Generative Personalized Spider Silk (GPSS)

In the field of synthetic biology and bioinformatics, the design and optimization of proteins are of critical significance, especially in the generation of sequences. The repetitive segments in spider silk protein make it particularly challenging for biologists to determine which segments are most crucial. With advances in protein structure prediction and the continuous growth of sequence data, along with various deep learning models such as AlphaFold and OmegaFold, the possibilities for protein design have greatly expanded. Nevertheless, the challenge remains to develop a comprehensive tool that can efficiently manipulate protein sequences, evaluate their properties, and accurately display their structures.

Our innovative software tool, GPSS, enables users to effortlessly input spider silk sequence properties through an intuitive interface. Upon input, the tool generates multiple sequences and provides detailed visualizations of their structures and predicted properties. Additionally, this tool offers functionalities to assess the stability, functionality, and potential applications of the designed proteins, making it an invaluable resource for researchers and scientists. Furthermore, it integrates advanced algorithms to analyze sequence variability and potential critical motifs, facilitating more targeted and effective protein engineering.

Generative Pretrained Transformer (GPT) is a state-of-the-art model that has significantly advanced natural language processing and sequence generation tasks. In the context of this project, GPT plays a pivotal role in generating spider silk protein sequences with specific mechanical properties. GPT's strengths lie in its ability to generate coherent and contextually appropriate sequences based on prior training, making it highly suitable for bioinformatics tasks that require pattern recognition in sequences.

1. Sequence Coherence: GPT uses the Transformer architecture, which excels at capturing long-range dependencies. This is particularly important for protein sequences, where the order of amino acids can significantly impact their properties.
2. Scalability: GPT can handle large datasets and generate long sequences, making it ideal for bioinformatics applications involving long protein chains.
3. Flexibility:GPT is flexible and can be fine-tuned on specific datasets, such as spider silk proteins, to generate sequences that exhibit desired physical properties.
4. Context Awareness:Unlike traditional sequence generation models, GPT takes into account the context of the entire sequence, allowing it to generate more biologically relevant outputs.
5. Sampling Control: With hyperparameters like temperature and top-k/top-p sampling, GPT allows fine-grained control over the diversity and randomness of generated sequences, ensuring that the outputs can be both novel and specific to the input criteria.

GPT is based on the Transformer architecture, which relies on self-attention mechanisms. The core idea is to compute attention scores that determine how each token (or amino acid in our case) in the sequence relates to every other token. This allows GPT to efficiently capture relationships between distant elements in a sequence.

For our environmental monitoring system, we developed sensors to activate air filtration when high pollution levels are detected and to minimize filtration when air quality improves. Using the Simulink application in MATLAB, we tested and visualized the performance of these sensors before physical deployment. Furthermore, we conducted a comprehensive performance analysis to validate our design. Visit the Environmental Monitoring tab for more information.

For our environmental monitoring system, we developed sensors to activate air filtration when high pollution levels are detected and to minimize filtration when air quality improves. Using the Simulink application in MATLAB, we tested and visualized the performance of these sensors before physical deployment. Furthermore, we conducted a comprehensive performance analysis to validate our design. Visit the Environmental Monitoring tab for more information.