Research → Design protein binders.
(A) Design of proteins scaffolding immunogenic epitopes on RSV protein F (site II: PDB ID 3IXT chain P residues 254 to 277; site V: PDB ID 5TPN chain A residues 163 to 181). Comparisons of the RF hallucinated models to AF2 structure predictions from the design sequence are in fig. S9; here, because of space constraints, we show only the AF2 model (the two are very close in all cases). Here and in the following figures, we assess the extent of success in designing sequences that fold to structures harboring the desired motif through two metrics computed on the AF2 predictions: prediction confidence (AF pLDDT) and the accuracy of recapitulation of the original scaffolded motif (motif AF-RMSD). For RSV-F designs, these metrics are rsvf_ii_141 (85.0, 0.53 Å), rsvf_ii_158 (82.9, 0.51 Å), rsvf_ii_171 (88.4, 0.69 Å), rsvfv_hal_1 (82, 0.7 Å), rsvfv_hal_2 (88, 0.64 Å), and rsvfv_hal_3 (86, 0.65 Å). (B) Design of COVID-19 receptor trap based on ACE2 interface helix (PDB ID 6VW1 chain A residues 24 to 42). Design metrics: ace2_76 (89.1, 0.55 Å), ace2_1157 (80.4, 0.47 Å), and ace2_1007 (83.3, 0.57 Å). Colors: native protein scaffold, light yellow; native functional motif, orange; hallucinated scaffold, gray; hallucinated motif, purple; and binding partner, blue. See table S2 for additional metrics on each design. From doi: 10.1126/science.abn2100
- Wiki: De novo binder design with BindCraft by Martin Pačesa.
- https://colab.research.google.com/github/martinpacesa/BindCraft/blob/main/notebooks/BindCraft.ipynb
- https://github.com/martinpacesa/BindCraft by Martin Pačesa.
Binder design settings - Author: Martin Pačesa
Enter path where to save your designs. We recommend to save on Google Drive so that you can continue generating at any time.
Enter the name that should be prefixed to your binders (generally target name).
The path to the .pdb structure of your target. Can be experimental or AlphaFold2. Trim the structure as needed to reduce runtime.
Which chains of your PDB to target? Can be one or multiple, comma-separated.
What positions to target? Example: 1,2-10 or chain-specific: C101-111. If blank, pipeline selects automatically.
Minimum and maximum size of binders to design (e.g. 70,150).
How many binder designs passing filters do you require?
Path to previous target settings JSON (e.g. /content/drive/MyDrive/BindCraft/[binder_name].json). If empty, will generate a new one.
Advanced settings
Which binder design protocol to run? "Default" is recommended. "Beta-sheet" promotes more beta-sheeted proteins but needs more sampling. "Peptide" is optimized for helical peptide binders.
What prediction protocol to use? "Default" uses single sequence prediction. "HardTarget" improves prediction for difficult targets using initial guess, but may introduce bias.
What interface design method to use? "AlphaFold2" (default) generates interface with AF2. "MPNN" uses soluble MPNN to optimize interface.
What target template protocol to use? "Default" allows limited flexibility. "Masked" allows greater flexibility at both sidechain and backbone level.
Filters
Which filters for designs to use? "Default" are recommended. "Peptide" for peptide binders. "Relaxed" are more permissive but may reduce experimental success. "Peptide_Relaxed" is for non-helical peptides. "None" is for benchmarking only.
