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10.1038/s41598-018-24760-x http://scihub22266oqcxt.onion/10.1038/s41598-018-24760-x C5910428!5910428 !29679026     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\29679026 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Sci+Rep 2018 ; 8 (1 ): 6349 Nephropedia Template TP {{tp|p=29679026 |t=2018. Computational Protein Design with Deep Learning Neural Networks |pdf=|usr=}}{{29679026 }} gab.com Text Computational Protein Design with Deep Learning Neural Networks JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=29679026 #FOAvip Computational protein design has a wide variety of applications. Despite its remarkable success, designing a protein for a given structure and function is still a challenging task. On the other hand, the number of solved protein structures is rapidly increasing while the number of unique protein folds has reached a steady number, suggesting more structural information is being accumulated on each fold. Deep learning neural network is a powerful method to learn such big data set and has shown superior performance in many machine learning fields. In this study, we applied the deep learning neural network approach to computational protein design for predicting the probability of 20 natural amino acids on each residue in a protein. A large set of protein structures was collected and a multi-layer neural network was constructed. A number of structural properties were extracted as input features and the best network achieved an accuracy of 38.3%. Using the network output as residue type restraints improves the average sequence identity in designing three natural proteins using Rosetta. Moreover, the predictions from our network show ~3% higher sequence identity than a previous method. Results from this study may benefit further development of computational protein design methods. Twit Text FOAVip Computational Protein Design with Deep Learning Neural Networks ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=29679026 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=29679026 #FOAvip English Wikipedia <ref>{{Cite pmid|29679026 }}</ref> Computational Protein Design with Deep Learning Neural Networks #MMPMID29679026 Wang J ; Cao H ; Zhang JZH ; Qi Y Sci Rep 2018[Apr]; 8 (1 ): 6349 PMID29679026 show ga Computational protein design has a wide variety of applications. Despite its remarkable success, designing a protein for a given structure and function is still a challenging task. On the other hand, the number of solved protein structures is rapidly increasing while the number of unique protein folds has reached a steady number, suggesting more structural information is being accumulated on each fold. Deep learning neural network is a powerful method to learn such big data set and has shown superior performance in many machine learning fields. In this study, we applied the deep learning neural network approach to computational protein design for predicting the probability of 20 natural amino acids on each residue in a protein. A large set of protein structures was collected and a multi-layer neural network was constructed. A number of structural properties were extracted as input features and the best network achieved an accuracy of 38.3%. Using the network output as residue type restraints improves the average sequence identity in designing three natural proteins using Rosetta. Moreover, the predictions from our network show ~3% higher sequence identity than a previous method. Results from this study may benefit further development of computational protein design methods. |Algorithms [MESH] |Amino Acid Sequence [MESH] |Amino Acids [MESH] |Big Data [MESH] |Computational Biology [MESH] |Deep Learning [MESH] |Machine Learning [MESH] |Neural Networks, Computer [MESH] |Probability [MESH] |Protein Engineering/*methods [MESH]Computational Protein Design with Deep Learning Neural Networks (epmc).pdf DeepDyve Pubget Overpricing