A step-by-step classification algorithm of protein secondary structures based on double-layer SVM model

Ge, Yongzhen; Zhao, Shuo; Zhao, Xin

doi:10.1016/j.ygeno.2019.11.006

Cited by 8 publications

(5 citation statements)

References 26 publications

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“…Considering the ubiquitous application of SVM in the field of bioinformatics, we made comparison with it to verify the effectiveness of RF-PSSM [42][43][44][45] . In specificity, we obtained the best kernel function, c and g by grid search, respectively [41,46] .…”

Section: Comparison With Svm-based Methodsmentioning

confidence: 99%

“…When samples are linearly inseparable, the kernel technique and soft interval maximization can be used to learn the nonlinear SVM. SVM has good generalization ability and has excellent performance in various fields, including antifungal peptides prediction [42] , cancer prediction [43,44] , protein secondary structure prediction [45] , and so on.…”

Section: Support Vector Machinementioning

confidence: 99%

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RF-PSSM: A Combination of Rotation Forest Algorithm and Position-Specific Scoring Matrix for Improved Prediction of Protein-Protein Interactions Between Hepatitis C Virus and Human

Liu

Ya-ping

Wang

et al. 2023

Big Data Min. Anal.

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The identification of hepatitis C virus (HCV) virus-human protein interactions will not only help us understand the molecular mechanisms of related diseases but also be conductive to discovering new drug targets. An increasing number of clinically and experimentally validated interactions between HCV and human proteins have been documented in public databases, facilitating studies based on computational methods. In this study, we proposed a new computational approach, rotation forest position-specific scoring matrix (RF-PSSM), to predict the interactions among HCV and human proteins. In particular, PSSM was used to characterize each protein, two-dimensional principal component analysis (2DPCA) was then adopted for feature extraction of PSSM. Finally, rotation forest (RF) was used to implement classification. The results of various ablation experiments show that on independent datasets, the accuracy and area under curve (AUC) value of RF-PSSM can reach 93.74% and 94.29%, respectively, outperforming almost all cutting-edge research. In addition, we used RF-PSSM to predict 9 human proteins that may interact with HCV protein E1, which can provide theoretical guidance for future experimental studies.

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Section: Comparison With Svm-based Methodsmentioning

confidence: 99%

Section: Support Vector Machinementioning

confidence: 99%

RF-PSSM: A Combination of Rotation Forest Algorithm and Position-Specific Scoring Matrix for Improved Prediction of Protein-Protein Interactions Between Hepatitis C Virus and Human

Liu

Ya-ping

Wang

et al. 2023

Big Data Min. Anal.

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“…Chai et al (2019) used the hmmscan similarity search algorithm to develop a web service, called HMMCAS, for identifying and annotating Cas proteins. Currently, support vector machine (SVM) as a machine learning method has been widely used in pattern recognition and classification in bioinformatics (Ge, Zhao & Zhao, 2020). Therefore, we built a web service named CASPredict, which is composed of two modules.…”

Section: Introductionmentioning

confidence: 99%

CASPredict: a web service for identifying Cas proteins

Yang

Huang

2021

PeerJ

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Clustered regularly interspaced short palindromic repeats (CRISPR) and their associated (Cas) proteins constitute the CRISPR-Cas systems, which play a key role in prokaryote adaptive immune system against invasive foreign elements. In recent years, the CRISPR-Cas systems have also been designed to facilitate target gene editing in eukaryotic genomes. As one of the important components of the CRISPR-Cas system, Cas protein plays an irreplaceable role. The effector module composed of Cas proteins is used to distinguish the type of CRISPR-Cas systems. Effective prediction and identification of Cas proteins can help biologists further infer the type of CRISPR-Cas systems. Moreover, the class 2 CRISPR-Cas systems are gradually applied in the field of genome editing. The discovery of Cas protein will help provide more candidates for genome editing. In this paper, we described a web service named CASPredict (http://i.uestc.edu.cn/caspredict/cgi-bin/CASPredict.pl) for identifying Cas proteins. CASPredict first predicts Cas proteins based on support vector machine (SVM) by using the optimal dipeptide composition and then annotates the function of Cas proteins based on the hmmscan search algorithm. The ten-fold cross-validation results showed that the 84.84% of Cas proteins were correctly classified. CASPredict will be a useful tool for the identification of Cas proteins, or at least can play a complementary role to the existing methods in this area.

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“…Their method, unfortunately, is not suitable for the majority of proteins, as 3D crystallographic structures are unavailable for most proteins. Later studies on predicting antifreeze proteins used modern machine learning algorithms, which have demonstrated their ability in other protein-related research, such as identifying membrane proteins and their subcategories (Chou and Shen, 2007), predicting subcellular localization of multi-label proteins (Javed and Hayat, 2019), and classifying protein secondary structures (Ge et al, 2019). Most of these studies focused on amino acid compositionrelated features, and various physicochemical properties of amino acid sequences have been extensively used to identify antifreeze proteins (Kandaswamy et al, 2011;Yu and Lu, 2011;Mondal and Pai, 2014;Pratiwi et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Identifying Antifreeze Proteins Based on Key Evolutionary Information

Sun

Ding

Wang

et al. 2020

Front. Bioeng. Biotechnol.

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Antifreeze proteins are important antifreeze materials that have been widely used in industry, including in cryopreservation, de-icing, and food storage applications. However, the quantity of some commercially produced antifreeze proteins is insufficient for large-scale industrial applications. Further, many antifreeze proteins have properties such as cytotoxicity, severely hindering their applications. Understanding the mechanisms underlying the protein-ice interactions and identifying novel antifreeze proteins are, therefore, urgently needed. In this study, to uncover the mechanisms underlying protein-ice interactions and provide an efficient and accurate tool for identifying antifreeze proteins, we assessed various evolutionary features based on position-specific scoring matrices (PSSMs) and evaluated their importance for discriminating of antifreeze and non-antifreeze proteins. We then parsimoniously selected seven key features with the highest importance. We found that the selected features showed opposite tendencies (regarding the conservation of certain amino acids) between antifreeze and non-antifreeze proteins. Five out of the seven features had relatively high contributions to the discrimination of antifreeze and non-antifreeze proteins, as revealed by a principal component analysis, i.e., the conservation of the replacement of Cys, Trp, and Gly in antifreeze proteins by Ala, Met, and Ala, respectively, in the related proteins, and the conservation of the replacement of Arg in non-antifreeze proteins by Ser and Arg in the related proteins. Based on the seven parsimoniously selected key features, we established a classifier using support vector machine, which outperformed the state-of-the-art tools. These results suggest that understanding evolutionary information is crucial to designing accurate automated methods for discriminating antifreeze and non-antifreeze proteins. Our classifier, therefore, is an efficient tool for annotating new proteins with antifreeze functions based on sequence information and can facilitate their application in industry.

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A step-by-step classification algorithm of protein secondary structures based on double-layer SVM model

Cited by 8 publications

References 26 publications

RF-PSSM: A Combination of Rotation Forest Algorithm and Position-Specific Scoring Matrix for Improved Prediction of Protein-Protein Interactions Between Hepatitis C Virus and Human

RF-PSSM: A Combination of Rotation Forest Algorithm and Position-Specific Scoring Matrix for Improved Prediction of Protein-Protein Interactions Between Hepatitis C Virus and Human

CASPredict: a web service for identifying Cas proteins

Identifying Antifreeze Proteins Based on Key Evolutionary Information

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