Run the binder design script
sbatch ./bindcraft.slurm --settings './settings_target/gp42-binder.json' --filters
'./settings_filters/default_filters.json' --advanced './settings_advanced/default_4stage_multimer.json'settings_target/PDL1.json
{
"design_path": "/content/",
"binder_name": "gp42-binder",
"starting_pdb": "gp42/gp42_clean.pdb",
"chains": "C",
"target_hotspot_residues": "C101-111",
"lengths": [
70,
150
],
"number_of_final_designs": 200
}settings_filters/default_filters.json
{
"MPNN_score": {
"threshold": null,
"higher": false
},
"MPNN_seq_recovery": {
"threshold": null,
"higher": false
},
"Average_pLDDT": {
"threshold": 0.8,
"higher": true
},
"1_pLDDT": {
"threshold": 0.8,
"higher": true
},
"2_pLDDT": {
"threshold": 0.8,
"higher": true
},
"3_pLDDT": {
"threshold": null,
"higher": true
},
"4_pLDDT": {
"threshold": null,
"higher": true
},
"5_pLDDT": {
"threshold": null,
"higher": true
},
"Average_pTM": {
"threshold": 0.55,
"higher": true
},
"1_pTM": {
"threshold": 0.55,
"higher": true
},
"2_pTM": {
"threshold": 0.55,
"higher": true
},
"3_pTM": {
"threshold": null,
"higher": true
},
"4_pTM": {
"threshold": null,
"higher": true
},
"5_pTM": {
"threshold": null,
"higher": true
},
"Average_i_pTM": {
"threshold": 0.5,
"higher": true
},
"1_i_pTM": {
"threshold": 0.5,
"higher": true
},
"2_i_pTM": {
"threshold": 0.5,
"higher": true
},
"3_i_pTM": {
"threshold": null,
"higher": true
},
"4_i_pTM": {
"threshold": null,
"higher": true
},
"5_i_pTM": {
"threshold": null,
"higher": true
},
"Average_pAE": {
"threshold": null,
"higher": false
},
"1_pAE": {
"threshold": null,
"higher": false
},
"2_pAE": {
"threshold": null,
"higher": false
},
"3_pAE": {
"threshold": null,
"higher": false
},
"4_pAE": {
"threshold": null,
"higher": false
},
"5_pAE": {
"threshold": null,
"higher": false
},
"Average_i_pAE": {
"threshold": 0.35,
"higher": false
},
"1_i_pAE": {
"threshold": 0.35,
"higher": false
},
"2_i_pAE": {
"threshold": 0.35,
"higher": false
},
"3_i_pAE": {
"threshold": null,
"higher": false
},
"4_i_pAE": {
"threshold": null,
"higher": false
},
"5_i_pAE": {
"threshold": null,
"higher": false
},
"Average_i_pLDDT": {
"threshold": null,
"higher": true
},
"1_i_pLDDT": {
"threshold": null,
"higher": true
},
"2_i_pLDDT": {
"threshold": null,
"higher": true
},
"3_i_pLDDT": {
"threshold": null,
"higher": true
},
"4_i_pLDDT": {
"threshold": null,
"higher": true
},
"5_i_pLDDT": {
"threshold": null,
"higher": true
},
"Average_ss_pLDDT": {
"threshold": null,
"higher": true
},
"1_ss_pLDDT": {
"threshold": null,
"higher": true
},
"2_ss_pLDDT": {
"threshold": null,
"higher": true
},
"3_ss_pLDDT": {
"threshold": null,
"higher": true
},
"4_ss_pLDDT": {
"threshold": null,
"higher": true
},
"5_ss_pLDDT": {
"threshold": null,
"higher": true
},
"Average_Unrelaxed_Clashes": {
"threshold": null,
"higher": false
},
"1_Unrelaxed_Clashes": {
"threshold": null,
"higher": false
},
"2_Unrelaxed_Clashes": {
"threshold": null,
"higher": false
},
"3_Unrelaxed_Clashes": {
"threshold": null,
"higher": false
},
"4_Unrelaxed_Clashes": {
"threshold": null,
"higher": false
},
"5_Unrelaxed_Clashes": {
"threshold": null,
"higher": false
},
"Average_Relaxed_Clashes": {
"threshold": null,
"higher": false
},
"1_Relaxed_Clashes": {
"threshold": null,
"higher": false
},
"2_Relaxed_Clashes": {
"threshold": null,
"higher": false
},
"3_Relaxed_Clashes": {
"threshold": null,
"higher": false
},
"4_Relaxed_Clashes": {
"threshold": null,
"higher": false
},
"5_Relaxed_Clashes": {
"threshold": null,
"higher": false
},
"Average_Binder_Energy_Score": {
"threshold": 0,
"higher": false
},
"1_Binder_Energy_Score": {
"threshold": 0,
"higher": false
},
"2_Binder_Energy_Score": {
"threshold": 0,
"higher": false
},
"3_Binder_Energy_Score": {
"threshold": null,
"higher": false
},
"4_Binder_Energy_Score": {
"threshold": null,
"higher": false
},
"5_Binder_Energy_Score": {
"threshold": null,
"higher": false
},
"Average_Surface_Hydrophobicity": {
"threshold": 0.35,
"higher": false
},
"1_Surface_Hydrophobicity": {
"threshold": 0.35,
"higher": false
},
"2_Surface_Hydrophobicity": {
"threshold": 0.35,
"higher": false
},
"3_Surface_Hydrophobicity": {
"threshold": null,
"higher": false
},
"4_Surface_Hydrophobicity": {
"threshold": null,
"higher": false
},
"5_Surface_Hydrophobicity": {
"threshold": null,
"higher": false
},
"Average_ShapeComplementarity": {
"threshold": 0.6,
"higher": true
},
"1_ShapeComplementarity": {
"threshold": 0.55,
"higher": true
},
"2_ShapeComplementarity": {
"threshold": 0.55,
"higher": true
},
"3_ShapeComplementarity": {
