pocketz.doi.bio/2026-01-16_063415_marananomeai_tools

MARA

URL: https://mara.nanome.ai/tools

Saved: 2026-01-16T14:34:15.221Z

NanomeAI Powered by MARA New Chat History Tools Workflows Workspaces Login via Device Code sness23 0 / 50Credits Remaining BUY MORE CREDITS version v0.19.2-2025-11-14 Tools What's New? 9 Request Tutorial Create All Secrets All categories All Custom Nanome Disabled Align a reference polymer structure to a set of mobile structures from PDB or mmCIF Files Input: Reference PDB/mmCIF file and list of mobile PDB/mmCIF files. Output: List of aligned PDB/mmCIF files with RMSD values. Uses CEAlign to align mobile structures to the reference. Align small molecules from SDF File Input: Reference and mobile SDF files. Output: SDF file with aligned molecules. Uses RDKit CrippenO3A for alignment. Apply rotamer library to a residue from a PDB or mmCIF File Input: PDB/mmCIF file and residue selection [chain, number]. Output: Multi-model PDB/mmCIF file. Applies Dunbrack rotamers to specified residue. Apply SMARTS pattern to a List of SMILES and draw an image with matching atoms highlighted Input: List of SMILES and SMARTS pattern. Output: List of PNG image files with highlighted matches. Boltz write single protein yaml, simple Python Simple tool that reads a .fasta or .fa file. Creates a new file for each sequence in the fasta file. Boltz write two proteins yaml, simple Python Simple tool that reads two .fasta or .fa files and writes an unconstrained prediction. Calculate Chemical Descriptors from a list of SMILES using RDKit Input: SMILES list. Output: List of Dictionary of Chemical Descriptors. Returns chemical descriptors (aka chemical properties) including MW, LogP, TPSA, HBA, HBD, RB, QED. Calculate Tanimoto Similarity between molecules of an SDF File Input: Two SDF files. Output: List of float values representing Tanimoto similarities. Compares the first molecule of the reference SDF against all molecules in the other SDF. Calculate Tanimoto Similarity between two SMILES Input: Two SMILES. Output: Float value representing Tanimoto similarity between the compounds. One conformer is generated for each SMILES. Check if two molecules are enantiomers from two SMILES Input: Two SMILES. Output: Boolean indicating if they are enantiomers. Check Organization Members Online Status Lists current active users in the organization, including their workspace IDs and names. Useful for checking if your teammates are online in Nanome v2. Clean up PDB Python This tool should take in a PDB that fails to load in Nanome and fix it so it can be opened in Nanome. Cluster aligned sequences based on similarity Python Using an intermediate result from a multiple sequence alignment, cluster the sequences based on their similarity and generate a dendrogram.

Input

intermediatepklfile - file path to the pickle file created from the "Multiple sequence alignment and visualization" tool

Output

• A dendrogram showing the clustering of the sequences Combine PDBQT and Multi-pose SDF Python This tool combines a docking input file and the resulting poses. Combine structures of a protein PDB and multiple ligands in SDF Python Input:
• A PDB file as the protein target (This should be the protein-only PDB file)
• If a PDBQT file exists, use this over any PDB available.
• A SDF file that contains multiple structures (This should come from the docking result)

Output:

• A multi-frame PDB file that contains the protein structure and all the ligands

Warning: This tool will lose information, SDF comments and bond order/aromaticity will be lost Warning 2: This tool can produce non-standard PDB files where different ligands are written in PDB models

