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<html>
<head>
<title></title>
<meta name="viewport" content="width=device-width, initial-scale=1">
<meta charset="UTF-8">
</head>
<body>
<div id='content'>
<h1 id="antifold">AntiFold</h1>
<p>Code for AntiFold paper, accepted for <a href="https://openreview.net/forum?id=bxZMKHtlL6">NeurIPS 2023 GenBio spotlight</a></p>
<p>Webserver: <a href="https://opig.stats.ox.ac.uk/webapps/AntiFold/">OPIG webserver</a></p>
<p>Code: <a href="https://opig.stats.ox.ac.uk/data/downloads/AntiFold/antifold_code.zip">antifold_code.zip</a></p>
<p>Model: <a href="https://opig.stats.ox.ac.uk/data/downloads/AntiFold/models/model.pt">model.pt</a></p>
<p>License: <a href="https://opig.stats.ox.ac.uk/data/downloads/AntiFold/LICENSE">BSD 3-Clause</a></p>
<h1 id="colab">Colab</h1>
<p>To test the method out without installing it you can try this: <a href="https://colab.research.google.com/drive/1TTfgjoZx3mzF5u4e9b4Un9Y7b_rqXc_4"><img src="images/colab-badge.svg" alt="Open In Colab" /></a></p>
<h2 id="install-and-run-antifold">Install and run AntiFold</h2>
<p>Use the <a href="https://opig.stats.ox.ac.uk/data/downloads/AntiFold/notebook.ipynb">Jupyter notebook</a> or follow this guide with example PDBs:</p>
<h3 id="download-antifold">Download AntiFold</h3>
<pre><code class="hljs bash language-bash"><span class="hljs-comment"># Download code</span>
<span class="hljs-built_in">mkdir</span> -p antifold_code && <span class="hljs-built_in">cd</span> antifold_code
wget https://opig.stats.ox.ac.uk/data/downloads/AntiFold/antifold_code.zip
unzip antifold_code.zip
<span class="hljs-comment"># Download model</span>
<span class="hljs-built_in">mkdir</span> -p models
wget -P models/ https://opig.stats.ox.ac.uk/data/downloads/AntiFold/models/model.pt
</code></pre>
<h3 id="install-antifold-cpu">Install AntiFold (CPU)</h3>
<pre><code class="hljs bash language-bash">conda create --name antifold python=3.10 -y
conda activate antifold
conda install -c pytorch pytorch
conda install -c pyg pyg -y
conda install -c conda-forge pip -y
<span class="hljs-comment"># Install AntiFold from antifold_code directory</span>
pip install .
pip install torch-geometric==2.4.0
</code></pre>
<h3 id="install-antifold-gpu">Install AntiFold (GPU)</h3>
<pre><code class="hljs bash language-bash">conda create --name antifold python=3.10 -y
conda activate antifold
conda install -c conda-forge pytorch-gpu
conda install -c pyg pyg -y
conda install -c conda-forge pip -y
<span class="hljs-comment"># Install AntiFold from antifold_code directory</span>
pip install .
pip install torch-geometric==2.4.0
</code></pre>
<h3 id="run-antifold-residue-probabilities-embeddings-sampled-sequences">Run AntiFold (residue probabilities, embeddings, sampled sequences)</h3>
<pre><code class="hljs bash language-bash"><span class="hljs-comment"># Residue probabilities, sample 10 sequences in CDRH3 from single PDB</span>
python antifold/main.py \
--out_dir output/single_pdb \
--pdb_file data/pdbs/6y1l_imgt.pdb \
--heavy_chain H \
--light_chain L \
--num_seq_per_target 10 \
--sampling_temp <span class="hljs-string">"0.2"</span> \
--regions <span class="hljs-string">"CDRH3"</span>
<span class="hljs-comment"># Residue probabilities, sample 10 sequences at temperatures 0.20 & 0.30 in CDRs 1-3 from folder of PDBs</span>
python antifold/main.py \
--out_dir output/example_pdbs \
--pdbs_csv data/example_pdbs.csv \
--pdb_dir data/pdbs \
--num_seq_per_target 10 \
--sampling_temp <span class="hljs-string">"0.20 0.30"</span> \
--regions <span class="hljs-string">"CDR1 CDR2 CDR3"</span>
<span class="hljs-comment"># Extract ESM-IF1 embeddings from PDBs in folder, from all chains listed in untested.csv (not limited to VH/VL)</span>
python antifold/main.py \
--out_dir output/untested/ \
--pdbs_csv data/untested.csv \
--pdb_dir data/untested/ \
--use_esm_if1_weights \
--custom_chain_mode \
--extract_embeddings
</code></pre>
<h2 id="input-parameters">Input parameters</h2>
<p>Required parameters:</p>
<pre><code class="hljs text language-text">- Option 1: PDB file (--pdb_file) + specified heavy and light chain (--heavy_chain and --light_chain)
- Option 2: PDB folder (--pdb_dir) + CSV file specifying chains (--pdbs_csv)
- Output directory (--out_dir) for output inverse folding probabilities CSV and optional generated sequences FASTA
</code></pre>
<p>Parameters for generating new sequences:</p>
<pre><code class="hljs text language-text">- Number of sequences to generate (--num_seq_per_target)
- Region to mutate (--region) based on inverse folding probabilities. Select from list in IMGT_dict (e.g. 'CDRH1 CDRH2 CDRH3')
- Sampling temperature (--sampling_temp) controls generated sequence diversity, by scaling the inverse folding probabilities before sampling. Temperature = 1 means no change, while temperature ~ 0 only samples the most likely amino-acid at each position (acts as argmax).