"threshold": null,
"higher": true
},
"4_ShapeComplementarity": {
"threshold": null,
"higher": true
},
"5_ShapeComplementarity": {
"threshold": null,
"higher": true
},
"Average_PackStat": {
"threshold": null,
"higher": true
},
"1_PackStat": {
"threshold": null,
"higher": true
},
"2_PackStat": {
"threshold": null,
"higher": true
},
"3_PackStat": {
"threshold": null,
"higher": true
},
"4_PackStat": {
"threshold": null,
"higher": true
},
"5_PackStat": {
"threshold": null,
"higher": true
},
"Average_dG": {
"threshold": 0,
"higher": false
},
"1_dG": {
"threshold": 0,
"higher": false
},
"2_dG": {
"threshold": 0,
"higher": false
},
"3_dG": {
"threshold": null,
"higher": false
},
"4_dG": {
"threshold": null,
"higher": false
},
"5_dG": {
"threshold": null,
"higher": false
},
"Average_dSASA": {
"threshold": 1,
"higher": true
},
"1_dSASA": {
"threshold": 1,
"higher": true
},
"2_dSASA": {
"threshold": 1,
"higher": true
},
"3_dSASA": {
"threshold": null,
"higher": true
},
"4_dSASA": {
"threshold": null,
"higher": true
},
"5_dSASA": {
"threshold": null,
"higher": true
},
"Average_dG/dSASA": {
"threshold": null,
"higher": false
},
"1_dG/dSASA": {
"threshold": null,
"higher": false
},
"2_dG/dSASA": {
"threshold": null,
"higher": false
},
"3_dG/dSASA": {
"threshold": null,
"higher": false
},
"4_dG/dSASA": {
"threshold": null,
"higher": false
},
"5_dG/dSASA": {
"threshold": null,
"higher": false
},
"Average_Interface_SASA_%": {
"threshold": null,
"higher": true
},
"1_Interface_SASA_%": {
"threshold": null,
"higher": true
},
"2_Interface_SASA_%": {
"threshold": null,
"higher": true
},
"3_Interface_SASA_%": {
"threshold": null,
"higher": true
},
"4_Interface_SASA_%": {
"threshold": null,
"higher": true
},
"5_Interface_SASA_%": {
"threshold": null,
"higher": true
},
"Average_Interface_Hydrophobicity": {
"threshold": null,
"higher": true
},
"1_Interface_Hydrophobicity": {
"threshold": null,
"higher": true
},
"2_Interface_Hydrophobicity": {
"threshold": null,
"higher": true
},
"3_Interface_Hydrophobicity": {
"threshold": null,
"higher": true
},
"4_Interface_Hydrophobicity": {
"threshold": null,
"higher": true
},
"5_Interface_Hydrophobicity": {
"threshold": null,
"higher": true
},
"Average_n_InterfaceResidues": {
"threshold": 7,
"higher": true
},
"1_n_InterfaceResidues": {
"threshold": 7,
"higher": true
},
"2_n_InterfaceResidues": {
"threshold": 7,
"higher": true
},
"3_n_InterfaceResidues": {
"threshold": null,
"higher": true
},
"4_n_InterfaceResidues": {
"threshold": null,
"higher": true
},
"5_n_InterfaceResidues": {
"threshold": null,
"higher": true
},
"Average_n_InterfaceHbonds": {
"threshold": 3,
"higher": true
},
"1_n_InterfaceHbonds": {
"threshold": 3,
"higher": true
},
"2_n_InterfaceHbonds": {
"threshold": 3,
"higher": true
},
"3_n_InterfaceHbonds": {
"threshold": null,
"higher": true
},
"4_n_InterfaceHbonds": {
"threshold": null,
"higher": true
},
"5_n_InterfaceHbonds": {
"threshold": null,
"higher": true
},
"Average_InterfaceHbondsPercentage": {
"threshold": null,
"higher": true
},
"1_InterfaceHbondsPercentage": {
"threshold": null,
"higher": true
},
"2_InterfaceHbondsPercentage": {
"threshold": null,
"higher": true
},
"3_InterfaceHbondsPercentage": {
"threshold": null,
"higher": true
},
"4_InterfaceHbondsPercentage": {
"threshold": null,
"higher": true
},
"5_InterfaceHbondsPercentage": {
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},
"Average_n_InterfaceUnsatHbonds": {
"threshold": 4,
"higher": false
},
"1_n_InterfaceUnsatHbonds": {
"threshold": 4,
"higher": false
},
"2_n_InterfaceUnsatHbonds": {
"threshold": 4,
"higher": false
},
"3_n_InterfaceUnsatHbonds": {
"threshold": null,
"higher": false
},
"4_n_InterfaceUnsatHbonds": {
"threshold": null,
"higher": false
},
"5_n_InterfaceUnsatHbonds": {
"threshold": null,
"higher": false
},
"Average_InterfaceUnsatHbondsPercentage": {
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"higher": false
},
"1_InterfaceUnsatHbondsPercentage": {
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"higher": false
},
"2_InterfaceUnsatHbondsPercentage": {
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},
"3_InterfaceUnsatHbondsPercentage": {
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},
"4_InterfaceUnsatHbondsPercentage": {
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"higher": false
},
"5_InterfaceUnsatHbondsPercentage": {
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"higher": true
},
"Average_Interface_Helix%": {
"threshold": null,
"higher": true
},
"1_Interface_Helix%": {