Description: This tool takes a protein PDB file and a SDF file that contains multiple ligands as input. It then combines the protein structure with all the ligands to create a multi-frame PDB file. The user can browse through the different frames of the PDB file to see the protein structure and all the ligands in the same coordinate space. This tool is useful for visualizing the docking results and understanding the interactions between the protein and the ligands. It should be the final step of a docking workflow. Compute docking box size from a list of residues and a PDB or mmCIF File Input: PDB/mmCIF file and residue list [(chain, number)]. Output: Box corner coordinates. Computes oriented bounding box around specified residues. Compute drug likeness filters from a list of SMILES Input: List of SMILES. Output: List of dictionaries containing filter results. Keys of a dictionary are Ro5 (Rule of 5), Ro4 (Rule of 4), Compute non-covalent interactions between a protonated protein and ligands Input: PDB or mmCIF File and SDF File. Output: List of dictionaries containing non-covalent interactions. Computes non-covalent bonds between protonated protein and ligands. Only the first model of the protein file is used, and only hydrogen bonds are currently computed. Compute QEPPI from a list of SMILES Input: SMILES list. Output: List of QEPPI scores. Compute shape and electrostatic potential similarities for a list of SMILES Input: Query SMILES and list of comparison SMILES. Output: Dictionary of shape and electrostatic potential similarities for each molecule. Compute shape and electrostatic potential similarities for a list of SMILES using a maximum common substructure MCS Input: Query SMILES, MCS SMARTS pattern, and list of comparison SMILES strings. Output: Dictionary of shape and electrostatic potential similarities for each molecule. Compute shape and electrostatic potential similarities from a SDF File Input: SDF file and similarity metric type. Output: Dictionary with shape and electrostatic potential similarities for each probe molecule. Uses the first molecule as reference. Compute Solvent Accessible Surface Area (SASA) of a structure from a PDB or mmCIF File Input: PDB/mmCIF file. Output: Dictionary of polar/apolar SASA values. Uses FreeSASA to compute surface areas. Compute synthetic accessibility score from a list of SMILES Input: List of SMILES. Output: List of synthetic accessibility scores. Convert .cub to molecular cloud Python Takes a .cub file and creates a pdb with hydrogens replacing the volume of the molecular orbitals. Convert a Chemdraw CDX file to a list of SMILES Input: CDX file. Output: List of SMILES. Parses a Chemdraw CDX file to extract molecular structures and converts each fragment to SMILES format. Fragments that cannot be converted are ignored. Convert a mmCIF File to a PDB File Input: mmCIF File. Output: PDB File and warning messages. Converts mmCIF to PDB using Maxit. Notes: Atom serials >99999 become 99999; 2-letter chain names use columns 21-22. Convert a multi conformer SDF File into a CSV Input: SDF file. Output: CSV file. Note that the user may use the terms 'Table', 'CSV', and 'Spreadsheet' interchangeably. Example prompts: 'Create a table for x.sdf', 'Convert x.sdf to a CSV', 'Turn x.sdf to a spreadsheet' Convert a PDB File to a mmCIF File Input: PDB File. Output: mmCIF File. Converts a PDB file to mmCIF format using Maxit. Output filename uses input name with .cif extension. Convert Genbank file to FASTA file Input: Genbank file. Output: FASTA file. Convert Mol2 file into SDF and PDB Files Input: Mol2 file. Output: SDF and/or PDB files. Each molecule of the input Mol2 file is categorized as either polymer (more than 100 heavy atoms) or ligand. Polymer molecules are written to PDB Files, and ligands are written to a single SDF File. Convert PDB to PDBQT for AutoDock Vina-based docking Python This script takes a PDB file as input and optionally a boolean value describing whether hydrogens will be added to the structure. It then runs a file conversion step to create a PDBQT file that can be used to define the receptor for the docking.

