DOI: 10.5061/dryad.wstqjq2wk
title: The predicted and experimental peptide binding information (PEPBI) database described in: A paired database of predicted and experimental protein peptide binding information

description: Peptides are important biomolecules and their binding interactions with
 proteins make them useful in a variety of sensing and therapeutic
 applications. The development of computational methods to design peptides
 can benefit from having high-quality structures of peptide-protein
 complexes matched with experimental measurements of their thermodynamic
 properties. The Predicted and Experimental Peptide Binding Information
 (PEPBI) database contains 329 predicted peptide-protein complexes, with
 each complex based on an experimentally determined structure, and
 corresponding experimental measurements of changes in Gibbs free energy
 (DG), enthalpy (DH), and entropy (DS). In addition, PEPBI includes 40
 predicted properties for each complex that were computationally calculated
 using the Rosetta Interface Analyzer. PEPBI is expected to be of use in
 the development of computational methods for designing peptides that bind
 to protein targets.
description: This database has an associated publication and the methods of
 its generation are described in comprehensive detail there. Briefly, PEPBI
 was curated using a five-step process. The first step was the definition
 of the criteria for complexes to be included. In the next step, a
 literature review was conducted to identify the peptide-protein complexes
 that should be part of PEPBI. Once they were known, complexes that exactly
 match the proteins and peptides used in the ITC experiments were
 computationally predicted. This was followed by calculating properties of
 those complexes using the Rosetta Interface Analyzer. Ultimately, this
 yielded the PEPBI database of 329 peptide-protein complexes with matched
 thermodynamic data.
description: # Predicted and Experimental Peptide Binding Information (PEPBI) Database
 --- [Access this dataset on
 Dryad](https://doi.org/10.5061/dryad.wstqjq2wk) This dataset is the
 publicly available data associated with *A Paired Database of Predicted
 and Experimental Protein Peptide Binding Information*, submitted to
 *Scientific Data* and hereafter referred to as the PEPBI manuscript. The
 Predicted and Experimental Peptide Binding Information (PEPBI) dataset
 provides curated thermodynamic and structural data for protein–peptide
 complexes. It includes: * **Experimental Data**: Isothermal titration
 calorimetry (ITC) data collected through an extensive literature review,
 compiled in the `PEPBI.xlsx` Excel spreadsheet. * **Computational Data**:
 Computationally predicted protein–peptide structures in `.pdb` format,
 each derived from a shared reference structure (template). These templates
 are experimentally determined structures from the Protein Data Bank (PDB)
 and are used as the foundation for generating computational models of
 mutated variants when experimental structures for those variants are not
 available. Variants generated from the same template are grouped together
 and referred to as a binding group. All predicted structures were analyzed
 using Rosetta’s Interface Analyzer (RIA), and the resulting binding
 interface metrics are compiled in the PEPBI.xlsx spreadsheet. Structural
 modifications used to generate the models are documented in accompanying
 .docx files. * **Technical Validation Data**: Binding energy distribution
 data from external protein–peptide and antibody–antigen benchmarks used to
 evaluate PEPBI predictions. These are compiled in the `Technical
 Validation.csv` spreadsheet. * **Utility Script**: A Python script,
 `PEPBI.py`, that enables users to convert the dataset into a `.csv` file
 or load it directly as a pandas DataFrame for analysis. This dataset is
 intended to support research in protein–peptide interaction prediction,
 computational modeling, and thermodynamic characterization. --- ##
 Description of the Data and File Structure The PEPBI database consists of
 **A**. an excel spreadsheet containing sequence information, experimental
 thermodynamic measurements, and computationally predicted binding
 interaction metrics, **B**. computationally predicted structural models,
 **C**. an excel spreadsheet containing technical validation data, and
 **D**. supporting documentation. Below is a description of each component
 in the repository, along with an overview of how the data is organized.
 ### 1. PEPBI Spreadsheet (`PEPBI.xlsx`) This Excel file contains curated
 data for the protein-peptide complexes included in the PEPBI database.
