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15 lines
1.3 KiB
Bash
Executable File
15 lines
1.3 KiB
Bash
Executable File
#!/bin/bash
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# Here, we're generating peptide binders, inspired by the work in Vazquez-Torres et al
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# We want to make a binder to a specific peptide sequence, which we know can adopt a roughly helical geometry
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# We therefore model the peptide as an ideal helix, docked into a grooved scaffold
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# We then noise the whole structure, but provide RFdiffusion with the sequence of the peptide
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# RFdiffusion can then predict the structure of the peptide, while designing an improved binder to it (by sampling around the groove scaffold topology)
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# In the command, we specify the output path and input pdb.
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# We then describe the protein with the contig input. The groove scaffold is 172 amino acids long, so we specify:
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# - 172-172/0 which is 172 residues followed by a chainbreak (between the scaffold and the peptide)
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# - 34-34 The peptide is 34 residues long
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# This contig will lead to the whole input pdb being noise by 10 steps (partial_T=10)
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# However, we provide the sequence of the peptide (the last 20 residues in the contig), with provide_seq=[172-205]. This is 0-indexed
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../scripts/run_inference.py inference.output_prefix=example_outputs/design_partialdiffusion_peptidewithsequence inference.input_pdb=input_pdbs/peptide_complex_ideal_helix.pdb 'contigmap.contigs=["172-172/0 34-34"]' diffuser.partial_T=10 inference.num_designs=10 'contigmap.provide_seq=[172-205]'
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