• Please carefully read through the conversation to see whether a pdb file is already downloaded. If not, please make sure to run "Download PDB File from RCSB Protein Data Bank using PDB Code" tool first to obtain a pdb file.
• The input pdb file should be protein-only, which means the "Save a protein-only PDB file" should always be run beforehand to obtain a protein-only PDB structure
• This is key to preparing a protein for docking
• This tool is only used for converting protein structure. Do not use it for the ligand SDF file! Create Nanome Workspace Creates a new Nanome workspace with the specified name and description. Create pdb with surrounding residues Python This tool will take in a 3 letter residue code and a radius in angstroms and return a new pdb file with all residues within that sphere. Create Scene in Nanome Workspace Creates a new scene within the specified Nanome workspace ID. Delete chain from a PDB or mmCIF File Input: PDB/mmCIF file and chain name. Output: Modified PDB/mmCIF file without specified chain. Delete Entry in Nanome Workspace Deletes a specified entry from the Nanome workspace. This action is irreversible. Always ask the user if they are sure before running this tool. Delete Nanome Workspace Deletes a specified Nanome workspace ID. This action is irreversible. Always ask the user if they are sure before running this tool. Delete Scene in Nanome Workspace Deletes a specified scene from the Nanome workspace. This action is irreversible. Always ask the user if they are sure before running this tool. Download ligand SDF Files linked to PDB Code from RCSB Protein Data Bank Input: PDB code. Output: List of SDF files. Downloads all ligand structures associated with given PDB. Download mmCIF or PDB File from RCSB Protein Data Bank using PDB Code Input: PDB code. Output: mmCIF or PDB file. Downloads structure file from the RCSB Protein Data Bank. Bio-assembly option returns full biological assembly in mmCIF format. Force PDB option attempts to get PDB format instead of mmCIF. Download SDF File from ChEMBL using ChEMBL ID Input: ChEMBL ID. Output: SDF File. Download SDF File from ChEMBL using InChIKey Input: InChIKey. Output: SDF File. Download SDF File from UniChem ID Input: UniChem ID (UCI). Output: SDF file. Download SDF File using EBI DB Ligand ID from RCSB Protein Data Bank Input: EBI ligand ID. Output: SDF file. Downloads ligand structure using 3-letter EBI identifier. Draw 2D Molecule from SMARTS Input: SMARTS pattern. Output: PNG image file of the 2D molecular structure. Draw 2D Molecule from SMILES Input: SMILES. Output: PNG image file of the 2D molecular structure. Draw Grid of Molecules from CSV data column Python This tool creates a montage of molecules in a grid by taking SMILES strings from a column in a CSV file. Optional parameters controls the number of molecules per row, and whether the structures are aligned according to the maximum common structure Draw Grid of Molecules from List of SMILES Input: List of SMILES. Output: PNG image file containing a grid of 2D molecular structures. Draw Molecule from SDF File Input: SDF file. Output: PNG image file of the 2D molecular structure. Draw Molecule from UniChem ID Input: UniChem ID. Output: SVG image file. Draw Molecule with Highlighted Atoms using SMILES and RDKit Atom IDs Input: SMILES and List of RDKit Atom IDs. Output: PNG image file with highlighted atoms. Edit Nanome Workspace Updates the name and/or description of an existing Nanome workspace. Enumerate stereo-isomers from a list of SMILES Input: List of SMILES. Output: List of lists of SMILES. Generate stereo-isomers for the input SMILES using RDKit. Enumerate tautomers from a list of SMILES Input: List of SMILES. Output: List of lists of SMILES. Generate the canonical SMILES and potential tautomers for the input SMILES using RDKit. Evaluate multi-ligand docking pose results Python This tool will take a .pdbqt file and a .sdf with multiple poses from a docking run. Each frame of the .sdf will be concatenated with the .pdbqt file. Then each new pdb file will undergo an analysis of the hydrogen bonding interactions and clashes between the ligand and the protein residues. Multiple csv files will be output for the user Export Data to CSV file Writes rows from a list into a specified CSV file, creating or overwriting it in the process. Ideal for exporting data into CSV format. Export Ligand from Nanome Workspace to SDF Exports a specific ligand from the specified Nanome workspace ID to an SDF file. Export Multiple Ligands from Nanome Workspace to Multi-Model SDF Exports multiple ligands from the specified Nanome workspace ID to a single multi-model SDF file. Extract Functional Groups from SMILES Input: SMILES. Output: List of identified functional groups present in the molecule. Extract protein from PDB or mmCIF File Input: PDB/mmCIF file. Output: Modified PDB/mmCIF file containing only protein residues. Extract Protein Sequence per Chain from PDB or mmCIF File Input: PDB or mmCIF file, boolean flag for SEQRES usage. Output: FASTA file. Extracts amino acid sequence per chain, ignoring ligands. If use_seqres=False, uses structure-derived sequence. Extract Raw Sequence From a FASTA File Input: FASTA file. Output: List of sequence strings. Returns a list of raw sequences, one per FASTA entry. Extract SMILES of Ligand Residues from PDB or mmCIF File Input: PDB or mmCIF File. Output: List of SMILES. Takes a PDB or mmCIF file and identifies ligand residues from Chemical Component dictionary to retrieve their SMILES. Only the first model is used. Filter and extract CSV data based on a query Python This tool takes a csv file as input and optionally a query string that can be accepted by pandas dataframe query method and a string of column names to be retained connected with comma (,).
• Prior to identifying the name of the columns to be retained, it is suggested to run the "Understand csv file content" tool to see all the available columns in the csv file, and make sure the name of the columns passed to the argument match the name of the columns actually in the csv file, and do conversions when necessary. If the column names can be found in the context, then there is no need to run that tool again and you should directly use the information from the context.
• For any column name in the query that contains space, make sure it is wrapped with ... symbols. For example if the user want all lines with molecular weight > 300, and the column name is just "molecular weight", then the query should be "molecular weight > 300"
• When the query string is provided, it will run the query and return the resulting pandas dataframe, otherwise all rows are retained.
• When the list of column names is provided it will only retain these columns. Otherwise all columns will be retained.
• The new data will be returned as a new csv file. Find Compounds with same substructure in ChEMBL using SMILES Input: SMILES. Output: List of ChEMBL IDs and SMILES. Find Similar Compounds in ChEMBL using SMILES Input: SMILES and similarity threshold (0-100). Output: List of ChEMBL IDs. Find the maximum common substructure from a list of SMILES Input: List of SMILES. Output: SMARTS pattern representing the maximum common substructure. Fold a sequence using ESMFold Python Run ESMFold using the ESM Metagenomics Atlas to predict the folded structure of a sequence