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"higher": true
},
"2_Interface_Helix%": {
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"higher": true
},
"3_Interface_Helix%": {
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"higher": true
},
"4_Interface_Helix%": {
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},
"5_Interface_Helix%": {
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},
"Average_Interface_BetaSheet%": {
"threshold": null,
"higher": true
},
"1_Interface_BetaSheet%": {
"threshold": null,
"higher": true
},
"2_Interface_BetaSheet%": {
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"higher": true
},
"3_Interface_BetaSheet%": {
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"higher": true
},
"4_Interface_BetaSheet%": {
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"higher": true
},
"5_Interface_BetaSheet%": {
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"higher": true
},
"Average_Interface_Loop%": {
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"higher": false
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"1_Interface_Loop%": {
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"2_Interface_Loop%": {
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"3_Interface_Loop%": {
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"4_Interface_Loop%": {
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},
"5_Interface_Loop%": {
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"Average_Binder_Helix%": {
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"higher": true
},
"1_Binder_Helix%": {
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"higher": true
},
"2_Binder_Helix%": {
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"3_Binder_Helix%": {
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"4_Binder_Helix%": {
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"5_Binder_Helix%": {
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"Average_Binder_BetaSheet%": {
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"1_Binder_BetaSheet%": {
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},
"2_Binder_BetaSheet%": {
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"3_Binder_BetaSheet%": {
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"4_Binder_BetaSheet%": {
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},
"5_Binder_BetaSheet%": {
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},
"Average_Binder_Loop%": {
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},
"1_Binder_Loop%": {
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},
"2_Binder_Loop%": {
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},
"3_Binder_Loop%": {
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},
"4_Binder_Loop%": {
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"5_Binder_Loop%": {
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"Average_InterfaceAAs": {
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"C": {
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"D": {
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"M": {
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"R": {
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"W": {
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"Y": {
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"1_InterfaceAAs": {
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"D": {
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"W": {
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"Y": {
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"2_InterfaceAAs": {
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"higher": false
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"C": {
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"higher": false
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"D": {
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"Q": {
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"W": {
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"Y": {
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},
"3_InterfaceAAs": {
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"C": {
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"higher": false
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"D": {
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"E": {
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"F": {
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"M": {
