McCutchan, D. the linear peptide environment. The normalization factor Cyclo(RGDyK) allows for an unbiased comparison of solvent convenience for residues of different amino acid types (and sizes). A weighting factor (set to Cyclo(RGDyK) 1 1.0) was used to convert the normalized SASA value into a score. The final step of our method entails a stochastic search Cyclo(RGDyK) for the optimal set of arginine residues based on the RAPDF and solvent convenience scores decided in step two. Each cycle of the search begins by randomly Cyclo(RGDyK) selecting a seed residue from your set of CDR residues. All residues within a 5.0 ? radius of the seed residue were removed (flagged) from your set of selectable residues in the current cycle. We next decided the closest residue greater than 5.0 ? (measured from your -carbon atom) away from the current seed residue. This residue was now set as the seed residue and all residues within a 5.0 ? radius were removed from the set of selectable residues. The process is usually repeated until all residues have been selected. At the end of each cycle, the total score for the final set of seed residues was decided from your look-up tables generated in step two. The total score was computed as follows: F= [ref PMID: 23671333] and then filtered based on their heavy chain V and J germline gene assignments. All sequences with the IgVH 3C20 and IgJH 2 germline gene assignments were retained for further processing. To enrich the pool of transcripts with long CDR H3s, we filtered out any sequences with CDR H3 lengths (Kabat) less than 24 amino acids. Duplicated sequences were removed and problematic sequences were edited to bring the transcript into the correct translational frame. A multiple sequence alignment for the final set of sequences was generated using which is part of the PHYLIP package v3.69 (http://evolution.genetics.washington.edu/phylip.html). The S option was set to No to provide a more thorough optimization. The inferred intermediates were derived from the ML tree. Nucleotide sequences for CH01CCH04 were Rabbit polyclonal to Neuron-specific class III beta Tubulin downloaded from GenBank (accession # “type”:”entrez-nucleotide”,”attrs”:”text”:”JQ267523.1″,”term_id”:”380865831″,”term_text”:”JQ267523.1″JQ267523.1-“type”:”entrez-nucleotide”,”attrs”:”text”:”JQ267526.1″,”term_id”:”380865837″,”term_text”:”JQ267526.1″JQ267526.1). Donor CAP256 The ML tree was obtained from the study of Doria-Rose et al. 16 The nucleotide sequence for the CAP256-VRC26.UCA was downloaded from GenBank (accession #: “type”:”entrez-nucleotide”,”attrs”:”text”:”KJ134860.1″,”term_id”:”612405039″,”term_text”:”KJ134860.1″KJ134860.1). Donor IAVI24 nucleotide sequences for PG9 (accession #: “type”:”entrez-nucleotide”,”attrs”:”text”:”GU272045.1″,”term_id”:”281185524″,”term_text”:”GU272045.1″GU272045.1) and PG16 (accession #: “type”:”entrez-nucleotide”,”attrs”:”text”:”GU272043.1″,”term_id”:”281185522″,”term_text”:”GU272043.1″GU272043.1) were aligned and the alignment used to generate an ML tree using the program using the same process as was done for donor CH0219. The inferred intermediate was derived from the ML tree. Donor IAVI84 454 NGS sequences were downloaded from your SRA (accession # SRP018335) and processed in the same manner as was carried out for donor CH0219. Sequences where then filtered according to their V germline gene assignments. All sequences with the IgVH1-8 germline gene assignment were retained for further processing using intra-donor phylogenetic analysis. The major objective of intra-donor phylogenetic analysis is to bracket all phylogenetically comparable sequences on a Neighbor-Joining (NJ) tree using known neutralizing antibody sequences derived from the same donor. For the analysis here, we used all possible pairs of neutralizing antibody sequences derived from this donor PGDM1400-1412 (accession #: “type”:”entrez-nucleotide-range”,”attrs”:”text”:”KP006370-KP006382″,”start_term”:”KP006370″,”end_term”:”KP006382″,”start_term_id”:”724470918″,”end_term_id”:”724470942″KP006370-KP006382) and PGT141C145 (accession #: “type”:”entrez-nucleotide”,”attrs”:”text”:”JN201906.1″,”term_id”:”344323240″,”term_text”:”JN201906.1″JN201906.1-“type”:”entrez-nucleotide”,”attrs”:”text”:”JN201910.1″,”term_id”:”344323248″,”term_text”:”JN201910.1″JN201910.1). Intra-donor phylogenetic analysis works in comparable fashion to the cross-donor phylogenetic analysis described previously11. Briefly, the method begins by randomly shuffling all the sequences in a data set to remove any potential bias in the order of the sequences and enhances the convergence of the method. After sequence shuffling, the data set were split into FASTA.