Input

sequence - the sequence to be folded

Output

A .pdb file for the folded structure Generate a 2D or 3D scatter plot from CSV file Creates scatter plots from CSV data. Input: CSV file and column names for X, Y, and optionally Z axes. Output: PNG image file of the scatter plot. Generate conformers from a SDF File and write them to SDF Files Input: SDF file. Output: List of SDF files containing generated conformers. Generate, minimize and cluster conformers for each molecule using RDKit and Butina method. Generate conformers from a SMILES list and write them to an SDF File Input: SMILES list. Output: SDF file. Generate, minimize and cluster conformers using RDKIT and Butina method. Generate constrained conformers from a SDF File Input: SDF file and SMILES list. Output: SDF file with generated conformers. Uses reference molecule to constrain conformer generation. All molecules must share a substructure with the reference molecule. Generate enantiomers from a list of SMILES Input: List of SMILES. Output: List of lists of isomeric SMILES. Generate fragments from SMILES Input: SMILES. Output: List of SMILES representing molecular fragments. Generate ligand interaction image using PandaMap Python Uses python package for PandaMap to show interactions of a ligand with nearby residues from the protein.

Input

a pdb file with ligand name

Output

a png Get ChEMBL ID from Generic or Brand Name of a Compound Input: Generic or Brand Name of a Compound. Output: ChEMBL ID. Get ChEMBL ID from UniChem ID Input: UniChem ID. Output: ChEMBL ID. Returns a ChEMBL ID corresponding to the UniChem ID. Get ChEMBL IDs by searching ChEMBL for the input query Input: query string and optional maxresult. Output: List of ChEMBL IDs and names. Returns a list of ChEMBL IDs of molecules or targets with the name or synonym name matching the query string on ChEMBL. Get ChEMBL IDs for Predicted Target Interactions with the Molecule using ChEMBL ID Input: ChEMBL ID and activity threshold. Output: List of ChEMBL IDs predicted to interact with the molecule sorted by threshold activity. Get FASTA File from an Uniprot Id Input: UniProt ID. Output: FASTA file. Retrieves sequence from UniProtKB and saves it to a FASTA file. Get functional and structural annotations FunPDBe for a given PDB Code from PDBe Input: PDB code. Output: Dictionary containing annotations. Includes data from 3Dcomplex, p2rank, ProKinO, and Kincore. Get IC50 Activities and ChEMBL IDs for Targets Tested Against ChEMBL ID Input: ChEMBL ID. Output: List of IC50 activity and target ChEMBL IDs. Get if the molecule is purchasable from UniChem ID Input: UniChem ID. Output: List of vendor URLs. Returns a list of URLs of vendors for the molecule defined by a UniChem ID using PubChem. Get InChI from a list of SMILES Input: List of SMILES. Output: List of InChI identifiers. Get InChI from InChIKey using UniChem Input: InChIKey. Output: InChI. Get InChI from PubChem CID Input: PubChem CID. Output: InChI. Returns InChI string of the corresponding molecule, fetching data from PubChem. Get InChI from UniChem ID Input: UniChem ID. Output: InChI. Get InChI of protein structure targets from a small molecule SMILES Input: SMILES. Output: Dictionary of structure target names and their InChI/affinity data. Identifies protein targets that interact with the given molecule. Get InChIKey from a list of SMILES Input: List of SMILES. Output: List of InChIKey identifiers. Get InChIKey from InChI using UniChem Input: InChI. Output: InChIKey. Get InChIKey from PubChem CID Input: PubChem CID. Output: InChIKey. Returns InChIKey string of the corresponding molecule, fetching data from PubChem. Get InChIKey from UniChem ID Input: UniChem ID. Output: InChIKey. Get Ligand SMILES from PDB Code Input: PDB code. Output: List of dictionaries of ligands. Returns a dictionary containing canonical SMILES and chain/residue information, for all ligands in structure from the Protein Data Bank. All molecular groups with molecular weight inferior to minmolecularweight are discared. Get ligand SMILES interacting with a protein defined by an Uniprot Id Input: Uniprot Id. Output: List of SMILES and affinity. Returns a list of SMILES strings that have known binding interactions with the specified protein. Get metadata associated with an Uniprot Id Input: UniProt ID. Output: Dictionary of metadata. Retrieves detailed protein information including names, organism data, and database references. Get Molecular Properties from ChEMBL ID using ChEMBL Input: ChEMBL ID. Output: Dictionary of molecular properties containing alogp, aromaticrings, cxlogd, cxlogp, cxmostapka, cxmostbpka, fullmolformula, fullmwt, hba, hbalipinski, hbd, hbdlipinski, heavyatoms, molecularspecies, mwfreebase, mwmonoisotopic, nplikenessscore, numlipinskiro5violations, numro5violations, psa, qedweighted, ro3pass, and rtb. Get number of data in a CSV file Python This tool reads in a csv file using pandas, and return the number of data points in it Get PDB codes corresponding to a query on RCSB Input: Query string. Output: List of PDB codes. Full text search of RCSB Protein Data Bank. Get PDB Codes from InChIKey Input: InChIKey. Output: List of PDB codes where the molecule can be found. Get PDB Codes from UniProt ID Input: UniProt ID. Output: List of PDB codes. Takes a UniProt ID and returns associated PDB codes from the UniProt database. Get Protein Sequence from PDB Code using RCSB Input: PDB code. Output: Amino acid sequence. Returns single-letter amino acid sequence. Get protein structure predictions from the AlphaFold database Input: UniProt ID. Output: List of mmCIF files. Fetches AlphaFold predictions from EBI AlphaFoldDB. Get PubChem CIDs of similar molecules with fast similarity 2D method, from InChI Input: InChI and similarity threshold. Output: List of PubChem CIDs. Uses PubChem's fast 2D similarity search. Get PubChem CIDs of similar molecules with fast similarity 3D method, from InChI Input: InChI and similarity threshold. Output: List of PubChem CIDs. Uses PubChem's fast 3D similarity search. Get PubChem CIDs of similar molecules with fast substructure method, from InChI Input: InChI. Output: List of PubChem CIDs. Uses PubChem's fast substructure similarity search. Get residue names and serials per chain from a PDB or mmCIF File