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Rapid, accurate and serotype independent pipeline for in silico epitope mapping of SARS-CoV-2 antigens: a combined machine learning and Chou’s pseudo amino acid composition method
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作者 Arash Rahmani Mokhtar Nosrati 《Medical Data Mining》 2023年第3期1-9,共9页
Here,a new integrated machine learning and Chou’s pseudo amino acid composition method has been proposed for in silico epitope mapping of severe acute respiratorysyndrome-like coronavirus antigens.For this,a training... Here,a new integrated machine learning and Chou’s pseudo amino acid composition method has been proposed for in silico epitope mapping of severe acute respiratorysyndrome-like coronavirus antigens.For this,a training dataset including 266 linear B-cell epitopes,1,267 T-cell epitopes and 1,280 non-epitopes were prepared.The epitope sequences were then converted to numerical vectors using Chou’s pseudo amino acid composition method.The vectors were then introduced to the support vector machine,random forest,artificial neural network,and K-nearest neighbor algorithms for the classification process.The algorithm with the highest performance was selected for the epitope mapping procedure.Based on the obtained results,the random forest algorithm was the most accurate classifier with an accuracy of 0.934 followed by K-nearest neighbor,artificial neural network,and support vector machine respectively.Furthermore,the efficacies of predicted epitopes by the trained random forest algorithm were assessed through their antigenicity potential as well as affinity to human B cell receptor and MHC-I/II alleles using the VaxiJen score and molecular docking,respectively.It was also clear that the predicted epitopes especially the B-cell epitopes had high antigenicity potentials and good affinities to the protein targets.According to the results,the suggested method can be considered for developing specific epitope predictor software as well as an accelerator pipeline for designing serotype independent vaccine against the virus. 展开更多
关键词 severe acute respiratory syndrome-like coronavirus machine learning Chou’s pseudo amino acid composition epitope based vaccine
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The Significant and Profound Impacts of Chou’s Pseudo Amino Acid Composition or PseAAC 被引量:1
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作者 Kuo-Chen Chou 《Natural Science》 2020年第9期647-658,共12页
In this short review paper, the significant and profound impacts of the Pseudo Amino Acid Composition or PseAAC have been briefly presented with crystal clear convincingness.
关键词 pseudo amino Acid composition PseAAC Significant Impacts Profound Impacts
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Structural Prediction of Membrane Protein:Application to Known Structures
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作者 赵培英 丁永生 《Journal of Donghua University(English Edition)》 EI CAS 2010年第2期289-292,共4页
Membrane proteins are embedded in the lipid bilayer,which creates a suitable environment for their actions. It is important to decide which tpye it belongs to because it is closely relevant to its biological function ... Membrane proteins are embedded in the lipid bilayer,which creates a suitable environment for their actions. It is important to decide which tpye it belongs to because it is closely relevant to its biological function and its interaction process with other molecules in a biological system. Membrane proteins have different types. The function of a membrane protein is closely correlated with the type it belongs to. In this study,on the basis of the concept of pseudo amino acid (PseAA) composition originally introduced by Chou,the value of approximate entropy (ApEn) of the query membrane protein was used to integrate the complementary information. By fusing fifteen powerful individual fuzzy K-nearest neighbor ( FKNN) classifiers,an ensemble classifier was presented. Each basic classifier was trained in PseAA composition of membrane protein sequences with different parameters. The results of experiments demonstrate it is efficient for the structural prediction of membrane proteins. 展开更多
关键词 pseudo amino acid composition approximate entropy ensemble classifiers
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