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)