Input: PDB/mmCIF file. Output: JSON file mapping chains to residue info. Each residue has name and serial number. Get SIFTS functional and structural annotations for a given PDB Code from PDBe Input: PDB code. Output: Dictionary containing annotations. Includes UniProt, GO, InterPro, Pfam, EC, CATH, CATH-B, SCOP, HMMER, Ensembl. SIFTS means Structure Integration with Function, Taxonomy and Sequence Get Similar CDD Vault Molecules HTTP Use this to retrieve molecules from CDD Vault that are similar to the listed SMILES. Get SMARTS Murcko scaffold from a list of SMILES Input: SMILES list. Output: List of SMARTS patterns representing Murcko scaffolds. Get SMILES from ChEMBL ID Input: ChEMBL ID. Output: SMILES. Get SMILES from Generic or Brand Name of a Compound using ChEMBL Input: Generic or Brand Name of a Compound. Output: SMILES. Get SMILES from InChIKey Input: InChIKey. Output: SMILES. Get SMILES from SDF File Input: SDF file. Output: List of SMILES. Get source organism for a given PDB Code from RCSB Input: PDB Code. Output: Scientific name and NCBI Taxonomy ID for PDB entry. Get Substructure RDKit Atom IDs from SMILES using SMARTS Input: SMILES and SMARTS pattern. Output: List of matching RDKit atom indices. Get SureChEMBL IDs from UniChem ID Input: UniChem ID. Output: List of (SureChEMBL ID, URL) pairs. Returns a list of (SureChEMBL IDs, url) of the molecule defined by a UniChem ID. Get UniChem ID from InChI Input: InChI. Output: UniChem ID. Get UniChem ID from InChIKey Input: InChIKey. Output: UniChem ID. Get UniChem ID from PubChem CID Input: PubChem CID. Output: UniChem ID. Returns UniChem ID of the corresponding molecule, fetching data from PubChem. Get UniProt Accession Code from RCSB Protein Data Bank using a PDB Code Input: PDB code. Output: UniProt accession code. Retrieves UniProt mapping for specified chain/entity. Get UniProt IDs for Proteins Containing the Target ChEMBL ID using ChEMBL Input: ChEMBL ID. Output: List of UniProt IDs. Give Permission to Nanome Workspace Grants a specified permission level to a list of emails for a given Nanome workspace. Identify Aromatic Rings RDKit Atom IDs from SMILES Input: SMILES. Output: List of atom indices belonging to aromatic rings. Ligand Centroid Python This tool takes in a .pdb file and a ligand residue id, then prints the centroid of the ligand. List Entries in Nanome Workspace Retrieves a list of all entries within the specified Nanome workspace ID, including their serial and name. List Ligands in Nanome Workspace Retrieves a list of all ligands within the specified Nanome workspace ID, including their IDs, names, and ratings. Chemical properties and SDF metadata can be optionally included if the user requests it. The chemical properties include information such as molecular weight and SMILES, while the metadata includes experimental data and annotations. List Nanome Workspaces Retrieves a list of all the user's Nanome workspaces including their IDs (GUID), names, owners, and created/updated timestamps. List Permissions for Nanome Workspace Retrieves a list of all permissions for a specified Nanome workspace ID, including user emails and their permission levels. Permission level 1 is Viewer, 2 is Editor, and 3 is Owner. List Scenes in Nanome Workspace Retrieves a list of all scenes within the specified Nanome workspace ID. Load Molecular file into Nanome Workspace Loads a molecular file into the specified Nanome workspace ID. Supported file formats currently only include .cif, .pdb, .sdf. Other formats need to be converted first. Look for close residues from a PDB or mmCIF File and a search radius Input: PDB/mmCIF file, radius, and residue selection [chain, number]. Output: JSON file of nearby residues. Finds residues within specified radius. Example of input: {'filename': '1kx2.pdb', 'radius': 5, 'selection': ['A', 90]}, example of output: {'A': [{'name': 'ASN', 'serial': 51}, {'name': 'HEC', 'serial': 90}]} Lookup description and citations for a given PDB Code from RCSB Input: PDB code. Output: Dictionary with descriptions and citations. Returns title, keywords and citation in CSE format with URL when available. Manage Scene Representations in Nanome Workspace Create and modify molecular visualization components within a Nanome workspace. Supports complex filtering of molecular structures (such as by entry, chain, residue, atom, or substructure type), and allows the application of a wide range of visualization styles (including atomistic, cartoon/ribbon, molecular surface, residue labels, and interaction types). Can adjust coloring schemes, visualization parameters, and visibility settings, and can respond to requests to show, hide, or modify specific molecular selections or visualizations. Designed to interpret user instructions and translate them into structured component definitions for molecular visualization in Nanome v2. Can answer questions about the current state of a scene in a workspace. If user refers to a specific scene, be sure to list the scenes first to get the correct serial. Map residues from reference pdb to mobile pdb Python Takes two pdb files after an alignment, then maps residues from the reference pdb to the mobile pdb and prints the equivalent residues in the mobile pdb Merge a list of SDF Files into a single SDF File Input: List of SDF files. Output: Single merged SDF file. For each SDF File, a list of conformers can be used by specifying conformerid_list, one list per SDF File Minimize from a SDF File Input: SDF file. Output: SDF file with minimized structures and list of energies. Mutate Protein residues from a PDB or mmCIF File Input: PDB/mmCIF file and mutation list. Output: Modified structure file and clash information. Mutates specified residues using BioBB mutateside. Mutations format: 'Chain', 'Residue Serial', 'New Residue Name', e.g. 'A', '336', 'Trp'. Prepare ligands at specific pH and optimize geometry from SDF Python This tool takes in single molecule SDF's, an array of SD files, or a multi-molecule SDF and adds hydrogens based on the desired pH, then optimizes geometry for a specified number of steps. Protein sequence aligner and visualiser (resizable image) Python Take multiple sequences and align them with visual output. If further output is created, summarise the key differences in the text.