</code></pre>
<p>Optional parameters:</p>
<pre><code class="hljs text language-text">- Multi-chain mode for including antigen or other chains (--custom_chain_mode) - Nb, experimental (see example above)
- Extract latent representations of PDB within model (--extract_embeddings)
- Use ESM-IF1 instead of AntiFold model weights (--model_path "ESM-IF1")
</code></pre>
<h2 id="example-output">Example output</h2>
<p>Output CSV with residue log-probabilities: Residue probabilities: <a href="https://opig.stats.ox.ac.uk/data/downloads/AntiFold/output/example_pdbs/6y1l_imgt.csv">6y1l_imgt.csv</a></p>
<ul>
<li>pdb_pos - PDB residue number</li>
<li>pdb_chain - PDB chain</li>
<li>aa_orig - PDB residue (e.g. 112)</li>
<li>aa_pred - Top predicted residue by AntiFold (argmax) for this position</li>
<li>pdb_posins - PDB residue number with insertion code (e.g. 112A)</li>
<li>perplexity - Inverse folding tolerance (higher is more tolerant) to mutations. See paper for more details.</li>
<li>Amino-acids - Inverse folding scores (log-likelihood) for the given position</li>
</ul>
<pre><code class="hljs csv language-csv">pdb_pos,pdb_chain,aa_orig,aa_pred,pdb_posins,perplexity,A,C,D,E,F,G,H,I,K,L,M,N,P,Q,R,S,T,V,W,Y
2,H,V,M,2,1.6488,<span class="hljs-string">-4</span>.9963,<span class="hljs-string">-6</span>.6117,<span class="hljs-string">-6</span>.3181,<span class="hljs-string">-6</span>.3243,<span class="hljs-string">-6</span>.7570,<span class="hljs-string">-4</span>.2518,<span class="hljs-string">-6</span>.7514,<span class="hljs-string">-5</span>.2540,<span class="hljs-string">-6</span>.8067,<span class="hljs-string">-5</span>.8619,<span class="hljs-string">-0</span>.0904,<span class="hljs-string">-6</span>.5493,<span class="hljs-string">-4</span>.8639,<span class="hljs-string">-6</span>.6316,<span class="hljs-string">-6</span>.3084,<span class="hljs-string">-5</span>.1900,<span class="hljs-string">-5</span>.0988,<span class="hljs-string">-3</span>.7295,<span class="hljs-string">-8</span>.0480,<span class="hljs-string">-7</span>.3236
3,H,Q,Q,3,1.3889,<span class="hljs-string">-10</span>.5258,<span class="hljs-string">-12</span>.8463,<span class="hljs-string">-8</span>.4800,<span class="hljs-string">-4</span>.7630,<span class="hljs-string">-12</span>.9094,<span class="hljs-string">-11</span>.0924,<span class="hljs-string">-5</span>.6136,<span class="hljs-string">-10</span>.9870,<span class="hljs-string">-3</span>.1119,<span class="hljs-string">-8</span>.1113,<span class="hljs-string">-9</span>.4382,<span class="hljs-string">-6</span>.2246,<span class="hljs-string">-13</span>.3660,<span class="hljs-string">-0</span>.0701,<span class="hljs-string">-4</span>.9957,<span class="hljs-string">-10</span>.0301,<span class="hljs-string">-6</span>.8618,<span class="hljs-string">-7</span>.5810,<span class="hljs-string">-13</span>.6721,<span class="hljs-string">-11</span>.4157
4,H,L,L,4,1.0021,<span class="hljs-string">-13</span>.3581,<span class="hljs-string">-12</span>.6206,<span class="hljs-string">-17</span>.5484,<span class="hljs-string">-12</span>.4801,<span class="hljs-string">-9</span>.8792,<span class="hljs-string">-13</span>.6382,<span class="hljs-string">-14</span>.8609,<span class="hljs-string">-13</span>.9344,<span class="hljs-string">-16</span>.4080,<span class="hljs-string">-0</span>.0002,<span class="hljs-string">-9</span>.2727,<span class="hljs-string">-16</span>.6532,<span class="hljs-string">-14</span>.0476,<span class="hljs-string">-12</span>.5943,<span class="hljs-string">-15</span>.4559,<span class="hljs-string">-16</span>.9103,<span class="hljs-string">-17</span>.0809,<span class="hljs-string">-10</span>.5670,<span class="hljs-string">-13</span>.5334,<span class="hljs-string">-13</span>.4324
...
