The iGEM spirit of open and collaborative work and the philosophy of building something for other teams encouraged us to build tools that can be used for further research and development. We have imbibed this spirit in almost all aspects of our work, and have something in each area that can be used by future teams in their own projects. Here is brief summary of our key contributions to the iGEM community, science and society:

TailScout Software

In order to provide an easy and automated approach to get the secondary structure of the engineered protein and its sequence, we developed an open-source software TailScout. This software is an improvement upon the software developed by team TAU Israel with better performance and features as a result of the unique design and build choices. TailScout is a web-based software which simply computationalizes the protein design process and predicts the secondary structure of the pyocin-bacteriophage fusion protein. It can be used by any future iGEM team to build on this work or take it as a reference. This software also serves as a tool that can be used by researchers to create new fusion pyocins for other pathogens in the ESKAPE category. In this way, it allows for a collaborative effort to combat AMR by utilizing the modular system developed by our team currently for A. baumannii. For further details, check out our software page and Github Repository .

Phage Tail Library

TailScout is based on the fusion of pyocin and bacteriophage tail fiber. In this process, we created a database of phage tail fibers using their protein sequence. This library can serve future teams as a direct source of information around phage tail sequences.

Parts (BioBricks)

We have added 5 parts to the registry for the use of future iGEM teams, that provides them with a basic backbone to conveniently produce Seekercins with their own targets. In this way, we encourage new teams to explore further around the idea that we developed. This will, in some way, allow us to explore and realise the true potential of the pyocin based antimicrobial therapy. These parts are meant to be used modularly with each other. For further information refer to the Parts page.

Protocols

As pyocins and by extension Seekercins are unique molecules, they require the use of slightly different protocols for production, purification and characterization when compared to other antimicrobial agents or phages. Our Protocols page. is a compilation of procedures created based on the literature and our own thought process that are specific to Seekercins, and should make working with them convenient for future researchers.

Education and Awareness

Education and AMR Awareness Tools

“Knowledge is Power”, and once you are well aware of things happening around the globe, you can conquer anything and everything. So, we formulated resource booklets (according to the learning standards of the children) in order to aware them about one of the most significant and pressing issues of the near future, i.e. Antimicrobial Resistance. We put in a lot of thought and consideration while developing original content for children. We have also developed interesting and attractive AMR mythbusters posters in 2 languages- Hindi and English. These are already widely circulated in the communities around us and we would love to share these with new iGEM teams working on AMR related projects. You can find all the documents and approach in designing them on the Human Practices page.

Machine Learning

Machine Learning Based Prediction Tool

Our ML algorithm is an important contribution in the global efforts to understand resistant bacterial species on a genetic level. Our tool helps in evaluating and understanding the impact of mutations in the gene on the resistance phenotype of bacterial strains. The combination of novel targets and mutational analysis help in prioritizing wet-lab experiments by providing the relative importance of various genes. The approach can be easily adapted to any other pathogen of interest. Check out our approach and model development process at Machine Learning page.