The size of the image created can be changed through the imagesize available, allowing a user to rerun this tool to make a bigger or smaller image. If a different image size is requested the the value can be multiplied by the factor the user has requested, if the user doesn't specify exactly how much they want to scale the image, the image size variable can be increased up or down by 50% compared to the last time the tool was run, providing the value given to imagesize is an integer.

The output from this tool: . MUST display the generated image . MUST include a similarity score . MUST include any difference between the lengths of the sequences . MUST include a written summary of the key differences of the residues and their locations between the sequences based on the text outputted by the tool. . If there are differences at the beginning OR the end of the sequences, then the output shouldn't describe these differences as it is just an aspect of how the alignment process works. . Differences after the beginning and end alignment regions of the output MUST be described to the user as those in the middle of the sequences.

Output description: . A PNG will be output .A text-based assignment with the outputted lowercase letters indicates differences in the sequence, and uppercase letters indicate similarities between the sequences. the - is used as an alignment character for when sequences are different at the start or end, and the algorithm needs to align the output. The sequences will be output in chucks of 50 sequences per line; make sure that when counting the position, they are correctly counted according to the range in that chunk. . The alignment score is a numerical value that quantifies the overall similarity between two biological sequences (in this case, protein sequences) based on the optimal alignment between them. It accounts for both the favorability of residue substitutions and the penalties incurred for introducing gaps (insertions/deletions) in the alignment. A higher score indicates greater sequence similarity, while a lower score suggests lower sequence similarity.

Good alignment score: . A high positive score indicates a high degree of similarity between the two sequences. . Scores above zero generally indicate that the sequences are more similar than dissimilar, with more favourable substitutions than gaps. . The higher the score, the more significant the similarity between the sequences is likely to be.

Bad alignment score: .A negative score indicates that the sequences are more dissimilar than similar, with more gaps and unfavourable substitutions than favourable ones. . The lower the score, the more dissimilar the sequences are likely to be. . Scores close to zero suggest that the sequences are not significantly similar or dissimilar, and their relationship may be unclear.