</code></pre>
<p>Output FASTA file with sampled sequences: <a href="https://opig.stats.ox.ac.uk/data/downloads/AntiFold/output/example_pdbs/6y1l_imgt.fasta">6y1l_imgt.fasta</a></p>
<ul>
<li>T: Temperature used for design</li>
<li>score: average log-odds of residues in the sampled region</li>
<li>global_score: average log-odds of all residues (IMGT positions 1-128)</li>
<li>regions: regions selected for design</li>
<li>seq_recovery: # mutations / total sequence length</li>
<li>mutations: # mutations from original PDB sequence</li>
</ul>
<pre><code class="hljs fasta language-fasta">>6y1l_imgt , <span class="hljs-attribute">score</span>=0.2934, <span class="hljs-attribute">global_score</span>=0.2934, regions=[<span class="hljs-string">'CDR1'</span>, <span class="hljs-string">'CDR2'</span>, <span class="hljs-string">'CDRH3'</span>], <span class="hljs-attribute">model_name</span>=AntiFold, <span class="hljs-attribute">seed</span>=42
VQLQESGPGLVKPSETLSLTCAVSGYSISSGYYWGWIRQPPGKGLEWIGSIYHSGSTYYN
PSLKSRVTISVDTSKNQFSLKLSSVTAADTAVYYCAGLTQSSHNDANWGQGTLVTVSS/V
LTQPPSVSAAPGQKVTISCSGSSSNIGNNYVSWYQQLPGTAPKRLIYDNNKRPSGIPDRF
SGSKSGTSATLGITGLQTGDEADYYCGTWDSSLNPVFGGGTKLEIKR
> <span class="hljs-attribute">T</span>=0.20, <span class="hljs-attribute">sample</span>=1, <span class="hljs-attribute">score</span>=0.3930, <span class="hljs-attribute">global_score</span>=0.1869, <span class="hljs-attribute">seq_recovery</span>=0.8983, <span class="hljs-attribute">mutations</span>=12
VQLQESGPGLVKPSETLSLTCAVSGASITSSYYWGWIRQPPGKGLEWIGSIYYSGSTYYN
PSLKSRVTISVDTSKNQFSLKLSSVTAADTAVYYCAGLYGSPWSNPYWGQGTLVTVSS/V
LTQPPSVSAAPGQKVTISCSGSSSNIGNNYVSWYQQLPGTAPKRLIYDNNKRPSGIPDRF
SGSKSGTSATLGITGLQTGDEADYYCGTWDSSLNPVFGGGTKLEIKR
<span class="hljs-built_in">..</span>.
