lib310 Python package
Sample Usages
Protein Functional Annotation
# 1. import lib310
import lib310
# 2. Get Spike SARS2 related proteins from database
seqs = lib310 .db .fetch (
name = "SPIKE_SARS2" ,
feature = 'sequence' ,
limit = 500
)
# 3. Instantiate a pre-trained GO Annotation machine learning model (e.g. TALE)
goa = lib310 .ml .GoAnnotation .from_pretrained (model = "prot_bert" , v = "latest" )
# 4. Predict!
results = goa .run (seqs )
# 5. Visualization
lib310 .plot .umap (results , color = 'protein_families' )
Protein Generation
# 1. import lib310
import lib310
# 2. Instantiate a pre-trained Generative Machine Learning model (e.g. GPT3)
lm = lib310 .ml .AutoRegressiveLM .from_pretrained (model = "ProtGPT3" , v = "latest" )
# 3. Predict!
generated_sequences = lm .run (num_samples = 1024 )
# 4. Downstream Analysis...
clusters = lib310 .tools .cluster (generated_sequences , method = 'kcluster' )
# 5. Visualization
lib310 .plot .umap (generated_sequences , clusters )