 Each row represents a unique complex. To aid interpretation, the contents
 of the spreadsheet are organized into three general classifications:
 Sequence Information, Experimental Data, and Computational Data. These
 classifications serve as descriptive groupings to help users navigate the
 file structure and are not explicit headers in the spreadsheet itself.
 This section is organized as follows: 1. First-level bullet points
 represent the three broad classifications. 2. Second-level bullet points
 describe logical groupings of related data (e.g., Protein–Peptide Complex
 Identifiers, Protein Sequence Information, Peptide Sequence Information,
 etc.), which correspond to the first row of headers within the
 spreadsheet. 3. Third-level bullet points list the individual column names
 found under each grouping. 4. Any additional indentation provides
 supporting information such as formatting details or naming conventions
 related to column entries. * **Sequence Information**: Descriptive
 information for each protein–peptide complex, including identifiers,
 sequence-level details, and information about its relationship to the
 reference structure. This metadata helps users interpret the data by
 specifying what each complex is and how it connects to other entries in
 the database. The following columns are presented in the order they appear
 in the spreadsheet: * **Protein–Peptide Complex Identifiers**: * **Binding
 Group**: A set of protein–peptide complexes derived from the same template
 structure, each representing a variant of that reference. * **Naming
 Convention** * `Protein - Peptide`, where: * `Protein` = protein name *
 `Peptide` = peptide name * **Example**: Fyn SH3 - P2L * **PEPBI Complex
 Name**: A unique identifier assigned to each complex in the dataset. These
 names correspond directly to the filenames of the computationally
 predicted `.pdb` structures. The naming convention uses the protein and
 peptide names (often abbreviated) separated by underscores, followed by an
 index used to distinguish each variant within the same binding group. *
 **Naming Convention** * `protein_peptide_#`, where: * `protein` = protein
 name * `peptide` = peptide name * `#` = index * **Example**: fyn_p2l_1 *
 **PDB ID**: The accession code for each entry's template structure in
 the Protein Data Bank (PDB). * **Crystallographic Unit**: The number of
 protein/peptide copies present in the crystal structure. Each
 crystallographic unit is labeled with `A` for the protein and `B` for the
 peptide, each followed by the number of copies present in the structure. *
 **Example**: A1B1 * **Unit Copy**: Indicates which instance of the
 crystallographic unit the structure represents when multiple versions of
 the same unit are available. Entries are labeled `C#` for each alternative
 copy (e.g., `C1`, `C2`), or `X` if no alternative copies exist. *
 **Protein Sequence Information**: * **Protein Name**: The name of the
 protein as reported in the source manuscript. * **Protein Sequence**: The
 protein’s amino acid sequence using one-letter code. * **Protein Length**:
 The total number of amino acids in the protein sequence. * **Protein
 Tag**: Any experimental tags (e.g., `His-tag`, `GST-tag`) that were
 present on the protein during the ITC experiments. * **Peptide Sequence
 Information**: * **Peptide Name**: The name of the peptide as reported in
 the source manuscript. * **Peptide Sequence**: The peptide's amino
 acid sequence using one-letter code. * **Peptide Length**: The total
 number of amino acids in the peptide sequence. * **Peptide Motif**: A
 recurring peptide sequence important for binding as reported in the source
 manuscript. * **Protein-Peptide Complex Variations**: * **Change from
 Binding Group Reference Complex**: General classifications of how each
 complex differs from its template structure. These classifications may
 appear individually or in combination when there are multiple changes
 needed. If no changes were made, the entry is marked with an `X`. Possible