</code></pre>
<h2 id="example-notebook">Example notebook</h2>
<p>Notebook: <a href="https://opig.stats.ox.ac.uk/data/downloads/AntiFold/notebook.ipynb">notebook.ipynb</a></p>
<pre><code class="hljs python language-python"><span class="hljs-keyword">import</span> pandas <span class="hljs-keyword">as</span> pd
<span class="hljs-comment"># Put IMGT numbered PDBs (Fv only, IMGT position 1-128) to process and load a CSV file with PDB names and heavy/light chains</span>
<span class="hljs-comment"># Define the PDB and chains in DataFrame</span>
pdb_dir = <span class="hljs-string">"data/pdbs"</span>
df_pdbs = pd.read_csv(<span class="hljs-string">"data/example_pdbs.csv"</span>)
<span class="hljs-comment"># Regions to mutate (IMGT)</span>
regions_to_mutate = [<span class="hljs-string">"CDR1"</span>, <span class="hljs-string">"CDR2"</span>, <span class="hljs-string">"CDR3H"</span>]
<span class="hljs-comment"># Load model</span>
<span class="hljs-keyword">import</span> antifold.main <span class="hljs-keyword">as</span> antifold
model = antifold.load_IF1_model(<span class="hljs-string">"models/model.pt"</span>)
<span class="hljs-comment"># Sample from PDBs, 10 sequences each at temperature 0.50 in regions CDR1, CDR2, CDR3H</span>
pdb_output_dict = antifold.sample_pdbs(
model,
pdbs_csv_or_dataframe=df_pdbs, <span class="hljs-comment"># Path to CSV file, or a DataFrame</span>
regions_to_mutate=regions_to_mutate,
pdb_dir=<span class="hljs-string">"data/pdbs"</span>,
sample_n=<span class="hljs-number">10</span>,
sampling_temp=<span class="hljs-number">0.50</span>,
limit_expected_variation=<span class="hljs-literal">False</span>
)
<span class="hljs-comment"># Output dictionary with sequences, and residue probabilities or log-odds</span>
pdbs = pdb_output_dict.keys()
<span class="hljs-comment"># Residue log probabilities</span>
df_logprobs = pdb_output_dict[<span class="hljs-string">"6y1l_imgt"</span>][<span class="hljs-string">"logprobs"</span>]
<span class="hljs-comment"># Sampled sequences</span>
fasta_dict = pdb_output_dict[<span class="hljs-string">"6y1l_imgt"</span>][<span class="hljs-string">"sequences"</span>]
</code></pre>
<h2 id="usage">Usage</h2>
<pre><code class="hljs bash language-bash">usage:
<span class="hljs-comment"># Predict on example PDBs in folder</span>
python antifold/main.py --pdbs_csv data/example_pdbs.csv --pdb_dir data/pdbs --out_dir output/
Predict inverse folding probabilities <span class="hljs-keyword">for</span> antibody variable domain, and sample sequences with maintained <span class="hljs-built_in">fold</span>.
PDB structures should be IMGT-numbered, paired heavy and light chain variable domains (positions 1-128).
For IMGT numbering PDBs use SAbDab or https://opig.stats.ox.ac.uk/webapps/sabdab-sabpred/sabpred/anarci/
options:
-h, --<span class="hljs-built_in">help</span> show this <span class="hljs-built_in">help</span> message and <span class="hljs-built_in">exit</span>
--pdb_file PDB_FILE Input PDB file (<span class="hljs-keyword">for</span> single PDB predictions)
--heavy_chain HEAVY_CHAIN
Ab heavy chain (<span class="hljs-keyword">for</span> single PDB predictions)
--light_chain LIGHT_CHAIN
Ab light chain (<span class="hljs-keyword">for</span> single PDB predictions)
--antigen_chain ANTIGEN_CHAIN
Antigen chain (experimental)
--pdbs_csv PDBS_CSV Input CSV file with PDB names and H/L chains (multi-PDB predictions)
--pdb_dir PDB_DIR Directory with input PDB files (multi-PDB predictions)
--out_dir OUT_DIR Output directory
--regions REGIONS Space-separated regions to mutate. Default <span class="hljs-string">'CDR1 CDR2 CDR3H'</span>
--num_seq_per_target NUM_SEQ_PER_TARGET
Number of sequences to sample from each antibody PDB (default 0)
--sampling_temp SAMPLING_TEMP
A string of temperatures e.g. <span class="hljs-string">'0.20 0.25 0.50'</span> (default 0.20). Sampling temperature <span class="hljs-keyword">for</span> amino acids. Suggested values 0.10, 0.15, 0.20, 0.25, 0.30. Higher values will lead to more diversity.
