InSilicoEvolution

Hallucination based protein design method with Alphafold2 as an oracle and SOLeNNoID discriminator network to produce solenoid proteins.

🚧 Work in progress.. 🚧

Installation

git clone https://github.com/yourusername/InSilicoEvolution.git
cd InSilicoEvolution

Running the program

python3 src/main.py \
  --parent_dir /path/to/output \
  --population_size 20 \
  --rounds 50 \
  --solenoid_type alphabeta

Command-Line Arguments

Argument	Description	Default
--parent_dir	Root directory for input/output	.
--input_dir	Input FASTA folder name	in_silico_evolution_input
--output_dir	ColabFold output folder name	in_silico_evolution_output
--final_output_dir	Where final results are stored	output_statistics
--num_repeats	Repeats of the sequence in FASTA	6
--population_size	Genetic algorithm population size	10
--parent_strategy	Parent selection strategy	wright-fisher
--beta	Mutation strength parameter	0.1
--children_proportion	Proportion of children per generation	0.8
--rounds	Number of design rounds	30
--sequences_batch_size	Number of sequences processed per batch	1
--model_queries_per_batch	Number of queries per generation	30
--starting_sequence	Provide a starting sequence	"" (auto-generated)
--sequence_length	Length of generated starting sequence	30
--min_solenoid	Threshold for solenoid confidence	0.6
--min_plddt	Threshold for pLDDT confidence	0.7
--solenoid_type	Solenoid class to target (beta, alphabeta, alpha)	beta