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"N": {
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"P": {
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"Q": {
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"higher": false
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"R": {
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"higher": false
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"S": {
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"T": {
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"V": {
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"W": {
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"Y": {
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},
"4_InterfaceAAs": {
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"higher": false
},
"C": {
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"higher": false
},
"D": {
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"E": {
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"F": {
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"G": {
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"H": {
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"I": {
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"K": {
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"L": {
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"M": {
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"N": {
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"P": {
"threshold": null,
"higher": false
},
"Q": {
"threshold": null,
"higher": false
},
"R": {
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},
"S": {
"threshold": null,
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},
"T": {
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"V": {
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"higher": false
},
"W": {
"threshold": null,
"higher": false
},
"Y": {
"threshold": null,
"higher": false
}
},
"5_InterfaceAAs": {
"A": {
"threshold": null,
"higher": false
},
"C": {
"threshold": null,
"higher": false
},
"D": {
"threshold": null,
"higher": false
},
"E": {
"threshold": null,
"higher": false
},
"F": {
"threshold": null,
"higher": false
},
"G": {
"threshold": null,
"higher": false
},
"H": {
"threshold": null,
"higher": false
},
"I": {
"threshold": null,
"higher": false
},
"K": {
"threshold": null,
"higher": false
},
"L": {
"threshold": null,
"higher": false
},
"M": {
"threshold": null,
"higher": false
},
"N": {
"threshold": null,
"higher": false
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"P": {
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"Q": {
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"higher": false
},
"R": {
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"higher": false
},
"S": {
"threshold": null,
"higher": false
},
"T": {
"threshold": null,
"higher": false
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"V": {
"threshold": null,
"higher": false
},
"W": {
"threshold": null,
"higher": false
},
"Y": {
"threshold": null,
"higher": false
}
},
"Average_Hotspot_RMSD": {
"threshold": 6,
"higher": false
},
"1_Hotspot_RMSD": {
"threshold": 6,
"higher": false
},
"2_Hotspot_RMSD": {
"threshold": 6,
"higher": false
},
"3_Hotspot_RMSD": {
"threshold": null,
"higher": false
},
"4_Hotspot_RMSD": {
"threshold": null,
"higher": false
},
"5_Hotspot_RMSD": {
"threshold": null,
"higher": false
},
"Average_Target_RMSD": {
"threshold": null,
"higher": false
},
"1_Target_RMSD": {
"threshold": null,
"higher": false
},
"2_Target_RMSD": {
"threshold": null,
"higher": false
},
"3_Target_RMSD": {
"threshold": null,
"higher": false
},
"4_Target_RMSD": {
"threshold": null,
"higher": false
},
"5_Target_RMSD": {
"threshold": null,
"higher": false
},
"Average_Binder_pLDDT": {
"threshold": 0.8,
"higher": true
},
"1_Binder_pLDDT": {
"threshold": 0.8,
"higher": true
},
"2_Binder_pLDDT": {
"threshold": 0.8,
"higher": true
},
"3_Binder_pLDDT": {
"threshold": 0.8,
"higher": true
},
"4_Binder_pLDDT": {
"threshold": 0.8,
"higher": true
},
"5_Binder_pLDDT": {
"threshold": 0.8,
"higher": true
},
"Average_Binder_pTM": {
"threshold": null,
"higher": true
},
"1_Binder_pTM": {
"threshold": null,
"higher": true
},
"2_Binder_pTM": {
"threshold": null,
"higher": true
},
"3_Binder_pTM": {
"threshold": null,
"higher": true
},
"4_Binder_pTM": {
"threshold": null,
"higher": true
},
"5_Binder_pTM": {
"threshold": null,
"higher": true
},
"Average_Binder_pAE": {
"threshold": null,
"higher": false
},
"1_Binder_pAE": {
"threshold": null,
"higher": false
