Editing Antibody Design by AI (section)

== Data preparation ==

We obtained ~10^7 HiSeq raw reads of length 76 of the nucleotide sequences around CDRH3 region for ranibizumab from all 3 rounds of panning, 4,493,746 reads from round 1, 3,983,153 reads from round 2 and 3,526,752 reads from round 3. The fixed flanking sequences on the boundary of variable region (12 base pairs on the head, 9 base pairs on the tail) was used as template to locate and segment out the CDR-H3 sequence. We used'''BLAST 25''' for short reads alignment to align the template with each read, allowing maximum of 2 mismatches on each side. We then took the sequence between end position of head template and start position of tail template, and discarded it if the length was not a multiple of three. On average, 58.90% of the raw reads were successfully parsed. The translation to amino acid sequences was done using '''EMBOSS 26.''' The resulting sequences were checked again for abnormal length (smaller than 8 or longer than 20 amino acids) or a stop codon. We obtained 572,647 unique CDR-H3 sequences for ranibizumab in round 1, 297,290 in round 2 and 171,568 in round 3. The same sequencing and preprocessing procedures were used for the other three targets and details can be found in Table S2. In the replicate of the same experiment, we obtained 8,022,860 MiSeq raw reads of length 150, in which over 85% were successfully parsed. In the replicate the number of unique CDR-H3 sequences is 558,400 for round 1, 265,563 for round 2 and 96,912 for round 3. We obtained 1,895,225 MiSeq raw reads of length 150 for ranibizumab-bevacizumab panning round 3 from 2 sample preparation each with 2 technical replicates, in which 82.9% were successfully parsed. The reduced sequence diversity in subsequent rounds indicated selection was occurring as expected.

To reduce noise in the training data for ranibizumab, we only '''retained sequences that had more than 5 read counts in at least one panning round, or had non-zero reads in all rounds'''. We then built 4 datasets for training machine learning models. In the classification dataset sequences with a round 3 frequency > 5e-5 or that is higher than its round 2 frequency by more than 1e-6 was labeled as positive, while sequence whose round 3 frequency is lower than round 2 frequency by more than 1e-6 and whose round 3 frequency is < 5e-5 was labeled as negative. This resulted in a training set of 51,130 sequences, with a distinct separation between the positive and negative examples. In the regression datasets, the R2-to-R3 enrichment was used as label and each dataset comprised subset of the whole pool. We added a pseudo-count of 1 to the read counts of all sequences to avoid errors when computing the ratio. The average size of the regression sets was 64,856.

For the multi-output training set, we retained sequences that had more than 3 read counts in at least one panning round and computed the R2-to-R3 enrichment as regression label for each of the targets. We then concatenated the labels, allowing missing values in one of the targets, and balanced the positive and negative samples by down-sampling negative/missing value sequences to have at most twice the size of the positive samples. This resulted in a training set of size 121,485 for etanercept and trastuzumab.

In the experiment with 1:100 diluted library of ML-proposed sequences to synthetic library sequences, we obtained 6,365,482 MiSeq reads with length 150 for standard washing condition and 7,016,792 reads for stringent washing condition for all 3 rounds. Over 85% of the reads were parsed successfully. We again observed decreasing diversity in subsequent panning rounds, and the most significant change of diversity happened between round 1 and round 2 where the number of unique sequences dropped from 1,044,401 to 76,928 for standard washing and from 1,135,356 to 53,809 in stringent washing. Our hypothesis was that the inclusion of highly competitive sequences generated by machine learning methods led to a more drastic competition in early rounds and thus accelerated the selection of good binders. Therefore, we looked at the R1-to-R3 enrichment in addition to R2-to-R3 enrichment when evaluating the enrichment for this experiment.