Team:Florida Atlantic/Experiments
Florida_Atlantic
Experiments
Wet Lab Protocols
LB Broth 25g LB Broth, Miller 1L Water Mix LB powder into water, autoclave for 15 minutes. Supplement with antibiotics (Chloramphenicol, 35μL/mL) as needed. LB Agar 25g LB Broth, Miller 15g Granulated Agar 1L Water Mix LB powder and Agar into water. Heat until agar is dissolved and solution is clear, avoid boiling over. Autoclave for 15 minutes. Supplement with antibiotics (Chloramphenicol, 35μL/mL) as needed. Pour 20mL into sterile culture plates and let cool.
Transformation of Competent Cells
50μL Competent Cells (DH5α E. coli) 10ng Plasmid DNA 450μL LB LB/Chloramphenicol Plate Thaw Competent cells on ice and add plasmid DNA. Let sit for 20 minutes on ice. Heat shock cells at 45 o C for 30 seconds and then return to ice for 2 minutes. Add LB and incubate at 37 o C for 2-3 hours. Plate 100μL of the cells on a LB/Chloramphenicol plate and incubate at 37 o C overnight.
Dry Lab Protocols
Protein Reverse Translation Isolate the protein sequence of interest and reverse translate using the E. coli preferred codon library in SnapGene. After reverse translation, look for out-of- frame coding regions and alter the codons so that no transcription is likely to occur. Finally, run a BLASTX protocol to ensure that the nucleotide sequence still encodes the protein of interest.
Machine Learning Protocols
LSTM model was coded using Tensorflow library and imported to Jupyter notebooks for 3 main experiments.
Artemisinin Binding
- Import LSTM model. - Collected and import positive dataset for proteins that bind to Artemisinin and negative dataset for proteins that do not bind to Artemisinin by hand from NCBI and literature. - Train model to learn binding vs. not binding. - Establish consistent parameters for learning process (number of proteins per dataset, learning rate, network size, and sub-sequence length viewed). - Run test and repeat to establish consistency.
Artemisinin Consensus Sequence
- Import LSTM. - Import dataset (from previous model^). - Train model to look at specified sequence length for binding vs. not binding. - Establish consistent parameters for learning process (number of proteins per dataset, learning rate, network size, and sub-sequence length viewed). - Set variables consistent (batch size, epochs, and sequence length to run in range loop). - Run test and repeat to establish consistency.
Homeobox Consensus Sequence
- Import LSTM. - Import dataset from Uniprot data website for proteins containing homeo-domain sequence and proteins and proteins not containing homeodomain sequence. - Train model to look at specified sequence length for binding vs. not binding. - Establish consistent parameters for learning process (number of proteins per dataset, learning rate, network size, and sub-sequence length viewed). - Set variables consistent (batch size, epochs, and sequence length to run in range loop). - Run test and repeat to establish consistency.
