Team:AFCM-Egypt/Contribution
Contribution
Contribution
In 2020, there were 2.3 million women diagnosed with breast cancer and 685,000 deaths globally. As of the end of 2020, there were 7.8 million women alive who were diagnosed with breast cancer in the past 5 years, making it the world’s most prevalent cancer. There are more lost disability-adjusted life years (DALYs) by women to breast cancer globally than any other type of cancer. Breast cancer occurs in every country of the world in women at any age after puberty but with increasing rates in later life.
Women are not only part of our society, the concept of a woman is much deeper than that. She could be a mother, sister, wife, and daughter to each and every one of us. So, our team put in mind that our project should be about the most aggressive disease they face. We hope that our vaccine will play a role in curing this problem and saving the lives of many women and also protecting families from the impact of seeing their loved ones suffer from the consequences of this disease.
Novel Directed Evolution approach and innovative new software:
AFCM-Egypt has developed a computational approach that integrates predictions of mutational effects on evolutionary fitness with encodings for local and global features representations of protein sequences, in addition to physicochemical parameters to produce mutant proteins with enhanced parameters that can contribute to achieving many breakthroughs in the field of protein engineering.
The aforementioned features were used to design two machine learning frameworks in an approach to accurately predict fit mutant protein variants without necessitating experimental inputs in an attempt to create zero-N directed evolution of proteins.
Our software can be adapted for the engineering of various protein classes, which in turn makes it a perfect platform for diverse applications.
By this, we have provided other teams with a step-wise approach and guidance to forming their own mutational landscapes and a pipeline to finally reach positive fit mutants with high evolutionary fitness.
This approach can be used by other iGEM teams to produce mutant variants of proteins that are more stable with increased fitness properties which will add widespread benefits for the scientific community and future projects.
Contribution of New Parts:
New 17 parts have been added to the registry, each part has been added after modification along with descriptive provisions that would help teams to design more logic gates and add more knowledge to the platform.
This can be explicitly displayed by A large library of parts that would be useful for future IGEM Teams and projects shown by: -
Parts allowing them to make advanced and system-sensitive riboswitches and logic gates that would allow them to make a complete logical system that would allow them to have more control over their plasmids and circuits. Such as Toehold switches (BBa_K3743010, BBa_K3743011) & various riboswitches (BBa_K3743015) with the rest being displayed in Parts.
Addition of characterization of multiple previous parts using mathematical modelling which has been performed in order to characterize and model their dynamics. Such as BBa_K2365048,BBa_K2100068,BBa_K2100050 And even previous parts of our own such as NSPs (BBa_K3504000-BBa_K3504003).
Addition of characterized and enhanced oncolytic parts like bax-mutant(BBa_K3743013) & Fas associated death domain (FADD)(BBa_K3743000).
Working on directed evolution we were able to provide future teams with a mutational landscape showing positive fit mutants, Therefore enhancing various parts for future teams which would work with highest efficacy
RNA binding proteins RBPs such as L7ae and MS2 which have witnessed improvements by adding (Cas12g) and (DD) respectively by fusion.
These added parts could be generalized among different vaccines to be used by futuristic IGEM teams as safety is the first priority.
Introduction of new VLPs to the synthetic biology community:
We’ve provided a new approach for immunotherapy for breast cancer using VLPs loaded with epitopes of TNBC neoantigens. This was followed by testing them for structural stability and molecular dynamics to select the ones with the highest stability in our model.
We believe that this approach can be very useful in various other applications. It is also available for other iGEM teams to use and improve on.
We provided teams with steps of creating their own vaccines using VLPs and this pipeline could even be implemented to create various different immunotherapeutic approaches using VLPs.
We used the Hepatitis B virus core protein as a backbone for our virus-like particle due to its ability to easily self-assemble having the same size as the virus particle but lacking its genetic material to assure the maximum level of safety for the recipient.
Our VLPs have been properly designed using well-characterized viral proteins with protein segments that do not include any standard primers used for viral detection. It also provides stability for core structure and flexibility for the epitope. That’s why we expect it to be used more by iGEM teams to achieve the same results.