 'change' classifications include: * `ITC Temperature (T)` *
 `Peptide Sequence` * `Protein Sequence` * `Binding Site` * **Binding
 Site**: Indicates which binding site the peptide occupies when the protein
 has multiple known binding sites. Entries are labeled as `Site 1`, `Site
 2`, etc. If only one binding site is known or applicable, the entry is
 marked as `X`. Documentation explaining the distinction between binding
 sites is provided in a later section (*see 2. Computational Structures
 (`Binding Group Structures.zip`)*). * **Protein Mutation(s)**: Describes
 mutations/modifications made to the protein sequence relative to the
 template structure. * **Single-site mutation**: `A5L` (alanine at position
 5 mutated to leucine) * **Insertion**: `ins940QEPE` (insertion of
 "QEPE" beginning at position 940) * **Single deletion**:
 `T20del` (threonine at position 20 deleted) * **Range deletion**:
 `F32_A34del` (residues from position 32 to 34 deleted) * **Multiple
 modifications**: Listed and separated by commas (e.g., `A5L, T20del`) *
 **Peptide Mutation(s)**: Describes mutations made to the peptide sequence
 using the same annotation format as for protein mutations. *
 **Experimental Data**: Thermodynamic parameters obtained from isothermal
 titration calorimetry (ITC) experiments, compiled through an extensive
 literature review. All values have been converted to consistent units for
 comparability across entries. The following columns appear in the order
 presented in the spreadsheet: * **Experimental Isothermal Titration
 Calorimetry (ITC) Data**: * **ΔG (kcal/mol)**: Gibbs free energy * **ΔG SD
 (kcal/mol)**: Standard deviation of ΔG * **KD (M)**: Dissociation constant
 * **KD SD (M)**: Standard deviation of KD * **ΔH (kcal/mol)**: Enthalpy *
 **ΔH SD (kcal/mol)**: Standard deviation of ΔH * **TΔS (kcal/mol)**:
 Temperature-scaled entropy contribution * **TΔS SD (kcal/mol)**: Standard
 deviation of TΔS * **ΔS (kcal/mol·K)**: Entropy * **ΔS SD (kcal/mol·K)**:
 Standard deviation of ΔS * **N**: Stoichiometry * **N SD**: Standard
 deviation of N * **T (K)**: Temperature in Kelvin * **Thermodynamic
 Calculations from ITC Data**: These values were calculated from
 experimentally reported ITC measurements using standard thermodynamic
 equations. * **Calculated ΔG (kcal/mol)**: Gibbs free energy, calculated
 as:\ ΔG = –RTln(1/KD)\ where R = 0.001987 kcal/mol·K * **Calculated TΔS
 (kcal/mol)**: Temperature-scaled entropy contribution, calculated as:\ TΔS
 = ΔH – ΔG * **Calculated ΔS (kcal/mol·K)**: Entropy change, calculated
 as:\ ΔS = TΔS/T * **Computational Data**: Includes 40 binding interface
 metrics generated using Rosetta’s Interface Analyzer (RIA). The following
 columns are direct outputs from RIA and are presented in the order they
 appear in the spreadsheet. Descriptions of these metrics can be found in
 **Table 2: The RIA-calculated Properties** of the corresponding PEPBI
 manuscript (can also be found in Supplemental Information Zenodo link). *
 **Computational Rosetta Interface Analyzer (RIA) Data**: * total_score *
 complex_normalized * dG_cross * dG_cross/dSAASAx100 * dG_separated *
 dG_separated/dSASAx100 * dSASA_hphobic * dSASA_int * dSASA_polar *
 delta_unsatHbonds * dslf_fa13 * fa_atr * fa_dun * fa_elec * fa_intra_rep *
 fa_intra_sol_xover4 * fa_rep * fa_sol * hbond_E_fraction * hbond_bb_sc *
 hbond_lr_bb * hbond_sc * hbond_sr_bb * hbonds_int * lk_ball_wtd * nres_all
 * nres_int * omega * p_aa_pp * packstat * per_residue_energy_int *
 pro_close * rama_prepro * ref * sc_value * side1_normalized * side1_score
 * side2_normalized * side2_score * yhh_planarity --- ### 2. Computational
 Structures (`Binding Group Structures.zip`) This zip folder contains 32
 subfolders, each corresponding to a Binding Group in PEPBI as listed in
 **Table 1: PEPBI Database Summary**. In cases where a binding group
 contains multiple crystallographic units, additional subfolders are
 included and labeled according to the corresponding crystallographic unit.
