effectiveness of the DDE, recently, many researchers implemented it and significantly enhanced the performance of the models including the prediction of ubiquitin proteins, identification of anticancer peptides, detection of the enzyme-specific protein domain, and recognition of ion-binding proteins …”

Section: Methodsmentioning

confidence: 99%

“…17 Due to the effectiveness of the DDE, recently, many researchers implemented it and significantly enhanced the performance of the models including the prediction of ubiquitin proteins, 18 identification of anticancer peptides, 19 detection of the enzyme-specific protein domain, 20 and recognition of ion-binding proteins. 21 DDE is formulated by three parameters: the theoretical variance (T v ), the dipeptide composition measure (D c(i) ), and the theoretical mean (T m(i) ). 22 D c(i) is formulated as (4) Here, f i denotes the frequency of dipeptide i and N represents the number of dipeptides in a protein sequence.…”

Section: Data Setsmentioning

confidence: 99%

See 2 more Smart Citations

AFP-SPTS: An Accurate Prediction of Antifreeze Proteins Using Sequential and Pseudo-Tri-Slicing Evolutionary Features with an Extremely Randomized Tree

Khan

Uddin

Ali

et al. 2023

J. Chem. Inf. Model.

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The development of intracellular ice in the bodies of cold-blooded living organisms may cause them to die. These species yield antifreeze proteins (AFPs) to live in subzero temperature environments. Additionally, AFPs are implemented in biotechnological, industrial, agricultural, and medical fields. Machine learning-based predictors were presented for AFP identification. However, more accurate predictors are still highly desirable for boosting the AFP prediction. This work presents a novel approach, named AFP-SPTS, for the correct prediction of AFPs. We explored the discriminative features with four schemes, namely, dipeptide deviation from the expected mean (DDE), reduced amino acid alphabet (RAAA), grouped dipeptide composition (GDPC), and a novel representative method, called pseudo-position-specific scoring matrix tri-slicing (PseTS-PSSM). Considering the advantages of ensemble learning strategy, we fused each feature vector into different combinations and trained the models with five machine learning algorithms, i.e., multilayer perceptron (MLP), extremely randomized tree (ERT), decision tree (DT), random forest (RF), and AdaBoost. Among all models, PseTS-PSSM + RAAA with an extremely randomized tree attained the best outcomes. The proposed predictor (AFP-SPTS) boosted the accuracies of AFPs in the literature by 1.82 and 4.1%.

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Section: Validation Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Data Setsmentioning

confidence: 99%

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AFP-SPTS: An Accurate Prediction of Antifreeze Proteins Using Sequential and Pseudo-Tri-Slicing Evolutionary Features with an Extremely Randomized Tree

Khan

Uddin

Ali

et al. 2023

J. Chem. Inf. Model.

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“…CKSAAP is a coding scheme based on the interaction between amino acid pairs, which has been widely used in protein prediction ( Yuan et al, 2022 ). CKSAAP can represent amino acids as a combination of multiple amino acid pairs with spacing K ( Chen et al, 2011 ), reflecting the short-range interaction between amino acid pairs.…”

Section: Methodsmentioning

confidence: 99%

Antimicrobial Peptides Prediction method based on sequence multidimensional feature embedding

Dong

Jiang

et al. 2022

Front. Genet.

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Antimicrobial peptides (AMPs) are alkaline substances with efficient bactericidal activity produced in living organisms. As the best substitute for antibiotics, they have been paid more and more attention in scientific research and clinical application. AMPs can be produced from almost all organisms and are capable of killing a wide variety of pathogenic microorganisms. In addition to being antibacterial, natural AMPs have many other therapeutically important activities, such as wound healing, antioxidant and immunomodulatory effects. To discover new AMPs, the use of wet experimental methods is expensive and difficult, and bioinformatics technology can effectively solve this problem. Recently, some deep learning methods have been applied to the prediction of AMPs and achieved good results. To further improve the prediction accuracy of AMPs, this paper designs a new deep learning method based on sequence multidimensional representation. By encoding and embedding sequence features, and then inputting the model to identify AMPs, high-precision classification of AMPs and Non-AMPs with lengths of 10–200 is achieved. The results show that our method improved accuracy by 1.05% compared to the most advanced model in independent data validation without decreasing other indicators.

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“…Since proteins have 20 kinds of normal amino acids, there are 400 kinds of dipeptides [60]. We need to count the numbers of these 400 dipeptides in the protein, and then calculate their frequencies in the whole sequence.…”

Section: Dipeptide Composition (Dc)mentioning

confidence: 99%

A First Computational Frame for Recognizing Heparin-Binding Protein

Zhu

Yuan

et al. 2023

Diagnostics

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Heparin-binding protein (HBP) is a cationic antibacterial protein derived from multinuclear neutrophils and an important biomarker of infectious diseases. The correct identification of HBP is of great significance to the study of infectious diseases. This work provides the first HBP recognition framework based on machine learning to accurately identify HBP. By using four sequence descriptors, HBP and non-HBP samples were represented by discrete numbers. By inputting these features into a support vector machine (SVM) and random forest (RF) algorithm and comparing the prediction performances of these methods on training data and independent test data, it is found that the SVM-based classifier has the greatest potential to identify HBP. The model could produce an auROC of 0.981 ± 0.028 on training data using 10-fold cross-validation and an overall accuracy of 95.0% on independent test data. As the first model for HBP recognition, it will provide some help for infectious diseases and stimulate further research in related fields.

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IBPred: A sequence-based predictor for identifying ion binding protein in phage

Cited by 13 publications

References 77 publications

AFP-SPTS: An Accurate Prediction of Antifreeze Proteins Using Sequential and Pseudo-Tri-Slicing Evolutionary Features with an Extremely Randomized Tree

AFP-SPTS: An Accurate Prediction of Antifreeze Proteins Using Sequential and Pseudo-Tri-Slicing Evolutionary Features with an Extremely Randomized Tree

Antimicrobial Peptides Prediction method based on sequence multidimensional feature embedding

A First Computational Frame for Recognizing Heparin-Binding Protein

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