Use ProteinMPNN

Official Neurosnap webserver for accessing ProteinMPNN online.

Inverse Folding Protein Design

Overview

ProteinMPNN is a powerful inverse folding model that is capable of not only predicting the amino acids of a protein structure, but also certain chains, and complexes. Additionally, ProteinMPNN can be used as a way to create functional homologs / mutants of existing proteins by inverse folding their structures and sampling the sequence space.

Neurosnap Overview

The ProteinMPNN online webserver allows anybody with a Neurosnap account to run and access ProteinMPNN, no downloads required. Information submitted through this webserver is kept confidential and never sold to third parties as detailed by our strong terms of service and privacy policy.

View Paper

Features

  • Predecessor to LigandMPNN. Note this service has been largely replaced by LigandMPNN and should no longer be used.
  • Utilizes the faster & more feature rich ColabDesign implementation of ProteinMPNN.
  • Supports SolubleMPNN.
  • Allows you to specify fixed chains and positions.
  • Allows you to inverse fold any protein or complex of proteins.
  • Supports homo-oligomers.
  • Supports different sampling techniques to better explore the protein landscape.
  • Includes per sequence metrics such as an overall score and sequence recovery.
  • Includes amino acid probabilities by position.
  • Includes sampling temperature adjusted amino acid probabilities by position.

Statistics

Neurosnap periodically calculates runtime statistics based on job execution data. These estimates provide a general guideline for how long your job may take, but actual runtimes can vary significantly depending on factors like input size or settings used.

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API Request

Access ProteinMPNN using the Neurosnap API by sending a request using any programming language with HTTP support. To safely generate an API key, visit the API tab of your overview page.

Video Tutorial

The following youtube video describes how to use ProteinMPNN using Neurosnap's online webserver. If you have any questions or want to suggest improvements for future tutorials please contact us here.

Job Note

Provide a name or description for your job to help you organize and track its results. This input is solely for organizational purposes and does not impact the outcome of the job.

Configuration & Options

Service Inputs

The input protein structure to predict the amino acid sequence of.

Design Options

Specify whether the structure is a homo-oligomer (homomer). Lengths of chains should be the same for correct symmetric tying.

Fixed positions are positions that will not be inverse folded or affected in anyway by ProteinMPNN. This option allows you to specify which chains, residues, and residue ranges should be fixed. To fix an entire chain simply enter the chain's ID (e.g., "C" to fix all residues of chain C). To fix specific positions use <chain ID><residue ID> (e.g., A10 to fix residue 10 on chain A). To fix a range of residues use <chain ID><start residue ID>-<end residue ID> (e.g., A10-20 will fix all residues between 10 and 20 on chain A). Multiple positions, chains, and ranges can be fixed all at once by comma delimiting your options (e.g., A15,A20-23,B will fix residues 15, 20, 21, 22, 23, and all residues on chain B).

Invert the selected fixed positions above. Basically if you decide to fix a position like A1-10 above and enable this mode then instead of fixing A1-10, everything will be fixed except for A1-10.

The number of output sequences to generate.

Specify sampling temperature lower numbers produce higher probability sequences, higher numbers produce more diverse sequences. A sampling temperature greater than 1.0 means random sampling.

Advanced Settings

Select the model you want to use to predict your structure. The first number presents the number of edges (48), the 2nd number represents the deviation in ångströms (020 = 0.2Å). The best performing option is usually either v_48_030 or v_48_020 (according to the ProteinMPNN paper).

Select whether you want to use the original ProteinMPNN weights (default) or if you want to use the newer SolubleMPNN weights which is a version of ProteinMPNN trained on only soluble proteins. If your goal is to design soluble proteins then SolubleMPNN might be more useful.

Advanced Residue Biases

The following inputs can be used to bias the frequency in which ProteinMPNN will sample certain amino acids. Biases greater than 0 will positively increase the frequency of the biased amino acid. Biases with negative values (values less than 0) will decrease the frequency of those residues being selected. A default bias of 0 will have no impact on amino acid frequency.

For example, ProteinMPNN can sometimes favor certain charged residues for some structures. If you would like to decrease the frequency of these residues being selected by ProteinMPNN than provide a negative bias.

The recommended range for bias values is generally between -2 and 2. To completely exclude an amino acid set the value to -25.

Increased values increase frequency, lower values decrease frequency. Default of 0 will have no impact on amino acid frequency.

Increased values increase frequency, lower values decrease frequency. Default of 0 will have no impact on amino acid frequency.

Increased values increase frequency, lower values decrease frequency. Default of 0 will have no impact on amino acid frequency.

Increased values increase frequency, lower values decrease frequency. Default of 0 will have no impact on amino acid frequency.

Increased values increase frequency, lower values decrease frequency. Default of 0 will have no impact on amino acid frequency.

Increased values increase frequency, lower values decrease frequency. Default of 0 will have no impact on amino acid frequency.

Increased values increase frequency, lower values decrease frequency. Default of 0 will have no impact on amino acid frequency.

Increased values increase frequency, lower values decrease frequency. Default of 0 will have no impact on amino acid frequency.

Increased values increase frequency, lower values decrease frequency. Default of 0 will have no impact on amino acid frequency.

Increased values increase frequency, lower values decrease frequency. Default of 0 will have no impact on amino acid frequency.

Increased values increase frequency, lower values decrease frequency. Default of 0 will have no impact on amino acid frequency.

Increased values increase frequency, lower values decrease frequency. Default of 0 will have no impact on amino acid frequency.

Increased values increase frequency, lower values decrease frequency. Default of 0 will have no impact on amino acid frequency.

Increased values increase frequency, lower values decrease frequency. Default of 0 will have no impact on amino acid frequency.

Increased values increase frequency, lower values decrease frequency. Default of 0 will have no impact on amino acid frequency.

Increased values increase frequency, lower values decrease frequency. Default of 0 will have no impact on amino acid frequency.

Increased values increase frequency, lower values decrease frequency. Default of 0 will have no impact on amino acid frequency.

Increased values increase frequency, lower values decrease frequency. Default of 0 will have no impact on amino acid frequency.

Increased values increase frequency, lower values decrease frequency. Default of 0 will have no impact on amino acid frequency.

Increased values increase frequency, lower values decrease frequency. Default of 0 will have no impact on amino acid frequency.

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