 Each of these subfolders contains the appropriate .pdb files specific to
 that unit. Additionally, each binding group subfolder contains a single
 .docx file that documents the modifications made to generate each
 computational model from its corresponding template structure. When
 crystallographic unit subfolders are present, this .docx file remains in
 the main binding group folder rather than within the individual unit
 folders. * **Binding Group Subfolders**: Folder names correspond to the 32
 binding groups listed in the ‘Binding Group’ column of the `PEPBI.xlsx`
 spreadsheet and detailed in *Table 1: PEPBI Database Summary*. *
 **Crystallographic Unit Subfolders**: When present, these folders
 correspond to the ‘Crystallographic Unit’ column of the `PEPBI.xlsx`
 spreadsheet. * **Structure Files**: `.pdb` file names match the entries in
 the ‘PEPBI Complex Name’ column of the `PEPBI.xlsx` spreadsheet. *
 **Structure Generation Documents**: `.pdf` file names match those of the
 'Binding Group' column of the `PEPBI.xlsx` spreadsheet. Each
 `.pdf` file includes: * A visual representation of the template
 structure(s) * Annotated protein and peptide sequences of the template
 structure(s), including position numbers * A list of all modifications
 made for each complex (e.g., mutations, insertions, deletions) *
 Highlighted sequences showing the locations of modifications --- ### 3.
 Technical Validation Data (`Technical Validation.csv`) The binding energy
 data used to validate our computational predictions is provided in the
 `Technical Validation.csv` file. This dataset corresponds to the binding
 energy distributions shown in **Figure 3**. This CSV contains a
 consolidated set of binding energy values from multiple sources: * **PEPBI
 Experimental**: Calculated ΔG values (in kcal/mol) from the `Calculated ΔG
 (kcal/mol)` column in `PEPBI.xlsx`, derived from experimental
 measurements. * **PEPBI**: Predicted binding energies (in kcal/mol) using
 the `dG_separated` metric from Rosetta’s Interface Analyzer (RIA), as
 provided in the `dG_separated` column of `PEPBI.xlsx`. * **AB/AG**:
 Predicted binding energies (in kcal/mol) for mutated antigen–antibody
 complexes, using the `Mut_IE` metric (equivalent to `dG_separated` from
 RIA) sourced from
 [https://doi.org/10.1080/19420862.2024.2440586](https://doi.org/10.1080/19420862.2024.2440586). * **MM/PBSA**: Binding energies reported as ΔG values (in kcal/mol), converted from the original kJ/mol units, sourced from [https://pubs.acs.org/doi/10.1021/acs.jcim.3c00752](https://pubs.acs.org/doi/10.1021/acs.jcim.3c00752). &gt; **Note:** This CSV includes only the consolidated binding energy values used for Figure 3. It does not include the accompanying metadata (e.g., PDB identifiers, sequences, mutation details) that were originally reported in the source publications from which the binding energy data were obtained. The data values are listed in the same order as presented in the original sources to preserve consistency. --- ### 4. PEPBI Utility Script (`PEPBI.py`) `PEPBI.py` is a Python script designed to assist users in accessing and working with the PEPBI dataset by converting it from an Excel `.xlsx` format into a `.csv` file or loading it directly as a pandas DataFrame for further analysis. The script performs the following tasks: 1. **Loading the Spreadsheet into a DataFrame**: The script loads the `PEPBI.xlsx` file into a pandas DataFrame, skipping the first row (which contains merged group labels) and using the second row for column headers. 2. **Data Preview and Information**: After loading the data into the DataFrame, the script previews the first 10 rows and displays the number of rows and columns in the dataset. 3. **Symbol Replacement**: It replaces certain symbols (Δ, α, and β) in the column names and data with their corresponding letters (d, a, and b) for consistency and compatibility with CSV formatting. 4. **Saving to CSV**: Finally, the modified DataFrame is saved as a `.csv` file. The script only requires the `pandas` library and provides a simple way to prepare and save the dataset for analysis. It helps users easily access and manipulate the PEPBI dataset.