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BETA - MAY CONTAIN BUGS Protein Folding Molecular Docking

Overview

Protenix is an open-source reproduction of AlphaFold3, enabling researchers to predict 3D structures of biomolecular complexes. It incorporates innovations in model architecture, data processing, and confidence prediction, democratizing access to advanced tools for modeling biomolecular interactions.

Neurosnap Overview

The Protenix (AlphaFold3) online webserver allows anybody with a Neurosnap account to run and access Protenix (AlphaFold3), 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

  • Reproduces AlphaFold3's architecture for predicting 3D structures of biomolecular complexes.
  • Supports diverse biomolecular systems, including proteins, nucleic acids, and small molecules.
  • Employs innovative algorithms for MSA pairing, pocket-conditioning, and unified cropping.
  • Optimized confidence model for reliable biomolecular interaction predictions.
  • Significant speed and computational efficiency improvements over comparable models.
  • Extensive benchmark validation on CASP15 and curated test sets.

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 amino acid, DNA, and RNA sequences corresponding to the molecules you want to predict. For complex prediction simply provide multiple sequences in this input and they will all be folded at the same time together. For DNA and RNA sequences enter them in the 5′-->3′ direction. If you wish to model double-stranded DNA, please add a second dna sequence representing the reverse complement strand.

Input small molecules to include in prediction. All inputs are converted to SMILES format, with SMILES strings being the preferred input for optimal results.

Residue Modifications Instructions

Specify any residue modifications for amino acid, dna, or rna sequences. The format for residue modifications is SEQ_ID:RESIDUE_INDEX:CCD_CODE per line.


SEQ_ID: This is the title assigned to the sequence within the Input Sequences input.

RESIDUE_INDEX: This is the residue index / position of the amino acid you want to modify. Use a residue ID of 1 for the first residue in your sequence.

CCD_CODE: This is the CCD code of the modification you want to assign to the residue.

See above instructions for information on how to use this input.

Advanced Settings

Choose whether you want to use an MSA generated using mmseqs2 or if you want to use single sequence mode which leverages language model embeddings instead ("single_sequence"). The single sequence approach is much faster and achieves reasonable accuracy but MSA mode is generally preferred and more accurate. Warning: We query a 3rd party service to perform sequence searches, if you do not wish to share your sequence set this to "single_sequence".

Increased recycling steps tend to produce more accurate predictions but will also greatly increases prediction time. For smaller proteins and monomers we recommend 5 recycles, for bigger proteins we recommend 10 recycles.

The number of sampling steps to use for prediction.

The number of diffusion samples to use for prediction.

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