import sapiens

best = sapiens.predict_masked(
    '**QLV*SGVEVKKPGASVKVSCKASGYTFTNYYMYWVRQAPGQGLEWMGGINPSNGGTNFNEKFKNRVTLTTDSSTTTAYMELKSLQFDDTAVYYCARRDYRFDMGFDYWGQGTTVTVSS',
    'H'
)
print(best)
# QVQLVQSGVEVKKPGASVKVSCKASGYTFTNYYMYWVRQAPGQGLEWMGGINPSNGGTNFNEKFKNRVTLTTDSSTTTAYMELKSLQFDDTAVYYCARRDYRFDMGFDYWGQGTTVTVSS

import sapiens

scores = sapiens.predict_scores(
    '**QLV*SGVEVKKPGASVKVSCKASGYTFTNYYMYWVRQAPGQGLEWMGGINPSNGGTNFNEKFKNRVTLTTDSSTTTAYMELKSLQFDDTAVYYCARRDYRFDMGFDYWGQGTTVTVSS',
    'H'
)
scores.head()
#           A         C         D         E  ...
# 0  0.003272  0.004147  0.004011  0.004590  ... <- based on masked input
# 1  0.012038  0.003854  0.006803  0.008174  ... <- based on masked input
# 2  0.003384  0.003895  0.003726  0.004068  ... <- based on Q input
# 3  0.004612  0.005325  0.004443  0.004641  ... <- based on L input
# 4  0.005519  0.003664  0.003555  0.005269  ... <- based on V input
#
# Scores are given both for residues that are masked and that are present. 
# When inputting a non-human antibody sequence, the output scores can be used for humanization.

import sapiens

residue_embed = sapiens.predict_residue_embedding(
    'QVKLQESGAELARPGASVKLSCKASGYTFTNYWMQWVKQRPGQGLDWIGAIYPGDGNTRYTHKFKGKATLTADKSSSTAYMQLSSLASEDSGVYYCARGEGNYAWFAYWGQGTTVTVSS', 
    'H', 
    layer=None
)
residue_embed.shape
# (layer, position in sequence, features)
# (5, 119, 128)

seq_embed = sapiens.predict_sequence_embedding(
    'QVKLQESGAELARPGASVKLSCKASGYTFTNYWMQWVKQRPGQGLDWIGAIYPGDGNTRYTHKFKGKATLTADKSSSTAYMQLSSLASEDSGVYYCARGEGNYAWFAYWGQGTTVTVSS', 
    'H', 
    layer=None
)
seq_embed.shape
# (layer, features)
# (5, 128)