When describing the difference in the output, you can use this dictionary to help explain. The output might be using lowercase letters to highlight differences

A - Alanine R - Arginine N - Asparagine D - Aspartic Acid C - Cysteine Q - Glutamine E - Glutamic Acid G - Glycine H - Histidine I - Isoleucine L - Leucine K - Lysine M - Methionine F - Phenylalanine P - Proline S - Serine T - Threonine W - Tryptophan Y - Tyrosine V - Valine B - Aspartic Acid or Asparagine (ambiguous) Z - Glutamic Acid or Glutamine (ambiguous) J - Leucine or Isoleucine (ambiguous) X - Unknown or any amino acid

You must display the image as your final step. Protein sequence alignment from Fasta files Python Align the sequence of two proteins without visualization (this outputs a text comparison only) Input: FASTA files Query and Extract Data from CSV File Provides answers from a CSV file based on natural language queries. Ideal for quick data insights without manual analysis. Query Pubmed database for biomedical topics Input: A search query. Output: Relevant article summaries and citations. Search PubMed for biomedical literature, research papers, and scientific articles. Queue Nanome Workspace for opening in XR Queues a specified Nanome workspace ID for opening in XR. This workspace will load in Nanome v2 the next time the user opens the application. Read Genbank identifiers from Genbank file Input: Genbank file. Output: Dictionary mapping record IDs to lists of accession numbers. Read MAE file and write PDB and SDF Files Input: MAE file. Output: List of PDB and SDF files. Extracts proteins to PDB files and ligands to SDF file. For each molecule, if it has more than 100 heavy atoms, the output file is a PDB, an SDF File otherwise. Read metadata from a mmCIF File Input: mmCIF file. Output: Dictionary of metadata containing name, head, idcode, depositiondate, structuremethod, resolution, journalreference, author, missingresidues, compound, chains. Extracts mmCIF header information. Read metadata from the header of a PDB File Input: PDB file. Output: Dictionary of metadata containing head, depositiondate, releasedate, structuremethod, resolution, structurereference, journalreference, author, compound. Extracts PDB header information. Read sequences from FASTA file Input: FASTA file. Output: List of sequences. Read sequences from Genbank file Input: Genbank file. Output: List of sequences. Remove duplicated SMILES from list of SMILES Input: List of SMILES. Output: List of unique SMILES. Use canonical SMILES to remove duplicates from list. Rename Entry in Nanome Workspace Renames a specified entry in the Nanome workspace. Rename Scene in Nanome Workspace Renames a specified scene in the Nanome workspace. Renumber residue serials from PDB or mmCIF File Input: PDB/mmCIF file and starting number. Output: Modified PDB/mmCIF file with renumbered residues. Retrieve FASTA files from Genbank identifiers Input: List of Genbank identifiers. Output: List of FASTA files. Retrieve Genbank files from Genbank identifiers Input: List of Genbank identifiers. Output: List of Genbank files. Reverse translate Protein Sequences to DNA Sequences from a FASTA File Input: FASTA file, codon table ID. Output: FASTA file. Reverse translates protein sequences to DNA using the specified genetic code. Run molecular filters from a list of SMILES Input: List of SMILES and filter names. Output: List of SMILES that pass the filters. Available filters: ALL, BRENK, CHEMBL, CHEMBLBMS, CHEMBLDundee, CHEMBLGlaxo, CHEMBLInpharmatica, CHEMBLLINT, CHEMBLMLSMR, CHEMBLSureChEMBL, NIH, PAINS, PAINSA, PAINSB, PAINS_C, ZINC. Run online Blastp using a protein sequence from a FASTA File Input: FASTA file containing protein sequence. Output: List of UniProt codes. Uses Blastp to search UniProt database, results sorted by bit-score. Run online Blastx using a DNA sequence from a FASTA File Input: FASTA file containing DNA sequence. Output: List of UniProt codes. Uses Blastx to search UniProt database, results sorted by bit-score. Run P2Rank ligand binding site predictor on a file Input: PDB File. Output: List of dictionaries containing predicted pockets data [name, probability, center, residues]. Takes in a PDB File and returns predicted ligand binding sites using P2Rank 2.4.1. Run pdb2pqr on an input pdb file and return a pqr file Python This tool will convert a pdb file to a pqr file. Run this tool when a prompt is very close to "Run pdb2pqr on *.pdb and return a .pqr file". Use the 4 letter pdb code to find the file name that you need.