--limit_variation Limit variation to as many mutations as expected from temperature sampling
--extract_embeddings Extract per-residue embeddings from AntiFold / ESM-IF1
--custom_chain_mode Custom chain input (experimental, e.g. single chain, inclusion of antigen chain or any chains with ESM-IF1)
--exclude_heavy Exclude heavy chain from sampling
--exclude_light Exclude light chain from sampling
--batch_size BATCH_SIZE
Batch-size to use
--num_threads NUM_THREADS
Number of CPU threads to use <span class="hljs-keyword">for</span> parallel processing (0 = all available)
--seed SEED Seed <span class="hljs-keyword">for</span> reproducibility
--model_path MODEL_PATH
AntiFold model weights. See --use_esm_if1_weights flag to use ESM-IF1 weights instead of AntiFold
--use_esm_if1_weights
Use ESM-IF1 weights instead of AntiFold
--verbose VERBOSE Verbose printing
</code></pre>
<h2 id="imgt-regions-dict">IMGT regions dict</h2>
<p>Used to specify which regions to mutate in an IMGT numbered PDB</p>
<ul>
<li>IMGT numbered PDBs: <a href="https://opig.stats.ox.ac.uk/webapps/sabdab-sabpred/sabdab">https://opig.stats.ox.ac.uk/webapps/sabdab-sabpred/sabdab</a></li>
<li>Renumber existing PDBs with ANARCI: <a href="https://github.com/oxpig/ANARCI">https://github.com/oxpig/ANARCI</a></li>
<li>Read more: <a href="https://www.imgt.org/IMGTScientificChart/Numbering/IMGTIGVLsuperfamily.html">https://www.imgt.org/IMGTScientificChart/Numbering/IMGTIGVLsuperfamily.html</a></li>
</ul>
<pre><code class="hljs python language-python">IMGT_dict = {
<span class="hljs-string">"all"</span>: <span class="hljs-built_in">range</span>(<span class="hljs-number">1</span>, <span class="hljs-number">128</span> + <span class="hljs-number">1</span>),
<span class="hljs-string">"allH"</span>: <span class="hljs-built_in">range</span>(<span class="hljs-number">1</span>, <span class="hljs-number">128</span> + <span class="hljs-number">1</span>),
<span class="hljs-string">"allL"</span>: <span class="hljs-built_in">range</span>(<span class="hljs-number">1</span>, <span class="hljs-number">128</span> + <span class="hljs-number">1</span>),
<span class="hljs-string">"FWH"</span>: <span class="hljs-built_in">list</span>(<span class="hljs-built_in">range</span>(<span class="hljs-number">1</span>, <span class="hljs-number">26</span> + <span class="hljs-number">1</span>)) + <span class="hljs-built_in">list</span>(<span class="hljs-built_in">range</span>(<span class="hljs-number">40</span>, <span class="hljs-number">55</span> + <span class="hljs-number">1</span>)) + <span class="hljs-built_in">list</span>(<span class="hljs-built_in">range</span>(<span class="hljs-number">66</span>, <span class="hljs-number">104</span> + <span class="hljs-number">1</span>)),
<span class="hljs-string">"FWL"</span>: <span class="hljs-built_in">list</span>(<span class="hljs-built_in">range</span>(<span class="hljs-number">1</span>, <span class="hljs-number">26</span> + <span class="hljs-number">1</span>)) + <span class="hljs-built_in">list</span>(<span class="hljs-built_in">range</span>(<span class="hljs-number">40</span>, <span class="hljs-number">55</span> + <span class="hljs-number">1</span>)) + <span class="hljs-built_in">list</span>(<span class="hljs-built_in">range</span>(<span class="hljs-number">66</span>, <span class="hljs-number">104</span> + <span class="hljs-number">1</span>)),
<span class="hljs-string">"CDRH"</span>: <span class="hljs-built_in">list</span>(<span class="hljs-built_in">range</span>(<span class="hljs-number">27</span>, <span class="hljs-number">39</span>)) + <span class="hljs-built_in">list</span>(<span class="hljs-built_in">range</span>(<span class="hljs-number">56</span>, <span class="hljs-number">65</span> + <span class="hljs-number">1</span>)) + <span class="hljs-built_in">list</span>(<span class="hljs-built_in">range</span>(<span class="hljs-number">105</span>, <span class="hljs-number">117</span> + <span class="hljs-number">1</span>)),
<span class="hljs-string">"CDRL"</span>: <span class="hljs-built_in">list</span>(<span class="hljs-built_in">range</span>(<span class="hljs-number">27</span>, <span class="hljs-number">39</span>)) + <span class="hljs-built_in">list</span>(<span class="hljs-built_in">range</span>(<span class="hljs-number">56</span>, <span class="hljs-number">65</span> + <span class="hljs-number">1</span>)) + <span class="hljs-built_in">list</span>(<span class="hljs-built_in">range</span>(<span class="hljs-number">105</span>, <span class="hljs-number">117</span> + <span class="hljs-number">1</span>)),
