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Two disulfide mutants in domain I of <i>Bacillus thuringiensis</i>Cry3Aa δ-endotoxin increase stability with no effect on toxicity

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Advances in Biological Chemistry 2012年第2期2卷 123-131页

作者： Sheng-Jiun Wu Alvaro M. Florez Bradley J. Homoelle Donald H. Dean oscar alzate Biochemistry Department Ohio State University Columbus USA Department of Cell and Developmental Biology University of North Carolina Chapel Hill USA Laboratorio de Biología Molecular y Biotecnología Facultad de Medicina Universidad de Santander (UDES) Campus Univer- sitario Bucaramanga Colombia

To increase protein stability and test protein function, three double-cysteine mutations were individually introduced by protein engineering into the cysteine-free Cry3Aa δ-endotoxin from Bacillus thuringiensis. These mutations were designed to create disulfide bonds between α-helices 2 and 5 (positions 110 - 193), and α-helices 5 and 7 (positions 195 - 276 and 198 - 276). Comparison of the CD spectra of the wild-type and the double-cysteine mutant proteins indicates a tighter helical packing consistent with formation of at least two of the disulfide bonds between the central and the outer helices. Thermal stability analysis indi-cates that potential covalent linkages between the central α-helix 5 and the other helices increase resistance to thermal denaturation by 10?C to 14?C com-pared to the thermal stability of the wild-type protein. Spectroscopic analysis of the disulfide-specific absorbance band indicates that the double mutant proteins are more stable to temperature and denaturant (guanidine hydrochloride) than the wild-type protein, as a result of the formation of two of the disulfide bridges. These results indicate that the double muta-tions M110C/F193C and A198C/V276C successfully established disulfide bonds, resulting in a more stable structure of the entire toxin. Despite the increase in stability and structural changes introduced by the disulfide bonds, no effect on toxicity was observed. A possible mechanism involving the insertion of all of domain I of Cry3Aa toxin into the target membrane accounts for these observations.

关键词： Disulfide Bonds CD Spectra Cry3Aa Site Directed Mutagenesis

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Optimized Convolutional Neural Network Models for Skin Lesion Classification

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Computers, Materials & Continua 2022年第2期70卷 2131-2148页

作者： Juan Pablo Villa-Pulgarin Anderson Alberto Ruales-Torres Daniel Arias-GarzónMario Alejandro Bravo-Ortiz Harold Brayan Arteaga-Arteaga Alejandro Mora-RubioJesus Alejandro alzate-Grisales Esteban Mercado-Ruiz M.Hassaballah Simon Orozco-Arias oscar Cardona-Morales Reinel Tabares-Soto Department of Electronics and Automation Universidad Autónoma deManizalesManizales170001Colombia SEDMATEC Corporación Universitaria Autónoma de Nari駉Pasto520002Colombia Department of Computer Science Faculty of Computers and InformationSouth Valley UniversityQena83523Egypt Department of Computer Science Universidad Autónoma de ManizalesManizales170001Colombia Department of Systems and Informatics Universidad de CaldasManizales170001Colombia

Skin cancer is one of themost severe diseases,andmedical imaging is among themain tools for cancer *** images provide information on the evolutionary stage,size,and location of tumor *** paper focuses on the classification of skin lesion images considering a framework of four experiments to analyze the classification performance of Convolutional Neural Networks(CNNs)in distinguishing different skin *** CNNs are based on transfer learning,taking advantage of ImageNet ***,in each experiment,different workflow stages are tested,including data augmentation and fine-tuning *** CNN models based on DenseNet-201,Inception-ResNet-V2,and Inception-V3 are proposed and compared using the HAM10000 *** results obtained by the three models demonstrate accuracies of 98%,97%,and 96%,***,the best model is tested on the ISIC 2019 dataset showing an accuracy of 93%.The proposed methodology using CNN represents a helpful tool to accurately diagnose skin cancer disease.

关键词： Deep learning skin lesion convolutional neural network data augmentation transfer learning

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