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Revision as of 15:25, 1 November 2017


Software
SafetyNet
When performing large scale, automated directed evolution experiments a manual assertion of every sequence in the library is impossible. However profound background and quality checks on sequences are crucial in the automated context as the experimentator has no direct control of the processes. This especially holds true for in silico evolution, where the immediate effect of a mutation is not assessable. In order to safeguard our in vivo and in silico directed evolution experiments we developed Safetynet. Safetynet is a web available, neural network based sequence check. It does not only infer the function and species of origin, but does also assert the safety level assigned to the origin species and the potential harm of an input sequence. We applied SafetyNet throughout our directed evolution experiments to ensure safe and flawless sequence improvement all the while preventing the unintended emergence of harmful traits.

Method

Safetynet is based on two algorithmic pillars. The first one is a BLAST search of the input sequence against the swissprot database, performed through the NCBI web API. The request is POSTed to the NCBI server and the result is catched with GET request. Subsequently the result is parsed for the protein IDs of all non redundant matches. Next, the retrieved protein IDs are used to send a GET request to the UniProt database, requesting the entry of the protein in question. The entry is again parsed for key information, this time returning the assigned GO-Terms, the species of origin and the gene of origin. Subsequently the collected information on each entry is combined and a lookup on the safetynet internal databases is performed. These comprehensive databases list GO-Terms associated with cytotoxic, viral or pathogenic functions or pathways. Further we included the functional terms for proteases and nucleases, to account for destructive intracellular potential. The biological safety level of the retrieved species of origin is investigated by a database lookup on the biosafety-database of the german ministry of consumer and food safety (the german FDA).
The second algorithmic column applies a DeeProtein implementation in the browser. Upon user request the neural network inference can additionally be enabled to support the BLAST search in function classification. This is especially useful as the neural network is able to detect latent or "hidden" potential as it learned the sequence to function relation accross the whole respective functional domain, whereas the BLAST search is limited to direct sequence identity.
The browser integrated neural network is implemented in DeeplearnJS and features GPU support. It is a ResNet30, similar to the Architecture of DeeProtein, asserting the class probability for 886 classes. As the size of the ResNet-weigths is ~100MB we offer a selection mode to guarantee the use of the BLAST-part on mobile connections.
Finally the collected information is concatenated and presented in a easily understandable color coded scheme.
Check protein safety using BLAST
Results of safetyBLAST search.
Check safety using DeeProtein
Results of safetyBLAST search.

References