<span class="hljs-string">"FW1"</span>: <span class="hljs-built_in">range</span>(<span class="hljs-number">1</span>, <span class="hljs-number">26</span> + <span class="hljs-number">1</span>),
<span class="hljs-string">"FWH1"</span>: <span class="hljs-built_in">range</span>(<span class="hljs-number">1</span>, <span class="hljs-number">26</span> + <span class="hljs-number">1</span>),
<span class="hljs-string">"FWL1"</span>: <span class="hljs-built_in">range</span>(<span class="hljs-number">1</span>, <span class="hljs-number">26</span> + <span class="hljs-number">1</span>),
<span class="hljs-string">"CDR1"</span>: <span class="hljs-built_in">range</span>(<span class="hljs-number">27</span>, <span class="hljs-number">39</span>),
<span class="hljs-string">"CDRH1"</span>: <span class="hljs-built_in">range</span>(<span class="hljs-number">27</span>, <span class="hljs-number">39</span>),
<span class="hljs-string">"CDRL1"</span>: <span class="hljs-built_in">range</span>(<span class="hljs-number">27</span>, <span class="hljs-number">39</span>),
<span class="hljs-string">"FW2"</span>: <span class="hljs-built_in">range</span>(<span class="hljs-number">40</span>, <span class="hljs-number">55</span> + <span class="hljs-number">1</span>),
<span class="hljs-string">"FWH2"</span>: <span class="hljs-built_in">range</span>(<span class="hljs-number">40</span>, <span class="hljs-number">55</span> + <span class="hljs-number">1</span>),
<span class="hljs-string">"FWL2"</span>: <span class="hljs-built_in">range</span>(<span class="hljs-number">40</span>, <span class="hljs-number">55</span> + <span class="hljs-number">1</span>),
<span class="hljs-string">"CDR2"</span>: <span class="hljs-built_in">range</span>(<span class="hljs-number">56</span>, <span class="hljs-number">65</span> + <span class="hljs-number">1</span>),
<span class="hljs-string">"CDRH2"</span>: <span class="hljs-built_in">range</span>(<span class="hljs-number">56</span>, <span class="hljs-number">65</span> + <span class="hljs-number">1</span>),
<span class="hljs-string">"CDRL2"</span>: <span class="hljs-built_in">range</span>(<span class="hljs-number">56</span>, <span class="hljs-number">65</span> + <span class="hljs-number">1</span>),
<span class="hljs-string">"FW3"</span>: <span class="hljs-built_in">range</span>(<span class="hljs-number">66</span>, <span class="hljs-number">104</span> + <span class="hljs-number">1</span>),
<span class="hljs-string">"FWH3"</span>: <span class="hljs-built_in">range</span>(<span class="hljs-number">66</span>, <span class="hljs-number">104</span> + <span class="hljs-number">1</span>),
<span class="hljs-string">"FWL3"</span>: <span class="hljs-built_in">range</span>(<span class="hljs-number">66</span>, <span class="hljs-number">104</span> + <span class="hljs-number">1</span>),
<span class="hljs-string">"CDR3"</span>: <span class="hljs-built_in">range</span>(<span class="hljs-number">105</span>, <span class="hljs-number">117</span> + <span class="hljs-number">1</span>),
<span class="hljs-string">"CDRH3"</span>: <span class="hljs-built_in">range</span>(<span class="hljs-number">105</span>, <span class="hljs-number">117</span> + <span class="hljs-number">1</span>),
<span class="hljs-string">"CDRL3"</span>: <span class="hljs-built_in">range</span>(<span class="hljs-number">105</span>, <span class="hljs-number">117</span> + <span class="hljs-number">1</span>),
<span class="hljs-string">"FW4"</span>: <span class="hljs-built_in">range</span>(<span class="hljs-number">118</span>, <span class="hljs-number">128</span> + <span class="hljs-number">1</span>),
<span class="hljs-string">"FWH4"</span>: <span class="hljs-built_in">range</span>(<span class="hljs-number">118</span>, <span class="hljs-number">128</span> + <span class="hljs-number">1</span>),
<span class="hljs-string">"FWL4"</span>: <span class="hljs-built_in">range</span>(<span class="hljs-number">118</span>, <span class="hljs-number">128</span> + <span class="hljs-number">1</span>),
}
</code></pre>
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