},
"2_Binder_pAE": {
"threshold": null,
"higher": false
},
"3_Binder_pAE": {
"threshold": null,
"higher": false
},
"4_Binder_pAE": {
"threshold": null,
"higher": false
},
"5_Binder_pAE": {
"threshold": null,
"higher": false
},
"Average_Binder_RMSD": {
"threshold": 3.5,
"higher": false
},
"1_Binder_RMSD": {
"threshold": 3.5,
"higher": false
},
"2_Binder_RMSD": {
"threshold": 3.5,
"higher": false
},
"3_Binder_RMSD": {
"threshold": 3.5,
"higher": false
},
"4_Binder_RMSD": {
"threshold": 3.5,
"higher": false
},
"5_Binder_RMSD": {
"threshold": 3.5,
"higher": false
}
}settings_advanced/
# Choose advanced settings according to your design needs:
betasheet_4stage_multimer.json
betasheet_4stage_multimer_flexible.json
betasheet_4stage_multimer_flexible_hardtarget.json
betasheet_4stage_multimer_hardtarget.json
betasheet_4stage_multimer_mpnn.json
betasheet_4stage_multimer_mpnn_flexible.json
betasheet_4stage_multimer_mpnn_flexible_hardtarget.json
betasheet_4stage_multimer_mpnn_hardtarget.json
default_4stage_multimer.json
default_4stage_multimer_flexible.json
default_4stage_multimer_flexible_hardtarget.json
default_4stage_multimer_hardtarget.json
default_4stage_multimer_mpnn.json
default_4stage_multimer_mpnn_flexible.json
default_4stage_multimer_mpnn_flexible_hardtarget.json
default_4stage_multimer_mpnn_hardtarget.json
peptide_3stage_multimer.json
peptide_3stage_multimer_flexible.json
peptide_3stage_multimer_mpnn.json
peptide_3stage_multimer_mpnn_flexible.jsonParámetros generados por BindCraft
BindCraft, que está pensado para facilitar el diseño y evaluación de novo de protein binders, genera diversos parámetros como salida tras la ejecución de la pipeline. A continuación se explica brevemente qué miden y cuáles son los rangos óptimos esperados para esos parámetros, cuando se busca un buen binder basado en estructuras de hélices α o láminas β.
- dG - Energía libre de unión: mide la energía libre de unión (binding free energy), estimada con FoldX o Rosetta. Cuanto más negativa → mejor unión, o sea, más estable el complejo proteína - binder. Para binders buenos: dG < -5 kcal/mol y muy buenos: < -10 kcal/mol.
- dSASA - Área superficial enterrada al unirse: mide la diferencia de área superficial solvente accesible (SASA) entre las proteínas separadas y el complejo, o sea, cuánto área se entierra al formar la interfaz entre ambas proteínas. Cuanto más alto → mejor contacto entre las cadenas proteicas. Los buenos complejos: > 1000 Ų, y los muy buenos: 1500-2500 Ų. Interfaces helicoidales suelen tener menor dSASA que ricas en láminas β o loops.
- dG/dSASA - Eficiencia energética por área de contacto: mide la proporción entre la energía libre de unión y el área enterrada. Representa la eficiencia del contacto. Cuanto más negativo → mejor calidad del contacto por unidad de área. Valores ideales: < -0.02 kcal/mol/Ų. Malos: > -0.01.
- Interface_SASA_% - Porcentaje del SASA total en la interfaz: mide la proporción del SASA total del binder que participa en la unión. Cuanto más alto una mayor parte del binder interactúa con su proteína diana. Rango típico: 20-60 % dependiendo del tamaño del binder.
- pLDDT - Predicted Local Distance Difference Test (AlphaFold): mide la confianza por residuo en la estructura predicha por AlphaFold. 90-100: alta confianza, 70-90: confiable y < 70, baja confianza. Para diseñar buenos binders: media > 80-90.
- pTM - Predicted TM-score (AlphaFold): mide la confianza global en el plegamiento de la estructura. pTM > 0.7 → buena predicción, y > 0.85 → alta calidad estructural.
- i_pTM - Interface pTM: mide la confianza estructural de la interfaz entre binder y target. i_pTM > 0.6 → interfaz razonable y > 0.75 → buena interfaz.
- pAE - Predicted Alignment Error: mide el error esperado en la alineación entre pares de residuos. Bajo → mejor predicción estructural. Media global < 10 Å y para interfaces críticas: < 5 Å.
- i_pAE - Interfacial Predicted Alignment Error: mide la PAE centrado en la interfaz. i_pAE < 5 Å → interfaz confiable, e idealmente < 3 Å en hot-spots.
- i_pLDDT - pLDDT en la región de la interfaz: mide la confianza AlphaFold en los residuos que forman la interfaz. i_pLDDT > 85-90 → interfaz bien definida. i_pLDDT láminas β > 85 hélices α > 90.
- ss_pLDDT - pLDDT en elementos secundarios: mide la confianza estructural en regiones específicas (hélices, hojas β). Útil para analizar si los elementos estructurales deseados están bien plegados. ss_pLDDT > 80 en β-strands y > 85 en α-helices.
Versión extendida de este primer binder diseñado.