Example. Run pdb2pqr on 3f8f and return a .pqr file. pdb_id = 3f8f.pdb Run R-group decomposition on a list of SMILES Input: List of SMILES and core SMARTS pattern. Output: List of dictionaries containing R-groups. Dictionary keys are 'Core', 'R1', 'R2', … and values are SMILES Save a protein-only PDB file Python This tool takes a PDB file, remove anything that is not a protein, such as binding ligands, solvents, ions, etc., and save a cleaned PDB file as a new one. Optionally it can also add hydrogens to the resulting structure.

After this tool finishes, it should write the protein-only PDB file into context, so that subsequently the "Combine structures of a protein PDB and multiple ligands in SDF" can use this protein-only PDB file as input Search for compounds on eMolecules from an exact, similarity or substructure search using SMILES Input: SMILES string and search type. Output: List of dictionaries. Search eMolecules using exact match (ex), similarity (si) or substructure (ss) search. Results include molecular properties and vendor information. Search for RCSB PDB Codes of Proteins with Similar Amino Acid Sequences Input: Amino acid sequence or FASTA file. Output: List of PDB codes. Uses MMseq2 (formerly BLAST) to find proteins with similar sequences. For FASTA input, only first sequence is used. Search NCBI database nucleotide sequences Input: Query string. Output: List of Genbank identifiers. Search NCBI nucleotide database using search terms. Show distributions of numerical data from a csv file Python This tool takes an input csv file, and create a histogram plot for the distribution of the numerical values contained in a certain column in the provided csv file. The output is a png file. SMINA docking, detailed table, multi-frame sdf, do not sanitize Python This tool runs the SMINA program to dock a ligand into a protein target. The user should provide the protein target as a .pdbqt file, the ligand as a .sdf file, and the centerx, centery, center_z, size, and exhaustiveness values for the docking box. The tool will then run the docking algorithm and output a .sdf file containing the docked poses of the ligand. The final docking scores output will be written to a csv file.

Input:

• Protein target given as .pdbqt file. If no pdbqt file is available, you should run the "Convert PDB to PDBQT for AutoDock Vina-based docking" tool to obtain a pdbqt file.
• Ligand for docking, should be a .sdf file.
• centerx, centery, centerz: coordinates of the center of the docking box. You should first run "Find pockets for docking on the input protein" to find available pockets for docking, and ask the user to select a pocket, then you should use the centerx, centery, centerz values for the user selected pocket as input for this tool. Do not assume the binding site is known. Do not make up numbers. Always run "Find pockets for docking on the input protein" to make sure you have reliable information.
• size: size of the docking box. Unless the user has specifically specified this value, a default value of 25 should be used.
• exhaustiveness: exhaustiveness setting for the docking. Unless the user has specifically specified this value, a default value of 8 should be used.

Output:

• A .sdf file containing multiple poses of the ligand from the docking run.
• A .csv file containing the docking scores

General docking workflow

Docking is used to predict the binding structure of a ligand in the protein. So you should expect there is already a ligand file in the context (usually in the format of .sdf file). The protein file can be either a .pdb file or a .cif/.mmcif file.

Please always run "Combine structures of a protein PDB and multiple ligands in SDF" by using the protein only pdb file with the results sdf.

Here is a list of related tools for the complete docking process, and the order for running these tools. It is essential to follow this docking process and please follow exactly for the order and tools here. Otherwise the docked results might be unreasonable!

1. Extract protein from PDB or mmCIF File: this is used to make sure the protein used for docking is in .pdb format and does not contain any component not required for docking (PLEASE DO NOT SKIP THIS STEP whenever the user asked for docking, because the user provided pdb file or those downloaded from rcsb might contain components that will make docking fail!)
2. Convert PDB to PDBQT for AutoDock Vina-based docking: This step converts the protein-only PDB file from step 1 into a PDBQT file that is required to run docking Split specific chain from pdb file Python This tool will take in a chain id and split it from the main file to create a new file. Superimpose two PDB files Python This tool will concatenate two pdb files while changing the chain names and superimpose the structures. Translate DNA or RNA Sequences to Protein Sequences from a FASTA File Input: FASTA file. Output: FASTA file. Translates DNA/RNA sequences to protein sequences, using the longest open reading frame from both strands. Understand csv file content Python This tool will output the first lines (which ideally contains information about the columns) in the csv file. It should be triggered when the user ask about the structure of the csv file. If there is "Unnamed: 0" columns, it means that column does not have a title, and most likely should be treated as an index column. Additionally, it will print the values in the first couple of rows so that the agent can understand what the data is all about.

The results from this tool should be saved into context to allow other tools to utilize. Validate PDB or mmCIF File Compatibility with Nanome Standalone Devices Input: PDB or mmCIF File. Output: Yes/No response. Checks if a structure file is compatible with Nanome Standalone devices (Meta Quest, Vive Focus 3) by verifying the atom count is under 10000.